Conifers, Angiosperm Trees, and Lianas: Growth, Whole-Plant Water and Nitrogen Use Efficiency, and Stable Isotope Composition (δ¹³C and δ¹⁸O) of Seedlings Grown in a Tropical Environment

Growth

Following Masle and Farquhar (1988), and based on earlier treatments (Blackman, 1919; Evans, 1972), we write the following expression to describe factors that influence the relative rate of C accumulation of a plant:(1)where r is relative growth rate (mol C mol⁻¹ C s⁻¹), m_c is plant C mass (mol C), t is time (s), A is leaf photosynthetic rate (mol C m⁻² s⁻¹), l is the light period as a fraction of 24 h, ϕ_c is the proportion of C gained in photosynthesis that is subsequently used for respiration by leaves at night and by roots and stems during day and night, and ρ is the ratio of plant C mass to leaf area (mol C m⁻²). Equation 1 provides a useful tool for examining sources of variation in r among plant species and individuals within a species. It is similar to the classical decomposition of r into net assimilation rate (NAR; g m⁻² s⁻¹) and leaf area ratio (LAR; m² g⁻¹), but allows the assimilation term to be expressed as a net photosynthetic rate, such as would be measured using standard gas exchange techniques (Long et al., 1996). Table I provides definitions of all abbreviations and symbols used in this article.

NUE

Multiplying both sides of Equation 1 by the molar ratio of plant C to N yields an expression for the NUE of C accumulation:(2)where NUE is whole-plant NUE (mol C mol⁻¹ N s⁻¹), m_n is plant N mass (mol N), A_n is photosynthetic NUE (mol C mol⁻¹ N s⁻¹), and n_l is the proportion of plant N allocated to leaves. Equation 2 provides a basis for linking A_n, a trait often quantified in ecophysiological investigations, with NUE, an integrated measure of NUE at the whole-plant level.

Transpiration Efficiency and C Isotope Discrimination

The ratio of C gain to water loss at the leaf level during photosynthesis can be expressed as the ratio of the diffusive fluxes of CO₂ and water vapor into and out of the leaf, respectively (Farquhar and Richards, 1984):(3)where E is transpiration (mol H₂O m⁻² s⁻¹), c_a and c_i are CO₂ partial pressures in ambient air and leaf intercellular air spaces, respectively, v is the leaf-to-air vapor pressure difference, and 1.6 is the ratio of diffusivities of CO₂ and H₂O in air. The v is defined as e_i-e_a, where e_i and e_a are the intercellular and ambient vapor pressures, respectively. The ratio of C gain to water loss can be scaled to the whole-plant level by taking into account respiratory C use and water loss not associated with photosynthesis (Farquhar and Richards, 1984; Hubick and Farquhar, 1989):(4)where TE_c is the transpiration efficiency of C gain, and ϕ_w is unproductive water loss as a proportion of water loss associated with C uptake, the former mainly comprising water loss at night through partially open stomata. Thus, ϕ_w can be approximated as E_n/E_d, where E_n is nighttime transpiration and E_d is daytime transpiration.

We suggest that the leaf to air vapor pressure difference, v, can be written as the product of the air vapor pressure deficit (D), and a second term, ϕ_v, which describes the magnitude of v relative to D, such that v = Dϕ_v. This allows Equation 3 to be written as(5)Weighting TE_c by D facilitates comparison of the transpiration efficiency of plants grown under different environmental conditions by accounting for variation due to differences in atmospheric vapor pressure deficit (Tanner and Sinclair, 1983; Hubick and Farquhar, 1989). Thus, it accounts for variation in TE_c that is purely environmental. The D·TE_c has units of Pa mol C mol⁻¹ H₂O.

Photosynthetic discrimination against ¹³C (Δ¹³C) shares a common dependence with TE_c on c_i/c_a. The Δ¹³C in C₃ plants relates to c_i/c_a according to the following equation (Farquhar et al., 1982; Farquhar and Richards, 1984; Hubick et al., 1986):(6)where a is the discrimination against ¹³C during diffusion through stomata (4.4‰), b is discrimination against ¹³C during carboxylation by Rubisco (29‰), and d is a composite term that summarizes collectively the discriminations associated with dissolution of CO₂, liquid phase diffusion, photorespiration, and dark respiration (Farquhar et al., 1989a). The term d may be excluded from Equation 5, in which case the reduction in Δ¹³C caused by d is often accounted for by taking a lower value for b. The Δ¹³C is defined with respect to CO₂ in air as Δ¹³C = R_a/R_p − 1, where R_a is ¹³C/¹²C of CO₂ in air and R_p is ¹³C/¹²C of plant C. Combining Equations 5 and 6 gives(7)Equation 7 suggests a negative linear dependence of TE_c (or D·TE_c) on Δ¹³C, although it can be seen that there are many other terms in Equation 7 that have the potential to influence the relationship between the two.

O Isotope Enrichment

It has been suggested that measurements of the O isotope enrichment of plant organic material (Δ¹⁸O_p) can provide complementary information to that inferred from Δ¹³C in analyses of plant water-use efficiency (Farquhar et al., 1989b, 1994; Sternberg et al., 1989; Yakir and Israeli, 1995). Specifically, Δ¹⁸O_p could provide information about the ratio of ambient to intercellular vapor pressures, e_a/e_i, and thus about the leaf-to-air vapor pressure difference, e_i-e_a, during photosynthesis. Note that e_i-e_a is equal to v in Equation 3. In the steady state, water at the evaporative sites in leaves becomes enriched in ¹⁸O relative to water entering the plant from the soil, according to the following relationship (Craig and Gordon, 1965; Dongmann et al., 1974; Farquhar and Lloyd, 1993):(8)where Δ¹⁸O_e is the ¹⁸O enrichment of evaporative site water relative to source water, ε⁺ is the equilibrium fractionation between liquid water and vapor, ε_k is the kinetic fractionation that occurs during diffusion of water vapor out of the leaf, and Δ¹⁸O_v is the discrimination of ambient vapor with respect to source water. The Δ¹⁸O of any water or dry matter component is defined with respect to source water (water entering the roots from the soil) as Δ¹⁸O = R/R_s − 1, where Δ¹⁸O is the ¹⁸O enrichment of the component of interest and R and R_s are the ¹⁸O/¹⁶O ratios of the component of interest and source water, respectively. The ε⁺ can be calculated as a function of leaf temperature (Bottinga and Craig, 1969), and ε_k can be calculated by partitioning the resistance to water vapor diffusion between stomata and boundary layer, with the two weighted by appropriate fractionation factors (Farquhar et al., 1989b; Cappa et al., 2003). The Δ¹⁸O_v can be calculated from measurements of the δ¹⁸O of ambient vapor and source water. If such data are not available, a reasonable approximation is to estimate Δ¹⁸O_v as −ε⁺, which means that ambient vapor is assumed to be in isotopic equilibrium with soil water. An up-to-date summary of equations necessary for parameterization of Equation 8 can be found in Cernusak et al. (2007b).

