A worldwide analysis of within-canopy variations in leaf structural, chemical and physiological traits across plant functional types

Abstract

Extensive within-canopy light gradients importantly affect the photosynthetic productivity of leaves in different canopy positions and lead to light-dependent increases in foliage photosynthetic capacity per area (AA). However, the controls on AA variations by changes in underlying traits are poorly known. We constructed an unprecedented worldwide database including 831 within-canopy gradients with standardized light estimates for 304 species belonging to major vascular plant functional types, and analyzed within-canopy variations in 12 key foliage structural, chemical and physiological traits by quantitative separation of the contributions of different traits to photosynthetic acclimation. Although the light-dependent increase in AA is surprisingly similar in different plant functional types, they differ fundamentally in the share of the controls on AA by constituent traits. Species with high rates of canopy development and leaf turnover, exhibiting highly dynamic light environments, actively change AA by nitrogen reallocation among and partitioning within leaves. By contrast, species with slow leaf turnover exhibit a passive AA acclimation response, primarily determined by the acclimation of leaf structure to growth light. This review emphasizes that different combinations of traits are responsible for within-canopy photosynthetic acclimation in different plant functional types, and solves an old enigma of the role of mass- vs area-based traits in vegetation acclimation.

Keywords: acclimation; economics spectrum; leaf structure; light gradients; nitrogen (N) allocation; nitrogen content; photosynthetic capacity; plasticity.

Figure 1

Illustration of representative light-dependent variations in leaf dry mass per unit area (a), nitrogen content per dry mass (b) and area (c), photosynthetic nitrogen use efficiency (photosynthetic capacity per unit nitrogen, d), and light-saturated net assimilation rate at ambient CO₂ concentration (photosynthetic capacity) per dry mass (e) and area (f) in the perennial forb Solidago altissima (data of Hirose & Werger, 1987a; Hirose & Werger, 1987b; Werger & Hirose, 1988), perennial grass Phragmites australis (data of Hirose & Werger, 1994; Hirose & Werger, 1995) and in broad-leaved winter-deciduous tree Fagus crenata (data of Iio et al., 2005). Data were fitted by equation 8, and the negative relationship in F. crenata in (b) by equation S4.1 (Notes S4). All relationships are significant at least at P < 0.02, except for the non-significant correlations in F. crenata in (b) and (e) shown by dashed lines. The seasonal average daily integrated quantum flux density (Q_int) is defined as the average for 50 days since the start of leaf development or for the actual number of days for leaves younger than 50 days.

Figure 2

Variation in key foliage structural, chemical, allocational and photosynthetic traits among plant functional types (Eq. 1-5), and correlations among functional trait specific average trait values (insets in b, e, and f). All trait values were standardized to at a constant moderately high integrated seasonal average daily quantum flux density (Q_int, Fig. 1 for the definition) of 12 mol m^-2 d^-1. These Q_int-standardized values were derived from leaf trait vs. Q_int relationships fitted by Eq. 8 and S4.1 (Fig. 1 for sample relationships). The distribution of trait values for different plant functional types is characterized by the box plots, where the box length provides the interquartile range (I_QR), the bottom of the box the 25th percentile (first quartile, q₁) and the top the 75th percentile(third quartile, q₃), and the horizontal line within the box provides the median value. The lower whisker corresponds to q₁-1.5I_QR or to the minimum estimate, and the upper whisker to q₃+1.5I_QR or to the maximum estimate, and the dots stand for values outside the whisker limits. For each trait, the width of the box was set proportional to the square root of the number of gradients available for each trait (Table 1 for the total number of gradients). Plant species were classified among the functional types to include normally at least 20 light gradients for each functional type, but for traits with fewer gradients (Table 1), similar plant functional types with fewer observations were merged to form a coarser classification. The herbaceous plant functional types distinguished were annual and perennial forbs (forbs for the merged group), and annual and perennial C₃ grasses and C₄ grasses (grasses for the merged group). For woody species, the plant functional types separated were winter-deciduous, needle-leaved winter-deciduous, subtropical and tropical broad-leaved evergreens (Evergr. trop.), temperate and Mediterranean broad-leaved evergreens (Evergr. temp) and needle-leaved evergreens (Table S1 for plant functional types for any given species). Average trait values in the insets were fitted by non-linear regressions in the form y = ax^b (panels b and f) and y = ce^dx (all significant at P < 0.001). Table S3 provides significance estimates of log-transformed mean comparisons among plant functional types according to ANOVA followed by Tukey tests.

Figure 3

Variation of the degree of explained variance (r²) in leaf trait vs. integrated light (Q_int) relationships (Eq. 8 and S4.1 in Notes S4) among different foliage structural, chemical, allocational and photosynthetic traits and among different plant functional types illustrated by the box plots (Fig. 2 for a detailed explanation). The first number next to each box provides the percentage of statistically non-significant relationships (Notes S4 for the significance limits), while the second number provides the percentage of statistically significant negative relationships. Classification of data according to plant functional types as in Fig. 2. For significantly different r² values among plant functional types see Table S3.

Figure 4

Correlations of the median values of the relative light-dependent plasticity estimates (P_L, Eq. 9) for the light-saturated net assimilation rate per unit area (photosynthetic capacity, A_A), nitrogen content per area (N_A) and leaf dry mass per area (M_A) among forbs and winter-deciduous trees across different ranges of integrated light (Q_int, Fig. 1 for the definition). Due to non-linear dependence of leaf traits on Q_int, the plasticity decreases with increasing Q_int (Figure S2 for variation of P_L values for theses three traits with Q_int range used for P_L calculation). Although the P_L values for different traits vary with the Q_int range, the figure demonstrates that trait P_L rankings for different plant functional types are essentially insensitive to Q_int range.

