SEARCH

SEARCH BY CITATION

Keywords:

  • enthalpy balance;
  • growth and maintenance paradigm (GMP);
  • growth;
  • Pinus radiata;
  • respiration

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • • 
    Correlation methods originating in the growth and maintenance paradigm (GMP) are traditionally used to calculate a ‘growth coefficient’ (g) or the ‘growth potential’ (1/g) of entire plants. The enthalpy balance approach is usually applied to plant organs and relies on determination of both CO2 release and O2 reduction to provide a measure of instantaneous rates of enthalpic growth (RSGΔHB).
  • • 
    Aspects of both the approaches to explore physiological mechanisms that govern enthalpic growth (variation in rates of CO2 release versus rates of O2 reduction) were combined.
  • • 
    Respiration and growth rates of apical buds of Pinus radiata were affected strongly by canopy position, and moderately by branching order. A linear relation between enthalpic growth and CO2 respiration explained 69% of the observed variation. Despite faster rates of growth, enthalpic growth potential (1/gH) was comparatively low in the upper canopy. Low enthalpic growth potential entailed comparatively low enthalpy conversion efficiency (ηH, ratio of RSGΔHB to inline image; proportional to CO2:O2 and to carbon conversion efficiency ɛ) at large RSGΔHB. Maximizing enthalpic growth requires a large capacity for O2 reduction.
  • • 
    Relations between RSGΔHB and ηH could be described by hyperbolae using two parameters. One parameter, P1, is equivalent to enthalpic growth potential (1/gH).

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

More than 35 yr ago, observations that respiration of growing plants differed fundamentally from that of nongrowing plants led to formulation of the growth and maintenance paradigm (GMP; Thornley, 1970; de Wit et al., 1970; McCree, 1970, 1974). Early empirical studies showed that rates of respiration (inline image) were greater when plants were growing strongly, and could often be linearly related to rates of growth (RSG). The slope of the relation (the growth coefficient, g) depends on a number of environmental and developmental factors. The intercept (I) has often been interpreted as the maintenance component of respiration – that necessary to maintain cellular structures and processes in the absence of growth:

  • image(Eqn 1)

The GMP became a dominant paradigm for representation of respiration in plant growth models (Gifford, 2003), which seems to be a result of its putative strength of prediction. The correlation between plant growth and CO2 release enables definition of a ‘growth potential’ (1/g), that is, the increase in growth rate per unit increase of CO2 respiration:

  • image(Eqn 2)

where g is the growth coefficient in Eqn 1. Experimental determination of growth potential is tedious because of the need for many independent analyses of rates of growth and respiration. In part, growth potential is a genetically inherent trait, but it also depends on developmental stage, and interacts with environmental variables. Separate evaluation of each potentially influential factor (as required for modeling purposes), or identification of plant varieties with inherently high growth potential, is thus a laborious task. Consequently, growth coefficients are routinely and often unavoidably calculated with poor precision.

In general, the GMP cannot be used to determine the growth potential of an individual plant at any one point in time. As an example, measured energetic costs of constructing foliage (in units of glucose respired per gram biomass produced) are strongly dependent on developmental stage. The oxidation state of anabolic products at different times of ontogenetic development can cause large variations in growth potential (1/g), if determined according to the mass-based approach outlined in Eqns 1 and 2. Lipids and proteins, for example, are more reduced than cellulose, the latter being predominantly synthesized later in cell development (i.e. following cell extension). Simply put, more carbohydrates are required and more CO2 is respired per gram increase in biomass in early stages of cell development, resulting in comparatively large values of g (or a low, mass-based growth potential 1/g).

As pointed out by de Wit et al. (1970) and Amthor (2000), one of the most useful measures of growth within the framework of the GMP is conversion of substrate carbon (i.e. nonstructural carbohydrates) into new structure (i.e. structural carbohydrates, lignin, protein, lipid, nucleic acid, etc.), rather than changes in total dry mass. While equating growth with increases in mass is intuitively attractive and thus popular, expressing growth in terms of energy increment captures important differences among tissues and species that are not always well reflected in mass (e.g. differences between herbaceous and woody plants, between young and old tissues, or between sclerophyllous and nonsclerophyllous species; McDermitt & Loomis, 1981; Amthor, 2000).

Despite the initial popularity of the GMP, which appeared to give plant respiration a theoretical underpinning, attempts to further its development led to a range of intricate problems. Strictly, the GMP can only be applied to entire plants (Thornley & Johnson, 1990, pp. 265–284). However, most respiration measurements are conducted on leaves or stems and often only respiratory CO2 release is determined. Nonetheless, many authors have provided mechanistic interpretations of such results that frequently include division of distinct physiological process into ‘growth’ and ‘maintenance’ components. This approach has been applied despite the many physiological processes that are peculiar to functionally different plant organs that serve the entire plant. As examples, both processes of (i) sugar loading into phloem in leaves and (ii) nutrient uptake in roots require considerable amounts of respiratory energy and cannot simply be assigned to either ‘growth’ or ‘maintenance’ respiration (Cannell & Thornley, 2000; Thornley & Cannell, 2000).

Similarly, respiration rates and nitrogen concentrations are often correlated in plant tissues (Turnbull et al., 2003, Wright et al., 2004, Reich et al., 2006, Kruse & Adams, 2008a). This has been interpreted in light of respiratory energy demand for maintenance of functionally active proteins (Ryan et al., 1996). For this specific process, Amthor (2000) introduced the term ‘tool maintenance’ as a descriptor of the energy input required for the repair and maintenance of proteins associated with photosynthetic machinery (mainly). While this example might seem a precedent for ‘maintenance’ respiration, it also produces further difficult-to-resolve issues of logic.

Hansen, Criddle and co-workers (Criddle et al., 1991; Hansen et al., 1994, 1997, 1998; Criddle & Hansen, 1999) developed a calorimetry-based approach that may help to tackle some of the problems raised by the GMP. Briefly, rates of oxygen reduction are quickly and easily quantified via tissue heat rates (q) using calorimetry. Rates of oxidation of substrate (and production of CO2) are measured in the presence of an alkali trap. The techniques have been thoroughly described over the past 10 yr – including the required precautions, assumptions and caveats – and recent summaries are given in Hansen et al. (2004, 2007). Calorimetric approaches deliver measures of instantaneous rates of growth (enthalpic growth rates). They are therefore suitable for analysis of the relation between respiration and growth on the short time-scales most relevant to physiological processes. Enthalpic growth rate is determined from the energy made available by oxidation of substrate (inline image) and the loss of heat to the surroundings (q). As q is proportional to oxygen reduction in plant mitochondria (Hopkin, 1991, Ordentlich et al., 1991) and equal to inline image (Kemp, 2000), it follows that enthalpic growth can be defined:

  • image(Eqn 3)

where inline image and inline image are the specific rates of CO2 production and O2 consumption (nmol g−1 s−1), respectively. inline image is the oxycaloric equivalent that changes little with substrate, and is equal to inline image (–469 kJ mol−1) if only carbohydrates are respired (inline image varies from −430 to −480 kJ mol−1 O2 depending on substrate; Criddle & Hansen, 1999, Kemp, 2000). ΔHB is the total enthalpy change for incorporation of one mole of substrate carbon, including the enthalpy effects from all elements (kJ nmol−1). An equivalent expression of the ‘enthalpy balance’ during respiration is given by:

  • image(Eqn 4)

where q is the specific heat rate (µW mg−1) and is equal to inline image.

