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Summary statement We recommend that stomatal slope parameters (g₁) be inferred by inversion so that variations ing₁may be attributed to variations physiological and environmental conditions. Understandingg₁will advance predictions of plant gas exchange and performance under global climate. 

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. 

Abstract Observations show vulnerability segmentation between stems and leaves is highly variable within and between environments. While a number of species exhibit conventional vulnerability segmentation (stem leaf ), others exhibit no vulnerability segmentation and others reverse vulnerability segmentation (stem leaf ). We developed a hydraulic model to test hypotheses about vulnerability segmentation and how it interacts with other traits to impact plant conductance. We do this using a series of experiments across a broad parameter space and with a case study of two species with contrasting vulnerability segmentation patterns:Quercus douglasiiandPopulus trichocarpa. We found that while conventional vulnerability segmentation helps to preserve conductance in stem tissues, reverse vulnerability segmentation can better maintain conductance across the combined stem‐leaf hydraulic pathway, particularly when plants have more vulnerable s and have hydraulic segmentation with greater resistance in the leaves. These findings show that the impacts of vulnerability segmentation are dependent upon other plant traits, notably hydraulic segmentation, a finding that could assist in the interpretation of variable observations of vulnerability segmentation. Further study is needed to examine how vulnerability segmentation impacts transpiration rates and recovery from water stress. 

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. 

Abstract 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 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. Year-long 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 day-long simulations were hindered by faulty modelling 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 night-time 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. 

Abstract Classifying the diverse ways that plants respond to hydrologic stress into generalizable ‘water‐use strategies’ has long been an eco‐physiological research goal. While many schemes for describing water‐use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water‐use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.A generalizable and predictive framework for assessing plant water‐use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water‐use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water‐use strategy framework.This idea is explored using a plant hydraulics model to identify: (a) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water‐use responses, and (b) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water‐use strategies. This modelling exercise can bridge the divide between various conceptualizations of water‐use strategies and provide targeted hypotheses to advance the understanding and quantification of plant water status regulation across spatial and temporal scales.Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water‐use strategy concept, including further investigation into the below‐ground determinants of plant water relations, targeted data collection efforts and the potential to scale these concepts from individuals to whole regions. A freePlain Language Summarycan be found within the Supporting Information of this article. 

Eddy covariance data are invaluable for determining ecosystem water use strategies under soil water stress. However, existing stress inference methods require numerous subjective data processing and model specification assumptions whose effect on the inferred soil water stress signal is rarely quantified. These uncertainties may confound the stress inference and the generalization of ecosystem water use strategies across multiple sites and studies. In this research, we quantify the sensitivity of soil water stress signals inferred from eddy covariance data to the prevailing data and modeling assumptions (i.e., their robustness) to compile a comprehensive list of sites with robust soil water stress signals and assess the performance of current stress inference methods. To accomplish this, we identify the most prevalent assumptions from the literature and perform a digital factorial experiment to extract probability distributions of plausible soil water stress signals and model performance at 151 FLUXNET2015 and AmeriFlux-FLUXNET sites. We develop a new framework that summarizes these probability distributions to classify and rank the robustness of each site’s soil water stress signal, which we display with a user-friendly heat map. We estimate that only 5%–36% of sites exhibit a robust soil water stress signal due to deficient model performance and poorly constrained ecosystem water use parameters. We also find that the lack of robustness is site-specific, which undermines grouping stress signals by broad ecosystem categories or comparing results across studies with differing assumptions. Lastly, existing stress inference methods appear better suited for eddy covariance sites with grass/annual vegetation. Our findings call for more careful and consistent inference of ecosystem water stress from eddy covariance data.