BG3.8

In vast areas of the world, the growth of forests and vegetation is water-limited and plant survival depends on the ability to avoid catastrophic hydraulic failure. Therefore, it is surprising that plants take high hydraulic risks by operating at water potentials (ψ) that induce partial failure of the water conduits (xylem) ¹, and which makes them susceptible to drought mortality under climate change ². Here we present an eco-evolutionary optimality principle for xylem design that explains this phenomenon - xylem is adapted to maximize its effective conductivity. A simple relationship emerges between the xylem intrinsic tolerance to high negative water potential (ψ₅₀) and the environmentally dependent minimum ψ, which explains observed patterns across and within species. The theory provides a fundamental conduit-level principle that complements previously described principles at higher organizational levels, such as hydraulic-network scaling and drought-avoidance behavior. The new optimality principle may be universally valid, from within-individuals to across-species, and thus improve our basic understanding of drought tolerance of plants and forests globally.   

How to cite: Franklin, O., Fransson, P., Hofhansl, F., and Joshi, J.: Plants maximize the conductive efficiency of the xylem, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9863, https://doi.org/10.5194/egusphere-egu22-9863, 2022.

Optimality principles have been used to explain stomatal behavior, assuming that plants maximize carbon assimilation, while minimizing water expenditures. This optimization is often realized in models under arbitrary time horizons, from instantaneous optimization to unknown time periods of days and weeks. Here we introduce the concept of “discounting” to the optimization framework. Simply put, discounting makes the assumption that a plant cares more about its fluxes of carbon and water at the present moment than those in the future, where a “discount rate” is used to quantify the amount by which the present is more valued than the future. We explore how the plant continually updates its prior density functions (in the Bayesian sense) regarding future climatic conditions, and how this mechanism relates to memory. We also show that instantaneous optimization and the usual optimization over a fixed period of time are but the extremes in a rich spectrum of behavior in the discount rate axis. Finally, we discuss how to link the idea of discounting to risk attitudes and isohydricity.

How to cite: Mau, Y. and Bayer, Y.: Stomatal optimization under uncertain climate: the role of discounting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4652, https://doi.org/10.5194/egusphere-egu22-4652, 2022.

Optimality theory states that the plants balance the carbon cost of photosynthesis with the cost of absorbing water thus satisfying a.δ(E/A)/δχ = -b. δ(V_cmax/A)/δχ, where χ is the ratio of leaf intercellular to ambient partial pressure of CO₂, “a” is the cost of maintaining the transpiration rate (E), required to support assimilation at a rate A under normal daytime conditions. While “b” is the cost of maintaining carboxylation capacity (V_cmax) at the level required to support assimilation at the same rate. Thus, the “a” cost, theoretically, should express the unit of maintenance respiration of the sapwood per unit of transpiration.

Here, we developed a mathematical expression to calculate the expected “a” cost (a_exp) under the optimality framework of the P-model using eddy covariance measurements of CO₂ exchange combined with environmental and transpiration measurements from the SAPFLUXNET database.

We then compared a_exp against two theoretical formulations of “a”. One (noted a_theo1) was estimated as a function of the viscosity of water at a given temperature η(T) compared to that at 25°C, which was proposed by Wang et al., (2017, Nat. Plants). And a second one (noted a_theo2), proposed by Prentice et al., (2014, Ecol. Lett.) where “a” depends on the soil-leaf water potential gradient (Δψ), η(T) and parameters defining hydraulic traits and respiration which were obtained from the literature.

The seasonal pattern of a_exp suggests that it is more costly for the ecosystem to transpire during the dry months. We found that a_theo1 has opposite seasonal variations to a_exp and strongly underestimates “a” during dry months. In contrast, a_theo2 shows similar seasonal variations to a_exp but generally overestimates the a_exp values by almost 4 times. Simple regression analyses showed, as expected, that a_exp  is inversely proportional to Δψ, but that, opposite to what was expected, it increases with a reduction of the water viscosity.

Overall, our results suggest that an improved formulation of the cost ratio “a” should account for the effect of water stress on transpiration and assimilation in the optimality theory.

How to cite: Sandoval, D., Lavergne, A., and Prentice, C.: The carbon cost of Transpiration from Optimality Theory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9428, https://doi.org/10.5194/egusphere-egu22-9428, 2022.