Major sources of CO2 in respiring plant tissue are: (i) oxidative decarboxylation reactions within the tricarboxylic acid (TCA) cycle of the mitochondrial matrix (coupled to reduction of NAD+), and (ii) oxidative decarboxylation of carbohydrates in the oxidative pentose phosphate pathway of the cytosol (coupled to reduction of NADP+). Reducing equivalents produced in plant mitochondria are used for subsequent reduction of O2 (generating heat), whereas NADPH is needed for reductive anabolism (thermally neutral). Some of the energy released during electron transport in the inner mitochondrial membrane is captured for the synthesis of ATP. During biosynthesis, ATP drives some anabolic reactions that are otherwise not thermodynamically feasible (anabolic reactions are thus coupled to catabolic degradation of sugars to varying degrees). Alternately put, ATP acts as a catalyst for biosynthetic reactions and is rapidly turned over, while little or no energy (that was transiently stored in ATP) is retained in the anabolic products of respiration (Macfarlane et al., 2002).

Summarizing, some of the energy made available by the oxidation of substrate carbon (inline image) is retained in anabolic products (RSGΔHB), and some is lost as heat to the surroundings. Enthalpic growth rates can thus be interpreted as the preservation of electrons in anabolic products of respiration, or as the energy stored in the chemical bonds of newly synthesized anabolic products (compare with Kruse & Adams, 2008b and Kruse et al., 2008). If carbohydrates are completely oxidized to CO2 in plant mitochondria, then inline image and RSGΔHB = 0.

The enthalpy conversion efficiency (ηH) is a derived parameter, describing the ratio of the enthalpy retained in anabolic products (the ‘enthalpic growth’, RSGΔHB) to the sum of retained enthalpy plus the enthalpy dissipated as heat:

  • image(Eqn 5)

The enthalpy conversion efficiency (ηH) is proportional to the respiratory quotient CO2:O2, assuming carbohydrates as substrate (see Kruse et al., 2008). It also follows from Eqn 5 that:

  • image(Eqn 6)

Hence, enthalpic growth rates (i.e. the difference between rates of decarboxylation reactions and those of oxygen reduction; compare Eqn 3) depend on both rates of CO2 respiration and on the ratio of CO2 release to O2 reduction. We must note that Eqn 3 represents a static view or a snapshot of a distinct metabolic state of respiring tissue. Physiological mechanisms that relate cell growth (increases of enthalpy) to CO2 respiration might be elucidated by a more dynamic approach, such as an approach using the expected change in enthalpic growth per unit increase (or decrease) in CO2 respired; that is, and by analogy with Eqn 2:

  • image(Eqn 7)

where 1/gH is ‘enthalpic growth potential’ (compare with Eqn 2).

Combining features (or at least some of the broad concepts) of the GMP and the ‘enthalpy balance approach’ might enhance our knowledge of the physiology of respiration. For example, it is not clear if correlation methods originating in the GMP can be applied to lower levels of biological organization (i.e. to plant cells or organs; Matheson et al., 2004). Kruse & Adams (2008a) demonstrated that fast rates of respiration by foliage from the upper canopy of pine trees were not related to instantaneous rates of enthalpic growth in early and late summer. Instead, upper canopy foliage had switched from carbohydrate ‘sink’ to ‘source’ and rates of respiration reflected the large energy demand for phloem loading of sugars and maintenance of existing proteins, rather than synthesis of new cell structure (Kruse & Adams, 2008a).

Our first aim was to clarify whether rates of respiration of pine needles (inline image) could be related to rates of enthalpic growth, when cell metabolism is dominated by growth processes during the ‘sink stage’ of foliage development in early spring. We also investigated whether respiration and growth were related to pools of soluble sugars and amino acids during this remobilization period. Secondly, we explored variation in the relations among respiration parameters RSGΔHB, inline image, ηH and dηH (equivalent to 1/gH) attributable to environmental and developmental factors. For this purpose we used as our model apical buds (plus young, growing needles), differing in branching order and taken from different canopy positions of Pinus radiata plantations that were either thinned or left untreated as a control. Thirdly, we sought to develop a method that could simplify determination of ‘enthalpic growth potential’ (1/gH or dηH), without the necessity of traditional correlation methods. We hoped to enhance our knowledge of the physiological mechanisms that link foliage respiration to cellular growth. This might in turn be useful in modeling plant growth and identifying plant varieties with large growth potential.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Experimental design and separation of main effects

Experiments were conducted in 24-yr-old pine (Pinus radiata (D. Don)) plantations (planted 1982), located 10 km east of Mt Gambier, South Australia, in early spring 2006 (end of October, c. 4 wk after bud break in the upper canopy). The climate of the region is Mediterranean with cool wet winters and warm dry summers. Average annual rainfall is 720 mm, of which c. 70% falls in winter. Monthly mean maximum and minimum air temperatures vary, respectively, from 25.2 and 10.8°C in January (mid-summer) to 13.0 and 4.9°C in July (mid-winter; Australian Bureau of Meteorology). The soil can best be described as a deep, podsolic sand, and the plantations were established on flat terrain. Research plots were established in 1995 as a Latin square design with different combinations of fertilizer and thinning treatment.

In the current study we chose four unthinned plots and four nonfertilized thinned plots. These plots were thinned to a basal area of 31 m−2 ha−1 in 2002 (compared with 47 m−2 ha−1 in the unthinned stand; see Kruse & Adams, 2008b). We selected three trees per plot (i.e. c. 8% of the trees of the thinned and thinned + fertilized treatments, and 5% of the unthinned treatment) in a semi-random approach: the first tree between numbers 1 and 13 (or between 1 and 20 in unthinned plots) was selected at random, and subsequently every 13th tree (or 20th), thus ensuring a sufficient coverage of the plot area. During the 8 d of sampling, the two treatments were studied in alternating fashion.

