Semester of Graduation

Fall 2023


Master of Renewable Natural Resources (SRNR)


School of Renewable Natural Resources

Document Type



Climate change has increased the occurrence of flood events in many regions. Predicting the impacts on ecosystems is a major challenge. Dynamic global vegetation models that project ecosystem dynamics in response to disturbances rely on predictions of stomatal conductance (gsw). Stomatal Conductance can be modeled based on optimality theory, which finds that plant stomata have evolved to optimize gsw by maximizing the difference between the gain of carbon uptake (A) and the cost of water loss (Θ). However, the interplay between hydraulic and photosynthetic physiological mechanisms driving changes in gsw, particularly in flood scenarios, is not well understood. To address this lack of understanding, I conducted a greenhouse flood experiment on the tree species Liquidambar styraciflua and Nyssa aquatica, which vary in flood tolerance. I measured gsw, hydraulic conductance in roots, stems, leaves, and total root-to-canopy pathway (kroot, kstem, kleaf, and krc); the maximum rubisco carboxylation rate (Vc,max); the maximum quantum yield of CO2 assimilation (α); and light-adapted quantum efficiency (ΦPSII) in well-watered controls and flooded trees.First, I tested whether loss in photosynthetic and hydraulic capacity are coupled and associated with reduced gsw under waterlogging conditions (H1). I then parameterized the stomatal optimization model based on xylem hydraulics (SOX) developed by Eller et al. (2018) to test whether gsw tracks gopt in waterlogged trees (H2). I tested this by parameterizing SOX with the effects of waterlogging on photosynthetic and hydraulic traits and comparing predicted gopt and measured g­sw. In the flood-tolerant species, N. aquatica had higher kroot and kstem in the waterlogged treatment compared to well-watered controls. In my less flood-tolerant species, L. styraciflua, there were significant reductions in gsw, Vc,max, α, and ΦPSII in waterlogged individuals compared to well-watered controls. When the effects of flooding on hydraulic and photosynthetic traits were incorporated into a stomatal optimization model, I predicted a time series of gsw in both waterlogged and control individuals relatively well in L. styraciflua (R2 = 0.51) and less well in N. aquatica (R2 = 0.30). These results indicate the utility of stomatal optimization models in predicting waterlogged effects on plants using photosynthetic and hydraulic traits.



Committee Chair

Dr. Wolfe, Brett T.

Available for download on Friday, October 30, 2026