OPEN ACCESS ARTICLE

This Article Free via Open Access: OA OA Abstract Full Text (PDF) Supplemental Data OA All Versions of this Article: 148/1/642    most recent pp.108.123521v1 Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via ISI Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Cernusak, L. A. Articles by Turner, B. L. Search for Related Content PubMed PubMed Citation Articles by Cernusak, L. A. Articles by Turner, B. L. Agricola Articles by Cernusak, L. A. Articles by Turner, B. L. Related Collections Biology of Transpiration

WHOLE PLANT AND ECOPHYSIOLOGY

Conifers, Angiosperm Trees, and Lianas: Growth, Whole-Plant Water and Nitrogen Use Efficiency, and Stable Isotope Composition (¹³C and ¹⁸O) of Seedlings Grown in a Tropical Environment^1,[W],[OA]

Smithsonian Tropical Research Institute, Balboa, Ancon, Republic of Panama

	ABSTRACT

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 
Seedlings of several species of gymnosperm trees, angiosperm trees, and angiosperm lianas were grown under tropical field conditions in the Republic of Panama; physiological processes controlling plant C and water fluxes were assessed across this functionally diverse range of species. Relative growth rate, r, was primarily controlled by the ratio of leaf area to plant mass, of which specific leaf area was a key component. Instantaneous photosynthesis, when expressed on a leaf-mass basis, explained 69% of variation in r (P < 0.0001, n = 94). Mean r of angiosperms was significantly higher than that of the gymnosperms; within angiosperms, mean r of lianas was higher than that of trees. Whole-plant nitrogen use efficiency was also significantly higher in angiosperm than in gymnosperm species, and was primarily controlled by the rate of photosynthesis for a given amount of leaf nitrogen. Whole-plant water use efficiency, TE_c, varied significantly among species, and was primarily controlled by c_i/c_a, the ratio of intercellular to ambient CO₂ partial pressures during photosynthesis. Instantaneous measurements of c_i/c_a explained 51% of variation in TE_c (P < 0.0001, n = 94). Whole-plant ¹³C discrimination also varied significantly as a function of c_i/c_a (R² = 0.57, P < 0.0001, n = 94), and was, accordingly, a good predictor of TE_c. The ¹⁸O enrichment of stem dry matter was primarily controlled by the predicted ¹⁸O enrichment of evaporative sites within leaves (R² = 0.61, P < 0.0001, n = 94), with some residual variation explained by mean transpiration rate. Measurements of carbon and oxygen stable isotope ratios could provide a useful means of parameterizing physiological models of tropical forest trees.

Conifers dominated the world's forests prior to the Cretaceous radiation in angiosperm diversity. However, conifers are largely absent from the lowland tropical and subtropical forests of today. It has been suggested that one means by which angiosperm tree species are able to out-compete gymnosperm tree species in tropical environments is through faster seedling growth caused by improved hydraulic efficiency (Bond, 1989; Brodribb et al., 2005). Angiosperm xylem tissue contains vessels, specialized water-conducting cells that are generally larger in diameter, and therefore more conductive to water, than conifer tracheids (Sperry et al., 2006). Conifer tracheid diameters are biomechanically constrained because these cells must perform the dual function of conducting water and providing structural support to woody tissues, whereas vessels need not perform the latter function in angiosperm wood. Lianas are large woody vines that occur predominantly in tropical forests; by attaching themselves to neighboring trees, they have evolved an additional means of freeing their xylem tissues from structural constraints. Thus, angiosperm lianas may achieve further increases in hydraulic efficiency compared to angiosperm trees (Gartner et al., 1990).

In this study, we grew seedlings of several species of gymnosperm trees, angiosperm trees, and angiosperm lianas in a tropical environment. We used this functionally diverse range of species to quantify the physiological controls over their C and water fluxes. We also took advantage of the contrasting physiology of the study species to test the theoretical basis for variation in the C and oxygen (O) stable isotope composition of plant dry matter.

	THEORY

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 

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, _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 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.

