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KARL J. NIKLAS, Plant Allometry, Leaf Nitrogen and Phosphorus Stoichiometry, and Interspecific Trends in Annual Growth Rates, Annals of Botany, Volume 97, Issue 2, February 2006, Pages 155–163, https://doi.org/10.1093/aob/mcj021
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Abstract
• Background Life forms as diverse as unicellular algae, zooplankton, vascular plants, and mammals appear to obey quarter-power scaling rules. Among the most famous of these rules is Kleiber's (i.e. basal metabolic rates scale as the three-quarters power of body mass), which has a botanical analogue (i.e. annual plant growth rates scale as the three-quarters power of total body mass). Numerous theories have tried to explain why these rules exist, but each has been heavily criticized either on conceptual or empirical grounds.
• N,P-Stoichiometry Recent models predicting growth rates on the basis of how total cell, tissue, or organism nitrogen and phosphorus are allocated, respectively, to protein and rRNA contents may provide the answer, particularly in light of the observation that annual plant growth rates scale linearly with respect to standing leaf mass and that total leaf mass scales isometrically with respect to nitrogen but as the three-quarters power of leaf phosphorus. For example, when these relationships are juxtaposed with other allometric trends, a simple N,P-stoichiometric model successfully predicts the relative growth rates of 131 diverse C3 and C4 species.
• Conclusions The melding of allometric and N,P-stoichiometric theoretical insights provides a robust modelling approach that conceptually links the subcellular ‘machinery’ of protein/ribosomal metabolism to observed growth rates of uni- and multicellular organisms. Because the operation of this ‘machinery’ is basic to the biology of all life forms, its allometry may provide a mechanistic explanation for the apparent ubiquity of quarter-power scaling rules.
INTRODUCTION
Numerous quarter-power scaling rules appear to span all levels of biological organization, from molecules to ecosystems, across pro- and eukaryotes, plants and animals (Hemmingsen, 1960; Peters, 1983; Calder, 1984, 1996; Schmidt-Nielsen, 1984). For example, across animal species ranging in size from that of a mouse to an elephant, maximum life span in captivity, blood volume circulation, fast muscle contraction, and a host of other phenomena, each scale closely to the one-quarter power of body mass (Lindstedt and Calder, 1981). Perhaps the most famous of these rules is Kleiber's, which states that basal metabolic rates scale as the three-quarter power of body mass (Kleiber, 1932, 1961)—a scaling relationship that finds its analogue in the allometry of growth rate versus body mass across the polyphyletically and ecologically diverse unicellular algae and terrestrial vascular embryophytes (Banse, 1976; Niklas, 1994; Niklas and Enquist, 2001).
Yet, the identification of an unambiguous mechanistic explanation for the origin of these scaling rules remains an open theoretical problem. Numerous explanations have been advanced, but each has been viewed with a critical if not jaundiced eye (e.g. Blaxter, 1965; Blum, 1977; Gray, 1981; Economos, 1982, 1983; Heusner, 1982; Feldman and McMahon, 1983; Feldman, 1995; Prothero, 1986a). Among the most recent of these theories is that of West, Brown and Enquist, who assert that all quarter-power scaling rules (and their one-quarter multiples such as three-quarters) emerge from the interplay between the physical or geometric constraints resulting from three functional properties of every biological network transport system (West et al., 1997, 1999, 2001). Specifically, their theory (the ‘WBE’ theory) claims that all networks (a) are space-filling, hierarchical branching systems, (b) have terminal branch elements that are invariant in size, and (c), by virtue of natural selection, minimize the energy required to transport and deliver nutrients (and thus minimize either the time or distance nutrients are moved).
As so many theories before it, the WBE theory has been heavily criticized on empirical, theoretical and even strictly mathematical grounds (e.g. Dodds et al., 2001; Darveau et al., 2002; Weibel, 2002). Arguably, the first assumption (i.e. that biological delivery networks are ‘fractal’ in nature) is consistent with the ‘self-similarity’ typically observed when branched nutrient networks within multicellular organisms are dissected and numerically quantified. However, if the WBE theory is valid across all levels of biological organization, from that of molecules to ecosystems as claimed by its authors, fractal-like delivery networks must exist at each level. This is difficult to imagine for some (e.g. molecules) and undocumented for others (e.g. organelles and ecosystems). Similar concerns exist for the two remaining assumptions of the WBE theory, e.g. it has yet to be established that capillaries, bronchioles and terminal xylary elements are invariant in size or that they minimize the time and energy required to exchange mass or energy.
