The leaf economics spectrum and the prediction of photosynthetic light–response curves

Abstract

Summary 1. In this paper we determine whether interspecific variation in entire photosynthetic light–response curves correlates with the leaf traits of the ‘leaf economics spectrum’ (LES) and the degree to which such traits can predict interspecific variation in light–response curves. This question is important because light–response curves are included in many ecosystem models of plant productivity and gas exchange but such models do not take into account interspecific variation in such response curves. 2. We answer this question using original observations from 260 leaves from 130 plants of 65 different species of herbaceous (25) and woody (40) angiosperms. Herbs were grown in growth chambers and gas exchange measurements were taken in the laboratory. Leaf traits and gas exchange measurements for the woody plants were taken in the field. Leaf traits measured were leaf mass per area (LMA), leaf nitrogen concentration (N) and leaf chlorophyll concentration (Chl). We fitted the Mitscherlich and Michaelis–Menten equations of the light–response curve separately for each leaf. This gave (for the Mitscherlich equation) the light compensation point (ϕ), the quantum yield at the light compensation point (q(ϕ)), and maximum net photosynthesis (Amax) and (for the Michaelis–Menten equation), the maximum gross photosynthesis (Gmax), the half saturation coefficient (k) and the dark respiration rate (Rd). 3. Amax and q(ϕ) were highly correlated with the measured leaf traits but ϕ was not. All three parameters of the Michaelis–Menten equations were correlated with the leaf traits. Allometric equations predicting the parameters of the Mitscherlich and Michaelis–Menten equations by N and LMA are presented. Replacing the leaf‐specific parameters by these general allometric equations based on leaf N and LMA gave good predictions of net photosynthetic rates over the entire range of irradiance (r = 0·79–0·98) but with a downward bias for the herbs when the most general allometric equations are used. 4. These results further extend the generality of the LES and may allow available information from large leaf trait data bases to be incorporated into ecosystem models of plant growth and gas exchange.

Functional diversity of leaf nitrogen concentrations drives grassland carbon fluxes

“…To our knowledge this is the first study attempting to link ecosystem C light response curves to functional diversity indices; we identified predictors whose relevance needs to be further tested in order to extend our understanding from species‐specific light response curves (Marino et al . ) to ecosystem‐level responses. For example, the FDQ‐gs+CWM‐height model predicted best the curvature of the light response curves (θ) (Table ).…”

“…Alongside biomass and LAI effects, we found that the functional diversity indices based on leaf N concentrations (as measured by FD Q -leafN% and FD Q -SLN) are retained as relevant predictors for three of the four parameters (AQY, Rdark and maxNEE) describing the non-rectangular hyperbola function. To our knowledge this is the first study attempting to link ecosystem C light response curves to functional diversity indices; we identified predictors whose relevance needs to be further tested in order to extend our understanding from species-specific light response curves (Marino et al 2010) to ecosystem-level responses. For example, the FDQ-gs+CWM-height model predicted best the curvature of the light response curves (h) ( Table 2).…”

Genetic architecture of life history traits and environment-specific trade-offs

“…2005). Within environments, the relationship between SLA and net photosynthesis is robust, but the relationship may be decoupled across environments (Marino et al. 2010; Price & Weitz 2010; Blonder et al.…”

Interspecific covariation between stomatal density and other functional leaf traits in a local flora

“…Given this, it is surprising that so little is known about large scale interspecific covariation between this leaf attribute and other leaf traits. Recent work documenting and quantifying the major axes of variation in leaf functional traits, including some components of gas exchange (photosynthetic rate and stomatal conductance), have ignored variation in stomatal density (D) simply because this variable is rarely studied in an interspecific context (Reich et al 1997;Wright et al 2004;Shipley et al 2006;Marino et al 2010). However, recent comparative work on covariation between D, genome size, and cell size suggests some tantalizing relationships between D and leaf functional traits.…”