The effect of canopy position on dark respiration and apical growth was assessed by studying branches from different canopy positions. Canopies were stratified into three different layers of equal depth. Every morning, three branches per tree (one randomly selected from each layer), from each of the selected three trees, were shot down. Branches long enough to bear three needle age classes and with unscathed apical buds were re-cut under water and carried to the calorimetry facility which was in close proximity (200 m) to the research plots. For calorimetric measurements we chose the apical bud at the end of the main branch (first-order apical bud), an apical bud from one 2-yr-old side branch (second-order apical bud), and one bud from a small 1-yr-old side branch attached to an older side branch (third-order apical bud). Six to seven young, expanding needles adjacent to the apical bud were cut above the needle sheath. Apical buds (including the remaining needles in the sheath, c. 1 cm in length) were used for calorimetric measurements. After measurements apical buds were pooled with the respective needle tips (in order to obtain sufficient material for analysis), frozen in liquid nitrogen, and subsequently stored at −20°C until biochemical analysis.

Respiration measurements

Respiration measurements were conducted with two multi-cell differential-scanning calorimeters (CSC 4100; MC-DSC; Calorimetry Sciences Corporation, Provo, UT, USA) operating in the isothermal mode, at 25°C, as described by Kruse & Adams (2008a). Briefly, we detached apical buds (first, second and third order) from the selected branches and left them for 20 min to allow respiratory wound responses to dissipate. Routine calorimetric measurements require two steps. The heat rate of the sample (q) is first measured after 40 min of instrument stabilization and subsequently re-measured (after another 40 min) in the presence of NaOH (qNaOH). This second heat rate is generally greater than the first, as a result of the exothermic formation of carbonate from CO2 released from plant material. Rates of CO2 production (inline image) can be calculated from the difference between the second and first heat rates, taking into account the enthalpy change for carbonate formation (−108.5 kJ mol−1) (Criddle et al., 1991; Hansen et al., 1994; Criddle & Hansen, 1999). At a time of strong sink activity and rapid growth, both heat rates (q and qNaOH) continue to decline at a rate of c. 0.2 µW min−1 after the 40-min instrument stabilization period (when heat rates of respiring tissue in the ampoules were as much as 500 to 600 µW). We extrapolated q and qNaOH to the mid-point between two measurements to obtain the best possible estimate of inline image (compare with Kruse et al., 2008).

Determination of soluble sugars and amino acids

Frozen plant material was ground to a fine powder with mortar and pestle, using liquid nitrogen. Aliquots of 50 mg of the fine powder were weighed into 2-ml Eppendorf vials. The plant material was washed twice with 100% acetone to remove pigments. Sugars were extracted twice with 1 ml of 80% ethanol, and centrifuged for 4 min at 16 000 g. An aliquot of 50 µl of the supernatant was added to 1 ml of anthrone reagent (1 g of anthrone per 500 ml of 72% sulfuric acid; Hansen & Möller, 1975). The mixture was heated for 10 min at 100°C and subsequently cooled on ice. Absorption was measured at 630 nm, and glucose was used as a standard.

For amino acid analysis, 50 mg of the finely ground plant material was homogenized in a buffer containing 20 mM HEPES (pH 7.0), 5 mM ethyleneglycoltetraacetic acid (EGTA), 10 mM NaF, and 1.2 ml of chloroform:methanol (1.5 : 3.5, by volume) as described by Kruse et al. (2002). Water-soluble metabolites were extracted with double-distilled water and the aqueous phases were combined. Subsequently, 200 µl of the aqueous phase was added to 200 µl of ninhydrin reagent (1.0 g of ninhydrin and 0.1 g of hydrinantin dissolved in 25 ml of dimethylsulfoxide (DMSO), plus 25 ml of acetate buffer (4 M)). The mixture was heated for 15 min at 100°C. After cooling to room temperature, 1 ml of stabilizing solvent (50% ethanol) was added. Absorption was measured at 570 nm, and glutamic acid was used as a standard.

Statistical analysis

Statistical analysis was performed with the results of 12 independent replicates per treatment, canopy position and branching order. Main effects and the effects of their interactions on respiration variables, soluble sugars and amino acids were assessed by ANOVA (statistica, version 6.0; Statsoft Inc., Tulsa, OK, USA). Main effects (with n = 6 for canopy position and branching order, and n = 9 for treatment), but no interaction terms were calculated for the enthalpic growth potential (1/gH or dηH). Categorical predictor variables were coded according to sigma-restricted parameterization, and sums of squares were calculated according to Type VI. The statistical significance of main effects and their interactions is indicated by * if P < 0.05, ** if P < 0.01 and *** if P < 0.001.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Respiration variables

Respiration variables were most strongly affected by canopy position. Across both thinning treatments, inline image increased from 2.90 µW mg−1 in the lower canopy to 3.09 µW mg−1 in the middle canopy, and to 3.61 µW mg−1 in the upper canopy. By contrast, inline image increased from 2.48 µW mg−1 in the lower canopy to 2.67 µW mg−1 in the middle canopy, but did not further increase from the middle to the upper canopy. inline image averaged 3.06 µW mg−1 in third-order branches, 3.15 µW mg−1 in second-order branches and 3.39 µW mg−1 in first-order branches, whereas inline image was not significantly affected by the branching order (compare Table 2). Hence, the greatest rates of enthalpic growth were observed in apical buds from first-order branches of the upper canopy (compare Table 1; a significant interaction is shown in Table 2). Somewhat surprisingly, thinning significantly reduced both inline image and inline image by 5% on average. However, enthalpic growth and efficiencies of apical buds were not affected by thinning, demonstrating that respiration and growth rates are not necessarily correlated, if only one respiration parameter is taken into account – and often only inline image is considered.

Table 2.  Results of the multifactorial ANOVA, showing the significance of the main effects and their interactions for inline image, inline image, RSGΔHB, enthalpy conversion efficiency (ηH), soluble sugars and amino acids
 inline image (µW mg−1)inline image (µW mg−1)RSGΔHB (µW mg−1)Efficiency (ηH)Soluble sugars (nmol g−1)Amino acids (nmol g−1)
FPFPFPFPFPFP
  • *,

    P < 0.05;

  • **,

    P < 0.05;

  • ***,

    P < 0.05; ns, not significant.

  • See Table 1 for list of definitions.