Despite these concerns (or perhaps because of them), the WBE theory has engendered a renaissance in the field of allometric theory and empirical enquiry—one in which alternative theories for the existence of quarter-power scaling rules continue to be sought. It is in this context that recent developments in modelling the effects of nitrogen and phosphorus allocation patterns on protein synthesis rates and thus ‘growth’ are particularly exciting. These models emerge from this perspective that, irrespective of phyletic affinity or ecological preference, the growth rate of any kind of organism is positively correlated with ribosome number and rate of activity and negatively correlated with protein concentration (Dobberfuhl, 1999; Sterner and Elser, 2002; Elser et al., 2003; Ågren, 2004; Vrede et al., 2004). Conceptually, the amounts of ribosomes and proteins are thought of as respective measures of an organism's protein-production ‘machinery’ and the ‘overhead’ that must be produced per unit time to maintain a constant growth rate. Nitrogen and phosphorus (N and P) stoichiometry is emphasized, because large fractions of the total N and P are allocated to the construction of proteins and rRNA. Thus, N,P-stoichiometry is predicted to correlate with growth rate at the level of cells, tissues or the whole organism (Dobberfuhl, 1999; Sterner and Elser, 2002; Vrede et al., 2004). Specifically, growth rates should correlate positively with increasing rRNA (and P) investments relative to protein (and N) investments.
This prediction is particularly relevant to three previously reported allometric relationships for plants (Niklas and Enquist, 2001, 2002). First, annual growth rates in body mass across phyletically and ecologically diverse species appear to scale as the three-quarters power of body size. Secondly, growth rates scale linearly (isometrically) with the capacity to intercept sunlight. And, thirdly, total leaf N appears to scale as the three-quarters power of total leaf P, across and within some species (Niklas and Cobb, 2005; Niklas et al., 2005). The goal of this paper is to review these relationships and to explore them empirically with the aid of a recently expanded database for non-woody and woody plant species ranging across 11 orders of magnitude in total body size.
A STATISTICAL PROLEGOMENA
However, before proceeding with this review, it is important to consider first how allometric scaling relationships are adduced statistically, particularly because these techniques are used to evaluate the numerical parameters that describe all allometric relationships, including those discussed throughout this paper (e.g. Tables 1 and 2).
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Unicellular algae (H gauged by cell pigment concentration CP), N = 68 | ||||||||
H vs. GT | 0·95 (0·87; 1·03) | −3·51 (−4·43; −2·60) | 0·886 | 488 | ||||
GTvs. MT | 0·75 (0·73; 0·76) | −0·91 (−1·10; −0·70) | 0·995 | 9745 | ||||
H vs. MT | 0·71 (0·64; 0·78) | −4·38 (−5·25; −3·50) | 0·876 | 453 | ||||
Non-woody species (H gauged by standing leaf mass ML), N = 1147 | ||||||||
H vs. GT | 1·01 (0·97; 1·06) | −0·91 (−1·01; −0·80) | 0·903 | 3957 | ||||
GTvs. MT | 0·99 (0·95; 1·04) | 0·51 (0·39; 0·63) | 0·907 | 3046 | ||||
H vs. MT | 1·01 (0·99; 1·03) | −0·39 (−0·46; −0·32) | 0·975 | 8144 | ||||
Woody species (H gauged by standing leaf mass ML), N = 265 | ||||||||
H vs. GT | 0·90 (0·84; 0·97) | −0·11 (−0·19; −0·03) | 0·790 | 545 | ||||
GTvs. MT | 0·77 (0·71; 0·83) | −0·74 (−0·87; −0·61) | 0·804 | 581 | ||||
H vs. MT | 0·70 (0·64; 0·75) | −0·78 (−0·91; −0·65) | 0·766 | 1065 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Unicellular algae (H gauged by cell pigment concentration CP), N = 68 | ||||||||
H vs. GT | 0·95 (0·87; 1·03) | −3·51 (−4·43; −2·60) | 0·886 | 488 | ||||