Average lamina leaf water ¹⁸O enrichment (Δ¹⁸O_L) is generally less than that predicted for evaporative site water (Yakir et al., 1989; Flanagan, 1993; Farquhar et al., 2007), and carbohydrates exported from leaves have been observed to carry the signal of Δ¹⁸O_L rather than Δ¹⁸O_e (Barbour et al., 2000b; Cernusak et al., 2003, 2005; Gessler et al., 2007). The Δ¹⁸O_L has been suggested to relate to Δ¹⁸O_e according to the following relationship (Farquhar and Lloyd, 1993; Farquhar and Gan, 2003):(9)where ℘ is a Péclet number, defined as EL/(CD₁₈), where E is transpiration rate (mol m⁻² s⁻¹), L is a scaled effective path length (m), C is the molar concentration of water (mol m⁻³), and D₁₈ is the diffusivity of H₂¹⁸O in water (m² s⁻¹). The C is a constant, and D₁₈ can be calculated from leaf temperature (Cuntz et al., 2007). The constancy of L, or otherwise, is currently under investigation (Barbour and Farquhar, 2004; Barbour, 2007; Kahmen et al., 2008; Ripullone et al., 2008). If L is assumed relatively constant, Equation 9 predicts that Δ¹⁸O_L will vary as a function of both Δ¹⁸O_e and E. To test for an influence of E on Δ¹⁸O_L, it is necessary to first account for variation in Δ¹⁸O_L caused by Δ¹⁸O_e (Flanagan et al., 1994). To this end, the relative deviation of Δ¹⁸O_L from Δ¹⁸O_e (1 − Δ¹⁸O_L/Δ¹⁸O_e) can be examined, in which case Equation 9 can be written as(10)Equation 10 predicts that 1 − Δ¹⁸O_L/Δ¹⁸O_e should increase as E increases.

The transfer of the leaf water ¹⁸O signal to plant organic material can be described by the following equation (Barbour and Farquhar, 2000):(11)where Δ¹⁸O_p is ¹⁸O enrichment of plant dry matter, p_ex is the proportion of O atoms that exchange with local water during synthesis of cellulose, a primary constituent of plant dry matter, p_x is the proportion of unenriched water at the site of tissue synthesis, ε_wc is the fractionation between organic oxygen and medium water, and ε_cp is the difference in Δ¹⁸O between tissue dry matter and the cellulose component. For tree stems, p_exp_x has been found to be relatively constant at about 0.4 (Roden et al., 2000; Cernusak et al., 2005). The ε_wc is relatively constant at about 27‰ (Barbour, 2007), and for stem dry matter, ε_cp appears to be relatively constant at about −5‰ (Borella et al., 1999; Barbour et al., 2001; Cernusak et al., 2005). If these assumptions are valid, variation in Δ¹⁸O_p should primarily reflect variation in Δ¹⁸O_L. Thus, combining Equations 10 and 11 provides a means of testing for an influence of E on Δ¹⁸O_p, assuming that Δ¹⁸O_p provides a time-integrated record of Δ¹⁸O_L (Barbour et al., 2004):(12)

Growth, Photosynthesis, and Elemental Concentrations

Daytime meteorological conditions over the course of the experiment are shown in Table II . Dates of initiation of transpiration measurements and harvest for each species are shown in Table III . Table III also shows the initial and final dry masses, in addition to root/shoot ratios. Variation in relative growth rate, r, among species is shown in Figure 1A ; variation in the components of r, A, and 1/ρ, is shown in Figure 1, B and C, respectively. The r varied significantly among functional groups (P < 0.0001), and among species within functional groups (P < 0.0001). Gymnosperm trees had the lowest mean value of r, whereas angiosperm lianas had the highest mean value; angiosperm trees had a mean value of r intermediate between that of gymnosperm trees and angiosperm lianas (Fig. 1A). In contrast, there was less variation among species and functional groups in instantaneous photosynthesis rates expressed on a leaf area basis (Fig. 1B), although the species Pinus caribaea and Stigmaphyllon hypargyreum were notable for having relatively high values of A. Variation in r tended to be more closely associated with variation in 1/ρ than with variation in A. Gymnosperm trees had the lowest mean value of 1/ρ, whereas angiosperm trees had an intermediate mean value, and angiosperm lianas had the highest mean value (Fig. 1C). The liana species S. hypargyreum possessed swollen, tuberous roots, which caused it to have a root/shoot ratio much higher than any other species in the study (Table III), and to have a reduced 1/ρ relative to the other two liana species (Fig. 1C).

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Figure 1.

A to C, Variation among species in mean relative growth rate (A), net photosynthesis, expressed on a leaf area basis (B), and leaf area per unit plant C mass, 1/ρ (C). Error bars represent 1 se. Sample sizes for each species are given in Table III.

Variation in instantaneous photosynthesis, when expressed on a leaf mass basis, was a good predictor of variation in r (Fig. 2 ). The former was measured over several minutes, whereas the latter was measured over several months. Mass-based photosynthesis, A_m, is the product of A (mol m⁻² s⁻¹) and specific leaf area (SLA; m² kg⁻¹). The correlation between A_m and r was almost entirely driven by variation in SLA, because A on a leaf area basis was not significantly correlated with r (P = 0.14, n = 94).

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Figure 2.

Mean relative growth rate plotted against instantaneous measurements of photosynthesis expressed on a leaf mass basis. White symbols with internal cross-hairs refer to gymnosperm tree species; completely white symbols refer to angiosperm liana species; black symbols and black symbols with internal cross-hairs refer to angiosperm tree species.