Treatment (T)5.1*5.6*0.6ns0.2ns17.1***1.6ns
Canopy I58.3***11.4***53.8***41.2***18.1***71.2***
Order (O)12.8***0.3ns17.8***15.2***2.8ns7.7**
T × C1.3ns2.8*0.7ns2.1ns0.1ns0.3ns
T × O0.2ns0.2ns0.5ns0.6ns0.5ns1.2ns
C × O3.1*0.6ns2.6*0.8ns0.7ns0.5ns
Table 1.  Respiration rates (inline image and inline image), enthalpic growth rates (RSGΔHB) and enthalpy conversion efficiency (ηH) of apical buds from Pinus radiata
 Not thinned
Lower canopyMiddle canopyUpper canopy
Third orderSecond orderFirst orderThird orderSecond orderFirst orderThird orderSecond orderFirst order
inline image (µW mg−1)2.93 ± 0.482.99 ± 0.313.08 ± 0.253.07 ± 0.363.19 ± 0.253.28 ± 0.213.41 ± 0.423.39 ± 0.474.01 ± 0.79
inline image (µW mg−1)2.59 ± 0.262.50 ± 0.242.47 ± 0.282.75 ± 0.282.72 ± 0.272.83 ± 0.242.61 ± 0.302.66 ± 0.282.71 ± 0.22
RSGΔHB (µW mg−1)0.34 ± 0.340.49 ± 0.290.61 ± 0.310.32 ± 0.310.47 ± 0.290.45 ± 0.300.80 ± 0.350.73 ± 0.371.30 ± 0.67
Efficiency (ηH)0.11 ± 0.090.16 ± 0.080.19 ± 0.090.10 ± 0.080.15 ± 0.080.14 ± 0.090.23 ± 0.080.21 ± 0.090.31 ± 0.11
 Thinned stands
Lower canopyMiddle canopyUpper canopy
Third orderSecond orderFirst orderThird orderSecond orderFirst orderThird orderSecond orderFirst order
  1. Samples were collected from thinned stands and unthinned control stands and from three different canopy layers and branching orders. Data shown are means ± SD of 12 independent replicates in each case.

  2. RSG, specific rate of conversion of substrate carbon to biomass carbon; ΔHB, total enthalpy change associated with incorporation of substrate carbon; inline image, specific rate of CO2 production; inline image, enthalpy change for combustion of substrate carbon to carbon dioxide; inline image, specific rate of O2 reduction; inline image, enthalpy change associated with reduction of oxygen, usually substituted by Thornton's ‘constant’ or the oxycaloric equivalent (–455 ± 15 J m mol−1).

inline image (µW mg−1)2.73 ± 0.32.79 ± 0.272.89 ± 0.342.84 ± 0.282.94 ± 0.313.20 ± 0.313.34 ± 0.483.59 ± 0.53.88 ± 0.46
inline image (µW mg−1)2.51 ± 0.292.38 ± 0.212.43 ± 0.232.53 ± 0.232.58 ± 0.272.57 ± 0.182.72 ± 0.342.65 ± 0.332.64 ± 0.31
RSGΔHB (µW mg−1)0.23 ± 0.180.41 ± 0.280.46 ± 0.330.31 ± 0.260.36 ± 0.210.63 ± 0.320.62 ± 0.360.94 ± 0.251.24 ± 0.35
Efficiency (ηH)0.08 ± 0.060.14 ± 0.090.15 ± 0.100.10 ± 0.080.12 ± 0.070.19 ± 0.090.18 ± 0.080.26 ± 0.040.32 ± 0.07

In general, rates of enthalpic growth were positively related to the enthalpy conversion efficiency (Table 1), which is equivalent to the respiratory quotient if carbohydrates are assumed to be the substrate. However, this relation was influenced by numerous factors – in particular canopy position (as can be gleaned from the statistical analysis shown in Table 2). While both inline image and RSGΔHB were greatest for buds from first-order branches in the upper canopy, there was no significant effect of the interaction of branching order and canopy position on the enthalpy conversion efficiency (ηH; compare Table 2). This point will be reconsidered later, after examining variation in enthalpic growth potential (dηH or 1/gH).

Enthalpic growth coefficients of needle respiration

In the present study, enthalpic growth rates were strongly correlated with rates of CO2 respiration (Fig. 1a). We established an average enthalpic growth coefficient (gH) of 0.99, and an average enthalpic intercept (IH) of 2.59 by linear regression analysis (Fig. 1a). However, this situation held only for young, meristematic tissue in late October. A few weeks later in early December, and during later stages of needle development in March (Fig. 1b and c, respectively), correlations between inline image and RSGΔHB were weak. A fitted linear relation between inline image and RSGΔHB explained 69% of the overall observed variation in October (across all canopy positions, branching order and thinning treatments). Additional influences on gH and IH were exerted by the canopy position, branching order and thinning treatment, as depicted in Table 3. We calculated gH and IH for each factor separately (with n = 12). The growth coefficient (gH) was significantly dependent on canopy position (results of main factorial ANOVA; not shown). gH averaged 1.08 in the upper canopy and was significantly greater (at P < 0.05) than in the middle canopy (gH = 0.66) or lower canopy (average gH = 0.93; P < 0.1; Tukey Honest Significant Difference post hoc test). In summary, enthalpic growth potential (1/gH) averaged 1.07 in the lower canopy, 1.51 in the middle canopy and 0.93 in the upper canopy. Thinning reduced the enthalpic growth potential of apical buds (1.01 in thinned and 1.27 in unthinned control stands; P < 0.1; Tukey HSD post hoc test). Branching order had no effect on the gH of apical buds.

image

Figure 1. Correlation between instantaneous growth (RSGΔHB) and respiration rates (inline image). Respiration rates were strongly correlated with enthalpic growth rates in early spring, during cell division and rapid growth (Fig. 1a), but not at later stages of ontogenetic needle development (Fig. 1b and c, recalculated data from Kruse & Adams, 2008a). Note that in the current study (*) respiration and growth rates were based on units fresh weight, whereas the data from Kruse & Adams (2008a) were expressed as rates per unit dry weight. gH, the enthalpic growth coefficient, calculated as the slope of the linear correlation; IH, the intercept of the linear correlation.

Download figure to PowerPoint

Table 3.  Results of the linear regression analysis for calculation of the enthalpic growth coefficient (gH, the enthalpic growth potential) and the intercept (IH) of the correlation between inline image and RSGΔHB
 Not thinned
Lower canopyMiddle canopyUpper canopy
Third orderSecond orderFirst orderThird orderSecond orderFirst orderThird orderSecond orderFirst order
inline image (min; max)2.49; 3.252.32; 3.432.66; 3.452.57; 3.942.78; 3.622.90; 3.652.95; 4.643.05; 4.523.07; 5.70
gH1.180.750.470.810.450.410.841.041.14
IH2.532.632.782.812.983.092.732.632.54
R2 (n = 12)0.620.480.550.450.350.260.490.640.93
 Thinned stands
Lower canopyMiddle canopyUpper canopy
Third orderSecond orderFirst orderThird orderSecond orderFirst orderThird orderSecond orderFirst order
  1. Every regression reflects, to some extent, the influence of treatment, canopy position and branching order on gH and IH. Regressions were calculated with n = 12. Minimum and maximum respiration rates and R2 values are given. Each correlation was significant at P < 0.05.

