Dynamically optimizing stomatal conductance for maximum turgor-driven growth over diel and seasonal cycles

Abstract

SUMMARY Stomata have recently been theorized to have evolved strategies that maximize turgor-driven growth over plants’ lifetimes, finding support through steady-state solutions, in which gas exchange, carbohydrate storage, and growth have all reached an equilibrium. However, plants do not operate near steady-state as plant responses and environmental forcings vary diurnally and seasonally. It remains unclear how gas exchange, carbohydrate storage, and growth should be dynamically coordinated for stomata to maximize growth. We simulated the gas exchange, carbohydrate storage, and growth that dynamically maximize growth diurnally and annually. Additionally, we test whether the growth-optimization hypothesis explains nocturnal stomatal opening, particularly through diel changes in temperature, carbohydrate storage, and demand. Yearlong dynamic simulations captured realistic diurnal and seasonal patterns in gas exchange as well as realistic seasonal patterns in carbohydrate storage and growth, improving upon unrealistic carbohydrate responses in steady-state simulations. Diurnal patterns of carbohydrate storage and growth in daylong simulations were hindered by faulty modeling assumptions of cyclic carbohydrate storage over an individual day and synchronization of the expansive and hardening phases of growth, respectively. The growth-optimization hypothesis cannot currently explain nocturnal stomatal opening unless employing corrective ‘fitness factors’ or reframing the theory in a probabilistic manner, in which stomata adopt an inaccurate statistical ‘memory’ of nighttime temperature. The growth-optimization hypothesis suggests that diurnal and seasonal patterns of stomatal conductance are driven by a dynamic carbon-use strategy that seeks to maintain homeostasis of carbohydrate reserves.

Beyond source and sink control – toward an integrated approach to understand the carbon balance in plants

“…Without assuming instantaneous behavior, the optimal marginal carbon gain changes with time, becoming larger as soils dry (over multiple days to weeks) (Manzoni et al ., 2013). More recently, Potkay & Feng (2023a,b) modified the original stomatal optimization model by replacing photosynthesis with growth as the process to be optimized. The new model synthesizes the effects of gas exchange, water balance, carbohydrate storage, respiration and growth, thus balancing the two main factors regulating growth via stomatal regulation.…”

“…In the first case, NSC availability is assumed to be low, and the Potkay–Feng model causes the Lagrange modifier η (the optimal target value for marginal NSC use efficiency) to be high because NSC has a high value for the organism. This leads to stomata opening even when current soil or atmospheric water conditions are not optimal (Potkay & Feng, 2023b). Such a control fits the observations in the drought release experiment of Hagedorn et al .…”

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

“…To simplify their mathematical complexity, these models typically discretize the timespan into the shortest repeated units of time over which their constraints oscillate. This unit of time could be the length of a drydown period when soil moisture is the constraint (Katul et al, 2010;Manzoni et al, 2011;Mrad et al, 2019), or the length of a year when plant carbon storage is a focus (Potkay & Feng, 2023b). These models then maximize their reward functions over one of these units of time, instead of solving for the stomatal conductance that maximizes the reward over the entire timescale.…”

“…Some of these models maximize a reward function over a timescale that is assumed to be long and could be as long as the plant's entire lifespan. The mathematical and computational complexity of these models is a key limitation (Buckley, 2023), requiring numerical solutions to solve the problem either deterministically (Cowan, 1982;Manzoni et al, 2011;Mrad et al, 2019;Potkay & Feng, 2023b) or stochastically (Cowan, 1986;Lu et al, 2016Lu et al, , 2020. To simplify their mathematical complexity, these models typically discretize the timespan into the shortest repeated units of time over which their constraints oscillate.…”