GTvs. MT | 0·75 (0·73; 0·76) | −0·91 (−1·10; −0·70) | 0·995 | 9745 | ||||
H vs. MT | 0·71 (0·64; 0·78) | −4·38 (−5·25; −3·50) | 0·876 | 453 | ||||
Non-woody species (H gauged by standing leaf mass ML), N = 1147 | ||||||||
H vs. GT | 1·01 (0·97; 1·06) | −0·91 (−1·01; −0·80) | 0·903 | 3957 | ||||
GTvs. MT | 0·99 (0·95; 1·04) | 0·51 (0·39; 0·63) | 0·907 | 3046 | ||||
H vs. MT | 1·01 (0·99; 1·03) | −0·39 (−0·46; −0·32) | 0·975 | 8144 | ||||
Woody species (H gauged by standing leaf mass ML), N = 265 | ||||||||
H vs. GT | 0·90 (0·84; 0·97) | −0·11 (−0·19; −0·03) | 0·790 | 545 | ||||
GTvs. MT | 0·77 (0·71; 0·83) | −0·74 (−0·87; −0·61) | 0·804 | 581 | ||||
H vs. MT | 0·70 (0·64; 0·75) | −0·78 (−0·91; −0·65) | 0·766 | 1065 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Unicellular algae (H gauged by cell pigment concentration CP), N = 68 | ||||||||
H vs. GT | 0·95 (0·87; 1·03) | −3·51 (−4·43; −2·60) | 0·886 | 488 | ||||
GTvs. MT | 0·75 (0·73; 0·76) | −0·91 (−1·10; −0·70) | 0·995 | 9745 | ||||
H vs. MT | 0·71 (0·64; 0·78) | −4·38 (−5·25; −3·50) | 0·876 | 453 | ||||
Non-woody species (H gauged by standing leaf mass ML), N = 1147 | ||||||||
H vs. GT | 1·01 (0·97; 1·06) | −0·91 (−1·01; −0·80) | 0·903 | 3957 | ||||
GTvs. MT | 0·99 (0·95; 1·04) | 0·51 (0·39; 0·63) | 0·907 | 3046 | ||||
H vs. MT | 1·01 (0·99; 1·03) | −0·39 (−0·46; −0·32) | 0·975 | 8144 | ||||
Woody species (H gauged by standing leaf mass ML), N = 265 | ||||||||
H vs. GT | 0·90 (0·84; 0·97) | −0·11 (−0·19; −0·03) | 0·790 | 545 | ||||
GTvs. MT | 0·77 (0·71; 0·83) | −0·74 (−0·87; −0·61) | 0·804 | 581 | ||||
H vs. MT | 0·70 (0·64; 0·75) | −0·78 (−0·91; −0·65) | 0·766 | 1065 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Unicellular algae (H gauged by cell pigment concentration CP), N = 68 | ||||||||
H vs. GT | 0·95 (0·87; 1·03) | −3·51 (−4·43; −2·60) | 0·886 | 488 | ||||
GTvs. MT | 0·75 (0·73; 0·76) | −0·91 (−1·10; −0·70) | 0·995 | 9745 | ||||
H vs. MT | 0·71 (0·64; 0·78) | −4·38 (−5·25; −3·50) | 0·876 | 453 | ||||
Non-woody species (H gauged by standing leaf mass ML), N = 1147 | ||||||||
H vs. GT | 1·01 (0·97; 1·06) | −0·91 (−1·01; −0·80) | 0·903 | 3957 | ||||
GTvs. MT | 0·99 (0·95; 1·04) | 0·51 (0·39; 0·63) | 0·907 | 3046 | ||||
H vs. MT | 1·01 (0·99; 1·03) | −0·39 (−0·46; −0·32) | 0·975 | 8144 | ||||
Woody species (H gauged by standing leaf mass ML), N = 265 | ||||||||
H vs. GT | 0·90 (0·84; 0·97) | −0·11 (−0·19; −0·03) | 0·790 | 545 | ||||
GTvs. MT | 0·77 (0·71; 0·83) | −0·74 (−0·87; −0·61) | 0·804 | 581 | ||||
H vs. MT | 0·70 (0·64; 0·75) | −0·78 (−0·91; −0·65) | 0·766 | 1065 |
The objective of the vast majority of allometric studies is to determine the numerical values of logβ and α. When a predictive relationship is sought, simple ordinary least squares (OLS) regression analysis can be used. When the objective is to establish a functional relationship between Yo and Ya, as is generally in case, OLS regression analysis is ill equipped for this purpose, in part because it is based on the assumption that Ya is biologically independent of Yo and that it is measured without error. Three regression methods have been suggested to overcome this limitation, i.e. Bartlett's three-group method, principal axis regression, and reduced major axis regression (Sokal and Rohlf, 1980). Considerable controversy revolves around which of these methods is the most appropriate (Smith, 1980; Harvey, 1982; Seim, 1983; Rayner, 1985; Prothero, 1986b; McArdle, 1988, 2003; Jolicoeur, 1990). This issue is not trivial, especially when the goal is to ‘test’ when empirically determined scaling exponents agree statistically with those predicted by a particular theory, because the numerical values of α and logβ depend on the regression techniques used and because different techniques can produce significantly different numerical values even for the same data set.