  2. See Table 1 for list of definitions.

inline image (min; max)2.28; 3.342.41; 3.292.37; 3.392.34; 3.362.41; 3.532.67; 3.642.67; 4.132.80; 4.523.11; 4.82
gH1.381.030.790.710.750.800.971.540.94
IH2.382.412.532.612.672.742.742.152.71
R2 (n = 12)0.520.710.590.400.270.690.510.620.53

gH and IH were strongly correlated (Fig. 2). That is, at the level of the plant organ, interpretation of IH as a separate ‘maintenance’ component of respiration lacks meaning because gH and IH were not independent:

image

Figure 2. The relation between the intercept (IH) and the slope (gH, the growth coefficient). We calculated IH and gH for 18 individual subsets (with n = 12) of the data – as influenced by treatment, canopy position and branching order (compare with Table 3). The two variables were strongly correlated.

Download figure to PowerPoint

  • image(Eqn 8)

Relation between enthalpic growth rate (RSGΔHB) and enthalpy conversion efficiency (ηH)

Enthalpic growth rate (RSGΔHB) is a function of both rate of respiration (inline image) and enthalpy conversion efficiency (ηH; compare Eqn 5). At times of rapid cell development, enthalpic growth rates are linearly related to rates of respiration (compare Fig. 1a). However, more generally, the correlation between RSGΔHB and ηH was hyperbolic (compare Fig. 3):

image

Figure 3. Hyperbolic relation between enthalpic growth rates (RSGΔHB) and the enthalpy conversion efficiency (ηH). Circles, lower canopy; squares, middle canopy; triangles, upper canopy. The hyperbolic relation is significantly influenced by the canopy position. Needles from the upper canopy exhibited large growth rates and comparatively low enthalpy conversion efficiencies. The hyperbola depicted in Fig. 3 was calculated for all data (n = 216). The canopy effect on this hyberbolic relation becomes obvious when hyperbolae are calculated individually for each canopy layer (they yield different parameters P1 and P2, but the R2 of each correlation remains high). It can be shown that the parameter P1 is equivalent to the enthalpic growth potential (see text and compare Fig. 4).

Download figure to PowerPoint

  • image(Eqn 9)

Combining features of the enthalpy balance approach and the GMP, it follows that:

  • image(Eqn 10)

The term IH can be expressed as a function of gH (compare Eqn 8). Substitution of IH and rearrangement give:

  • image(Eqn 11)

By comparison between Eqns 9 and 11, it follows that:

  • image(Eqn 12)

and

  • image(Eqn 13)

When enthalpic growth (RSGΔHB) is rapid, as observed for apical buds from the upper canopy, enthalpy conversion efficiencies (ηH) are comparatively poor (compare Fig. 3). It can be demonstrated that this effect is related to the enthalpic growth potential (inline image).

Figure 4 depicts different hyperbolae, with values for P1 and P2 as defined by Eqns 11 and 12. The enthalpic growth potential of upper canopy needles was significantly less than those of needles from the middle (and lower) canopy (see preceding paragraph). Therefore, further increases in rates of growth in the upper canopy (where growth rates were already fast) would require an out-of-proportion increase in respiration rates (inline image), because enthalpy conversion efficiency reached a plateau at fast rates of enthalpic growth and low growth potentials (compare Fig. 4).

image

Figure 4. Impact of the enthalpic growth potential (1/gH) on the hyperbolic relation between enthalpic growth rates (RSGΔHB) and the enthalpy conversion efficiency (ηH). The parameter P1 is inversely related to gH (Fig. 4b), and low growth potential (1/gH) results in comparatively low enthalpy conversion efficiency at growth rates > 0.82 µW mg−1 s−1 (like those found in the upper canopy; compare Fig. 3).

Download figure to PowerPoint

The approach outlined above raises the possibility of predicting growth potential from simple point measurements of respiration rates in fast-growing plant tissue, provided that both CO2 release and O2 uptake are measured accurately (for precise determination of RSGΔHB and ηH): rearrangement of the hyperbola function depicted in Figs 3 and 4 gives:

  • image(Eqn 14)

where x is the enthalpic growth rate and y is the enthalpy conversion efficiency. A second algorithm describes P1 as a function of P2, and is given by Eqn 13.

After rearrangement of Eqns 13 and 14, P2 can be eliminated as follows:

  • image(Eqn 15)

The parameter P1 (that is, the enthalpic growth potential 1/gH) can now be calculated with the knowledge of the enthalpic growth rate (x) and the enthalpy conversion efficiency (y); both are dependent on inline image and inline image. Note that at x = RSGΔHB= 0.82, the parameter P1 cannot be defined (compare Eqn 15) and at this point lie the intercepts of all hyperbolae defined by different growth potentials (P1; compare Fig. 4a).

Amino acids and soluble sugars

Canopy position had a moderate but statistically significant impact on sugar contents of young, expanding foliage (Fig. 5, Table 2). On average, sugar contents increased from 38.9 nmol g−1 fresh weight (FW) in the lower to 42.3 nmol g−1 FW in the middle canopy, and further to 45.3 nmol g−1 FW in the upper canopy. Thinning increased sugar contents by 9% on average, compared with control stands. By contrast, thinning reduced inline image of buds by 5% (but increased their growth potential; see preceding paragraphs). Rates of respiration (inline image) were poorly correlated with sugar content of growing tissues (compare Fig. 6a).

image

Figure 5. Soluble sugar and amino acid contents in apical buds of Pinus radiata. Closed bars, stands that were not thinned; open bars, thinned stands. Frozen plant material was homogenized in liquid nitrogen. Soluble sugars and total amino-N were determined photometrically using anthrone and ninhydrine as test reagents, respectively. Sugar contents were increased moderately by the thinning treatment, and the canopy position had a moderate effect on sugar contents. Amino acid contents strongly increased with canopy height (and moderately with branching order), but the thinning treatment had no effect on amino acid contents.

Download figure to PowerPoint

image

Figure 6. Correlation between respiration rates (inline image) and soluble sugars (a), and between respiration rates and amino acids (b). In the present study, respiration rates were only poorly correlated with sugar contents, but roughly 50% of their variability could statistically be explained by the variation in total amino acid contents.

