The validity of optimal leaf traits modelled on environmental conditions

Self Cite

As the ratio of carbon uptake to water use by vegetation, water‐use efficiency (WUE) is a key ecosystem property linking global carbon and water cycles. It can be estimated in several ways, but it is currently unclear how different measures of WUE relate, and how well they each capture variation in WUE with soil moisture availability. We evaluated WUE in an Acacia‐dominated woodland ecosystem of central Australia at various spatial and temporal scales using stable carbon isotope analysis, leaf gas exchange and eddy covariance (EC) fluxes. Semi‐arid Australia has a highly variable rainfall pattern, making it an ideal system to study how WUE varies with water availability. We normalized our measures of WUE across a range of vapour pressure deficits using g1, which is a parameter derived from an optimal stomatal conductance model and which is inversely related to WUE. Continuous measures of whole‐ecosystem g1 obtained from EC data were elevated in the 3 days following rain, indicating a strong effect of soil evaporation. Once these values were removed, a close relationship of g1 with soil moisture content was observed. Leaf‐scale values of g1 derived from gas exchange were in close agreement with ecosystem‐scale values. In contrast, values of g1 obtained from stable isotopes did not vary with soil moisture availability, potentially indicating remobilization of stored carbon during dry periods. Our comprehensive comparison of alternative measures of WUE shows the importance of stomatal control of fluxes in this highly variable rainfall climate and demonstrates the ability of these different measures to quantify this effect. Our study provides the empirical evidence required to better predict the dynamic carbon–water relations in semi‐arid Australian ecosystems.

Section: Discussionsupporting

confidence: 72%

Water‐use efficiency in a semi‐arid woodland with high rainfall variability

Tarín

Nolan

Medlyn

et al. 2019

Self Cite

“…(e.g., μmol/mol) to pressure unit (Pa). All of them refer to growing season or monthly mean values in existing optimality-based models (Bernotas et al, 2019;Bloomfield et al, 2018;.…”

Section: Model Parameterization For V Cmax25c Seasonalitymentioning

confidence: 99%

An optimality‐based model explains seasonal variation in C3 plant photosynthetic capacity

Jiang

Ryu

Wang

et al. 2020

The maximum rate of carboxylation (V cmax) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V cmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/ chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V cmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V cmax,25C (V cmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V cmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m −2 s −1 , which suggests that seasonal changes in V cmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.

“…When net C‐gain is modelled using a common physiological framework but species‐specific Arrhenius parameter values (Figure , Supporting Information Figure S4), net C‐gain shows similar patterns across the climate scenarios, sites, and months for the assessed species as it does in the full model analysis. Since the Arrhenius parameters can be environmentally driven (Dillaway & Kruger, ; Kattge & Knorr, ), it may be possible to capture the range of modelled boreal C‐gain responses here with an environmentally‐based equation (for an example of this approach, see Wang et al, ; Bloomfield et al, ). In the seasonal regional warming with elevated CO 2 scenario, the P. glauca Arrhenius parameter values generated a small increase in net C‐gain across all sites and months.…”

Section: Discussionmentioning

confidence: 99%

“…While six of the seven species modelled here are in the boreal evergreen needleleaf tree PFT (and all species are in the Pinaceae), the large variation in parameterization and modelled net C-gain responses to climate could not be captured by a single set of physiological parameters. Approaches that expand beyond the traditional PFT approach may be more appropriate for modelling boreal tree responses to climate, which include accounting for within-PFT trait variation (Kattge, Knorr, Raddatz, & Wirth, 2009), using a plant functional trait approach (Butler et al, 2017;Peaucelle, Bellassen, Ciais, Peñuelas, & Viovy, 2017;Yang, Zhu, Peng, Wang, & Chen, 2015), incorporating variability in leaf traits (Reich, Rich, Lu, Wang, & Oleksyn, 2014), and recognizing environmentally driven traits (Bloomfield et al, 2018;Wang et al, 2017). However, since our parameterizations were derived from multiple sources, we suggest the need to better quantify physiological diversity in photosynthetic and respiratory parameters across many environments and species through the use of common garden experiments and measurements across species' environmental niches.…”

Section: Discussionmentioning

confidence: 99%

Modelled net carbon gain responses to climate change in boreal trees: Impacts of photosynthetic parameter selection and acclimation

Stinziano

Bauerle

2018

Boreal forests are crucial in regulating global vegetation‐atmosphere feedbacks, but the impact of climate change on boreal tree carbon fluxes is still unclear. Given the sensitivity of global vegetation models to photosynthetic and respiration parameters, we determined how predictions of net carbon gain (C‐gain) respond to variation in these parameters using a stand‐level model (MAESTRA). We also modelled how thermal acclimation of photosynthetic and respiratory temperature sensitivity alters predicted net C‐gain responses to climate change. We modelled net C‐gain of seven common boreal tree species under eight climate scenarios across a latitudinal gradient to capture a range of seasonal temperature conditions. Physiological parameter values were taken from the literature together with different approaches for thermally acclimating photosynthesis and respiration. At high latitudes, net C‐gain was stimulated up to 400% by elevated temperatures and CO2 in the autumn but suppressed at the lowest latitudes during midsummer under climate scenarios that included warming. Modelled net C‐gain was more sensitive to photosynthetic capacity parameters (Vcmax, Jmax, Arrhenius temperature response parameters, and the ratio of Jmax to Vcmax) than stomatal conductance or respiration parameters. The effect of photosynthetic thermal acclimation depended on the temperatures where it was applied: acclimation reduced net C‐gain by 10%–15% within the temperature range where the equations were derived but decreased net C‐gain by 175% at temperatures outside this range. Thermal acclimation of respiration had small, but positive, impacts on net C‐gain. We show that model simulations are highly sensitive to variation in photosynthetic parameters and highlight the need to better understand the mechanisms and drivers underlying this variability (e.g., whether variability is environmentally and/or biologically driven) for further model improvement.