Space precludes a detailed discussion of the merits and detractions of each of the three regression methods. However, reduced major axis (RMA) regression analysis has emerged as a ‘standard’ allometric technique over the past few years. Statistical software is available to perform RMA regression analyses, but access to this software is not critical, because OLS regression summary statistics provide all the necessary information to compute the numerical values of α and logβ, and their corresponding 95 % confidence intervals.
Eqns (3)–(6) are used throughout this paper to evaluate the numerical values of scaling exponents, allometric constants, and their respective 95 % confidence intervals (see Tables 1 and 2). As noted, the data base used to establish these numerical values has been recently expanded in terms of species number, body size range and phylogenetic diversity compared with that used previously to establish scaling exponents (e.g. Niklas, 1994, 2004; Niklas and Enquist, 2001, 2002).
LIGHT, GROWTH AND BODY SIZE
The isometric relationship between H and GT represented by eqn (7) makes some intuitive sense. Even though the ability to ‘harvest sunlight’ and its corresponding ‘energy use efficiency’ are very different biophysical phenomena, it is not unreasonable to expect growth rates to correlate linearly with the ability to capture radiant energy. In contrast, it is far less obvious why either annual growth rate or light-harvesting ability should scale as the three-quarters power of body mass. Early workers exploring the relationship between basal metabolic rates across animals differing in body size expected a two-thirds scaling exponent, because it was assumed that the ability of cells or entire organisms to exchange mass or energy with the environment is governed by body surface area (which scales as the square of any linear reference dimension L) and that the demand for nutrients is gauged by body volume (which scales as the cube of L). Of course the two-thirds scaling relationship between surface area and volume holds true only for a series of geometrically identical objects that retain the same shape as they increase in size—two conditions that are repeatedly violated by unicellular and multicellular organisms, either ontogenetically or phylogenetically.
Regardless of the mechanistic explanation for why the three scaling relationships exist, each receives reasonably strong statistical support when ‘tested’ against empirically observed trends for phylogenetically diverse unicellular algae and tree-sized dicots and conifers (Table 1). For these organisms, the 95 % confidence intervals of the slope of the log–log linear relationship between light harvesting capability and annual growth approach or include unity. Likewise, the intervals of the slope of the log–log linear relationship between annual growth and total body mass include 0·75. Thus, the proportional relationships summarized by H ∝ GT ∝ MT3/4 are reasonably accurate for unicellular algae and tree-sized plants.
In pointed contrast, the allometry of non-woody plants (i.e. herbaceous species and 1-year old-dicot and conifer tree species) deviates from that predicted by eqns (8) and (9) and observed for unicellular algae and tree-sized individuals, because it is strongly isometric in terms of all three biological variables, i.e. H ∝ GT ∝ MT (Table 1). In this sense, the three-quarters scaling ‘rule’ asserted for all unicellular and multicellular plants and animals is not ‘invariant’ as is sometimes claimed (although it cannot escape attention that isometry can emerge as a ‘quarter-power rule’ in the most general sense).
