The leaf economics spectrum and the prediction of photosynthetic
light response curves
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G. MARINO, M. AQIL and B. SHIPLEY1
Département de biologie, Université de Sherbrooke
and Centre d’étude de la forêt (CEF).
Sherbrooke, (Qc) J1K 2R1 CANADA
1
To whom correspondence should be addressed. [email protected]
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Summary
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with the leaf traits of the “leaf economics spectrum” and the degree to which such traits can
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predict interspecific variation in light response curves. This question is important because light
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response curves are included in many ecosystem models of plant productivity and gas exchange
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but such models do not take into account interspecific variation in such response curves.
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2. We answer this question using original observations from 260 leaves from 130 plants of 65
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different species of herbaceous (25) and woody (40) angiosperms. Herbs were grown in growth
1. We determine if interspecific variation in entire photosynthetic light response curves correlate
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chambers and gas exchange measurements were done in the laboratory. Leaf traits and gas
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exchange measurements for the woody plants were taken in the field. Leaf traits were leaf mass
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per area (LMA), leaf nitrogen content (N), leaf chlorophyll content (Chl), leaf absorbance,
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reflectance and transmittance and leaf lamina thickness; not all of these variables were measured
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for the woody species. Fitting the Mitscherlich equation of the light response curve separately
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for each leaf, we estimated the light compensation point ( φ ), the quantum yield at the light
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compensation point (q( φ )), and maximum net photosynthesis (Amax).
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3. Amax and q( φ ) were highly correlated with the measured leaf traits but φ was not. Variation
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in Amax was allometrically related to LMA and N. Variation in q( φ ) was allometrically related
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to LMA. Replacing Amax and q( φ ) in the Mitscherlich equation by the allometric equations gave
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good predictions of net photosynthetic rates over the entire range of irradiance (r=0.81).
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4. These results further extend the generality of the “leaf economics spectrum” and may allow
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available information from large leaf trait databases to be incorporated into ecosystem models of
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plant growth and gas exchange.
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Key words: light-response curves, leaf nitrogen content, plant allometry, plant gas exchange,
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net photosynthesis, specific leaf area, specific leaf mass.
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1
Introduction
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Carbon assimilation, the most important function of most leaves, is determined by many
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leaf traits embedded in a complex network of interactions. It is logically possible for the
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network of trait interactions to generate a different pattern of leaf trait correlations for each
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species, perhaps modified by environmental conditions. Recent empirical evidence, however,
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shows that this is not true (Reich et al. 1997, Wright et al. 2004). From the Arctic to the tropics,
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across a wide range of taxonomic groups, and across diverse environmental conditions, most of
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the covariation in maximum leaf net photosynthetic rate (Amax), Leaf respiration (Rd), leaf
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nitrogen content (N), specific leaf area (SLA), leaf dry matter content (LDMC) and leaf lifespan
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(LL) falls along a single major axis of variation, although Amax and LL also have a unique
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secondary link (Shipley et al. 2006).
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Together, this set of leaf traits has been called the “worldwide leaf economics spectrum”
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(LES) because this correlated suite of traits reflects the trade-off between the rapid acquisition of
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resources and the conservation of captured resources. The average values of all of these traits
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change predictably along major environmental gradients but the patterns of covariation are
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largely (but not completely) unaffected by environments. On the other hand, this empirical
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evidence is an eclectic collection of measurements taken by many different people, often using
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different methods, and mostly taken from plants growing under natural conditions. To what
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degree does the residual variation around the LES represent inherent interspecific differences as
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opposed to measurement error, plastic phenotypic responses to environments, or simply
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sampling variation and resulting averaging at the interspecific level? We evaluate this question
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by contrasting the results from a collection of species obtained in controlled growth conditions
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and from species measured in the field.
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Since maximum net photosynthetic rate (Amax) and daytime respiration rate (Rd) are part
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of the LES, and since these are also the two extremes of the photosynthetic light response curve,
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is it possible that the entire leaf photosynthetic light response curve is also constrained by this
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single axis of trait covariation? If so, could the morphological components of the LES be used to
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predict the entire light response curve? This question has theoretical importance. The light
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environment of a leaf is incredibly dynamic. Leaves experience fluctuating intermediate
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irradiance levels for most of the lifetimes and rarely reaching the saturating levels at which Amax
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is expressed. Therefore, claims for a general spectrum of leaf traits require that photosynthetic
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responses at intermediate light levels also be correlated with the known components of the LES.