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 _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, _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, _v, which describes the magnitude of v relative to D, such that v = D_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 (¹³C) shares a common dependence with TE_c on c_i/c_a. The ¹³C 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 ¹³C caused by d is often accounted for by taking a lower value for b. The ¹³C is defined with respect to CO₂ in air as ¹³C = 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 ¹³C, 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 (¹⁸O_p) can provide complementary information to that inferred from ¹³C in analyses of plant water-use efficiency (Farquhar et al., 1989b, 1994; Sternberg et al., 1989; Yakir and Israeli, 1995). Specifically, ¹⁸O_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 ¹⁸O_e is the ¹⁸O enrichment of evaporative site water relative to source water, ⁺ is the equilibrium fractionation between liquid water and vapor, _k is the kinetic fractionation that occurs during diffusion of water vapor out of the leaf, and ¹⁸O_v is the discrimination of ambient vapor with respect to source water. The ¹⁸O of any water or dry matter component is defined with respect to source water (water entering the roots from the soil) as ¹⁸O = R/R_s – 1, where ¹⁸O 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 ⁺ can be calculated as a function of leaf temperature (Bottinga and Craig, 1969), and _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 ¹⁸O_v can be calculated from measurements of the ¹⁸O of ambient vapor and source water. If such data are not available, a reasonable approximation is to estimate ¹⁸O_v as –⁺, 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 (¹⁸O_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 ¹⁸O_L rather than ¹⁸O_e (Barbour et al., 2000b; Cernusak et al., 2003, 2005; Gessler et al., 2007). The ¹⁸O_L has been suggested to relate to ¹⁸O_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 ¹⁸O_L will vary as a function of both ¹⁸O_e and E. To test for an influence of E on ¹⁸O_L, it is necessary to first account for variation in ¹⁸O_L caused by ¹⁸O_e (Flanagan et al., 1994). To this end, the relative deviation of ¹⁸O_L from ¹⁸O_e (1 – ¹⁸O_L/¹⁸O_e) can be examined, in which case Equation 9 can be written as

(10)

Equation 10 predicts that 1 – ¹⁸O_L/¹⁸O_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 ¹⁸O_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, _wc is the fractionation between organic oxygen and medium water, and _cp is the difference in ¹⁸O 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 _wc is relatively constant at about 27 (Barbour, 2007), and for stem dry matter, _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 ¹⁸O_p should primarily reflect variation in ¹⁸O_L. Thus, combining Equations 10 and 11 provides a means of testing for an influence of E on ¹⁸O_p, assuming that ¹⁸O_p provides a time-integrated record of ¹⁸O_L (Barbour et al., 2004):

(12)

	RESULTS

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 

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/, 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/ than with variation in A. Gymnosperm trees had the lowest mean value of 1/, 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/ relative to the other two liana species (Fig. 1C).

View larger version (16K): [in this window] [in a new window] [as a PowerPoint slide]   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.

View larger version (22K): [in this window] [in a new window] [as a PowerPoint slide]   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/ (C). Error bars represent 1 SE. Sample sizes for each species are given in Table III.

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.

View larger version (20K): [in this window] [in a new window] [as a PowerPoint slide]   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.

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) .

View larger version (22K): [in this window] [in a new window] [as a PowerPoint slide]   Figure 4. A to C, Variation among species in whole-plant NUE (A), photosynthetic NUE (B), and nl, 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.

View larger version (17K): [in this window] [in a new window] [as a PowerPoint slide]   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.

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).

View larger version (26K): [in this window] [in a new window] [as a PowerPoint slide]   Figure 6. A to D, The top two panels show instantaneous measurements of ci/ca 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.

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 ¹³C between leaves and the sum of stems plus roots, the heterotrophic component of the plant. Across all individuals, leaf ¹³C was more negative than stem ¹³C (P < 0.0001, n = 94) and root ¹³C (P < 0.0001, n = 94), whereas stem ¹³C was more negative than root ¹³C, but by a much smaller amount (P = 0.0008, n = 94); mean values for leaf, stem, and root ¹³C were –29.4, –28.1, and –27.8, respectively.