Plant functional traits are a key component of land vegetation models. We present global maps of specific leaf area (SLA) and leaf nitrogen content (N) by mass and area, derived from optimality principles. Leaf N per unit area (N_area) is proposed to be determined primarily by the amount of leaf tissue (is related to LMA = 1/SLA) and its metabolic activity (is related to  carboxylation capacity at 25˚C, known as V_cmax,25). SLA is predicted via optimality hypothesis that LMA maximizes average net carbon over the life cycle of the leaf, with separate calculations for evergreen and deciduous plant types. Global maps then use a remote sensing-based land cover product to assign fractional coverage of each type. V_cmax,25  is predicted via the coordination hypothesis, which posits that V_cmax under current growth conditions tends towards a value that balances the Rubisco- and electron transport-limited rates of photosynthesis.  Predicted trait values are compared to in-situ observations, showing good agreement for all three traits. Predicted global distributions are further compared with recently developed, data-based global trait maps. This research indicates how an optimality perspective can help to improve our understanding of vegetation functional diversity and ecosystem function, and potentially enhance vegetation models.

How to cite: Dong, N., Dechant, B., and Prentice, I. C.: Global patterns of leaf traits based on optimality theory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10673, https://doi.org/10.5194/egusphere-egu22-10673, 2022.

Photosynthesis is the largest flux of carbon between the atmosphere and Earth’s surface and is driven by proteins that require nitrogen. Thus, photosynthesis is a key linkage between the terrestrial carbon and nitrogen cycles, and the representation of this linkage is  critical for coupled carbon-nitrogen land surface models. Most models use a scheme that assumes that photosynthetic nitrogen is driven by soil nitrogen availability. This contributes to projected future reductions in the CO₂ fertilization of photosynthesis, as this fertilization becomes limited by nitrogen availability. However, recent results suggest that photosynthetic nitrogen is determined by leaf nitrogen demand, which is set by aboveground conditions, and that future increases in temperature and atmospheric CO₂ should reduce photosynthetic nitrogen demand. Here, we used recently developed photosynthetic optimality theory to incorporate the effect of reduced photosynthetic demand for nitrogen into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface processes under future elevated CO₂ conditions to 2100 using the RCP 8.5 scenario. Our simulations showed that photosynthesis increases under future conditions, but leaf nitrogen declines. This nitrogen savings led to an increase in simulated leaf area, which increased GPP and ecosystem carbon in 2100. These results suggest that land surface models may overestimate future nitrogen limitation of photosynthesis if they do not incorporate future reductions in photosynthetic nitrogen demand.

How to cite: Smith, N., Zhu, Q., Keenan, T., and Riley, W.: Reductions in photosynthetic nitrogen demand due to elevated CO2 increases simulated future ecosystem carbon storage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1847, https://doi.org/10.5194/egusphere-egu22-1847, 2022.

Theorethical Eco-Evolutionary Optimality (EEO) hypotheses are proving helpful in representing leaf-level processes, but are scarcely applied to whole plant levels. Applying EEO approaches at plant level can provide simple ways of representing plant- and ecosystem interactions and dynamics, especially when incorporating anthropogenic environmental impacts. An increase in plant productivity related to, for example, increased emission of CO₂ and Nitrogen (N), will likely increase limitation by other essential nutrients and minerals, such as phosphorus (P). Interacting effects of elevated CO₂ and limitation of essential nutrients are thought to affect plant tissue concentrations, organ growth rates, and photosynthetic capacities. However, it remains uncertain how plant-level reactions to varying nutritional resources affects optimality in plant functioning. Here we used plants with contrasting nutrient limitations to test EEO theoretical optimal photosynthetic traits and the corresponding internal nutrient allocation. It is hypothesised that (I) relative allocation of N and P towards the leaf will decrease under rising CO₂ to optimize photosynthesis in relation to transpiration and (II) effects of P deficiency on growth will be relatively stronger in plants grown in high CO₂ conditions compared to lower CO₂ concentrations. Preliminary data was collected from a phytotron experiment focussing on the combined effect of P limitation and CO₂ fertilization. In this experiment, plant photosynthetic traits (e.g. photosynthetic maximum carboxylation rate, Vcmax, and electron transport rate, Jmax) were measured on three different plant species, Holcus lanatus, Panicum miliaceum, and Solanum dulcamara (a C3 grass, a C4 grass, and a C3 herb respectively). They were grown at either low (150ppm), ambient (450ppm), or high (800ppm) CO₂ concentrations, and given one of either treatments; sufficient P in an N:P ratio of 1:1, or severely limiting P in an N:P ratio of 45:1 with a similar supply of N. Preliminary results suggest that decreased availability of P limits Vcmax and Jmax, constraining the maximum photosynthesis rate. This effect is amplified in low CO₂ conditions, as this triggers plants to increase their photosynthetic capacities when nutrients are sufficiently available. Measured leaf N and P concentrations, alongside Vcmax and Jmax, will be additionally used to determine leaf stoichiometry and photosynthetic P-use efficiency as a result of fertilization. Applied N and P will be compared with leaf concentrations to evaluate their relative allocation. Results will be used to validate EEO model predictions on optimality in suboptimal conditions.

