Applying Optimization Theory to Predict Stomatal Behavior in Waterlogged Trees Based on Photosynthetic and Hydraulic Traits

Author

Marisa J. BrennanFollow

Semester of Graduation

Fall 2023

Degree

Master of Renewable Natural Resources (SRNR)

Department

School of Renewable Natural Resources

Document Type

Thesis

Abstract

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 (g_sw). Stomatal Conductance can be modeled based on optimality theory, which finds that plant stomata have evolved to optimize g_sw 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 g_sw, 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 g_sw, hydraulic conductance in roots, stems, leaves, and total root-to-canopy pathway (k_root, k_stem, k_leaf, and k_rc); the maximum rubisco carboxylation rate (V_c,max); the maximum quantum yield of CO₂ assimilation (α); and light-adapted quantum efficiency (Φ_PSII) in well-watered controls and flooded trees.First, I tested whether loss in photosynthetic and hydraulic capacity are coupled and associated with reduced g_sw 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 g_sw tracks g_opt in waterlogged trees (H2). I tested this by parameterizing SOX with the effects of waterlogging on photosynthetic and hydraulic traits and comparing predicted g_opt and measured g­_sw. In the flood-tolerant species, N. aquatica had higher k_root and k_stem in the waterlogged treatment compared to well-watered controls. In my less flood-tolerant species, L. styraciflua, there were significant reductions in g_sw, V_c,max, α, and Φ_PSII 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 g_sw 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.

Date

11-15-2023

Recommended Citation

Brennan, Marisa J., "Applying Optimization Theory to Predict Stomatal Behavior in Waterlogged Trees Based on Photosynthetic and Hydraulic Traits" (2023). LSU Master's Theses. 5861.
https://repository.lsu.edu/gradschool_theses/5861

Committee Chair

Dr. Wolfe, Brett T.