The ¹³C_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 ¹³C_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 ¹³C_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 ¹³C_p-based estimates of 1 – c_i/c_a.

View larger version (15K): [in this window] [in a new window] [as a PowerPoint slide]   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.

View larger version (15K): [in this window] [in a new window] [as a PowerPoint slide]   Figure 8. The product of transpiration efficiency of C gain (TEc) and daytime vapor-pressure deficit of ambient air (Dg) 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.

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, ¹⁸O_p, from the mean ¹⁸O of irrigation water of –4.3. The observed ¹⁸O_p was significantly correlated with the predicted ¹⁸O enrichment of evaporative site water, ¹⁸O_e, weighted by predicted weekly growth increments (Fig. 9 ). We tested whether the residual variation in ¹⁸O_p, after accounting for variation in ¹⁸O_e, was related to transpiration rate by plotting 1 – [(¹⁸O_p – _wc – _cp)/(1 – p_exp_x)]/¹⁸O_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.

View larger version (16K): [in this window] [in a new window] [as a PowerPoint slide]   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 18Oe was weighted according to the predicted weekly growth increment for each individual plant. Different symbols refer to different species, as defined in Figure 2.

	DISCUSSION

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

Growth and NUE

We observed that the term 1/ 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/, 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/ and r, whereas angiosperm lianas had highest 1/ and r, with angiosperm trees intermediate between the two (Fig. 1). This pattern suggests that variation in 1/ 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 _c, the proportion of net C fixation used for respiration, has potential to influence r. It was previously observed for 24 herbaceous species that _c ranged from about 0.5 to 0.3, and that r was negatively correlated with _c, as predicted by Equation 1 (Poorter et al., 1990). Although we did not measure _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 _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 _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 _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 _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 _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 _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 – _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 _c varied among species from 0.3 to 0.5 (Poorter et al., 1990), the term 1 – _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 ¹³C_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, ¹³C_p, showed a strong correlation with instantaneous measurements of c_i/c_a (Fig. 7), suggesting that in general ¹³C_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 ¹³C_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 ¹³C_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 ¹³C_p and D_g·TE_c was generally much stronger in this study. For example, in a least-squares linear regression between ¹³C_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 ¹³C_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 ¹³C_p exhibited by the particular species that comprised the experiments. In the earlier study, mean values for ¹³C_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 ¹³C_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 ¹³C_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 ¹³C of leaves and that of stems and roots (Table VII). The mechanistic basis for such variation among species in the ¹³C 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, ¹⁸O_p, varied significantly among species, and much of the observed variation in ¹⁸O_p could be explained by variation in ¹⁸O_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 ¹⁸O_e. Species means for growth-weighted ¹⁸O_e ranged from 6.9 to 13.0 (14.6 for P. guatemalensis), and explained 73% of variation in the observed species means for ¹⁸O_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 ¹⁸O_p model are reasonable. Additionally, we observed that the term 1 – [(¹⁸O_p – _wc – _cp)/(1 – p_exp_x)]/¹⁸O_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 ¹⁸O_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 _cp for stem dry matter tends to be less variable within and among species than _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 ¹³C and ¹⁸O 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 ¹³C 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 ¹⁸O, 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 ¹⁸O_p and the predicted ¹⁸O_e (Fig. 9) supports this idea. However, our analysis also confirmed that the relationship between ¹⁸O_p and ¹⁸O_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 ¹⁸O_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 ¹⁸O_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).