How to cite: Lankhorst, J., Rebel, K., van Dijk, J., and de Boer, H.: Optimality in (sub)optimal conditions; Leaf stoichiometry in response to contrasting CO2 and phosphorus fertilization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8590, https://doi.org/10.5194/egusphere-egu22-8590, 2022.

The eco-evolutionary optimality principle, which states that natural selection rapidly eliminates uncompetitive combinations of traits, has proven to be a powerful source of testable hypotheses and predicting patterns in vegetation structure and composition. In this context, Prentice et al. (2014) proposed an optimality framework for plant functional ecology, which predicts relationships between parameters of photosynthetic biochemistry and stomatal conductance (g_s). Leaf morphology plays an essential role herein, as shown by the conservative g_s/g_smax ratio (McElwain et al., 2016) and the strong correlation between maximum photosynthesis rate and leaf hydraulic traits (Brodribb et al., 2007). The aim of this research is to determine how such leaf morphological adaptations relate to adaptations of photosynthetic traits, mainly  g_s/g_smax and V_cmax, over different timescales. Here we present empirical data to test predicted effects of changing CO₂ concentrations on V_cmax, C_i/C_a, J_max, g_s, and leaf morphology, according to the optimality framework.

The effects were tested in two genotypes of Solanum dulcamara (bittersweet) that were grown from seeds to maturity under 200, 400 and 800 ppm CO₂ in walk-in growth chambers with tight control on light, temperature and humidity. The genotypes were grown from two distinct natural populations; one adapted to well-drained sandy soil (the 'dry' genotype) and one adapted to poorly-drained clayey soil (the 'wet' genotype). Measurements of photosynthetic traits were obtained with a portable photosynthesis system. Morphological and developmental leaf traits were measured on microscopy images, after plant maturation.

The results show that the optimality framework is suitable to predict changes in the photosynthetic traits under changing atmospheric CO₂ concentrations. With higher concentrations, the V_cmax decreased in both S. dulcamara genotypes. Also, at each CO₂ growth level, the dry genotype showed a higher Huber value and a lower V_cmax than the wet genotype, indicating that the ‘dry’ genotype combines a relatively high cost of transpiration with a low cost of photosynthesis, and the ‘wet’ genotype vice versa. The down-regulation of V_cmax under high CO₂ was strongest in the dry genotype, and the downregulation of g_s the strongest in the wet genotype, in line with the predicted trade-off between the costs of transpiration and photosynthesis.

The two leaf morphological traits with the clearest CO₂ response were leaf vein density and guard cell length, which were also strongly correlated. Interestingly, stomatal density showed no CO₂ response in this species, but is correlated to the guard cell length. Overall, our empirical data support the optimality responses in photosynthetic traits and g_s, however, leaf morphological responses appear less consistent with the theory. More research, including experiments over a longer timescale will provide more insight in these relationships.

How to cite: Odé, A., Rebel, K., van der Ploeg, M., and de Boer, H.: Effects of rising CO2 concentrations on photosynthetic traits and leaf morphology to test optimality framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9596, https://doi.org/10.5194/egusphere-egu22-9596, 2022.

