Global photosynthetic capacity is optimized to the environment

Abstract

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (V_cmax ), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co-optimization of carboxylation and water costs for photosynthesis, suggests that optimal V_cmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field-measured V_cmax dataset for C₃ plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first-order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

Keywords: Carbon cycle; Carboxylation; Jmax; Vcmax; coordination; ecophysiology; electron transport; light availability; nitrogen availability; temperature.

Figure 1

Sensitivity of the theoretical model to environmental drivers. Sensitivity of the theoretical maximum rate of Rubisco carboxylation ($V_{\mathrm{cmax}}^{\prime }$; black, solid lines) and ratio of intercellular to atmospheric CO ₂ concentration (χ; grey dotted lines, panels f, g and h) to the main environmental parameters within the model: growing season mean for irradiance (I _g, panels a and e), air temperature (T _g, panels b and f) and vapour pressure deficit (D _g, panels c and g), as well as elevation (z, panels d and h). In panels a, b, c and d, $V_{\mathrm{cmax}}^{\prime }$ values were mean centred to aid in comparison across environmental parameters. In panels e, f, g and h, values were mean centred and scaled (divided by the standard deviation) to aid comparison of $V_{\mathrm{cmax}}^{\prime }$and χ sensitivities. Sensitivity analyses were done while keeping all other environmental variables at standard levels: I _g  = 800 μmol m⁻² s⁻¹, T _g  = 25 °C, D _g  = 1 kPa, z = 0 km. Note: χ is insensitive to I _g, and as such, no dashed grey line was plotted_.

Figure 2

Comparison of observed to optimal $V_{\mathrm{cmax}}^{\prime }$. Observed mean maximum rate of Rubisco carboxylation ($V_{\mathrm{cmax}}^{\prime }$) at 201 global sites plotted against the predicted $V_{\mathrm{cmax}}^{\prime }$value at that site from the theoretical model. Sites are coloured by Köppen climate classification. Tropical (first letter A), arid (first letter B), temperate (first letter C), boreal (first letter D) and polar (first letter E) regions are represented by red, yellow, green, blue and grey colours. Error bars represent standard errors of the mean. The solid black line is the best fit line from the reduced major axis regression. The grey‐shaded area represents a 95% confidence interval. The dotted black line is a 1:1 line. Köppen climate classification key: Af= tropical rainforest, Am= tropical monsoon, Aw= tropical wet savannah, BSh= hot arid steppe, BSk= cold arid steppe, BWh= hot arid desert, BWk= cold arid desert, Cfa= temperate hot summer without dry season, Cfb= temperate warm summer without dry season, Cfc= temperate cold summer without dry season, Csa= temperate hot summer with dry summer, Csb= temperate warm summer with dry summer, Cwa= temperate hot summer with dry winter, Cwb= temperate warm summer with dry winter, Dfa= boreal hot summer without dry season, Dfb= boreal warm summer without dry season, Dfc= boreal cold summer without dry season, Dsc= boreal cold summer with dry summer, Dwc= boreal cold summer with dry winter, EF= eternal winter, ET= tundra. A version of this figure with individual points can be found in the Supplementary Information (Figure S8).

Figure 3

Partial residuals of the observed bias (%) in maximum rate of Rubisco carboxylation ($V_{\mathrm{cmax}}^{\prime }$) predicted by the theoretical model at each of the 201 sites plotted against growing season light (I _g), growing season temperature (T _g), growing season leaf‐to‐air vapour pressure deficit (D _g), and elevation (z) (grey circles). Model bias was defined as $\frac{V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }-V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}{V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}\ast 100$, where $V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }$ is the predicted optimal $V_{\mathrm{cmax}}^{\prime }$ and $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$ is the observed $V_{\mathrm{cmax}}^{\prime }$. Data points are sized logarithmically by $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$. Lines indicate the modelled response from the multiple linear regression models. Shading indicates 95% confidence intervals for regression lines. Only significant trends (P < 0.05) are shown. Colours are as in Figure 2.

Figure 4

Partial residuals of the observed bias (%) in maximum rate of Rubisco carboxylation ($V_{\mathrm{cmax}}^{\prime }$) predicted by the theoretical model by site plotted against leaf nitrogen per leaf area (N _a; n = 98) and leaf mass per leaf area (LMA; n = 112) (grey circles). Model bias was defined as $\frac{V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }-V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}{V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}\ast 100$, where $V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }$ is the predicted optimal $V_{\mathrm{cmax}}^{\prime }$ and $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$ is the observed $V_{\mathrm{cmax}}^{\prime }$. Data points are sized logarithmically by $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$. Lines indicate the modelled response from the multiple linear regression models. Shading indicates 95% confidence intervals for regression lines. Only significant trends (P < 0.05) are shown. Colours are as in Figure 2.

Figure 5

Model bias in relation to soil variables. Partial residuals of the observed bias (%) in the maximum rate of Rubisco carboxylation predicted by the theoretical model ($V_{\mathrm{cmax}}^{\prime }$) by site plotted against soil cation exchange capacity (CEC, panel a), pH (panel b), carbon‐to‐nitrogen ratio (C:N, panel c), silt content (panel d), clay content (panel e), and an index of soil water availability (α; panel f) (black transparent circles). Model bias was defined as $\frac{V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }-V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}{V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }}\ast 100$, where $V_{\mathrm{cmax}\left[\mathrm{pred}\right]}^{\prime }$ is the predicted optimal $V_{\mathrm{cmax}}^{\prime }$ and $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$ is the observed $V_{\mathrm{cmax}}^{\prime }$. Data points are sized logarithmically by $V_{\mathrm{cmax}\left[\mathrm{obs}\right]}^{\prime }$. Lines indicate the modelled response from the multiple linear regression models. Shading indicates 95% confidence intervals for regression lines. Only significant trends (P < 0.05) are shown. Data are plotted for each of the 193 sites that had available soil data. Colours are as in Figure 2.

Figure 6

Globally predicted optimal rates of $V_{\mathrm{cmax}}^{\prime }$. Global ‘present‐day’ optimal rates of maximum Rubisco carboxylation ($V_{\mathrm{cmax}}^{\prime }$) computed using mean growing season irradiance, air temperature, vapour pressure deficit and elevation. Values were calculated at 0.5° resolution using effective growing season mean temperature (T _g; °C), atmospheric vapour pressure deficit (D _g; Pa) and incoming photosynthetically active radiation (I _g; μmol m⁻² s⁻¹) for each location from monthly data provided by the Climatic Research Unit (CRU TS3.24.01) (Harris et al. 2014). Growing season was defined as months having temperatures greater than 0 °C. Elevation (z; m) at each location was obtained from the WFDEI meteorological forcing dataset (Weedon et al. 2014). Atmospheric CO ₂ was assumed to be 400 μmol mol⁻¹ at z = 0 m and converted to Pa for each location based on z.