	CONCLUSION

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 
We observed that 1/ was an important control over relative growth rate, r, in a diverse group of seedlings grown under tropical field conditions, including gymnosperm trees, angiosperm trees, and angiosperm lianas. The gymnosperm trees generally had lower 1/ and r than the angiosperm species, and this may have reflected differences in the hydraulic efficiency of plant biomass among functional groups. Additionally, we observed that A_n, the photosynthetic NUE, was the primary control over whole-plant NUE, and that the gymnosperm species appeared to be at a significant disadvantage with respect to this trait compared to angiosperm species. Variation in whole-plant water use efficiency among species was primarily controlled by c_i/c_a, which in turn varied as a function of stomatal conductance. Whole-plant ¹³C discrimination was also controlled by c_i/c_a, and thus correlated with whole-plant water use efficiency. The ¹⁸O enrichment of stem dry matter of the experimental plants varied primarily as a function of the predicted ¹⁸O enrichment of evaporative site water within leaves, and secondarily as a function of the daytime MTR. Results provided quantitative information about the mechanisms controlling fluxes of C and water between forest trees and the atmosphere, and the coupling of these processes to plant N status. Moreover, our data set enabled rigorous testing of the theoretical basis for variation in ¹³C and ¹⁸O of plant dry matter; measurements of these stable isotope ratios could prove useful for parameterizing forest ecosystem process models.

	MATERIALS AND METHODS

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 

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 ¹⁸O_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 ¹⁸O_e. The ¹⁸O_v was assumed equal to –⁺, calculated from air temperature (Bottinga and Craig, 1969). Growth-weighted ¹⁸O_e, ¹⁸O_eg, was then calculated for each plant as in Equation 13, but replacing D_i with ¹⁸O_ei, the average daytime ¹⁸O_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 ¹³C, 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 ¹⁸O 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 ¹³C and ¹⁸O values were expressed in notation with respect to the standards of PeeDee Belemnite and Vienna Standard Mean Ocean Water, respectively. The ¹³C discrimination of plant dry matter (¹³C_p) was calculated as ¹³C_p = (¹³C_a – ¹³C_p)/(1 + ¹³C_p), where ¹³C_a is the ¹³C of CO₂ in air and ¹³C_p is that of plant dry matter. We assumed a ¹³C_a of –8. The oxygen isotope enrichment of stem dry matter (¹⁸O_p) was calculated as ¹⁸O_p = (¹⁸O_p – ¹⁸O_s)/(1 + ¹⁸O_s), where ¹⁸O_p is ¹⁸O of stem dry matter, and ¹⁸O_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 ¹⁸O_s. The tank water had a mean ¹⁸O of –4.3 ± 0.5 (mean ± 1 SD, n = 6); we therefore calculated ¹⁸O_p assuming a ¹⁸O_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.

	ACKNOWLEDGMENTS

 
We thank Milton Garcia and Aurelio Virgo for technical assistance, and Ben Harlow for carrying out isotopic and elemental analyses.

Received May 26, 2008; accepted June 23, 2008; published July 3, 2008.

	FOOTNOTES

 
¹ This work was supported by the Smithsonian Tropical Research Institute. L.A.C. was supported by a postdoctoral fellowship from the Smithsonian Institution and a Tupper Research Fellowship from the Smithsonian Tropical Research Institute.

² Present address: School of Environmental and Life Sciences, Charles Darwin University, Darwin, Northern Territory 0909, Australia.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Lucas A. Cernusak (lucas.cernusak{at}cdu.edu.au).

^[W] The online version of this article contains Web-only data.

^[OA] Open Access article can be viewed online without a subscription.

www.plantphysiol.org/cgi/doi/10.1104/pp.108.123521

^* Corresponding author; e-mail lucas.cernusak{at}cdu.edu.au.

	LITERATURE CITED

TOP ABSTRACT THEORY RESULTS DISCUSSION CONCLUSION MATERIALS AND METHODS LITERATURE CITED

 
Aerts R, Chapin FS (2000) The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Adv Ecol Res 30: 1–67

Atkin OK, Schortemeyer M, McFarlane N, Evans JR (1998) Variation in the components of relative growth rate in ten Acacia species from contrasting environments. Plant Cell Environ 21: 1007–1017[CrossRef]

Badeck FW, Tcherkez G, Nogues S, Piel C, Ghashghaie J (2005) Post-photosynthetic fractionation of stable carbon isotopes between plant organs—a widespread phenomenon. Rapid Commun Mass Spectrom 19: 1381–1391[CrossRef][Web of Science][Medline]