We present Plant-FATE, a trait-size-structured vegetation model in which the time evolution of the size distribution of multiple species is modelled using the McKendrick-von Foerster partial differential equation. In our model, trait structure allows for representing any number of functionally distinct species as points in trait space, while size structure allows for modelling competition for light. To account for the stomatal and biochemical responses of leaves to environmental conditions, including CO₂ concentration, vapour pressure deficit, and soil moisture, Plant-FATE incorporates ‘P-hydro’, a unified model for stomatal conductance and photosynthetic capacity [Joshi, J., et al. (2020). Towards a unified theory of plant photosynthesis and hydraulics. bioRxiv 2020.12.17.423132]. To model the resource allocation of plants, Plant-FATE uses an extended version of the ‘T-model’, accounting for crown geometry. In Plant-FATE, the vertical light profile attenuated by the canopy can be (optionally) modelled as a continuous light profile or via the ‘perfect plasticity approximation’ (PPA). Plant-FATE also includes a simple model for the acclimation of the crown leaf area index and an empirically derived model of plant mortality. Here, we present initial results exploring the effect of different trait combinations on the demographics of individual trees and single-species stands. We also analyse the outcomes of pairwise competition between species differing in their traits. Our approach is a step towards developing an eco-evolutionary vegetation model (EEVM) capable of simulating the adaptive responses of biodiverse plant communities to changing environmental conditions.

How to cite: Joshi, J., Prentice, I. C., Brännström, Å., Singh, S., Hofhansl, F., and Dieckmann, U.: Plant-FATE – Predicting the adaptive responses of biodiverse plant communities using functional-trait evolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9994, https://doi.org/10.5194/egusphere-egu22-9994, 2022.

Plants take up water from the soil via roots and release it into the atmosphere through stomata; uptake of CO₂ from the atmosphere also proceeds through the stomata, implying tight coupling of transpiration and photosynthesis. We distinguish leaf-level (biochemical and stomatal) responses to external stimuli on different timescales: fast responses taking place over seconds to hours, and longer-term (acclimation) responses taking place over weeks to months. Typically, land-surface models (LSMs) have focused on the fast responses, and have not accounted for acclimation responses, although these can be different in magnitude and even in sign. We have developed a method that explicitly separates these two timescales in order to implement an existing optimality-based model, the P model, with a sub-daily timestep; and, thereby, to include acclimated responses within an LSM framework. The resulting model, compared to flux-tower gross primary production (GPP) data in five “well-watered” biomes from boreal to tropical, correctly reproduces diurnal cycles of GPP throughout the growing season. No changes of parameters are required between biomes, because optimality ensures that current parameter values are always adapted to the local environment. This is a clear practical advantage because it eliminates the need to specify different parameter values for different plant functional types. However, in areas with large seasonal variations in moisture variability, the model does not perform well. Here we address the issue of soil-moisture controls on GPP, which is a challenging issue for LSMs in general. We note two problems: an error in magnitude, and an error in shape. The model tends to overestimate GPP in dry areas because it does not consider the effect of low soil moisture (as opposed to atmospheric dryness) on photosynthesis; and it does not simulate the ‘midday depression’ that is observed under very high vapour pressure deficits. Moving beyond commonly used (empirical) water-stress formulations, we have incorporated soil moisture limitation on photosynthesis in the sub-daily P model. The main idea is to control GPP via hydraulic limitation. The revised model firstly assesses the “demand”—the transpiration that would take place under well-watered conditions—then constrains the actual transpiration at a rate that does not exceed the canopy’s estimated hydraulic capacity. This transpiration rate is then used to obtain revised rates of stomatal conductance and GPP, “corrected” for water stress. Preliminary results evaluating the revised model’s performances against flux tower measurements at dry sites are encouraging, suggesting a route towards a parameter-sparse and globally applicable LSM.

How to cite: Mengoli, G., Harrison, S. P., and Prentice, I. C.: Towards a land surface model based on optimality principles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1331, https://doi.org/10.5194/egusphere-egu22-1331, 2022.

Photosynthesis is the core engine of vegetation productivity, usually estimated by Gross Primary Production (GPP), the rate of carbon fixed by photosynthesis per unit of ground area. A better understanding of ecosystem productivity relies on two main streams of information: observations and modelling. However, both streams have severe limitations with respect to GPP: 1- no large-scale measurements exist for GPP, 2- while satellites typically measure light reflectance by foliage, the light reactions (i.e., light absorption by photosystems generating reduction power and energy for carbon-fixation) are still described empirically in vegetation models.

