BG3.8

EDI
Exploring the use of optimality approaches in vegetation and land-surface models

Eco-evolutionary optimality (EEO) theory invokes the power of natural selection to eliminate uncompetitive trait combinations, and thereby shape predictable, general patterns in vegetation structure and composition. Although the implementation of process-based representations derived from EEO principles in vegetation and land-surface models is a relatively recent phenomenon, it is already yielding considerable improvements to our ability to simulate vegetation responses to changing climate and environmental conditions. For example, hypotheses derived from EEO principles are proving helpful in developing parsimonious representations of leaf-level processes such as photosynthesis and primary production, dark respiration, and stomatal behaviour. EEO approaches can also be applied to at whole plant and community levels, providing simple ways of representing plant interactions and ecosystem dynamics. Comparisons of EEO-based predictions against experimental data and field and remote-sensing observations provide a way of evaluating the robustness of the hypotheses, as well as discriminating between alternative EEO hypotheses.
This session is designed to bring together scientists applying EEO approaches to modelling plant behaviour from cellular to community scales, experimentalists and observationalists developing data sets that can be used to evaluate EEO hypotheses, and vegetation and land-surface modellers implementing EEO approaches in existing model frameworks. The session will explore the current state-of-the-art, as well as ways to move EEO-based approaches forward. The key objective is to bring together researchers from different communities working on EEO principles, promoting scientific exchanges that are much needed to develop robust, reliable and realistic next-generation Earth System Models.

Co-organized by CL5.3
Convener: Sandy Harrison | Co-conveners: Han WangECSECS, Hugo de Boer, Anna Agusti-Panareda
Presentations
| Wed, 25 May, 13:20–15:34 (CEST)
 
Room 2.95

Presentations: Wed, 25 May | Room 2.95

Chairpersons: Sandy Harrison, Hugo de Boer
13:20–13:26
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EGU22-995
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ECS
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Presentation form not yet defined
Wenjia Cai and Iain Colin Prentice

Leaf area index (LAI), defined as one-sided green leaf area per unit ground area, is an important biophysical property of terrestrial vegetation. As the primary locus of mass and energy exchange, leaf area is directly linked with carbon assimilation, evapotranspiration, and the energy and carbon balances of terrestrial ecosystems. Predicting the response of terrestrial vegetation under climate change requires accurate characterization of plant biophysical and biochemical processes in which LAI is a key determinant. Despite many successes, global vegetation and land surface models are still subject to systematic failures and divergences between model projections, indicating a need to develop and test more reliable representations of vegetation-climate interactions. LAI in particular is not well constrained by current models. Here we apply eco-evolutionary optimality (EEO) principles to derive a parsimonious approach to the prediction of LAI by balancing net carbon gain and water loss. Plants are expected to optimally allocate carbon to foliage for light capture and CO2 acquisition, until water losses via transpiration make further canopy development unsustainable. We hypothesize that LAI is limited by the minimum of two values determined by the energy supply for photosynthesis and the water supply by precipitation, respectively. With simple equations, requiring far fewer parameters than typical complex models, we demonstrate a gridded simulated annual maximum LAI that is broadly consistent with a similar measure derived from remotely sensed observations. Further development of this model over different time scales, and its incorporation into vegetation models, would be beneficial to achieve better carbon cycle projections in a changing world.

How to cite: Cai, W. and Prentice, I. C.: Applying eco-evolutionary optimality principles to predict leaf area index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-995, https://doi.org/10.5194/egusphere-egu22-995, 2022.

13:26–13:32
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EGU22-2886
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ECS
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On-site presentation
Remko C. Nijzink and Stanislaus J. Schymanski

Leaf area dynamics are often prescribed in terrestrial biosphere models (TBMs) or based on predefined carbon allocation rules and plant functional types. However, reliance on observational data hampers predictions under future scenarios, as leaf area dynamics and allocation patterns may change due to feedbacks with soil and atmosphere. Therefore, dynamical modelling of leaf area in TBMs based on fundamental principles could greatly improve our ability to better understand and predict vegetation response to environmental change.

The Vegetation Optimality Model (VOM, Schymanski et al., 2009) uses an optimality principle based on the maximization of the Net Carbon Profit (NCP) to predict vegetation properties such as root distributions, photosynthetic capacity and vegetation cover at the daily time scale, as well as water and CO2 exchange at the hourly scale. The NCP is defined as the difference between the total CO2 assimilated by photosynthesis and the carbon costs for construction and maintenance of the light and water harvesting plant organs. In a previous study (Nijzink et al. 2021), we found that the VOM systematically overestimated wet season light absorption and CO2 uptake along the North Australian Tropical Transect (NATT), suggesting that the original big-leaf approach may be missing self-shading effects at high leaf area index (LAI) values. Therefore, we extended the VOM to explicitly consider light absorption as a function of the LAI, and dynamically optimize LAI while considering the carbon costs and benefits of maintaining leaf area. The model was extended step-wise while its predictions were compared to measurements at five flux tower sites along the NATT, with a strong precipitation gradient from north to south.