Barbour MM (2007) Stable oxygen isotope composition of plant tissue: a review. Funct Plant Biol 34: 83–94[CrossRef]

Barbour MM, Andrews JT, Farquhar GD (2001) Correlations between oxygen isotope ratios of wood constituents of Quercus and Pinus samples from around the world. Aust J Plant Physiol 28: 335–348[Web of Science]

Barbour MM, Farquhar GD (2000) Relative humidity- and ABA-induced variation in carbon and oxygen isotope ratios of cotton leaves. Plant Cell Environ 23: 473–485[CrossRef]

Barbour MM, Farquhar GD (2004) Do pathways of water movement and leaf anatomical dimensions allow development of gradients in H₂¹⁸O between veins and the sites of evaporation within leaves? Plant Cell Environ 27: 107–121[Medline]

Barbour MM, Fischer RA, Sayre KD, Farquhar GD (2000a) Oxygen isotope ratio of leaf and grain material correlates with stomatal conductance and grain yield in irrigated wheat. Aust J Plant Physiol 27: 625–637[Web of Science]

Barbour MM, Roden JS, Farquhar GD, Ehleringer JR (2004) Expressing leaf water and cellulose oxygen isotope ratios as enrichment above source water reveals evidence of a Péclet effect. Oecologia 138: 426–435[CrossRef][Web of Science][Medline]

Barbour MM, Schurr U, Henry BK, Wong SC, Farquhar GD (2000b) Variation in the oxygen isotope ratio of phloem sap sucrose from castor bean. Evidence in support of the Péclet effect. Plant Physiol 123: 671–679[Abstract/Free Full Text]

Blackman VH (1919) The compound interest law and plant growth. Ann Bot (Lond) 33: 353–360

Bond WJ (1989) The tortoise and the hare: ecology of angiosperm dominance and gymnosperm persistence. Biol J Linn Soc 36: 227–249[Web of Science]

Borella S, Leuenberger M, Saurer M (1999) Analysis of ¹⁸O in tree rings: wood-cellulose comparison and method dependent sensitivity. J Geophys Res 104: 19,267–19,273[CrossRef]

Bottinga Y, Craig H (1969) Oxygen isotope fractionation between CO₂ and water, and the isotopic composition of marine atmospheric CO₂. Earth Planet Sci Lett 5: 285–295

Brodribb T (1996) Dynamics of changing intercellular CO₂ concentration (c_i) during drought and determination of minimum functional c_i. Plant Physiol 111: 179–185[Abstract]

Brodribb TJ, Holbrook NM, Hill RS (2005) Seedling growth in conifers and angiosperms: impacts of contrasting xylem structure. Aust J Bot 53: 749–755[CrossRef][Web of Science]

Buckley TN (2008) The role of stomatal acclimation in modelling tree adaptation to high CO₂. J Exp Bot 59: 1951–1961[Abstract/Free Full Text]

Cappa CD, Hendricks MB, DePaulo DJ, Cohen RC (2003) Isotopic fractionation of water during evaporation. J Geophys Res 108: 4525[CrossRef]

Cernusak LA, Aranda J, Marshall JD, Winter K (2007a) Large variation in whole-plant water-use efficiency among tropical tree species. New Phytol 173: 294–305[CrossRef][Web of Science][Medline]

Cernusak LA, Farquhar GD, Pate J (2005) Environmental and physiological controls over oxygen and carbon isotope composition of Tasmanian blue gum, Eucalyptus globulus. Tree Physiol 25: 129–146

Cernusak LA, Pate JS, Farquhar GD (2004) Oxygen and carbon isotope composition of parasitic plants and their hosts in southwestern Australia. Oecologia 139: 199–213[CrossRef][Web of Science][Medline]

Cernusak LA, Winter K, Aranda J, Turner BL, Marshall JD (2007b) Transpiration efficiency of a tropical pioneer tree (Ficus insipida) in relation to soil fertility. J Exp Bot 58: 3549–3566[Abstract/Free Full Text]

