Jianwei Zhang

Zhang is a research forester whose research focuses on the effect of forest management on long-term stand dynamics. By analyzing newer data with the archived long-term dataset collectively, he found that thinning both ponderosa pine and true fir stands reduced mortality while maintaining stand productivity. Other benefits by treating stand with lower density included significantly reducing hazardous fuels, increasing carbon sequestration, and enhancing health of residual trees and stand resilience to wildfires and biotic disturbances. A similar result was also found with competition control regardless of stand density in ponderosa pine. In addition, He also studied an impact of post-fire management regimes on carbon pools and forest restoration including assessing the rates of ecosystem carbon recovery. These results were extensively cited in the Environmental Assessment Documents in National Forests across northern California and by forest industries in their regional management strategies.

In past research projects, Zhang pioneered the use of a stable carbon isotope to study population variation in the water-use efficiency of forest trees, which provided a new, extensively cited tool to study the physiological response of trees to their environment. He used leaf gas exchange to quantify tree ozone uptake. He developed a technique that used large cuttings to increase the survival and growth of cottonwood plantations in the alluvial lands in the Mississippi River.

Zhang is interested in using physiological, community, and ecosystem ecology as well as silviculture, soil, and modeling approach to examine the responses of terrestrial ecosystems in general and forest ecosystems in particular to climate change, land use/management, fire, and biotic disturbance in California and globally. He will continue to study how the structure and function, biodiversity, and sustainability of forest ecosystems change and to determine how these changes affect ecosystem services. Furthermore, he tries to build a process-based or a mechanistic model to predict forest productivity under changing climate.