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The question also has practical importance at the level of community and ecosystem
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ecology, the level of interest in this paper. Models of gas exchange at the level of entire forests
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(Ollinger et al. In press), and process-oriented models of stand-level plant growth such as
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LIGNUM (Perttunen et al. 1996, Perttunen et al. 1998, Lo et al. 2001, Perttunen et al. 2001,
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Perttunen & Sievanen 2005) include a mathematical function describing the photosynthetic light
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response curve. Such mathematical functions must be parameterized. Since plant communities
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involve many species in many environments, it is impossible to empirically measure such
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parameters separately for each species in each environment. Because of this, such community
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and ecosystem models largely ignore such interspecific differences by choosing “typical” or
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average parameter values for the light response curve. However, if the interspecific properties of
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photosynthetic light response curves are also part of the LES, and since the LES is a general
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interspecific pattern, then one could include interspecific photosynthetic light responses into
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large-scale models of carbons fixation using easily measured leaf chemical and morphological
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properties. This would allow community and ecosystem models to include variation due to
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changes in species composition by exploiting information already available for several thousand
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species.
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Several different equations (Michaelis-Menten, regular and non-rectangular hyperbolae,
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Mitscherlich) have been used to describe the photosynthetic light response curve. The two most
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popular are the non-rectangular hyperbola and the Mitscherlich equation because they are
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sufficiently flexible to accurately describe curves ranging from a regular hyperbola to a
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Blackman light response (Blackman 1905); i.e. two intersecting straight lines. In choosing
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between these two equations we are not interested in mechanistic realism but rather in predictive
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accuracy, in ease of use, and in the ability to approximate species-specific parameter values of
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the light response curve by easily measured leaf traits from the LES.
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The non-rectangular hyperbola equation is most commonly used in physiology and
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ecophysiology (Lambers et al. 1998) because it is partly derived from biochemical
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considerations (Rabinowitch 1958, Chartier & Prioul 1976, Thornley 1976). However, it
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requires the fitting of four free parameters including a “shape” parameter (θ) that has no obvious
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biological interpretation and that can be difficult to accurately estimate (Lambers et al. 1998,
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Thornley 2002). Some modelling studies (Leverenz 1988, Buckley & Farquhar 2004) suggest
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that θ is related to leaf thickness and chlorophyll content per leaf surface but the only empirical
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evidence for this claim of which we are aware, from six species of conifer needles (Leverenz
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1987), was subsequently contradicted (Leverenz 1988, figure 5). Another reason to avoid the
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non-rectangular hyperbola is that this equation is undefined for particular combinations of
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parameters. When fitting the non-rectangular hyperbola to observed photosynthesis – irradiance
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values these problems are avoided by numerically preventing the parameter estimates from
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taking such values but when predicting the parameters from leaf traits and then substituting these
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predicted parameter values into the equation the problem can potentially arise.
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The Mitscherlich equation (Equation 1) has also been used in ecophysiology as a
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phenomenological, not a mechanistic, description of the photosynthetic light response (Potvin et
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al. 1990, Peek et al. 2002). It is easier to use and to fit than the non-rectangular hyperbola
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because it uses only three parameters, φ , q( φ ) and Amax, that are each linked to a clear
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biological process. φ is the light compensation point, q( φ ) is the apparent quantum yield (the
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net amount of carbon fixed per amount of photons received) at the light compensation point
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q( φ ), and Amax is the maximum net photosynthetic rate. The Mitscherlich equation has the same
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predictive ability as the more mechanistic non-rectangular hyperbola. Furthermore, this equation
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is defined over all possible parameter values, unlike the non-rectangular hyperbola. The
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Mitscherlich equation is:
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A( I ) = Amax (1 − e −γ ( I −φ ) ) = Amax − Amax e −γ ( I −φ )
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We can remove γ from Eqn. 1 by calculating the apparent quantum yield (dA/dI) at the
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(1)
light compensation point (I= φ ), i.e. q( φ ):
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dA(φ )
= q(φ ) = γAmax e −γ (φ −φ ) = γAmax
dI
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Thus, γ is the ratio of the quantum yield at the light compensation point to the maximum
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net photosynthetic rate. If the value of γ does not vary much between species, we should expect
(2)
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a positive linear relationship involving q( φ ) and Amax. Since Amax varies allometrically with
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leaf mass per area (LMA) and leaf nitrogen (N) (Reich et al. 1997, Wright et al. 2004), q( φ )
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should show a similar allometric relationship. Replacing γ in Equation (1) by its solution in
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Equation 2 gives the version of the Mitscherlich equation used in this paper:
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− q (φ ) ( I −φ )
⎞
⎛
A = Amax ⎜1 − e Amax ⎟
⎟
⎜
⎠
⎝
(3)
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We ask five questions based on measurements on leaves of 25 herbaceous species grown
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under controlled conditions, and on leaves of 40 species of trees and shrubs growing in the field:
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(1) Do the leaves of these species follow the LES?