The C and N concentrations of leaves, stems, roots, and whole plants for each species are given in Supplemental Table S1. For whole-plant C concentration, there was significant variation, both among functional groups (P < 0.0001), and among species within functional groups (P < 0.0001), as shown in Figure 3A . Gymnosperm trees had a mean C concentration of 49.6%, significantly higher than angiosperm trees and lianas. Angiosperm trees and lianas did not differ from each other in whole-plant C concentration, and had mean values of 45.4% and 44.9%, respectively. For whole plant N concentration, there was also significant variation among functional groups (P < 0.0001) and among species within functional groups (P < 0.0001), as shown in Figure 3B. Angiosperm lianas had the highest mean whole-plant N concentration at 1.22%, followed by angiosperm trees at 1.01%, then by gymnosperm trees at 0.82%. Accordingly, there was significant variation among functional groups (P < 0.0001) and among species within functional groups (P < 0.0001) in whole-plant C/N mass ratio (Fig. 3C). Gymnosperm trees had the highest mean whole-plant C/N at 64.7 g g⁻¹, followed by angiosperm trees at 49.6 g g⁻¹, then by angiosperm lianas at 39.0 g g⁻¹.

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Figure 3.

A to C, Variation among species in the C concentration of dry matter on a whole-plant basis (A), the N concentration of dry matter on a whole-plant basis (B), and the C/N mass ratio of dry matter on a whole-plant basis (C). Error bars represent 1 se. Sample sizes for each species are given in Table III.

Concentrations of phosphorus (P), calcium (Ca), and potassium (K), and the N/P mass ratio in leaf dry matter for each species are shown in Table IV . There was significant variation among functional groups (P < 0.0001) and among species within functional groups (P < 0.0001) for all elements and for N/P. Angiosperm lianas tended to have higher mean concentrations of P, Ca, and K in their leaf dry matter than angiosperm and gymnosperm trees. The mean leaf N/P was higher in angiosperm trees than in gymnosperm trees or angiosperm lianas; mean values were 9.3, 4.9, and 4.7 g g⁻¹, respectively.

When expressed on a leaf area basis, the leaf P concentration was significantly correlated with mean transpiration rate (MTR) across all individuals (R² = 0.24, P < 0.0001, n = 94). The equation relating the two was P_area = 0.096MTR + 2.75, where P_area is in mmol m⁻², and MTR is in mol m⁻² d⁻¹.

NUE

Equation 2 presents a means for analyzing variation among functional groups and species in whole-plant NUE (mol C mol⁻¹ N s⁻¹). We calculated NUE as the product of r and m_c/m_n, the whole-plant C to N molar ratio; thus, a relatively high C/N has the effect of increasing NUE. Figure 4A shows variation among species in NUE. There was significant variation among functional groups (P < 0.0001) and among species within functional groups (P < 0.0001). However, unlike results for r, angiosperm trees and lianas did not differ from each other with respect to NUE (P = 0.84). In contrast, NUE of gymnosperm trees was lower than that of both angiosperm trees (P < 0.0001) and angiosperm lianas (P < 0.0001). Figure 4, B and C, shows variation among species in the NUE components, A_n and n_l. Variation in NUE among species tended to reflect variation in A_n, the photosynthetic NUE (Fig. 4B). The A_n was also higher in angiosperm trees and lianas than in gymnosperm trees (Fig. 4B). This variation in A_n was offset to a lesser extent by variation in n_l, the proportion of plant N allocated to leaves (Fig. 4C). Gymnosperm trees had highest mean n_l, at 0.69, followed by angiosperm trees at 0.59, then by angiosperm lianas at 0.50. Thus, a higher allocation of N to leaves in gymnosperm trees compensated to some extent for their much lower A_n. However, the A_n was still the dominant control over NUE (Fig. 5) .

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Figure 4.

A to C, Variation among species in whole-plant NUE (A), photosynthetic NUE (B), and n_l, the leaf N content as a proportion of whole-plant N content (C). Error bars represent 1 se. Sample sizes for each species are given in Table III.

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Figure 5.

Whole-plant NUE plotted against photosynthetic NUE. Whole-plant NUE was calculated from mean relative growth rate, measured over several months, whereas photosynthetic NUE was calculated from instantaneous photosynthesis measurements, taken over several minutes. Different symbols refer to different species, as detailed in Figure 2.

Transpiration Efficiency

Mean values for each species for TE_c, the whole-plant transpiration efficiency of C gain, are shown in Table V . Also shown in Table V are the growth-weighted estimates of the daytime vapor pressure deficit, D_g, by species. There was significant variation, both among functional groups (P < 0.0001), and among species within functional groups (P < 0.0001), in both TE_c and D_g. However, across the full data set, TE_c and D_g were not significantly correlated (P = 0.11, n = 94), suggesting that D_g was not a primary control over TE_c. Taking the product of D_g and TE_c allows analysis of variation in TE_c independently of variation in D_g, as articulated in Equation 5. The D_g·TE_c also varied significantly among functional groups (P < 0.0001) and among species within functional groups (P < 0.0001). Angiosperm trees had the highest mean D_g·TE_c at 1.58 Pa mol C mol⁻¹ H₂O, followed by angiosperm lianas at 1.30 Pa mol C mol⁻¹ H₂O, then by gymnosperm trees at 1.11 Pa mol C mol⁻¹ H₂O. Among all species, there was a 3.7-fold variation in D_g·TE_c (i.e. the largest species mean was 3.7 times the smallest species mean).

Table V summarizes for each species the components of D_g·TE_c that we quantified: ϕ_v, the ratio of leaf-to-air vapor pressure difference to air vapor pressure deficit; ϕ_w, the ratio of unproductive to productive water loss; and c_i/c_a, the ratio of intercellular to ambient CO₂ partial pressures during photosynthesis. Although there was a 1.8-fold variation among species in ϕ_v, this parameter did not appear to be a primary control over D_g·TE_c: the term 1/ϕ_v explained only 13% of variation in D_g·TE_c (R² = 0.13, P = 0.0004, n = 94); moreover, the slope of the relationship between D_g·TE_c and 1/ϕ_v was negative, opposite to that predicted by Equation 5. The parameter ϕ_w similarly did not appear to exert a strong control over D_g·TE_c: although D_g·TE_c was positively correlated with 1/(1 + ϕ_w) (R² = 0.18, P < 0.0001, n = 92), there was only a 1.1-fold variation in this term among species, suggesting that it could only account for a variation in D_g·TE_c of approximately 10%. In contrast, the c_i/c_a appeared to be the primary control over D_g·TE_c. Among species, there was a 2.3-fold variation in instantaneous measurements of 1 − c_i/c_a, and c_i/c_a explained 46% of variation in D_g·TE_c. Regression coefficients are given in Table VI . Furthermore, instantaneous measurements of 1 − c_i/c_a explained 64% of variation in the composite term v_g·TE_c(1 + ϕ_w) (R² = 0.64, P < 0.0001, n = 94). Taking this product means that only the variables ϕ_c, c_a, and c_i/c_a remain on the right side of Equation 5.