Figure 5

Comparison of relative trait plasticity (P_L, Eq. 9) estimated for a moderately high integrated light (Q_int) range of 6-12 mol m^-2 d^-1 within and among key plant functional types. As defined, P_L values are directly comparable for different traits and for the same traits for light gradients with varying average trait values. The box plots for each trait and each plant functional type characterize the distribution of the given trait. Plant functional types and details of box plots as in Fig. 2. The total number of gradients for each trait is reported in Table 1, and statistically significant plasticity differences among plant functional types are demonstrated in Table S3.

Figure 6

Quantitative partitioning of the light-dependent variations in nitrogen content per area (N_A) due to leaf dry mass per unit area (M_A) and nitrogen content per dry mass (N_M, Eq. 3), within and among plant functional types according to the response coefficient analysis (a) and the correlations between the relative light-dependent plasticities in N_A and M_A for the light range of 6-12 mol m^-2 d^-1 (P_L, Eq. 9) in different plant functional types (b). The response coefficient (R_c) for a given trait (section IV.4 and Notes S6, Eq. S6.1-S6.4) provides the fraction of light-dependent variance in the target variable due to the light-dependent variation in the given trait. The response coefficients of N_A for M_A and N_m sum up to 1. Positive R_c values indicate a positive effect of the given trait on the target variable, while negative R_c values indicate a negative effect. R_c values close to zero indicate that light-dependent variation in the given variable has a minor effect on the variation of the target variable (Fig. 2 for the specifics of the box plots as used here and for the logic of separation of plant functional types). Each symbol in (b) corresponds to a different light gradient. For better visual assessment, the outliers in the box plots were suppressed in (a), while the data for the perennial grass Carex acutiformis exhibiting strongly negative P_L values between -0.027 to -0.063 for M_A (see the Results) were suppressed in (b). In (a), the statistical significance among the response coefficients for the given plant functional type (paired t-tests) is denoted as: *** - P < 0.001; ** - P < 0.01; * - P < 0.05; ns - not significantly different. In (b), the functional type specific regressions were compared by SMATR version 2.0 that provides tests for differences among slopes, intercepts and elevations of standardized major axis regressions (Warton et al., 2006). A common slope test indicated that the N_A vs M_A plasticity relationship in herbs had a greater elevation (P < 0.001 for all data and P = 0.02 for a dataset where Carex acutiformis data exhibiting large negative values of M_A plasticity were removed).

Figure 7

Variation in the response coefficients (R_c, section IV.4 and Notes S6, Eq. S6.1-S6.4) describing the light-dependent alteration of foliage photosynthetic capacity per area (A_A) within and among plant functional types as illustrated by the box plots (Fig. 2 for details of the box plots). As detailed in Fig. 6, the response coefficients provide a quantitative means to distribute the light-dependent variation in the target variable among the variations in the constituent traits. Here the response coefficients for the three different functional forms for the expression of the composite trait A_A are provided. The panel (a) demonstrates the response coefficients for leaf dry mass per unit area (M_A) and photosynthetic capacity per dry mass (A_M) for the functional relationship A_A = M_A A_M (Eq. 1), the panel (b) demonstrates the response coefficients for the nitrogen use efficiency (E_N) and nitrogen content per area (N_A) for the relationship A_A = E_N N_A (Eq. 4), and finally, the panel (c) demonstrates the response coefficients for E_N, M_A and N_M (Eq. 4). In all cases, the response coefficients for the given relationship sum up to 1. The latter relationship is the composite of that demonstrated in panel (b) and in Fig. 6a. Because the response coefficients for measured (A_A) and standardized (Notes S2, A_A,V) were strongly correlated (r² = 0.71-0.95) average response coefficients were calculated. The width of the boxes in each panel is proportional to the square root of the number of light gradients (n = 17-70 for all, except for the grasses in the functional relations including M_A where n = 9). The statistical significance among the mean values of the response coefficients for each plant functional type is shown as in Fig. 6.

Figure 8

Quantitative separation of light-dependent variation sources in foliage biochemical potentials by response coefficient (R_c) analyses within and among plant functional types illustrated by the box plots (Fig. 2 for the explanation of box plot characteristics). Foliage biochemical potentials are characterized by Farquhar et al. (1980) biochemical photosynthesis model characteristics, the capacity for ribulose 1,5-bisphosphate carboxylation, V_cmax, and the capacity for photosynthetic electron transport, J_max. As V_cmax and J_max plasticities (Table 1) and the corresponding response coefficients for individual V_cmax and J_max relationships were strongly correlated, we have used average response coefficients for “biochemical photosynthesis capacity” C_A. The response coefficients are shown for three different functional forms of expression of foliage biochemical potentials specified by Eq. 5-7. The panel (a) shows the response coefficients for M_A and C_M (average of the corresponding response confidents for V_cmax,M and J_max,M) for the functional relationship C_A = M_AC_M. The panel (b) shows the response coefficients for N_A and F_i (average of the response coefficients for the fractions of nitrogen in Rubisco, F_R, and in bioenergetics, F_B) for the functional relationship C_A ~ F_iN_A. Finally, the panel (c) shows the response coefficients for M_A, N_M and F_i for the functional relationship C_A ~ F_iM_AN_M. The statistical significance among the response coefficients for each plant functional type as in Fig. 7, except for the last comparison in (c), where P = 0.08.