LEAF N, P STOICHIOMETRY
That growth does not invariably scale as the three-quarters power of body mass is evident from the analyses of data for non-woody vascular plants presented in the previous section. Nevertheless, the claim that annual growth across ecologically and phyletically diverse unicellular and multicellular photoautotrophic eukaryotes scales isometrically or nearly so with respect to light-harvesting ability (see Niklas and Enquist, 2001, 2002) is statistically robust (Table 1). In the case of unicellular photoautotrophs, H is measured in units of photosynthetic pigment concentrations per cell, CP. However, for terrestrial embryophytes, H is measured in terms of standing dry leaf mass per plant, ML. Thus, annual growth appears to be inexorably linked to the ‘machinery’ of photosynthesis in some very basic way that cuts across otherwise sharply defined phyletic boundaries.
This linkage probably exists at numerous metabolic and structural levels, but the view advocated here is that it is sensitive to the manner in which nitrogen and phosphorus is allocated in light-harvesting structures. This perspective is based on the comparatively strong scaling relationships that exist between total leaf carbon mass (MLC) and total leaf nitrogen and phosphorus (MLN and MLP, respectively)—relationships that appear to obey their own quarter-power ‘rules’ across and within those species that have been examined in sufficient detail.
For example, based on stoichiometric data collected from 131 herbaceous species, including C3 and C4 species, Niklas et al. (2005) report that leaf nitrogen content scales almost isometrically with respect to increasing leaf carbon content, whereas leaf phosphorus content scales as the four-thirds power of leaf carbon content (Fig. 3A and Table 2). For these species, it follows from MLN ∝ MLC and MLP ∝ MLC4/3 that MLN ∝ MLP3/4 (Fig. 3B and Table 2). Although stoichiometric analyses of plant conspecifics differing in size are sparse, those few data that are available indicate that intraspecific trends may abide by the same ‘rules’. For example, in a recent study of Eranthis hyemalis (a perennial member of the Ranunculaceae), Niklas and Cobb (2005) report scaling exponents for MLN, MLP and MLC relationships that are statistically indistinguishable from the proportional relationships MLN ∝ MLC and MLP ∝ MLC4/3 and MLN ∝ MLP3/4 (Fig. 4A and Table 2).
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Across herbaceous species, N = 131 | ||||||||
MLNvs. MLC | 1·06 (0·95; 1·17) | −1·67 (−1·76; −1·58) | 0·941 | 2058 | ||||
MLPvs. MLC | 1·37 (1·27; 1·48) | −2·61 (−2·70; −2·52) | 0·968 | 3881 | ||||
MLNvs. MLP | 0·78 (0·72; 0·85) | −0·74 (−0·72; −0·76) | 0·948 | 2339 | ||||
Eranthis hyemalis, N = 17 | ||||||||
MLNvs. MLC | 1·00 (0·98; 1·03) | −1·33 (−1·39; −1·26) | 0·996 | 3929 | ||||
MLPvs. MLC | 1·37 (1·32; 1·42) | 0·77 (0·65; 0·90) | 0·993 | 2283 | ||||
MLNvs. MLP | 0·73 (0·70; 0·76) | −1·89 (−1·97; −1·82) | 0·996 | 3425 | ||||
Across Reich and Oleksyn (2004) data set, N = 7445 | ||||||||
MLNvs. MLP | 0·73 (0·71; 0·75) | 1·08 (1·07; 1·08) | 0·335 | 3753 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Across herbaceous species, N = 131 | ||||||||
MLNvs. MLC | 1·06 (0·95; 1·17) | −1·67 (−1·76; −1·58) | 0·941 | 2058 | ||||