Variation in instantaneous measurements of c_i/c_a was largely driven by variation in stomatal conductance, g_s, rather than by variation in photosynthesis, A. If g_s controls variation in c_i/c_a, then c_i/c_a should decrease as 1/g_s increases. The 1/g_s is equivalent to the stomatal resistance. In contrast, if A controls c_i/c_a, then c_i/c_a should decrease as A increases. Figure 6A shows that instantaneous c_i/c_a decreased as a linear function of 1/g_s. In contrast, Figure 6B shows that there was a weak tendency for c_i/c_a to increase as A increased, opposite to the trend that would be expected if A were controlling c_i/c_a.

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Figure 6.

A to D, The top two panels show instantaneous measurements of c_i/c_a plotted against stomatal resistance (A) and photosynthesis (B); the bottom two panels show whole-plant ¹³C discrimination plotted against stomatal resistance (C) and photosynthesis (D). Stomatal resistance is the inverse of stomatal conductance. Different symbols refer to different species, as described for Figure 2.

We used measurements of leaf temperature, taken with a hand-held infrared thermometer, to calculate values of ϕ_v for the species harvested on the second and third harvest dates (Table III). We then compared these instantaneous measurements of ϕ_v with our time-integrated estimates for each plant based on leaf energy balance predictions and meteorological data. The time-integrated estimates of ϕ_v compared favorably with the instantaneous measurements of ϕ_v (R² = 0.42, P < 0.0001, n = 55), thus providing some validation of the former.

Stable Isotope Composition

The C isotope composition of leaves, stems, roots, and whole plants is shown for each species in Table VII . Also shown is the difference in δ¹³C between leaves and the sum of stems plus roots, the heterotrophic component of the plant. Across all individuals, leaf δ¹³C was more negative than stem δ¹³C (P < 0.0001, n = 94) and root δ¹³C (P < 0.0001, n = 94), whereas stem δ¹³C was more negative than root δ¹³C, but by a much smaller amount (P = 0.0008, n = 94); mean values for leaf, stem, and root δ¹³C were −29.4, −28.1, and −27.8‰, respectively.

We converted plant δ¹³C values to ¹³C discrimination by assuming δ¹³C of atmospheric CO₂ to be −8‰. Whole-plant Δ¹³C, Δ¹³C_p, covered a range from 18.8‰ to 22.9‰ among species, corresponding to δ¹³C_p values ranging from −26.3‰ to −30.2‰ (Table VII). There was significant variation in Δ¹³C_p among functional groups (P = 0.002) and among species within functional groups (P < 0.0001). The Δ¹³C_p was lower in angiosperm trees than in gymnosperm trees, whereas angiosperm lianas did not differ significantly from angiosperm or gymnosperm trees. Mean values were 21.0‰, 21.2‰, and 21.5‰ for angiosperm trees, angiosperm lianas, and gymnosperm trees, respectively.

The Δ¹³C_p was significantly correlated with instantaneous measurements of c_i/c_a (Fig. 7 ), as predicted by Equation 6. We estimated the term d of Equation 6 by least-squares regression by assuming fixed values for a and b of 4.4‰ and 29‰, respectively. This resulted in an estimate for d of 3.1‰; the regression equation explained 57% of variation in Δ¹³C_p. Thus, the predictive power of this relationship was equivalent to that obtained with a standard linear regression, in which both the slope and intercept are free to vary (Fig. 7). Using the mean estimate of 3.1‰ for d, and values of 4.4‰ and 29‰ for a and b, respectively, we calculated a Δ¹³C_p-based estimate of c_i/c_a for each plant. Mean values of these estimates for each species are given in Table V. There was a 2.4-fold variation among species in the Δ¹³C_p-based estimates of 1 − c_i/c_a.

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Figure 7.

Whole-plant ¹³C discrimination plotted against the ratio of intercellular to ambient CO₂ partial pressures determined from instantaneous gas exchange measurements. Different symbols refer to different species, as defined in Figure 2.

Variation in Δ¹³C_p was significantly correlated with variation in D_g·TE_c (Fig. 8 ); the former explained 49% of variation in the latter. Regression coefficients and the coefficient of determination for least-squares linear regressions of TE_c, D_g·TE_c, and v_g·TE_c against Δ¹³C of leaves, stems, roots, and whole plants are given in Table VI. In general, whole-plant Δ¹³C was a better predictor of variation in TE_c than Δ¹³C of leaves, stems, or roots individually. Additionally, weighting of TE_c by D_g or v_g tended to result in modest increases in the proportion of variation explained by the regression models (Table VI).

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Figure 8.

The product of transpiration efficiency of C gain (TE_c) and daytime vapor-pressure deficit of ambient air (D_g) plotted against whole-plant ¹³C discrimination. Different symbols refer to different species, as defined in Figure 2. Daytime air vapor-pressure deficit was weighted according to the predicted weekly growth increment for each individual plant.

Correlations between Δ¹³C_p and 1/g_s and A further supported the conclusion that variation in c_i/c_a was largely driven by variation in g_s. The Δ¹³C_p decreased as a linear function of 1/g_s (Fig. 6C). In contrast, the Δ¹³C_p showed a weak tendency to increase as a function of A (Fig. 6D), opposite to the trend that would be expected if A controlled variation in c_i/c_a.

Variation among species in the O isotope composition of stem dry matter is given in Table VII. We calculated the ¹⁸O enrichment above source water of stem dry matter, Δ¹⁸O_p, from the mean δ¹⁸O of irrigation water of −4.3‰. The observed Δ¹⁸O_p was significantly correlated with the predicted ¹⁸O enrichment of evaporative site water, Δ¹⁸O_e, weighted by predicted weekly growth increments (Fig. 9 ). We tested whether the residual variation in Δ¹⁸O_p, after accounting for variation in Δ¹⁸O_e, was related to transpiration rate by plotting 1 − [(Δ¹⁸O_p − ε_wc − ε_cp)/(1 − p_exp_x)]/Δ¹⁸O_e against the MTR. As shown in Equation 12, this term should increase with an increasing transpiration rate if there is a significant Péclet effect. Our analysis detected a significant relationship between the two parameters (R² = 0.14, P = 0.0002, n = 94), supporting the notion of a significant Péclet effect, although there was considerable scatter in the relationship. Using the MTR and E_n/E_d, we calculated a daytime MTR, then used the nonlinear regression routine in SYSTAT to solve for an average value of L, the scaled effective path length, across the full data set. This analysis estimated a mean value of L for the full data set of 53 mm, with the 95% confidence interval ranging from 43 to 62 mm.

