- Top of page
- Materials and Methods
- Supporting Information
The crown architecture of plants is highly diverse, but it remains unclear how this diversity affects light interception and growth across species. Leaf display depends on a multitude of morphological traits (Halléet al., 1978; Barthélémy & Caraglio, 2007; Valladares & Niinemets, 2007), but it is difficult to generalize how these traits influence the light interception efficiency of individual plants. Moreover, different crown traits may result in similar light interception efficiencies (light interception per unit leaf area), suggesting functional equivalence of different architectural layouts (Valladares et al., 2002). In this paper, we develop a coherent theoretical framework that allows the diversity in crown architectures seen among individual plants to be compared, in terms of their light interception efficiency. As plant productivity over long time-scales is approximately proportional to intercepted light (Monteith, 1977; Cannell et al., 1987), it is hoped that such a framework can provide a basis for understanding differences in productivity between plants and vegetation types.
Although plants vary in a myriad of qualitative architectural properties, models predicting light interception typically focus on a small number of quantitative features of the canopy, such as total leaf area, leaf angle distribution, and leaf dispersion (clumped, random, or regular) (Campbell & Norman, 2000), as these can be reliably quantified for different species and vegetation types. The well-known Lambert–Beer model estimates light interception by horizontally homogeneous canopies, assuming that leaves are randomly distributed in space (Monsi & Saeki, 1953, 2005):
- (Eqn 1)
Qint, intercepted photosynthetic photon flux density (PPFD); Q0, incident PPFD above the canopy; L, the leaf area index; k, an extinction coefficient.) However, random leaf spacing is clearly a poor assumption for most real canopies, where foliage is clumped at shoot and whole-plant levels. As a result, model errors can be very large (Baldocchi et al., 1985; Whitehead et al., 1990; Cescatti, 1998). For this reason, a leaf dispersion parameter is frequently introduced in Eqn 1 (Nilson, 1971; Ross, 1981). The leaf dispersion parameter is typically estimated from inversion of PPFD transmission measurements in canopies (Nilson, 1971; Cescatti & Zorer, 2003), and is rarely related to direct measurements of canopy structure. A notable exception is a shoot clumping parameter developed for conifers (Oker-Blom & Smolander, 1988; Stenberg, 1996), but it is not clear how this parameter could be applied to other plant architectures. To this end, Sinoquet et al. (2007) developed a method based on the spatial variance of foliage in tree crowns, but this method seems to be difficult to apply to field measurements. The lack of a simple operational method to account for grouping of foliage is thus limiting our ability to link canopy light interception to plant and canopy structure.
A few attempts have been made to provide simplified models that account for grouping of foliage at the whole-plant scale (Jackson & Palmer, 1979; Kucharik et al., 1999; Chen et al., 2008; Ni-Meister et al., 2010). However, the resulting models are often complex, with many species-specific parameters that are time-consuming to obtain. Furthermore, available models do not provide estimates of light interception by individual plants, limiting their usage to stand-scale applications. Estimates of light interception on an individual plant level are needed in individual-based models of vegetation dynamics (e.g. Moorcroft et al., 2001; Falster et al., 2011). A different field of study has avoided simplification altogether by developing highly detailed three-dimensional plant models with spatially explicit representation of leaves and stems. These studies have provided valuable insights into the details of plant architecture and how it affects plant performance in specific environments (see reviews by Valladares & Pearcy, 1999; Pearcy et al., 2005; Vos et al., 2010). However, detailed architecture studies are yet to discover which aspects of canopy structure most influence whole-plant light interception, in part because of the limited sample sizes that necessarily result when using such methods.
Generally speaking, plant crowns are defined by the number, size, shape, three-dimensional distribution and orientation of their leaves. Together, these variables determine the size of the crown, the arrangement of leaves inside the crown, and the average leaf overlap (‘self-shading’) when viewed from a given direction. We define light interception efficiency as the ratio of displayed (i.e. exposed) to total leaf area, averaged over the entire sky hemisphere () (Farque et al., 2001; Delagrange et al., 2006) (see Table 1 for a list of symbols). relates directly to the amount of diffuse radiation intercepted by the plant, which can play an important role in determining total carbon uptake (Ackerly & Bazzaz, 1995; Roderick et al., 2001). Direct light interception also scales with the sunlit leaf area fraction (Campbell & Norman, 2000), which is probably correlated with . Simple indices of self-shading – such as have also been shown to predict total carbon uptake. For example, comparing branches from 38 perennial species, Falster & Westoby (2003) found that > 90% of variation in whole-branch CO2 assimilation rate expressed per unit leaf area (excluding differences in leaf photosynthetic capacity) was accounted for by an index of self-shading. has also been used in other applications to rank light interception efficiency of whole plants (Delagrange et al., 2006; Sinoquet et al., 2007) and shoots (Niinemets et al., 2005), suggesting that it provides a reliable indicator of plant performance.
