Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species

Abstract

The leaf maximum carboxylation rate (Vcmax) is a crucial parameter in determining the photosynthetic capacity of plants. Providing accurate estimates of leaf Vcmax, that cover large geographic areas and incorporate plant seasonality is central to correctly predicting carbon fluxes within the terrestrial global carbon cycle. Chlorophyll, as the main photon‐harvesting molecule in leaves, is closely linked to plant photosynthesis. However, how the nature of the relationship between the leaf maximum carboxylation rate (scaled to 25°C; Vcmax,25) and leaf chlorophyll content varies according to plant type is uncertain. In this study, we investigate whether a universal and stable relationship exists between leaf Vcmax,25 and leaf chlorophyll content across different plant types and verify it using field experiments. Measurements of leaf chlorophyll content (Chl) and CO2 response curves were made on 283 crop, shrub, tree, and vegetable leaves, across 13 species, in China and southern Ontario, Canada. A strong relationship was found between the leaf Vcmax,25 and chlorophyll content across different plant types (R2 = 0.65, p < 0.001). Cross‐validation showed that the model performs well, producing an RMSE value of 15.4 μmol m−2 s−1. The results confirm that leaf chlorophyll content can be a reliable proxy for estimating Vcmax,25, opening the door to accurate, spatially continuous estimates of Vcmax,25 at the global scale.

A Photosynthetic Light Acclimation Model Accounting for the Effects of Leaf Age, Chlorophyll Content, and Intra-Leaf Radiation Transfer

“…Being an integration variable (e.g., Equation 10) it also influences intra-leaf absorption parameters via Gaussian process regression. This fits well with recent observations in various species of V cmax and J max - Chl relations being better predictors than leaf nitrogen ( Qian et al, 2021 ). However, neither its repeatable measurement nor its empirical prediction of Chl in time and space seems to be trivial.…”

Leaf chlorophyll parameters and photosynthetic characteristic variations with stand age in a typical desert species (Haloxylon ammodendron)

“…Chl is a key factor affecting photosynthetic capacity and vegetation productivity and can be accurately retrieved, so it can be used as a proxy for V cmax25 at large scales to resolve the uncertainty in V cmax25 in C cycle modeling ( Croft et al., 2013 ; Croft et al., 2017 ; Qian et al., 2021 ). LUE max is also an important parameter for productivity estimation.…”

“…Photosynthesis is not only the core component of the carbon cycle in terrestrial ecosystems and the initial driving force of materials and energy cycles on earth but also has a key role in maintaining the carbon–oxygen balance of the atmosphere ( Sellers et al., 1997 ; Zhang et al., 2020 ; Qian et al., 2021 ). Leaf chlorophyll (Chl), being the most important and abundant pigment in the plant photosystem (PS), contributes to the conversion of solar radiation into chemical energy ( Croft et al., 2020 ; Qian et al., 2021 ). Photosynthetic characteristics can reflect the plant’s photosynthetic capacity, physiological and ecological adaptation patterns to different environments, and light utilization strategies ( Li et al., 2010 ).…”

“…Regrettably, the accurate simulation of LUE max is still an issue that needs to be addressed. Scientists hypothesized a relationship between chlorophyll content and GPP and confirmed that chlorophyll content is a better proxy for photosynthetic capacity than leaf N ( Croft et al., 2017 ; Qian et al., 2021 ). This idea is innovative, so we ponder whether chlorophyll content can be used as a proxy for LUE max .…”

Global net biome CO2exchange predicted comparably well using parameter–environment relationships and plant functional types

“…Indeed, macroclimatic and biophysical factors like temperature, atmospheric aridity, water supply, and nutrient availability strongly impact the strategies by which plants grow, allocate resources, and respond to stress (e.g., Woodward, 1987). In practice, this concept-which broadly underlies certain large-scale predictive ecological frameworks like FLUXCOM (Jung et al, 2020)-is implemented by deriving mathematical relationships between community mean traits and environmental covariates (e.g., Boonman et al, 2020;Butler et al, 2017;Chaney et al, 2016;Moreno-Martínez et al, 2018;Ordoñez et al, 2009;Peaucelle et al, 2019;Qian et al, 2021). However, while recent work focusing on a small subset of model parameters shows that these flexible, data-driven EF relationships can be feasibly implemented directly within large-scale TBMs (Verheijen et al, 2013(Verheijen et al, , 2015Walker et al, 2017), the degree to which such an approach may impact the quality of simulated carbon fluxes-including NBE predictions-is not known.…”