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Figure 9.

The ¹⁸O enrichment of stem dry matter relative to irrigation water plotted against the predicted ¹⁸O enrichment of water at the evaporative sites in leaves. The predicted Δ¹⁸O_e was weighted according to the predicted weekly growth increment for each individual plant. Different symbols refer to different species, as defined in Figure 2.

Growth and NUE

We observed that the term 1/ρ was the primary control over variation in r, and that A was a relatively conservative parameter among species (Fig. 1). These results agree with those presented previously for 24 herbaceous species (Poorter and Remkes, 1990; Poorter et al., 1990), and for many woody species (Cornelissen et al., 1996; Atkin et al., 1998; Wright and Westoby, 2000). For the herbaceous species, SLA was also a key component of 1/ρ, such that variation in r was not correlated with variation in A, but was strongly correlated with variation in A_m, the product of A and SLA (Poorter et al., 1990). It should be noted that A was measured near the end of the experiment, and our results thus do not preclude the possibility that variation in A may have modulated r earlier in plant development. Nonetheless, our measurements of A_m explained more than two-thirds of total variation in r (Fig. 2). Among the species that we grew, the gymnosperm trees generally had lowest 1/ρ and r, whereas angiosperm lianas had highest 1/ρ and r, with angiosperm trees intermediate between the two (Fig. 1). This pattern suggests that variation in 1/ρ could be related to hydraulic efficiency, with the tracheid-bearing gymnosperm species constrained by a lower hydraulic conductance for a given plant mass, and thereby requiring a greater plant mass to support a given amount of assimilative, and thus evaporative, surface area. On the other hand, the angiosperm liana species, having freed themselves from the constraint of structural self-sufficiency, and possessing hydraulically efficient vessels, might then have required a smaller plant mass to deliver water to a given leaf surface area. Measurements of whole-plant hydraulic conductance per unit plant mass would be necessary to confirm this hypothesis. However, it would be consistent with differences in hydraulic conductivity observed previously between gymnosperm and angiosperm seedlings (Brodribb et al., 2005).

As shown in Equation 1, the term ϕ_c, the proportion of net C fixation used for respiration, has potential to influence r. It was previously observed for 24 herbaceous species that ϕ_c ranged from about 0.5 to 0.3, and that r was negatively correlated with ϕ_c, as predicted by Equation 1 (Poorter et al., 1990). Although we did not measure ϕ_c in our study, we can speculate that the slower-growing species had higher values, because they generally had higher whole-plant C concentrations (Fig. 3A), which would correlate with higher tissue construction costs (Vertregt and Penning de Vries, 1987; Poorter, 1994). The r was negatively correlated with whole-plant C concentration across the full data set (R² = 0.35, P < 0.0001, n = 94).

The leaf N/P mass ratios that we observed (Table IV) were generally low for tropical vegetation (Reich and Oleksyn, 2004). However, they are consistent with a previous study conducted under similar conditions, but with variable amounts of rice (Oryza sativa) husk mixed into the experimental soil (Cernusak et al., 2007b). At a similar rice husk/soil mixture as used in this study, we previously observed a mean leaf N/P ratio of 5.9 for Ficus insipida (Cernusak et al., 2007b), whereas the overall mean for all species in this study was 7.2. The generally low leaf N/P ratios suggest that plant growth in this study was constrained primarily by N availability, rather than by P availability (Koerselman and Meuleman, 1996; Aerts and Chapin, 2000). This is consistent with the addition of rice husks increasing the C/N ratio of the experimental soil, thereby favoring microbial immobilization of soil N, and thus reducing N availability to plants. This provided a useful experimental basis for comparing NUE among the species in our study, because N was likely the nutrient most limiting plant growth.

We calculated whole-plant NUE as the product of r and m_c/m_n, the whole-plant molar ratio of C to N. Although the gymnosperm species had higher C/N than the angiosperm species (Fig. 4), they were still at a marked disadvantage with respect to NUE. Such disadvantage resulted primarily from a lower A_n (Figs. 4 and 5). This difference between gymnosperm and angiosperm seedlings, whereby the former employed N much less efficiently than the latter for accumulating C, could be decisive in determining competitive outcomes between the two. Although productivity in tropical forests is generally considered P limited, it has also been reported that N availability can constrain tree growth in both montane (Tanner et al., 1998) and lowland (LeBauer and Treseder, 2008) tropical forests. Thus, a higher NUE may contribute to angiosperm dominance in tropical environments. The low A_n in the gymnosperm species compared to the angiosperm species resulted primarily from lower SLA, because A was generally similar between the two groups, and leaf N concentration was lower in gymnosperms than in angiosperms (Supplemental Table S1).

Transpiration Efficiency

The species included in the study exhibited a large variation in the transpiration efficiency of C uptake, TE_c (Table V). This is consistent with previous results showing large variation in TE_c among seven tropical tree species (Cernusak et al., 2007a). When the TE_c for each individual plant was normalized according to its growth-weighted mean daytime vapor pressure deficit, D_g, the variation among species was still apparent, suggesting that D_g was not a primary control over TE_c. Additionally, the relative ranking among species in this study was consistent with results for three species that were also measured previously (Cernusak et al., 2007a); in this study Platymiscium pinnatum had the highest D_g·TE_c, Swietenia macrophylla an intermediate value, and Tectona grandis the lowest value (3.05, 1.12, and 0.82 Pa mol C mol⁻¹ H₂O, respectively). Previously, we observed that TE_c for these three species was 3.97, 2.88, and 1.63 mmol C mol⁻¹ H₂O, respectively (Cernusak et al., 2007a).