MLPvs. MLC | 1·37 (1·27; 1·48) | −2·61 (−2·70; −2·52) | 0·968 | 3881 | ||||
MLNvs. MLP | 0·78 (0·72; 0·85) | −0·74 (−0·72; −0·76) | 0·948 | 2339 | ||||
Eranthis hyemalis, N = 17 | ||||||||
MLNvs. MLC | 1·00 (0·98; 1·03) | −1·33 (−1·39; −1·26) | 0·996 | 3929 | ||||
MLPvs. MLC | 1·37 (1·32; 1·42) | 0·77 (0·65; 0·90) | 0·993 | 2283 | ||||
MLNvs. MLP | 0·73 (0·70; 0·76) | −1·89 (−1·97; −1·82) | 0·996 | 3425 | ||||
Across Reich and Oleksyn (2004) data set, N = 7445 | ||||||||
MLNvs. MLP | 0·73 (0·71; 0·75) | 1·08 (1·07; 1·08) | 0·335 | 3753 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Across herbaceous species, N = 131 | ||||||||
MLNvs. MLC | 1·06 (0·95; 1·17) | −1·67 (−1·76; −1·58) | 0·941 | 2058 | ||||
MLPvs. MLC | 1·37 (1·27; 1·48) | −2·61 (−2·70; −2·52) | 0·968 | 3881 | ||||
MLNvs. MLP | 0·78 (0·72; 0·85) | −0·74 (−0·72; −0·76) | 0·948 | 2339 | ||||
Eranthis hyemalis, N = 17 | ||||||||
MLNvs. MLC | 1·00 (0·98; 1·03) | −1·33 (−1·39; −1·26) | 0·996 | 3929 | ||||
MLPvs. MLC | 1·37 (1·32; 1·42) | 0·77 (0·65; 0·90) | 0·993 | 2283 | ||||
MLNvs. MLP | 0·73 (0·70; 0·76) | −1·89 (−1·97; −1·82) | 0·996 | 3425 | ||||
Across Reich and Oleksyn (2004) data set, N = 7445 | ||||||||
MLNvs. MLP | 0·73 (0·71; 0·75) | 1·08 (1·07; 1·08) | 0·335 | 3753 |
. | αRMA (95 % CI) . | log βRMA (95 % CI) . | r2 . | F . | ||||
---|---|---|---|---|---|---|---|---|
Across herbaceous species, N = 131 | ||||||||
MLNvs. MLC | 1·06 (0·95; 1·17) | −1·67 (−1·76; −1·58) | 0·941 | 2058 | ||||
MLPvs. MLC | 1·37 (1·27; 1·48) | −2·61 (−2·70; −2·52) | 0·968 | 3881 | ||||
MLNvs. MLP | 0·78 (0·72; 0·85) | −0·74 (−0·72; −0·76) | 0·948 | 2339 | ||||
Eranthis hyemalis, N = 17 | ||||||||
MLNvs. MLC | 1·00 (0·98; 1·03) | −1·33 (−1·39; −1·26) | 0·996 | 3929 | ||||
MLPvs. MLC | 1·37 (1·32; 1·42) | 0·77 (0·65; 0·90) | 0·993 | 2283 | ||||
MLNvs. MLP | 0·73 (0·70; 0·76) | −1·89 (−1·97; −1·82) | 0·996 | 3425 | ||||
Across Reich and Oleksyn (2004) data set, N = 7445 | ||||||||
MLNvs. MLP | 0·73 (0·71; 0·75) | 1·08 (1·07; 1·08) | 0·335 | 3753 |
GROWTH AND A SIMPLE N, P-MODEL
Equation (10) takes on significance when it is wedded to a recently developed stoichiometric model for predicting the relative growth rates of diverse organisms based on their cell or tissue N and P contents.
Using estimates or published numerical values of the variables required by this model, eqn (13) (or its variants) has been used to successfully predict the relative growth rates of different unicellular algae and small aquatic animals (e.g. Nielsen et al., 1996; Klausmeier et al., 2004; Vrede et al., 2004) despite the assumptions that NT and PT allocation patterns are ontogenetically invariant, that balanced growth has been achieved, and the supposition that resources are not limiting.
TESTING THE MODEL
Equation (15) has three significant attributes. First, it directly incorporates an allometric relationship for leaf nitrogen and phosphorus allocation; secondly, it relates the N, P-stoichiometry of leaves directly to relative growth rates (and thus prior allometric theory treating the relationship between leaf dry mass and total plant annual growth); and, thirdly, it can be examined empirically based on observed leaf growth rates, thereby setting limits on the numerical values of α and other allometric or physiological parameters.