Download figure to PowerPoint

Amino acid contents of growing apical buds (plus needle tissue) were more strongly affected by canopy position than sugar contents (Fig. 5, Table 2). In contrast to sugars, we also observed a significant effect of branching order on amino acid contents (tissue from first-order branches contained the greatest amounts of amino acids). Roughly 50% of variation in inline image could be explained by variable amino acid contents of growing apical buds and needle tissue (Fig. 6b).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Dynamics of crown development in early spring

Simultaneous determination of respiratory CO2 release and mitochondrial oxygen reduction (which is proportional to the evolution of heat from plant tissue; Kemp, 2000) yields a measure of the energy increment in the anabolic products of respiration (RSGΔHB). The accuracy of estimation of RSGΔHB partly depends upon the assumptions made. We assumed that carbohydrates were used as the substrate of respiration because rapidly growing bud tissue depends on carbohydrates delivered via the phloem. Enthalpic rates of growth determined in the present study reliably reflected patterns of growth of foliage within pine canopies. Enthalpic growth of apical buds was most strongly affected by the canopy position, and to a lesser extent by the branching order (largest growth rates were observed for buds from first-order branches of the upper canopy). These results, in combination with those obtained by Kruse & Adams (2008a), demonstrate that respiration and growth processes within the canopy are coordinated (even optimized) to ensure effective crown development. In early spring, needle growth is hastened in the upper canopy, such that photosynthetic carbon gain can be maximized as long as environmental conditions are favorable (in particular water availability). During this period of time, respiration rates (inline image) were closely related to rates of needle growth (increase of enthalpy; see Fig. 1a). Respiration rates were also correlated with soluble amino acids, but not with sugars. Within tree canopies, nitrogen is often allocated according to patterns that follow time-integrated irradiance (Hirose & Werger, 1987; Niinemets et al., 2004), in order to allow for enhanced photosynthesis early in the growth season (Evans, 1989). Thus, nitrogen allocation and availability seem to be the crucial drivers of growth and respiration (also compare Kruse et al., 2008), whereas sugar availability does not appear to be a limiting factor for foliage development during the remobilization period in early spring. Ögren (2000) noted that respiration and sugar content were related in foliage of Pinus contorta and Picea abies, but only during the dormant period. There seems little cause to expect that tissue sugar content would be a major restriction on respiration in rapidly growing tissues where much of the carbon used is imported via the phloem.

Enthalpic growth potential, enthalpy conversion efficiency, and carbon conversion efficiency

Light gradients within tree canopies affect not only nitrogen allocation patterns but also synthesis of various compounds. For example, lignin contents of foliage increase under conditions of high light (Niinemets, 1999; Niinemets et al., 2002), as do those of soluble phenolics (Yamasaki & Kikuzawa, 2003; Poorter et al., 2006). Respiratory energy is also needed for the production of ascorbate which plays a crucial role in antioxidant defense, preventing the accumulation of reactive oxygen species under conditions of high light (Millar et al., 2003; Bartoli et al., 2006). Variation in oxidation state among the chemical constituents of biomass (e.g. lignin is more reduced than cellulose; Lambers et al., 1998) produces large variations in costs of construction. For this reason, the chemical composition of biomass affects plant carbon balance. In the upper canopy, biosynthesis of foliage rich in protein and lignin requires more substrate carbon, and is associated with comparatively faster rates of CO2 respiration (or a low mass-based growth potential, i.e. low 1/g), than foliage from lower in the canopy. That is, the mass-based growth potential can be distorted by variable oxidation states among the anabolic products of respiration; an effect that is avoided (or taken into account) by employing the ‘enthalpy balance approach’ of the present study. Comparatively low ‘enthalpic growth potential’ (1/gH as opposed to 1/g; compare Eqns 2 and 5) of foliage from the upper canopy cannot be explained on the basis of its high energetic costs (as compared with needles from lower canopy levels). Low ‘enthalpic growth potential’ is associated with low enthalpy conversion efficiency at higher rates of enthalpic growth – like those observed for foliage from the upper canopy. This result suggests that maximizing enthalpic growth necessitates a large capacity for respiratory oxygen reduction (i.e. the enthalpy conversion efficiency and CO2:O2 are comparatively small when rates of growth are fast).

We have demonstrated elsewhere that alternative oxidase (AOX) activity is greater in young, expanding foliage than in mature foliage, and that AOX activity is correlated with the respiratory capacity of mitochondrial oxygen reduction (cytochrome C oxidase (COX) + AOX; Kruse & Adams, 2008b). Although operation of the alternative path may appear wasteful (because it is not coupled to ATP production), it has long been argued that AOX helps regulate fluctuating demand for carbon-skeleton intermediates, reducing power and ATP (Vanlerberge & McIntosh, 1997). In rapidly growing tissue, demand for ATP is less than for carbon skeleton intermediates and reducing power (Amthor, 2000; Buchanan et al., 2000). Under these circumstances, and perhaps paradoxically, large AOX activity ensures fast rates of growth (also compare Stucki, 1982). Alternatively, cycles of ATP production and hydrolysis (which are sometimes also considered to be futile; Amthor, 2000) can help maintain a large capacity for oxygen reduction, as catalyzed by COX. Such physiological mechanisms clearly affect the carbon conversion efficiency (ɛ) of substrates during growth, because a large and variable proportion of the energy stored in substrate carbon (carbohydrates) is dissipated and not translated into anabolic products (Ribas-Carbo et al., 1995). Carbon conversion efficiency is defined as (compare Hansen et al., 1998, Kruse & Adams, 2008a):

  • image(Eqn 16)

Rearrangement of Eqn 16 gives:

  • image(Eqn 17)

Combining Eqn 17 with Eqn 6 (see Introduction) gives:

  • image(Eqn 18)

Eqn 18 demonstrates that substrate carbon conversion efficiency (ɛ) during growth is dependent on two terms. The oxidation state of newly synthesized biomass affects ɛ (and ΔHB can be obtained from the chemical composition or heat of combustion of biomass; McDermitt & Loomis, 1981), as has been discussed above. However, variability of inline imageHB is certainly less than variability of ηH (which depends on variation of physiological processes, i.e. inline image versus inline image, and not on the oxidation state of anabolic products), such that substrate carbon conversion efficiency (ɛ) is mainly a function of enthalpy conversion efficiency (ηH). Substrate carbon conversion efficiency increases as growth rates increase, mainly because increasing quantities of respiratory intermediates are withdrawn from the main catabolic pathways for biosynthesis (glycolysis, the TCA cycle and the oxidative pentose phosphate pathway; Buchanan et al., 2000).