Table 1. Symbols, their definitions and units
|L||Leaf area index||m2 m−2|
|AC||Crown surface area – total surface area of 3D convex hull wrapped around the leaf cloud||m2|
|AL||Total plant leaf area||m2|
|AD,Ω||Displayed leaf area from angle Ω||m2|
|HΩ||Crown silhouette area from angle Ω||m2|
|PΩ||Crown porosity from angle Ω||–|
|Displayed leaf area averaged over all angles||m2|
|aL||Mean leaf area of individual leaves||m2|
|N||Total plant leaf number||–|
|K||Leaf projection coefficient averaged over all viewing angles||m2 m−2|
|k||Extinction coefficient for a homogenous canopy||m2 m−2|
|kΩ||Leaf projection coefficient from angle Ω||m2 m−2|
|Silhouette to total area ratio, averaged over all viewing angles||m2 m−2|
|β||Leaf dispersion parameter||–|
|Ω||Viewing angle (elevation, azimuth pair)||–|
|fΩ||Weighting function for AD,Ω||–|
|O5||Observed average distance to five nearest leaves||m|
|E5||Expected average distance to five nearest leaves for a random distribution||m|
As possible predictors of , we propose two simple whole-plant variables: crown density and leaf dispersion. These variables were selected based on a statistical model predicting shading within the crown, based on similar models independently developed by Sinoquet et al. (2007) and Duursma & Mäkelä (2007), but with a new leaf dispersion parameter. The model is derived (see the Materials and Methods section) by viewing the plant first from one direction, and estimating the leaf area displayed in that direction from the silhouette of the crown, the number of leaves, the mean leaf area, and the mean leaf angle. After making simplifying assumptions, this leads to an approximation for the average leaf area displayed in all directions:
- (Eqn 2)
(AL/AC, the crown density (ratio of plant leaf area, AL, to crown surface area, AC); β, a leaf dispersion parameter; ε and φ, empirical parameters.) The ‘extinction coefficient’K is constant because we integrate over the entire hemisphere (Stenberg, 2006) (see the Materials and Methods section). The crown surface area is defined as the total surface area of a crown, that is, the area of a sheet wrapped around the crown. It is calculated as the surface area of the three-dimensional shape constructed by joining all outlying points of the plant crown, so that the shape is convex (i.e. there are no indentations in the three-dimensional shape). The remarkable aspect of the approximation (Eqn 2) is that only two plant variables, crown density (AL/AC) and leaf dispersion (β), are needed to estimate , in addition to the two (constant) parameters. In this paper we test the hypothesis that, across plants of diverse architecture, size, and growth environments, variation in can be explained by crown density and leaf dispersion. To do so, we used a database of 1831 virtual plants, reconstructed from precise digitization of the position and orientation of leaves and stems of real plants, to estimate (cf. Farque et al., 2001). We then compared these empirical estimates to those given by the simplified model. Finally, we tested the hypothesis that declines with increasing plant size for a given species (Farque et al., 2001; Niinemets et al., 2005; Delagrange et al., 2006; Sinoquet et al., 2007), and whether this decline is related to changes in leaf dispersion or crown density.
- Top of page
- Materials and Methods
- Supporting Information
Figure 3. (a) Illustration of the two components of the leaf dispersion parameter. The mean distance to five nearest leaves is a decreasing function of the leaf number density (number of leaves per unit crown volume). For a random distribution, these mean distances (E5) were obtained by numerical simulation. The thick line indicates the mean, and the thin lines the 5 and 95% quantiles; the variation in E5 at a given leaf number density is the result of edge effects and, to a lesser extent, stochastic effects of the numerical simulation. For real plants (grey circles), the mean distance to five nearest leaves (O5) was calculated for each leaf, and averaged. The leaf dispersion parameter (β) is calculated as O5/E5, so that β < 1 indicates a clumped distribution, and β > 1 a more uniform distribution. (b) Histogram of the leaf dispersion parameter as estimated for all plants in the data set.