Here we present the insights gained from this process, including the importance of considering sunlit and shaded leaf area fractions, and separate optimization of photosynthetic capacity for each. In a first step, dynamical leaf area was introduced in the VOM without considering shading, which led to a relatively high CO2-assimilation. Nevertheless, including shaded and sunlit leaf fractions in the big leaf approach of the VOM was not sufficient, as in nature, shaded leaves in the lower canopy have lower photosynthetic capacities than the mostly sunlit upper canopy leaves. For this reason, a separate optimization of photosynthetic capacities, in order to maximize the NCP, was included for shaded and sunlit leaves. Eventually, we will compare the modelled leaf area dynamics and fluxes with remotely sensed LAI and locally measured fluxes at the different flux tower sites along the NATT.

 

References

Nijzink, R. C., Beringer, J., Hutley, L. B., and Schymanski, S. J.:, 2021. Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-265, accepted

Schymanski, S.J., Sivapalan, M., Roderick, M.L., Hutley, L.B., Beringer, J., 2009. An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resources Research 45. https://doi.org/10.1029/2008WR006841

How to cite: Nijzink, R. C. and Schymanski, S. J.: Prediction of leaf area dynamics by maximizing the Net Carbon Profit, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2886, https://doi.org/10.5194/egusphere-egu22-2886, 2022.

13:32–13:38
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EGU22-1752
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ECS
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Presentation form not yet defined
Ruijie Ding, Ning Dong, Jian Ni, and Iain Colin Prentice

Recently developed ecosystem models based on eco-evolutionary optimality hypotheses can predict many aspects of the carbon, water and nutrient economy of ecosystems. These models have focused on various key plant functional traits and their environmental controls. Gross primary production (GPP) is partly determined by the ratio of intercellular to ambient CO2 concentrations (χ), which can be inferred from leaf stable carbon isotope ratios (δ13C). The effect of nitrogen (N) supply on GPP is mediated by the allocation of carbon (C) to leaves, while leaf-level photosynthetic traits (e.g. χ and photosynthetic capacity) and morphological traits (e.g leaf size and leaf mass per area, LMA) are modified or constrained by climate. The amount of N in the leaf is related in part to the quantity of photosynthetic enzymes, indexed by carboxylation capacity at standard temperature (Vcmax,25), and in part to LMA – as all plant tissues, including cell walls, contain N. Plant N isotope ratios (δ15N) are sensitive to the partitioning of N loss from soil between the gaseous and leaching pathways (a balance that is strongly under climatic control), and also to plants’ N uptake strategy (mycorrhizal type or symbiotic N-fixation).

Plant and ecosystem data collected on the Northeast China Transect (NECT) are used here to test a series of quantitative trait predictions based on optimality principles. The NECT is characterized by a long continuous gradient in precipitation and community structure, ranging from moist forests in the east, via grasslands, to semi-desert in the west. We investigated the relationships among leaf traits, ecosystem properties and N loss pathways, including χ, LMA, leaf N per unit area (Narea), leaf area index (LAI, inferred from satellite data), above-ground biomass, and δ15N. The calculations involve testable predictions of intermediate quantities, including community-mean photosynthetic capacity and GPP. By reproducing observed patterns of trait variation along the NECT, this analysis has provided empirical support for an emerging, optimality-based theory for the coupling of C and N cycles in terrestrial ecosystems.

 

How to cite: Ding, R., Dong, N., Ni, J., and Prentice, I. C.: Optimal trait theory: testing predictions on the Northeast China Transect, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1752, https://doi.org/10.5194/egusphere-egu22-1752, 2022.

13:38–13:44
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EGU22-1029
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Virtual presentation
Ziqi Zhu, Han Wang, Sandy P. Harrison, I. Colin Prentice, Shengchao Qiao, and Shen Tan

Recent increases in vegetation cover, observed over much of the world, reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming appears to be declining. Here we examine changes in vegetation cover on the Tibetan Plateau over the past 35 years. Although the climate trends are similar across the Plateau, drier regions have become greener by 0.31±0.14% yr−1 while wetter regions have become browner by 0.12±0.08% yr–1. This divergent response is predicted by a universal model of primary production accounting for optimal carbon allocation to leaves, subject to constraint by water availability. Rising CO2 stimulates production in both greening and browning areas; increased precipitation enhances growth in dry regions, but growth is reduced in wetter regions because warming increases below-ground allocation costs. The declining sensitivity of vegetation to climate change reflects a shift from water to energy limitation. 