The relation between enthalpy conversion efficiency and growth is hyperbolic, and ηH (and therefore also ɛ) levels off and approaches an asymptote as rates of growth increase. The asymptote is dictated by enthalpic growth potential (1/gH): low enthalpic growth potential is associated with comparatively low ηH and ɛ at high rates of growth. Maximizing rates of growth requires maintenance of high rates of substrate conversion (AOX, for example, is essential to operation of the TCA cycle), but these are associated with comparatively low efficiency – that is, further increases in growth rates can only be achieved by an over-proportional increase in rates of respiration (inline image). This putative disadvantage can be outweighed if carbohydrates are abundant and growth is limited to comparatively brief periods of favorable environmental conditions.

Practical applications of theoretical insights?

It is a long-standing hope that theoretical insights into physiological mechanisms operating at cellular or organ levels (e.g. leaves) will eventually help predict whole-plant performance. In past decades, there has been tremendous progress in our understanding of photosynthesis and its underlying mechanisms (i.e. Farquhar et al., 1980). However, photosynthetic performance alone is frequently a poor predictor of plant growth, largely as a consequence of rapid respiration of 30 to 80% of acquired CO2 (Atkin et al., 2005). DeLucia et al. (2007) recently emphasized this point in an analysis of carbon use efficiency (CUE) which they showed was far from constant across ecosystems and species.

We clearly require a better understanding of physiological processes associated with respiration in order to comprehensively quantify the carbon balance of plants. While separation of respiratory processes into ‘growth’ and ‘maintenance’ components has been regarded in many quarters as the best means of helping close the gap in knowledge of the complex process of respiration, it has done little to enhance our capacity to predict whole-plant performance (Hansen et al., 1998). A key to understanding plant growth lies in a better understanding of the link between carbohydrate production, the respiration rate (inline image) and – most importantly – the efficiency of conversion of carbohydrates into biomass.

The enthalpy balance approach helps characterize respiration in a straightforward manner. In summary, the enthalpy balance approach treats respiration as a ‘black box’ and provides a measure of the efficiency of respiratory energy conversion, without assumption of any specific physiological pathway (Mathews et al., 2000). That said, the temperature response of respiration (both inline image and inline image; see Kruse & Adams, 2008b; Kruse et al. 2008), as measured using calorimetry, can also help to identify and characterize respiratory pathways (alternative oxidase and cytochrome C oxidase). We have demonstrated here that point measurements of inline image and inline image are sufficient to characterize enthalpic growth potentials of plant tissues. We note again that enthalpic growth potential (1/gH) depends on temperature (Matheson et al., 2004), as the temperature response of inline image differs from that of inline image (Kruse et al., 2008).

A question arising is: can we employ this method for identification of plant varieties with inherently high growth potential? The approach and methods developed here are most useful when applied to rapidly growing sink tissue, where growth and respiration rates are correlated. Accurate determination of growth potential (i.e. inline image) will be most readily achieved if the methods are applied to tissue exhibiting fast rates of enthalpic growth. This is exemplified by considering the increasingly prevalent effect of enthalpic growth potential (inline image) on the hyperbolic relation between enthalpic growth rates (RSGΔHB) and the enthalpy conversion efficiency (ηH), especially at growth rates > 1.0 µW mg−1 FW (compare Fig. 4a).

It follows that, in order to identify cultivars with inherently high growth potential, this method is best applied to plants growing under optimal, controlled conditions. Equally clearly, growth potential can be more readily determined for herbaceous annuals (i.e. for many economically important agricultural crops) than for the slow-growing pine needles in the present study.