Download figure to PowerPoint
Figure 4. Measured (silhouette to total area ratio, averaged over all viewing angles) of the virtual plants (estimated with YPLANT) compared against the modelled estimates from the summary model (Eqn 2). The dashed line is a regression line (y = −0.011 + 1.03x, R2 = 0.848, RMSE, root mean squared error = 0.025). Each point is a plant (1831 plants).
Download figure to PowerPoint
Table 2. Diagnostics of the nonlinear regression fit of the simple model of (Eqn 2) to the whole data set
|Model||R2||RMSE (m2 m−2)|
Variation between and within species dwarfed the influences of family and division membership on differences in light interception efficiency. The model performed equally well – or better – when fitted to each plant family separately, with one exception (Myrtaceae) (Fig. S2). However, the estimated parameter values (φ and ε) did not vary greatly between families. These results show that the excellent fit of the model to the full data set was not confounded by taxonomic relatedness, and that the same model can predict for a variety of taxa. However, there was some indication of taxa-specific effects that are not accounted for in the simple model. Partitioning of the residual variance from the full model showed that 14.1% was related to division (Gymnosperm or Angiosperm), 3.7% to family, 36.9% to species, and the remaining 45.6% to individual within species. Thus, an additional 8.25% of total variance in (55% of residual variance) could be attributed to species, family, or division, in addition to the 85% of total variance already accounted for by crown density and leaf dispersion.
Within species, the leaf dispersion parameter was closely related to total leaf number (N). There was a strong tendency for plants with more leaves to exhibit a more clumped leaf dispersion (Fig. 6a). This observed correlation was directly related to N, because at a given AL, β increased with mean leaf size (i.e. plants with smaller leaves have more clumped foliage) (Fig. 6b). Secondly, at a given leaf size, the dispersion parameter (β) decreased with AL (i.e. larger plants had more clumped foliage) (Fig. 6c). These two patterns are confounded when leaf dispersion is plotted against either leaf size or leaf area, because larger leaved species tended to have greater total area in our data set. To illustrate this, we fit a simple regression model to leaf dispersion (β) as a function of total plant leaf area (AL) and mean leaf size (aL), as β = bALc×aLd (see Fig. 6), which can also be rearranged as β = aNc*aL(c+d). Because c ≈ −0.15 and d ≈ 0.15, the leaf size component cancels, leaving a dependence of β on number of leaves (with exponent ≈ −0.15).
Figure 6. Leaf dispersion is related to number of leaves per plant (N), total plant leaf area (AL) and mean leaf size (aL). Leaf dispersion (a) decreases markedly with N, and (b) increases with aL at a given total plant leaf area (AL), and (c) decreases with AL at a given aL. The solid black curves are predictions from a regression model of the form: , fitted to the entire data set by linear regression after log-transformation (R2 = 0.71, b = 0.39, c = −0.15, d = 0.15; interaction not significant).
Download figure to PowerPoint
Finally, we tested the hypothesis that declines with plant size. We found a strong relationship between and total leaf number (N) (Fig. 7a). Out of the 65 species in the data set that had more than five replicates, 50 showed a significant negative correlation between and N. To assess whether this decrease in with increasing N was attributable to leaf dispersion (Fig. 6) or crown density (AL/AC), we tested for size-related trends in AL/AC by assessing the scaling of AL as AL = aACb (Fig. 7b). If the exponent b > 1, this would result in an increase in AL/AC with AC, and would lead to decreased light interception efficiency with increasing crown size. Out of the 65 species, 38 showed an exponent not significantly different from unity (the mean exponent estimated by SMA was 1.05; SD = 0.25). For the remainder of the data set, 18 species had an exponent > 1, and only nine had an exponent < 1. There is, then, no general evidence that AL/AC increased with plant size in this data set, and the decrease in with N is entirely attributable to a more clumped leaf dispersion in larger plants.
Figure 7. (a) The relationship between total leaf number (N) and (silhouette to total area ratio, averaged over all viewing angles) for a subset of the data set, including 65 species with at least five replicates (1647 plants). The lines are standardized major axis fits to each species separately. (b) Scaling of total plant leaf area (AL) with crown surface area (AC) for the same data set as in (a). The lines are standardized major axis fits to each species separately. The dashed line is a 1 : 1 line.
Download figure to PowerPoint