Cernusak LA, Wong SC, Farquhar GD (2003) Oxygen isotope composition of phloem sap in relation to leaf water in Ricinus communis. Funct Plant Biol 30: 1059–1070[CrossRef]

Cornelissen JHC, Diez PC, Hunt R (1996) Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. J Ecol 84: 755–765[CrossRef][Web of Science]

Craig H, Gordon LI (1965) Deuterium and oxygen-18 variations in the ocean and the marine atmosphere. In E Tongiorgi, ed, Proceedings of a Conference on Stable Isotopes in Oceanographic Studies and Palaeotemperatures. Lischi and Figli, Pisa, Italy, pp 9–130

Cuntz M, Ogée J, Farquhar GD, Peylin P, Cernusak LA (2007) Modelling advection and diffusion of water isotopologues in leaves. Plant Cell Environ 30: 892–909[CrossRef][Medline]

Dongmann G, Nurnberg HW, Förstel H, Wagener K (1974) On the enrichment of H₂¹⁸O in the leaves of transpiring plants. Radiat Environ Biophys 11: 41–52[CrossRef][Web of Science][Medline]

Evans GC (1972) The quantitative analysis of plant growth. Blackwell Scientific, Oxford

Farquhar GD, Cernusak LA, Barnes B (2007) Heavy water fractionation during transpiration. Plant Physiol 143: 11–18[Free Full Text]

Farquhar GD, Condon AG, Masle J (1994) Use of carbon and oxygen isotope composition and mineral ash content in breeding for improved rice production under favorable, irrigated conditions. In KG Cassman, ed, Breaking the Yield Barrier. International Rice Research Institute, Manila, Philippines, pp 95–101

Farquhar GD, Ehleringer JR, Hubick KT (1989a) Carbon isotope discrimination and photosynthesis. Annu Rev Plant Physiol Plant Mol Biol 40: 503–537[CrossRef][Web of Science]

Farquhar GD, Gan KS (2003) On the progressive enrichment of the oxygen isotopic composition of water along leaves. Plant Cell Environ 26: 801–819[Medline]

Farquhar GD, Hubick KT, Condon AG, Richards RA (1989b) Carbon isotope fractionation and plant water-use efficiency. In PW Rundel, JR Ehleringer, KA Nagy, eds, Stable Isotopes in Ecological Research, Springer-Verlag, New York, pp 21–46

Farquhar GD, Lloyd J (1993) Carbon and oxygen isotope effects in the exchange of carbon dioxide between terrestrial plants and the atmosphere. In JR Ehleringer, AE Hall, GD Farquhar, eds, Stable Isotopes and Plant Carbon-Water Relations. Academic Press, San Diego, pp 47–70

Farquhar GD, O'Leary MH, Berry JA (1982) On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Aust J Plant Physiol 9: 121–137[Web of Science]

Farquhar GD, Richards RA (1984) Isotopic composition of plant carbon correlates with water-use efficiency in wheat genotypes. Aust J Plant Physiol 11: 539–552[Web of Science]

Flanagan LB (1993) Environmental and biological influences on the stable oxygen and hydrogen isotopic composition of leaf water. In JR Ehleringer, AE Hall, GD Farquhar, eds, Stable Isotopes and Plant Carbon-Water Relations. Academic Press, San Diego, pp 71–89

Flanagan LB, Phillips SL, Ehleringer JR, Lloyd J, Farquhar GD (1994) Effect of changes in leaf water oxygen isotopic composition on discrimination against C¹⁸O¹⁶O during photosynthetic gas exchange. Aust J Plant Physiol 21: 221–234[Web of Science]

Gartner BL, Bullock SH, Mooney HA, Brown VB, Whitbeck JL (1990) Water transport properties of vine and tree stems in tropical deciduous forest. Am J Bot 77: 742–749[CrossRef][Web of Science]

Gessler A, Peuke AD, Keitel C, Farquhar GD (2007) Oxygen isotope enrichment of organic matter in Ricinus communis during the diel course and as affected by assimilate transport. New Phytol 174: 600–613[CrossRef][Web of Science][Medline]