In this study, we were able to confirm that c_i/c_a was the primary control over D_g·TE_c. The ϕ_v also showed a moderate variation among species, suggesting that it could be an important source of variation in D_g·TE_c (Table V); however, the ϕ_v tended to be negatively correlated with c_i/c_a, due to a mutual dependence of the two parameters on g_s. Thus, it appeared that variation in ϕ_v mostly served to dampen what would have been the full effect of variation in c_i/c_a on D_g·TE_c. For example, plants with low g_s tended to have low c_i/c_a (Fig. 6), which would increase D_g·TE_c, as shown in Equation 5. All else being equal, the low g_s would also cause leaf temperature to increase, thereby increasing ϕ_v, which would then cause a counteracting decrease in D_g·TE_c. However, it is clear from the large variation in D_g·TE_c and its correlation with c_i/c_a (Table VI) that variation in ϕ_v only dampened, and did not completely cancel, the effect of c_i/c_a on D_g·TE_c. Of the other terms in Equation 5, we found that ϕ_w, the unproductive water loss as a proportion of that associated with photosynthesis, played only a minor role in modulating D_g·TE_c, in agreement with previous results (Cernusak et al., 2007b). Finally we consider variation in 1 − ϕ_c: although this term is an important functional trait, and may play an important role in modulating r, it likely plays a lesser role in controlling D_g·TE_c than c_i/c_a. For example if ϕ_c varied among species from 0.3 to 0.5 (Poorter et al., 1990), the term 1 − ϕ_c would vary from 0.5 to 0.7, whereas we observed variation in 1 − c_i/c_a from 0.12 to 0.27 (Table V). Thus, the former would be associated with a 1.4-fold variation in D_g·TE_c, and the latter with a 2.3-fold variation in D_g·TE_c.

There was significant variation in D_g·TE_c among the plant functional groups that we studied, such that angiosperm trees had highest D_g·TE_c, on average, and gymnosperm trees lowest, with angiosperm lianas intermediate between the two. Angiosperm trees also had an advantage over gymnosperm trees if water-use efficiency was analyzed as TE_c, v_g·TE_c, c_i/c_a, or Δ¹³C_p. Thus, in addition to having an advantage over gymnosperm seedlings in NUE, angiosperm seedlings may also have a competitive advantage in terms of water-use efficiency, when grown in tropical environments. However, it should be emphasized that our experiment was carried out under well-watered conditions, and thus may not necessarily be indicative of trends when water availability is limiting to plant growth.

Of the gymnosperm species that we grew, only one, Podocarpus guatemalensis, occurs naturally in the tropical forests of Panama. Although generally associated with highland forests, this species also occurs on low-lying islands off both the Pacific and Atlantic coasts. Unfortunately, only one individual of P. guatemalensis survived in our experiment, and we therefore excluded it from all species-level analyses. However, the lone surviving individual was interesting in that it had the highest D_g·TE_c and lowest c_i/c_a of any plant in the study (Figs. 7 and 8). The c_i/c_a of 0.63 that we observed for this individual of P. guatemalensis is similar to a c_i/c_a of about 0.60 observed previously for well-watered Podocarpus lawrencii (Brodribb, 1996). Further research is necessary to determine whether high water-use efficiency is a common trait within the genus Podocarpus, and whether this trait contributes to the ability of Podocarpus to persist in otherwise angiosperm-dominated tropical forests.

Stable Isotope Composition

Whole-plant ¹³C discrimination, Δ¹³C_p, showed a strong correlation with instantaneous measurements of c_i/c_a (Fig. 7), suggesting that in general Δ¹³C_p was a faithful recorder of c_i/c_a, as predicted by Equation 6. The mean value for d that we estimated for the full data set was 3.1‰, reasonably similar to a value of 4.0‰, recently estimated for Ficus insipida (Cernusak et al., 2007b). The Δ¹³C_p was also a reasonably good predictor of variation in TE_c, D_g·TE_c, and v_g·TE_c (Table VI). We previously observed that the relationship between Δ¹³C_p and TE_c broke down at the species level, appearing to reflect species-specific offsets in the relationship between the two parameters (Cernusak et al., 2007a). Whereas there was some evidence of similar behavior in this study, as can be seen in Figure 8, the species-level relationship between Δ¹³C_p and D_g·TE_c was generally much stronger in this study. For example, in a least-squares linear regression between Δ¹³C_p and D_g·TE_c using species means, the former explained 57% of variation in the latter (R² = 0.57, P = 0.002, n = 14); if P. guatemalensis was included in the regression, the Δ¹³C_p explained 77% of variation in D_g·TE_c (R² = 0.77, P < 0.0001, n = 15). The main difference between the earlier study (Cernusak et al., 2007a) and this study was likely the range of variation in Δ¹³C_p exhibited by the particular species that comprised the experiments. In the earlier study, mean values for Δ¹³C_p at the species level ranged from only 20.3 to 21.7‰, whereas in this study, species means ranged from 18.8‰ to 22.9‰; including the individual of P. guatemalensis would further extend the lower range to 16.9‰.

Although our results show a generally strong correlation between Δ¹³C_p and D_g·TE_c, we suggest that it is best to err on the side of caution when interpreting variation among species in the former as indicative of variation among species in the latter. As shown in Equation 7, there are many terms with potential to influence the relationship between Δ¹³C_p and D_g·TE_c, not the least of which is variation in d, which could be associated with variation among species in mesophyll conductance to CO₂ (Lloyd et al., 1992; Warren and Adams, 2006; Seibt et al., 2008). Moreover, as was previously the case (Cernusak et al., 2007a), we observed significant variation among species in the difference between δ¹³C of leaves and that of stems and roots (Table VII). The mechanistic basis for such variation among species in the δ¹³C difference between leaves and heterotrophic tissues is not well understood (Hobbie and Werner, 2004; Badeck et al., 2005).

The ¹⁸O enrichment of stem dry matter, Δ¹⁸O_p, varied significantly among species, and much of the observed variation in Δ¹⁸O_p could be explained by variation in Δ¹⁸O_e, the predicted ¹⁸O enrichment of evaporative sites within leaves (Fig. 9). Because plants were grown over different time periods throughout the year, and due to predicted differences in leaf temperature, there was a reasonable variation among species in predicted Δ¹⁸O_e. Species means for growth-weighted Δ¹⁸O_e ranged from 6.9‰ to 13.0‰ (14.6‰ for P. guatemalensis), and explained 73% of variation in the observed species means for Δ¹⁸O_p (R² = 0.73, P = 0.0001, n = 14), or 75% if P. guatemalensis was included (R² = 0.75, P < 0.0001, n = 15). These correlations suggest that the assumptions described in the theory section for the Δ¹⁸O_p model are reasonable. Additionally, we observed that the term 1 − [(Δ¹⁸O_p − ε_wc − ε_cp)/(1 − p_exp_x)]/Δ¹⁸O_e was significantly related to the daytime MTR across the full data set, providing evidence for a Péclet effect, as articulated in Equation 12. This result is consistent with an experiment involving three temperate tree species (Barbour et al., 2004). In this study, we estimated a mean scaled effective path length, L, for the full data set of 53 mm, similar to a value of 54 mm estimated previously for Eucalyptus globulus (Cernusak et al., 2005). Analysis of Δ¹⁸O_p in stem dry matter likely provides an advantage over analysis of leaf dry matter for data sets such as ours, which comprise diverse sets of species, because ε_cp for stem dry matter tends to be less variable within and among species than ε_cp for leaf dry matter (Borella et al., 1999; Barbour et al., 2001; Cernusak et al., 2004, 2005).