However, this model can be evaluated empirically only if the numerical values of all physiological variables are stipulated. For bacteria and animals, the values of some of these variables are comparatively well known, i.e. ks = 2·5 × 10−11 µg protein ribosome−1 d−1, re = 0·60, F = 0·80 and mr = 4·53 × 10−12 µg rRNA ribosome−1 (Campana and Schwartz, 1981; McKee and Knowles, 1987; Mathers et al., 1993; Sadava, 1993; Vrede et al., 2004). Assuming that these values are equally applicable to plants, it follows that ksreF/mr = 2·648. Prior work also shows that between 16 % and 27 % of total leaf nitrogen is incorporated in Rubisco (Evans, 1989) and that, depending on whether ambient light conditions are high or low, between 15 % and 60 % of total leaf nitrogen is found in chloroplast thylakoids [pigment–protein complexes, electron transport constituents, reaction centres, components of the electron transport chain (particularly cyto b/f) and ferredoxin] (Evans, 1989). Based on published nitrogen allocation to Rubisco and thylakoids, it is reasonable to suppose that fN ∼ 0·55 across otherwise diverse species.
Despite its simplicity and numerous assumptions, the behaviour of the model accords reasonably well with empirical trends observed for 131 ecologically and phyletically diverse herbaceous species (for details, see Niklas et al., 2005). For these plants, observed μL scales as the 0·33 power of total leaf nitrogen (r2 = 0·72), as the 0·25 power of total leaf phosphorus (r2 = 0·76) and as the 0·22 power of total leaf carbon mass (r2 = 0·78; see Fig. 5). Likewise, as predicted, μL decreases as the quotient MLN/MLP increases (Fig. 6). And, finally, the OLS regression curve for predicted versus observed μL is log–log linear with a slope very near unity.
CAVEATS
Nevertheless, considerable variation exists when observed leaf growth rates are plotted as a function of leaf carbon mass (see Fig. 3A). This feature can be attributed to a number of factors not addressed in the model but that are nevertheless of great ecological importance. Among the more obvious of these are species-specific differences or ecotypic variation in protein synthesis rates or retention efficiencies (Mathers et al., 1993; Sadava, 1993; Güsewell, 2004), differences in the fractional allocation of leaf nitrogen to proteins as a consequence of leaf age or different ambient light intensities (Evans, 1989; Ryser et al., 1997), morphological and anatomical differences in leaf construction (Nielsen et al., 1996), recruitment of N and P from older organs during early growth (Meyer and Tukey, 1965), differences in leaf tissue ploidy and nitrogen-use efficiency (Brown, 1978), and changes in N or P allocation to leaf components during leaf ontogeny.
Despite the remarkable success of the model in light of the undoubted influence of these and other physiological and ecological variables on growth, there are grounds for concern. As noted, one of the attributes of eqn (16) is that it can be challenged empirically by comparing the numerical values of physiological variables used to predict observed growth rates with those that are actually reported in the literature. One of these variables is the decimal fraction of total body phosphorus allocated to RNA construction, denoted by fP. Inspection of Fig. 5 indicates that the relative growth rates observed for 131 plant species plot between those predicted by eqn (16) when fP is a priori set equal to 0·05 and 0·15. The fact that all observed leaf growth rates plot within the ‘corridor’ defined by these two values suggests that between 5 % and 15 % of total leaf phosphorus is invested in the construction of RNA.
However, this range does not resonate well with fP-values reported for other life forms such as bacteria, small aquatic heterotrophs, or unicellular algae. For these species, the decimal fraction of total cell or body phosphorus committed to RNA ranges between 0·20 and 0·90 (with an average value of 0·50 for animals) (see Rhee, 1978; Elser et al., 2003). Therefore, the range 0·05 ≤ fP ≤ 0·15 identified by the model to ‘contain’ the leaf growth rates of vascular plants is unusually low.
This discrepancy may be the result of systematically underestimating plant growth rates because of their ability to store and annually recycle large pools of N and P. Specifically, the relative growth rates plotted in Fig. 5 are based on the difference in leaf N and P levels measured early and late in the growing season (see Niklas et al., 2005). In retrospect, this procedure may have introduced a systematic bias because there is substantial evidence that vascular plants recruit N and P from older organs as new tissues and organs are produced during the early growth season (e.g. Mochizuki and Hanada, 1958; Meyer and Tukey, 1965; Taylor, 1967; Taylor and May, 1967; Niklas and Cobb, 2005) and resorb substantial quantifies of N and P toward the end of the growing season, e.g. an average of 50 % of total P is reabsorbed before leaf senescence (50 % of which comes from nucleic acid hydrolysis; see Chapin and Kedrowski, 1983; Aerts, 1996). Therefore, N and P levels in newly formed leaves may be significantly higher than those reached once constant growth rates are achieved (thus violating a basic assumption of the model), and N and P levels may be on the decline even before visible signs of leaf senescence. If leaf relative growth rates are systematically underestimated for these (or any other) reasons, the upper and lower fP-levels identified by the model to fit the data would be likewise underestimated.