Hobbie EA, Werner RA (2004) Intramolecular, compound-specific, and bulk carbon isotope patterns in C₃ and C₄ plants: a review and synthesis. New Phytol 161: 371–385[CrossRef][Web of Science]

Hubick KT, Farquhar GD (1989) Carbon isotope discrimination and the ratio of carbon gained to water lost in barley cultivars. Plant Cell Environ 12: 795–804[CrossRef]

Hubick KT, Farquhar GD, Shorter R (1986) Correlation between water-use efficiency and carbon isotope discrimination in diverse peanut (Arachis) germplasm. Aust J Plant Physiol 13: 803–816[Web of Science]

Kahmen A, Simonin K, Tu KP, Merchant A, Callister A, Siegwolf R, Dawson TE, Arndt SK (2008) Effects of environmental parameters, leaf physiological properties and leaf water relations on leaf water ¹⁸O enrichment in different Eucalyptus species. Plant Cell Environ 31: 738–751[CrossRef]

Koerselman W, Meuleman AFM (1996) The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. J Appl Ecol 33: 1441–1450[CrossRef]

Laurance WF, Oliveira AA, Laurance SG, Condit R, Nascimento HEM, Sanchez-Thorin AC, Lovejoy TE, Andrade A, D'Angelo S, Ribeiro JE, et al (2004) Pervasive alteration of tree communities in undisturbed Amazonian forests. Nature 428: 171–175[CrossRef][Web of Science][Medline]

LeBauer DS, Treseder KK (2008) Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89: 371–379[CrossRef][Web of Science][Medline]

Lloyd J, Farquhar GD (2008) Effects of rising temperatures and [CO₂] on the physiology of tropical forest trees. Philos Trans R Soc Lond B Biol Sci 363: 1811–1817[Abstract/Free Full Text]

Lloyd J, Syvertsen JP, Kriedemann PE, Farquhar GD (1992) Low conductances for CO₂ diffusion from stomata to the sites of carboxylation in leaves of woody species. Plant Cell Environ 15: 873–899[CrossRef]

Long SP, Farage PK, Garcia RL (1996) Measurement of leaf and canopy photosynthetic CO₂ exchange in the field. J Exp Bot 47: 1629–1642[Abstract/Free Full Text]

Masle J, Farquhar GD (1988) Effects of soil strength on the relation of water-use efficiency and growth to carbon isotope discrimination in wheat seedlings. Plant Physiol 86: 32–38[Abstract/Free Full Text]

Poorter H (1994) Construction costs and payback time of biomass: a whole plant perspective. In J Roy, E Garnier, eds, A Whole Plant Perspective on Carbon-Nitrogen Interactions. SPB Academic Publishing, The Hague, The Netherlands, pp 11–127

Poorter H, Remkes C (1990) Leaf-area ratio and net assimilation rate of 24 wild species differing in relative growth rate. Oecologia 83: 553–559[CrossRef][Web of Science]

Poorter H, Remkes C, Lambers H (1990) Carbon and nitrogen economy of 24 wild species differing in relative growth rate. Plant Physiol 94: 621–627[Abstract/Free Full Text]

Reich PB, Oleksyn J (2004) Global patterns of plant leaf N and P in relation to temperature and latitude. Proc Natl Acad Sci USA 101: 11001–11006[Abstract/Free Full Text]

Ripullone F, Matsuo N, Stuart-Williams H, Wong SC, Borghetti M, Tani M, Farquhar GD (2008) Environmental effects on oxygen isotope enrichment of leaf water in cotton leaves. Plant Physiol 146: 729–736[Abstract/Free Full Text]

Roden JS, Lin GG, Ehleringer JR (2000) A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. Geochim Cosmochim Acta 64: 21–35[CrossRef][Web of Science]

Seibt U, Rajabi A, Griffiths H, Berry JA (2008) Carbon isotopes and water use efficiency: sense and sensitivity. Oecologia 155: 441–454[CrossRef][Web of Science][Medline]