Given a sound theoretical understanding of sources of variation in δ¹³C and δ¹⁸O in plant dry matter, it should be possible to use such isotopic data to constrain physiological models of tropical forest trees. Data from the present experiment support the suggestion that measurements of δ¹³C can be used to make time-integrated estimates of c_i/c_a at the tree or stand scale. The photosynthetic rate, A, can then be predicted from c_i, assuming c_a is known or can be predicted. Finally, g_s can be calculated from A, c_i, and c_a. An example of this modeling approach was recently provided (Buckley, 2008), along with a discussion of its advantages and disadvantages. In the case of δ¹⁸O, it should be possible to use this signal to reconstruct the ratio of ambient to intercellular vapor pressures, e_a/e_i, during photosynthesis (Farquhar et al., 1989b; Sternberg et al., 1989). The strong relationship that we observed between stem dry matter Δ¹⁸O_p and the predicted Δ¹⁸O_e (Fig. 9) supports this idea. However, our analysis also confirmed that the relationship between Δ¹⁸O_p and Δ¹⁸O_e was further modified by transpiration rate, E, suggesting that it may be necessary to obtain information independently about E to calculate e_a/e_i from Δ¹⁸O_p. Such information might be obtained from sap flux measurements or eddy covariance data, for example. Assuming e_a is known or can be predicted, an estimate of e_i based on Δ¹⁸O_p might then be particularly valuable, as it was recently suggested that the leaf-to-air vapor pressure difference, e_i-e_a, will likely be an important control over productivity in tropical forest trees in the face of changing climate (Lloyd and Farquhar, 2008).

Study Site and Plant Material

The study was carried out at the Santa Cruz Experimental Field Facility, a part of the Smithsonian Tropical Research Institute, located in Gamboa, Republic of Panama (9°07′ N, 79°42′ W), at an altitude of approximately 28 m above sea level. Average meteorological conditions at the study site during the experiment are shown in Table II. These values were calculated from data collected on site every 15 min by an automated weather station (Winter et al., 2001, 2005). Seedlings of Cupressus lusitanica, Pinus caribaea, and Thuja occidentalis were obtained from a commercial nursery in Chiriqui Province, Republic of Panama. All other species were grown from seed collected in the Panama Canal watershed, or obtained as seedlings from PRORENA, a native species reforestation initiative operated through the Center for Tropical Forest Science at the Smithsonian Tropical Research Institute. Familial associations for each species are shown in Table III. The species C. lusitanica and P. caribaea are conifers, with native distributions extending from Mexico to Nicaragua. T. occidentalis is a conifer native to northeastern North America, and Tectona grandis is a timber species native to south and southeast Asia. All other species included in the study occur naturally in Panama.

The initial dry mass at the commencement of transpiration measurements for each species is shown in Table III; these dry masses were estimated by harvesting three to five individuals judged to be similar in size to the seedlings retained for the experiment. Seedlings were transplanted individually into 38-L plastic pots (Rubbermaid Round Brute; Consolidated). Each pot contained 25 kg of dry soil mixture, which comprised 60% by volume dark, air-dried top soil, and 40% by volume air-dried rice (Oryza sativa) husks. The rice husks were added to improve soil structure and drainage. The pot water content was brought to field capacity by the addition of 8 kg of water. The soil surface was covered with 2 kg of gravel to minimize soil evaporation, and the outer walls of each pot were lined with reflective insulation to minimize heating by sunlight. A metal trellis was added to each pot containing a liana seedling. The pots were situated under a rain shelter with a glass roof, such that there was essentially no interception of rain by the pots in the otherwise open-air conditions. The initiation of measurements varied among species, due to the temporal variation in the availability of seed and seedlings. Harvest dates also varied, depending on the date of initiation of measurements and growth rates; there were three harvests in total. The start and end dates of transpiration measurements for each species are given in Table III.

Growth and Transpiration Efficiency Measurements

Pots were weighed at a minimum frequency of once per week to the nearest 5 g with a 64-kg capacity balance (Sartorius QS64B; Thomas). After the mass was recorded, water was added to each pot to restore it to its mass at field capacity. As plant water use increased with increasing plant size, the pots were weighed and watered more frequently. We endeavored to maintain pot water content above 5 kg at all times, such that the range of soil water contents experienced by the plants ranged approximately from field capacity to 60% of field capacity. Control pots without plants were deployed among the pots with plants in a ratio of one control pot to each six planted pots. The control pots were weighed each week to estimate soil evaporation. Cumulative plant water use was calculated as the sum of pot water loss over the course of the experiment minus the average water loss of control pots for the same time period. Shortly before plant harvest, the pots were weighed at dawn and dusk for 2 d to calculate nighttime transpiration separately from daytime transpiration. Immediately following plant harvest, total plant leaf area was measured with a leaf area meter (LI-3100; LI-COR). Leaves, stems, and roots were separated at harvest and oven-dried to a constant mass at 70°C; they were then weighed to the nearest 0.02 g.

The mean relative growth rate, r, of each plant was calculated as r = [ln(m_c2) − ln(m_c1)]/t, where ln(m_c2) and ln(m_c1) are natural logarithms of the C mass at the end and beginning of the experiment, respectively, and t is the duration of the experiment (Blackman, 1919). The MTR over the course of the experiment was calculated as the cumulative water transpired divided by the leaf area duration (Sheshshayee et al., 2005): MTR = E_t/[(LA₁ + LA₂)0.5t], where E_t is cumulative water transpired, and LA₁ and LA₂ are the leaf area at the beginning and end of the experiment, respectively. The transpiration efficiency of C gain, TE_c, was calculated as TE_c = (m_c2 − m_c1 + l_c)/E_t, where l_c is the C mass of leaf litter abscised during the experiment.