VARIATION AND FUTURE DIRECTIONS
That significant differences in leaf N,P-stoichiometry exist even among vascular plants is strongly suggested by the allometric and stoichiometric trends reviewed here. As noted, the relationships between total leaf mass, annual growth rates, and total body mass differ between non-woody and woody plants (see Table 1). Across non-woody plants, total leaf mass scales isometrically with respect to both annual growth rate and total body mass (i.e. ML ∝ GT ∝ MT), whereas, across woody plants, leaf mass scales as the three-quarters power to total body mass (i.e. ML ∝ GT ∝ MT3/4). Yet, based on the data presented here and elsewhere, total leaf mass (in units of carbon mass) scales isometrically with respect to total leaf nitrogen and allometrically albeit roughly as the three-quarters power of total leaf phosphorus (i.e. MLC ∝ MLN ∝ MLP3/4) (see Table 2). Assuming that these relationships are definitive, it follows that total annual growth rates should scale across all species as the three-quarters power of total leaf phosphorus across (i.e. GT ∝ MLP3/4). However, it also follows that total leaf phosphorus should increase as the four/thirds power of the total body mass for non-woody plants (i.e. MLP ∝ MT4/3) but scale isometrically with respect to the body mass for woody plants (i.e. MLP ∝ MT).
These predictions have yet to be explored empirically, but they are in accord with the observation that much of the total body mass of woody plants is composed of physiologically inert material (heartwood) that increases in volume fraction with each passing year. Indeed, we need to know much more about the allometry and stoichiometry of what may be called ‘necromass’—organic constituents that contribute to total body mass but that do not participate in metabolic activity or resource utilization, e.g. cell wall materials and secondary metabolites sequestered in the lumens of dead cells, which continue to accumulate throughout the lifetime of the multicellular individual. We also need to know much more about the allometry of annual biomass accumulation with respect to the N, P stoichiometry of meristematic tissues, both for herbaceous non-woody and woody species.
It is also clear that the juxtaposition of allometric theory and observation with the potential insights gained from N, P stoichiometric models is in its infancy. This approach clearly offers great promise (if for no other reason than that it helps to identify and quantify interdependencies across every level of biological organization, from molecules to ecosystems, and across bacteria to multicellular eukaryotes) but it is perhaps best viewed as a heuristic device with which to explore important conceptual issues. To be more effective, this juxtaposition would benefit greatly from more detailed measurements of how stoichiometric parameters vary ontogenetically and phylogenetically. In particular, more detailed data sets are needed for protein synthesis rates per ribosome, protein retention efficiencies, and the proportion of total P and N committed, respectively, to the construction of rRNA and nonstructural proteins. Mutants of unicellular algae, like those of Chlamydomonas, and parasitic plants with a ‘leaf-stem’ construction, like Monotropa, should be used to ‘dissect’ how total N and P cell or tissue contents are allocated to the construction of different parts of the photosynthetic machinery. Perhaps in this way, we will be able to explain why tissue and organ nitrogen levels and dry mass scale as the three-quarters power of phosphorus and why so many other phenomena seem to obey similar quarter-power rules.
This paper is based in part on an Annals of Botany annual lecture invited by Prof. J. W. Shipley (Université de Sherbrooke), regional editor of the Annals of Botany. The author thanks Prof. Shipley for the invitation, Prof. T. L. Rost (Invited Review's editor; University of California, Davis) for overseeing the review process, and Prof. H.-C. Spatz (Universität Freiburg) and an anonymous additional reviewer for valuable suggestions on how to improve the manuscript. Funding from the College of Agriculture and Life Sciences (Cornell University) is also gratefully acknowledged.
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