Sheshshayee MS, Bindumadhava H, Ramesh R, Prasad TG, Lakshminarayana MR, Udayakumar M (2005) Oxygen isotope enrichment (¹⁸O) as a measure of time-averaged transpiration rate. J Exp Bot 56: 3033–3039[Abstract/Free Full Text]

Sperry JS, Hacke UG, Pittermann J (2006) Size and function in conifer tracheids and angiosperm vessels. Am J Bot 93: 1490–1500[Abstract/Free Full Text]

Sternberg LSL, Mulkey SS, Wright SJ (1989) Oxygen isotope ratio stratification in a tropical moist forest. Oecologia 81: 51–56[CrossRef][Web of Science]

Tanner CB, Sinclair TR (1983) Efficient water use in crop production: research or re-search. In H Taylor, ed, Limitations to Efficient Water Use in Crop Production. ASA-CSSA-SSSA, Madison, WI, pp 1–28

Tanner EVJ, Vitousek PM, Cuevas E (1998) Experimental investigation of nutrient limitation of forest growth on wet tropical mountains. Ecology 79: 10–22[Web of Science]

Vertregt N, Penning de Vries FWT (1987) A rapid method for determining the efficiency of biosynthesis of plant biomass. J Theor Biol 128: 109–119[CrossRef][Web of Science]

Warren CR, Adams MA (2006) Internal conductance does not scale with photosynthetic capacity: implications for carbon isotope discrimination and the economics of water and nitrogen use in photosynthesis. Plant Cell Environ 29: 192–201[CrossRef][Medline]

Winter K, Aranda J, Garcia M, Virgo A, Paton SR (2001) Effect of elevated CO₂ and soil fertilization on whole-plant growth and water use in seedlings of a tropical pioneer tree, Ficus insipida Willd. Flora 196: 458–464

Winter K, Aranda J, Holtum JAM (2005) Carbon isotope composition and water-use efficiency in plants with crassulacean acid metabolism. Funct Plant Biol 32: 381–388[CrossRef]

Wright IJ, Westoby M (2000) Cross-species relationships between seedling relative growth rate, nitrogen productivity and root vs leaf function in 28 Australian woody species. Funct Ecol 14: 97–107[CrossRef]

Wright SJ (2005) Tropical forests in a changing environment. Trends Ecol Evol 20: 553–560[CrossRef][Medline]

Yakir D, DeNiro MJ, Rundel PW (1989) Isotopic inhomogeneity of leaf water: evidence and implications for the use of isotopic signals transduced by plants. Geochim Cosmochim Acta 53: 2769–2773[CrossRef][Web of Science]

Yakir D, Israeli Y (1995) Reduced solar irradiance effects on net primary productivity (NPP) and the ¹³C and ¹⁸O values in plantations of Musa sp. Musaceae. Geochim Cosmochim Acta 59: 2149–2151[CrossRef][Web of Science]

This article has been cited by other articles:

				L. Llorens, C. P. Osborne, and D. J. Beerling Water-use responses of 'living fossil' conifers to CO2 enrichment in a simulated Cretaceous polar environment Ann. Bot., July 1, 2009; 104(1): 179 - 188. [Abstract] [Full Text] [PDF]

				G. J. Lanigan, N. Betson, H. Griffiths, and U. Seibt Carbon Isotope Fractionation during Photorespiration and Carboxylation in Senecio Plant Physiology, December 1, 2008; 148(4): 2013 - 2020. [Abstract] [Full Text] [PDF]

This Article Free via Open Access: OA OA Abstract Full Text (PDF) Supplemental Data OA All Versions of this Article: 148/1/642    most recent pp.108.123521v1 Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via ISI Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Cernusak, L. A. Articles by Turner, B. L. Search for Related Content PubMed PubMed Citation Articles by Cernusak, L. A. Articles by Turner, B. L. Agricola Articles by Cernusak, L. A. Articles by Turner, B. L. Related Collections Biology of Transpiration