How to cite: Zhu, Z., Wang, H., Harrison, S. P., Prentice, I. C., Qiao, S., and Tan, S.: Optimality principles explaining divergent responses of alpine vegetation to environmental change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1029, https://doi.org/10.5194/egusphere-egu22-1029, 2022.

13:44–13:50
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EGU22-9863
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On-site presentation
Oskar Franklin, Peter Fransson, Florian Hofhansl, and Jaideep Joshi

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.

13:50–13:56
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EGU22-949
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ECS
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Virtual presentation
Huiying Xu, Han Wang, I. Colin Prentice, Sandy Harrison, and Ian Wright

Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quanti- tative relationships between hydraulic and photosynthetic traits. We propose a basis for these relationships based on optimality theory, and test its predic- tions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000-m elevation gradient. Hydraulic and leaf economic traits were less plastic, and more closely associated with phy- logeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, vH). The observed coordination between vH and sapwood hydraulic conductivity (KS) and photosynthetic capacity (Vcmax) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between KS and vH. Leaf drought tolerance (inferred from turgor loss point, –Ψtlp) increased with wood density, but the trade-off between hydraulic efficiency (KS) and –Ψtlp was weak. Plant trait effects on vH were dominated by variation in KS, while effects of environment were dominated by variation in temperature. This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a com- mon theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.

How to cite: Xu, H., Wang, H., Prentice, I. C., Harrison, S., and Wright, I.: Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-949, https://doi.org/10.5194/egusphere-egu22-949, 2022.

13:56–14:02
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EGU22-4652
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Presentation form not yet defined
Yair Mau and Yuval Bayer

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.

14:02–14:08
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EGU22-9428
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ECS
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Virtual presentation
David Sandoval, Alienor Lavergne, and Colin Prentice

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(Vcmax/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 (Vcmax) 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 (aexp) 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 aexp against two theoretical formulations of “a”. One (noted atheo1) 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 atheo2), 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 aexp suggests that it is more costly for the ecosystem to transpire during the dry months. We found that atheo1 has opposite seasonal variations to aexp and strongly underestimates “a” during dry months. In contrast, atheo2 shows similar seasonal variations to aexp but generally overestimates the aexp values by almost 4 times. Simple regression analyses showed, as expected, that aexp  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.

14:08–14:14
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EGU22-10673
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ECS
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Presentation form not yet defined
Ning Dong, Benjamin Dechant, and Iain Colin Prentice

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 (Narea) 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 Vcmax,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. Vcmax,25  is predicted via the coordination hypothesis, which posits that Vcmax 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.

14:14–14:20
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EGU22-1847
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ECS
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Highlight
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On-site presentation
Nicholas Smith, Qing Zhu, Trevor Keenan, and William Riley

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.

14:20–14:26
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EGU22-8590
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ECS
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On-site presentation
Jan Lankhorst, Karin Rebel, Jerry van Dijk, and Hugo de Boer

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.

14:26–14:32
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EGU22-9596
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ECS
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On-site presentation
Astrid Odé, Karin Rebel, Martine van der Ploeg, and Hugo de Boer

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 (gs). Leaf morphology plays an essential role herein, as shown by the conservative gs/gsmax 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  gs/gsmax and Vcmax, over different timescales. Here we present empirical data to test predicted effects of changing CO2 concentrations on Vcmax, Ci/Ca, Jmax, gs, 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 Vcmax decreased in both S. dulcamara genotypes. Also, at each CO2 growth level, the dry genotype showed a higher Huber value and a lower Vcmax 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 Vcmax under high CO2 was strongest in the dry genotype, and the downregulation of gs 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 gs, 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.

14:32–14:38
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EGU22-1751
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ECS
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On-site presentation
Jiaze Li and Iain Colin Prentice

Phylogenetic niche conservatism (PNC) in climate space implies consistent climate responses of plant taxa distributed across different geographical regions. PNC can be considered as an expected consequence of optimizing selection. Optimizing selection favours appropriate combinations of plant traits and maintains these combinations over evolutionary time. It enables species to track their optimal environmental conditions, and governs the climatic tolerances of plant lineages. PNC is implicitly required by pollen-based palaeoclimate reconstruction. As pollen is rarely identifiable to the species level by morphological classification, climatic PNC at higher taxonomic levels can justify the use of geographically extensive data sets of contemporary pollen assemblages in the reconstruction of climates of the geologically recent past.