To compare the D experienced by species that were grown over different time periods (Table III), we calculated a growth-weighted D for each individual plant. Dry matter increments were predicted at weekly time steps for each plant using relative growth rates calculated over the full experiment. Thus, the dry matter increment for week 1, w₁, was calculated as w₁ = m₁ − m₀, where m₀ was initial plant dry mass, and m₁ was calculated as m₁ = m₀e^rt, with t = 7 d; the r was calculated as described above. The dry matter increment in week 2, w₂, was then calculated as w₂ = m₂ − m₁, where m₂ was calculated as m₂ = m₁e^rt, with t again set at 7 d, and so on. For each week during the experimental period, we also calculated an average daytime D from the meteorological data collected at 15-min intervals. Growth-weighted D, D_g, was then calculated as(13)where D_i is the average daytime D for week i (kPa), and w_i is the predicted dry matter increment for week i (g).

In addition to calculating a growth-weighted D for each species, we also predicted a growth-weighted v. Average weekly v for each plant was predicted using a leaf energy balance model developed by D.G.G. dePury and G.D. Farquhar (unpublished data), and described by Barbour et al. (2000a). The model was parameterized with weekly average daytime values for air temperature, relative humidity, irradiance, and wind speed taken from the data collected by the automated weather station. Stomatal conductance for each plant, measured as described below, was further used to parameterize the model. The model predicted average weekly daytime leaf temperature for each plant. The intercellular water vapor pressure, e_i, was then calculated as the saturation vapor pressure at leaf temperature, and this value was used to calculate an average weekly value of v for each plant. The growth-weighted v, v_g, was then calculated as in Equation 13, but replacing D_i with v_i, the average daytime v for week i.

In a similar fashion, we predicted a growth-weighted Δ¹⁸O_e for each plant. For each weekly time step, the predicted value of e_a/e_i was used with Equation 8 to calculate average weekly daytime Δ¹⁸O_e. The Δ¹⁸O_v was assumed equal to −ε⁺, calculated from air temperature (Bottinga and Craig, 1969). Growth-weighted Δ¹⁸O_e, Δ¹⁸O_eg, was then calculated for each plant as in Equation 13, but replacing D_i with Δ¹⁸O_ei, the average daytime Δ¹⁸O_e for week i.

Leaf Gas Exchange and Leaf Temperature Measurements

We measured leaf gas exchange on three to five leaves per plant in the week preceding plant harvest with a Li-6400 portable photosynthesis system (LI-COR). Leaves were illuminated with an artificial light source (6400-02B LED; LI-COR) at a photon flux density of 1,200 μmol m⁻² s⁻¹. Measurements were made during both the morning and afternoon for each plant, and the mean of the two sets of measurements was taken for each individual. The mean leaf temperature during measurements was 32.7 ± 1.5°C (mean ± 1 sd), and the mean v was 1.55 ± 0.46 kPa (mean ± 1 sd).

Several days prior to the harvests that took place on March 11, 2006 and May 10, 2006 (Table III), we made measurements of leaf temperature with a hand-held infrared thermometer (Raytek MT Minitemp; Forestry Suppliers). Measurements were repeated two to three times on three to five leaves per plant under clear-sky conditions near midday. Values for each plant were averaged, and v was calculated from measurements of relative humidity and air temperature, assuming e_i was at saturation at the average leaf temperature for each plant. Leaf temperature was not measured for the plants harvested on December 13, 2005 (Table III).

Stable Isotope and Elemental Analyses

Leaf, stem, and root dry matter were ground to a fine, homogeneous powder for analysis of isotopic and elemental composition. The δ¹³C, total C, and total N concentrations were determined on subsamples of approximately 3 mg, combusted in an elemental analyzer (ECS 4010; Costech Analytical Technologies) coupled to a continuous flow isotope ratio mass spectrometer (Delta XP; Finnigan MAT). The δ¹⁸O of stem dry matter was determined on subsamples of approximately 1 mg (Delta XP; Finnigan MAT), following pyrolysis in a high-temperature furnace (Thermoquest TC/EA; Finnigan MAT). Analyses were carried out at the Stable Isotope Core Laboratory, Washington State University. The δ¹³C and δ¹⁸O values were expressed in δ notation with respect to the standards of PeeDee Belemnite and Vienna Standard Mean Ocean Water, respectively. The ¹³C discrimination of plant dry matter (Δ¹³C_p) was calculated as Δ¹³C_p = (δ¹³C_a − δ¹³C_p)/(1 + δ¹³C_p), where δ¹³C_a is the δ¹³C of CO₂ in air and δ¹³C_p is that of plant dry matter. We assumed a δ¹³C_a of −8‰. The oxygen isotope enrichment of stem dry matter (Δ¹⁸O_p) was calculated as Δ¹⁸O_p = (δ¹⁸O_p − δ¹⁸O_s)/(1 + δ¹⁸O_s), where δ¹⁸O_p is δ¹⁸O of stem dry matter, and δ¹⁸O_s is that of irrigation (source) water. Irrigation water was drawn from two 800-L tanks, sealed to prevent evaporation, which were periodically refilled with tap water, to buffer against short-term variation in δ¹⁸O_s. The tank water had a mean δ¹⁸O of −4.3 ± 0.5‰ (mean ± 1 sd, n = 6); we therefore calculated Δ¹⁸O_p assuming a δ¹⁸O_s of −4.3‰.

Leaf dry matter was further analyzed for P, K, and Ca concentrations by acid digestion and detection on an inductively coupled plasma optical-emission spectrometer (Perkin Elmer). Leaf samples were prepared by digesting approximately 200 mg of sample material under pressure in polytetrafluoroethylene vessels with 2 mL of concentrated nitric acid.

Statistical Analyses

We analyzed relationships between continuous variables using least-squares linear regression. Variation among species and among functional groups (gymnosperm trees, angiosperm trees, and angiosperm lianas) was assessed with a nested design in the general linear model routine of SYSTAT 11 (SYSTAT Software); the functional group and species nested within the functional group were taken as independent factors. For these analyses, the number of observations was 93, the degrees of freedom for the functional group was 2, the degrees of freedom for species nested within the functional group was 11, and the degrees of freedom error was 79. Pairwise comparisons among species or functional groups were then carried out according to Tukey's method. Among the study species, there was one, Podocarpus guatemalensis, for which only one individual survived. All other species comprised between five and eight individuals, as shown in Table III. Because there was only one individual of P. guatemalensis, we excluded this species from analyses aimed at assessing variation among functional groups and species. However, we included the individual in linear regression analyses of continuous variables. We considered it important to report data for this individual, as it represents the only gymnosperm species in the study native to the tropical forests of Panama.

Supplemental Data

The following materials are available in the on-line version of this article.

• Supplemental Table S1. The C and N concentrations of experimental plants.