Part of the energy absorbed by the Chlorophyll pigments is radiatively dissipated through fluorescence. In the recent years, using narrow band observations in the oxygen A-band, first global Solar Induced Chlorophyll Fluorescence (SIF) measurements were obtained opening a new insight for estimates of vegetation photosynthesis. Yet, fluorescence quenching is a passive energy quenching and fluorescence yields are dependant of the faction of energy used for photochemistry and dissipated through non-photochemical quenching (NPQ). Thus direct comparison between GPP and SIF can lead to misinterpretation.

In this study, we append the P-model to include SIF simulations. The P-model is a new-generation vegetation model based on optimality principles and require minimal parametrisation. Two approaches to simulate fluorescence yield are tested. The first one is based on the van der Tol et al. (2014) fluorescence model and simulate fluorescence using an empirical method. The second is based on recent development from Johnson and Berry (Johnson and Berry, 2021), who proposed a process-based model for partitioning absorbed light between photochemistry, NPQ and fluorescence. The two approaches are evaluated and assessed against SIF satellite products.

How to cite: Morfopoulos, C., Gu, C., and Iain Colin, P.: Modelling solar-induced chlorophyll fluorescence using the P-model, an optimality-based model for vegetation productivity., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3963, https://doi.org/10.5194/egusphere-egu22-3963, 2022.

Understanding the mechanisms underlying changes in carbon isotope discrimination (Δ¹³C) in C₃ and C₄ plants is critical for predicting the C₃/C₄ fraction in mixed ecosystems. Variations in Δ¹³C are closely related to changes in the stomatal limitation of photosynthesis (i.e. the ratio of leaf internal to ambient partial pressure of CO₂, c_i/c_a), which are in turn determined by environmental variables, but also depend on the pathway of carbon assimilation. For instance, isotopic fractionation during the diffusion of CO₂ through the stomata primarily influences Δ¹³C in C₄ plants, while fractionation during Rubisco carboxylation has a stronger imprint on Δ¹³C in C₃ plants. As a result, C₃ plants are depleted in ¹³C compared to C₄ plants. Isotopic measurements can thus be used as tracers of physiological processes in plants.

Here we implement Δ¹³C formulations for C₃ and C₄ plants in the optimal P model to investigate the abundance of C₃ and C₄ plants at different locations across the globe. We first test model predictions of Δ¹³C (and hence c_i/c_a) for the two carbon pathways against a large network of isotopic measurements from leaves. We then predict the expected mean Δ¹³C in soil organic materials after plants decomposition using maps of C₃/C₄ plants distribution and assess model predictions with real isotopic measurements. Based on our results, we propose a model to predict the competition of C₃/C₄ plants as a response to environmental variations in different ecosystems.

How to cite: Lavergne, A., Harrison, S. P., and Prentice, I. C.: Investigating C3/C4 plants competition using carbon isotopes and optimality principles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1111, https://doi.org/10.5194/egusphere-egu22-1111, 2022.

Wheat sowing dates are currently used as an input for crop models that simulated wheat production. However, the optimal time for planting wheat will be affected by climate changes and human adaptations to these changes. In this paper, we present an optimality-based modelling approach, with additional constraints from low temperature and precipitation intensity, to estimate wheat sowing dates globally. This approach assumes that wheat could be sown at any time when the climate conditions are suitable, but the optimal sowing date that would be adopted by farmers would be that which maximises overall grain yields. We therefore run the model starting on every possible sowing date as determined by the climate constraints and then select the date which gives the highest yield in each location. We compare the modelled optimal sowing dates with an updated version of observed sowing dates created by merging census-based datasets and local agronomic information. Cold season temperatures are the major determinant of sowing dates in the extra-tropics, whereas the seasonal cycle of monsoon rainfall plays an important role in determining sowing dates in the tropics. The model captures the timing of reported sowing dates, with difference between estimated and observed sowing dates of less than one month (< 30 days) over much of the world;  maximum errors in tropical regions with large altitudinal gradients, such as Ethiopia, Bolivia and Peru, are up to two months. Discrepancies between the predictions and observations are larger in tropical regions than temperate and cold regions. Our approach for estimating optimal wheat sowing dates provides a way to examine human management decisions could mitigate the impacts of climate change on crop systems.

How to cite: Qiao, S., Harrison, S. P., Prentice, I. C., and Wang, H.: Optimality-based modelling of wheat sowing dates globally, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9084, https://doi.org/10.5194/egusphere-egu22-9084, 2022.