We set out to evaluate the PNC hypothesis in two genera, Picea and Quercus, that are widely distributed in the Holarctic phytogeographic realm. These genera are characteristic of boreal and temperate forest biomes, respectively. We characterized the realized climatic niches of 29 Picea and 160 Quercus species by their optima (u) and tolerances (t) using Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) in a three-dimensional climate space defined by a moisture index (MI, representing plant-available moisture), mean temperature of the coldest month (MTCO, representing winter cold) and growing degree days above a base level of 5 ℃ (GDD5, representing summer warmth). We then used phylogenetic analyses and published phylogenetic data to test whether more closely related species occupy more similar climatic niches. We designed an R function, and developed an index of niche overlap, to test whether the combined climatic ranges of species within each genus are coherent in present-day climate space.

The correlation between climatic niche separation and phylogenetic distance in Picea was found to be weak. This is probably either because (i) parallel evolution leads to similarity among distantly related species; or (ii) analyses on a small phylogenetic scale amplify the divergence among closely related species. Nevertheless, the genus Picea as a whole occupies a coherent climatic niche, consistent with PNC. Quercus showed positive correlations between climatic niche separation and phylogenetic distance. A consistent climatic differentiation between predominantly evergreen versus deciduous clades indicates climatic PNC within major Quercus clades.

These results indicate phylogenetically conserved climatic niches in plant clades with broad geographical distributions, and support the inference of Quaternary climate changes based on pollen assemblages at genus or subgenus levels.

How to cite: Li, J. and Prentice, I. C.: Phylogenetic niche conservatism of Picea and Quercus: analysis and implications for palaeoclimate reconstructions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1751, https://doi.org/10.5194/egusphere-egu22-1751, 2022.

14:38–14:44
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EGU22-9994
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ECS
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On-site presentation
Jaideep Joshi, Iain Colin Prentice, Åke Brännström, Shipra Singh, Florian Hofhansl, and Ulf Dieckmann

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.

14:44–14:50
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EGU22-1331
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ECS
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Highlight
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Presentation form not yet defined
Giulia Mengoli, Sandy P. Harrison, and Iain Colin Prentice

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.

Coffee break
Chairpersons: Han Wang, Anna Agusti-Panareda
15:10–15:16
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EGU22-3531
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ECS
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Virtual presentation
Yanghang Ren, Han Wang, Sandy P. Harrison, I. Colin Prentice, Peter B. Reich, Nicholas G. Smith, and Artur Stefanski

Leaf dark respiration (Rd) accounts for approximately 50% of plant respiration. The acclimation of plant respiration to temperature weakens the positive feedback to global warming. Most existing land surface models (LSMs) adopt an empirical leaf respiration scheme with a constant Rd25 (leaf dark respiration rate at 25°C) for each vegetation type, since there is no acceptable theory of Rd acclimation and how it varies temporally and spatially. Here we propose that Rd25 adjusts to prior nighttime temperature (Tnight) to maintain the ratio of Rd to photosynthesis capacity (Vcmax) approximately constant. To test this hypothesis and explore the time scale of acclimation, we predict Rd25 over different time windows and evaluate these predictions using data from 14 sites from two datasets (Boreal Forest Warming at an Ecotone in Danger (B4WarmED) experiment and Leaf Carbon Exchange dataset (LCE)), one of which provides measurements through time and the other across spatial gradients. Predictions that account for the combined effects of Vcmax and Tnight have better predictive power for all species (mean R2=0.4) than considering the effect of one factor alone. Predictions of acclimation on different timescales show that Vcmax and Tnight averaged over the past fortnight explain the most variation in observed Rd25. These results could provide an alternative solution to the leaf respiration schemes used in LSMs.

How to cite: Ren, Y., Wang, H., Harrison, S. P., Prentice, I. C., Reich, P. B., Smith, N. G., and Stefanski, A.: Nighttime temperature and optimal photosynthetic capacity over the past fortnight jointly control the acclimation of leaf respiration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3531, https://doi.org/10.5194/egusphere-egu22-3531, 2022.

15:16–15:22
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EGU22-3963
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Presentation form not yet defined
Catherine Morfopoulos, Chuanxin Gu, and Prentice Iain Colin

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.

15:22–15:28
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EGU22-1111
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ECS
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On-site presentation
Aliénor Lavergne, Sandy P. Harrison, and Iain Colin Prentice

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, ci/ca), 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 ci/ca) 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.

15:28–15:34
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EGU22-9084
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ECS
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Highlight
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Virtual presentation
Shengchao Qiao, Sandy P. Harrison, I. Colin Prentice, and Han Wang

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.