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ABSTRACT Stomata control plant water loss and photosynthetic carbon gain. Developing more generalized and accurate stomatal models is essential for earth system models and predicting responses under novel environmental conditions associated with global change. Plant optimality theories offer one promising approach, but most such theories assume that stomatal conductance maximizes photosynthetic net carbon assimilation subject to some cost orconstraintof water. We move beyond this approach by developing a new, generalized optimality theory of stomatal conductance, optimizing any non‐foliar proxy that requires water and carbon reserves, like growth, survival, and reproduction. We overcome two prior limitations. First, we reconcile the computational efficiency ofinstantaneousoptimization with a more biologically meaningfuldynamic feedbackoptimization over plant lifespans. Second, we incorporatenon‐steady‐statephysics in the optimization to account for the temporal changes in the water, carbon, and energy storage within a plant and its environment that occur over the timescales that stomata act, contrary to previous theories. Our optimal stomatal conductance compares well to observations from seedlings, saplings, and mature trees from field and greenhouse experiments. Our model predicts predispositions to mortality during the 2018 European drought and captures realistic responses to environmental cues, including the partial alleviation of heat stress by evaporative cooling and the negative effect of accumulating foliar soluble carbohydrates, promoting closure under elevated CO2. We advance stomatal optimality theory by incorporating generalized evolutionary fitness proxies and enhance its utility without compromising its realism, offering promise for future models to more realistically and accurately predict global carbon and water fluxes.
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Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length
Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plantsinstantaneouslyoptimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale (τ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value ofτto be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value ofτthat best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation toτare measures of the average number of dry days between rainfall events. Values ofτare similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.
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- PAR ID:
- 10504677
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- AGU Advances
- Volume:
- 5
- Issue:
- 2
- ISSN:
- 2576-604X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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