We have shown that a combination of concepts and a comparatively novel experimental technique can enlarge our mechanistic understanding of the relation between respiration and growth, which in turn may lead to several practical applications, including growth modeling and plant breeding.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We wish to thank Hancock Victorian Plantations Pty Limited for financial and logistic support and Najib Ahmady for technical assistance. We thank the ARC for financial support (Linkage Grant) and Phil Gerschwitz for assistance in the field. We are particularly grateful to the reviewers who greatly helped improve an earlier draft of this manuscript.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • Amthor JS. 2000. The McCree-de Wit-Penning de Vries-Thornley respiration paradigms: 30 years later. Annals of Botany 86: 120.
  • Atkin OK, Bruhn D, Hurry VM, Tjoelker MG. 2005. Evans Review No. 2. The hot and the cold: unraveling the variable response of plant respiration to temperature. Functional Plant Biology 32: 87105.
  • Bartoli CG, Yu J, Gómez F, Fernández L, McIntosh L, Foyer CH. 2006. Inter-relationships between light and respiration in the control of ascorbic acid synthesis and accumulation in Arabidopsis thaliana leaves. Journal of Experimental Botany 8: 16211631.
  • Buchanan BB, Gruissem W, Jones RL. 2000. Biochemistry and molecular biology of plants. Rockville, MD, USA: American Society of Plant Physiologists.
  • Cannell MGR, Thornley JHM. 2000. Modeling plant respiration: some guiding principles. Annals of Botany 85: 4554.
  • Criddle RS, Breidenbach RW, Hansen LD. 1991. Plant calorimetry: how to quantitatively compare apples and oranges. Thermochimica Acta 193: 6782.
  • Criddle RS, Hansen LD. 1999. Calorimetric methods for analysis of plant metabolism. In: KempRB, ed. Handbook of thermal analysis and colorimetry 4. From macromolecules to man . Amsterdam, the Netherlands: Elsevier, 711763.
  • DeLucia EH, Drake JE, Thomas RB, Gonzalez-Melers M. 2007. Forest carbon use efficiency: is respiration a constant fraction of gross primary production? Global Change Biology 13: 11571167.
  • Evans JR. 1989. Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78: 919.
  • Farquhar GD, Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149: 7990.
  • Gifford RM. 2003. Plant respiration in productivity models: conceptualization, representation and issues for global terrestrial carbon-cycle research. Functional Plant Biology 30: 171186.
  • Hansen J, Möller IB. 1975. Percolation of starch and soluble carbohydrates from plant tissue for quantitative determination with anthrone. Analytical Biochemistry 68: 8794.
  • Hansen LD, Breidenbach RW, Smith BN, Hansen JR, Criddle RS. 1998. Misconceptions about the relation between growth and respiration. Botanica Acta 111: 255260.
  • Hansen LD, Hopkin MS, Criddle RS. 1997. Plant calorimetry: a window to plant physiology and ecology. Thermochimica Acta 300: 183197.
  • Hansen LD, Hopkin MS, Rank DR, Anekonda TS, Breidenbach RW, Criddle RS. 1994. The relation between plant growth and respiration: a thermodynamic model. Planta 194: 7785.
  • Hansen LD, Macfarlane C, McKinnon N, Smith BN, Criddle RS. 2004. Use of calorespirometric ratios, heat per CO2 and heat per O2, to quantify metabolic paths and energetics of growin cells. Thermochimica Acta 422: 5561.
  • Hansen LD, Russell DJ, Choma CT. 2007. From biochemistry to physiology: the calorimetry connection. Cell Biochemistry and Biophysics 49: 125140.
  • Hirose T, Werger MJA. 1987. Maximizing daily canopy photosynthesis with respect to the leaf nitrogen allocation pattern in the canopy. Oecologia 72: 520526.
  • Hopkin MS. 1991. Calorimetric studies of plant physiology. PhD dissertation, Provo, UT, USA: Brigham Young University.
  • Kemp RB. 2000. ‘Fire burn and cauldron bubble’ (W. Shakespeare): what the calorimetric-respirometric (CR) ratio does for our understanding of cells. Thermochimica Acta 355: 115124.
  • Kruse J, Adams MA. 2008a. Sensitivity of respiratory metabolism and efficiency to foliar nitrogen during growth and maintenance. Global Change Biology 14: 12331251.
  • Kruse J, Adams MA. 2008b. Three parameters comprehensively describe the temperature response of respiratory oxygen reduction. Plant, Cell & Environment 31: 954967.
  • Kruse J, Hetzger I, Hänsch R, Mendel R-R, Walch-Liu P, Engels C, Rennenberg H. 2002. Elevated pCO2 favours nitrate reduction in the roots of wild-type tobacco (Nicotiana tabacum cv. Gat.) and significantly alters N-metabolism in transformants lacking functional nitrate reductase in the roots. Journal of Experimental Botany 379: 23512367.
  • Kruse J, Hopmans P, Adams MA. 2008. Temperature responses are a window to the physiology of dark respiration: differences between CO2 release and O2 reduction shed light on energy conservation. Plant, Cell & Environment 31: 901914.
  • Lambers H, Chapin FS, Pons TL. 1998. Plant physiological ecology. New York, NY, USA: Springer Verlag.
  • Macfarlane C, Adams MA, Hansen LD. 2002. Application of an enthalpy balance model of the relation between growth and respiration to temperature acclimation of Eucalyptus globulus seedlings. Proceedings Royal Society London, Series B 269: 14991507.
  • Matheson S, Ellingson DJ, McCarlie VW, Smith BN, Criddle RS, Rodier L, Hansen LD. 2004. Determination of growth and maintenance coefficients by calorespirometry. Functional Plant Biology 31: 929939.
  • Mathews CK, Van Holde KE, Ahern KG. 2000. Biochemistry. San Francisco, CA, USA: Benjamin/Cummings, an imprint of Addison Wesley Longman.
  • McCree KJ. 1970. An equation for the rate of dark respiration of white clover plants grown under controlled condition. In: ŠetlikI, ed. Prediction and measurement of photosynthetic productivity. Wageningen, the Netherlands: Centre for Agricultural Publishing and Documentation, 221229.
  • McCree KJ. 1974. Equations for the rate of dark respiration of white clover and grain sorghum, as functions of dry weight, photosynthesis rate, and temperature. Crop Science 14: 509514.
  • McDermitt DK, Loomis RS.1981. Elemental composition of biomass and its relation to energy content, growth efficiency and yield. Annals of Botany 48: 275290.
  • Millar AH, Mittova V, Kiddle G, Heazlewood JL, Bartoli CG, Theodoulou FL, Foyer CH. 2003. Control of ascorbate synthesis by respiration and its implications for stress response. Plant Physiology 133: 443447.
  • Niinemets Ü. 1999. Energy requirement for foliage formation is not constant along canopy light gradients in temperate deciduous trees. New Phytologist 141: 459470.
  • Niinemets Ü, Ellsworth DS, Lukjanova A, Tobias M. 2002. Dependence of needle architecture and chemical composition on canopy light availability in three North American Pinus species with contrasting needle length. Tree Physiology 22: 747761.
  • Niinemets Ü, Kull O, Tenhunen JD. 2004. Within-canopy variation in the rate of development of photosynthetic capacity is proportional to integrated quantum flux density in temperate deciduous trees. Plant, Cell & Environment 27: 293313.
  • Ögren E. 2000. Maintenance respiration correlates with sugar but not nitrogen concentration in dormant plants. Physiologia Plantarum 108: 295299.
  • Ordentlich A, Linzer RA, Raskin I. 1991. Alternative respiration and heat evolution in plants. Plant Physiology 97: 15451550.
  • Poorter H, Pepin S, Rijkers T, De Jong Y, Evans JR, Körner C. 2006. Construction costs, chemical composition and payback time of high and low irradiance leaves. Journal of Experimental Botany 2: 355371.
  • Reich PB, Tjoelker MG, Machado J-L, Oleksyn J. 2006. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439: 457461.
  • Ribas-Carbo M, Berry JA, Yakir D, Giles L, Robinson SA, Lennon AM, Siedow JN. 1995. Electron partitioning between the cytochrome and alternative pathways in plant mitochondria. Plant Physiology 109: 829837.
  • Ryan MG, Hubbart RM, Pongracic S, Raison RJ, McMurtrie RE. 1996. Foliage, fine-root, woody tissue and stand respiration in Pinus radiata in relation to nitrogen status. Tree Physiology 16: 333343.
  • Stucki JW. 1982. Thermodynamic optimizing principles in mitochondrial energy conversion. In: SiesH, ed. Metabolic compartmentation. London, USA: Academic Press, 3969.
  • Thornley JHM. 1970. Respiration, growth and maintenance in plants. Nature 227: 304305.
  • Thornley JHM, Cannell MGR. 2000. Modeling the components of plant respiration: representation and realism. Annals of Botany 85: 5567.
  • Thornley JHM, Johnson RL. 1990. Plant and crop modeling. A mathematical approach to plant and crop physiology. Oxford, UK: Oxford Science Publications.
  • Turnbull MH, Whitehead D, Tissue DT, Schuster WSF, Brown KJ, Griffin KL. 2003. Scaling foliar respiration in two contrasting forest canopies. Functional Ecology 17: 101108.
  • Vanlerberge GC, McIntosh L. 1997. Alternative oxidase: from gene to function. Annual Review of Plant Physiology and Plant Molecular Biology 48: 703734.
  • De Wit CT, Brouwer R, Penning de Vries FWT. 1970. The simulation of photosynthetic systems. In: SetlikI, ed. Prediction and measurement of photosynthetic productivity. Wageningen, the Netherlends: Centre for Agricultural Publishing and Documentation, 4770.
  • Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M et al . 2004. The worldwide leaf economics spectrum. Nature 428: 821827.
  • Yamasaki M, Kikuzawa K. 2003. Temporal and spatial variations in leaf herbivory within a canopy of Fagus crenata. Oecologia 137: 226232.