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(2) Are the parameters of the Mitscherlich equation (thus the entire photosynthetic light response
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curve) also related to the leaf traits of the LES?
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(3) How accurately can the parameters of the Mitscherlich equation be predicted from leaf traits
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of the LES?
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(4) How accurately can interspecific variation in photosynthesis due to changing irradiance be
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predicted using leaf traits of the LES?
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(5) Does the position of a leaf in the canopy (for the woody species) affect the parameters
of the light response curve independently of the relevant leaf traits of the LES?
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Methods
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Experiment 1: herbaceous species and controlled conditions
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We grew two plants each of 25 herbaceous species (Appendix S1 in Supporting
2
Information) from seed in fertile soil under controlled growth conditions. Each plant grew alone
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in its pot. Pots were spaced in the growth chamber to avoid shading. Fourteen-hour daytime
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temperatures were 21°C with 350 μmol m-2 s-1 PAR and night-time temperatures were 18°C.
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Plants were kept well-watered at all times. Gas exchange measurements were taken before
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plants had begun flowering and when they had sufficient leaf mass for the leaf nitrogen
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measurements.
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Plants were watered to field capacity the night before gas-exchange measurements were
taken. The next morning they were brought to the laboratory and allowed to habituate to
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ambient conditions (21°C) for at least 30 minutes before gas exchange measurements were taken
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using a Qubit Systems IRGA (Model S154 CO2 analyzer, model P650 gas pump, model F360
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flow meter and model G248 4-channel gas controller and monitor). CO2 concentration of the
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incoming air was precisely maintained at 400 μmol L-1 and leaf temperature was measured with a
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thermistor. Incoming CO2 concentrations were accurately controlled by first collecting the air
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for an entire light response curve in a large 96L air compressor and passing a portion of the
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incoming air through soda lime before reaching the leaf cuvette in order to maintain exactly 400
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μmol L-1. Irradiance was supplied by red light-emitting diodes. Irradiance levels were 0, 50,
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100, 200, 300, 400, 600, 800, 1000 and 1310 μmol m-2 s-1. Leaves were allowed to habituate to
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each irradiance level for 15 minutes before readings began. These light response curves were
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measured on each of the two leaves, taken from every plant. The leaves were chosen according
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to similarity in age.
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We removed these two leaves from the plant immediately following the measurements
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and extracted the chlorophyll in each leaf using DMSO and quantified its concentration
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following the protocol of Hiscox and Isrealstam (1979). The remaining leaves of the plant were
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then harvested and their total projected surface area was measured using WinFolia
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(www.regent.qc.ca) from scanned images. These leaves were then dried at 70°C for at least 48
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hours and the leaf mass per area (LMA) per plant, excluding petioles, was calculated as the dry
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mass divided by the projected leaf area. Finally, the dried leaf tissues were ground to a fine
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powder using a ball mill and, leaf nitrogen concentration was measured using a Macro Elemental
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CN Analyser (Elementar Analysesensysteme GmbH, Hanau, Germany).
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Experiment 2: woody species and field conditions
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Data were collected from May 22 until August 17, 2007 within a 5 km radius of the city
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of Sherbrooke (Quebec, Canada, latitude 45° 26’ 53’’ N, longitude 71° 52’ 55’’ W). This is the
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period in which leaves are fully expanded and before senescence begins. Two undamaged leaves
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per plant, 2 plants per species and 40 deciduous species of trees and shrubs were sampled
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(Appendix S1 in Supporting Information). Whenever possible, one leaf was sampled from the
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outside of the canopy and one leaf from within the canopy.
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Immediately following the photosynthetic measurements, two branches having a
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minimum of two other intact leaves were taken from the plant in question. These branches
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contained, or were in proximity to, the leaves used for photosynthetic measurements. The
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branches were transported back to the laboratory in a cooler and with the cut stem submerged in
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a tube filled with water. Upon arrival in the laboratory the end of the branch stem was again cut
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under water at approximately 5cm from the end to eliminate possible absorption of air in the
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xylem and it was stored overnight in this way in the dark and in water, in order to allow for
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evacuation of non-structural carbon from the leaves (Garnier et al. 2001).
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The following day two intact leaves were chosen from each branch. One leaf was
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separated into lamina and petiole. The lamina was then scanned and its projected surface area
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was calculated. After scanning, the lamina and petiole were dried at 70°C for 48 hours and re-
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weighted. LMA was measured using only the leaf lamina. Finally, this first leaf lamina was
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pulverized using a ball mill and its N concentration was measured as before. The second fresh
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leaf was utilized for the extraction of chlorophyll as described before.
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A field-measured photosynthetic light-response curve was estimated for each leaf using
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photosynthetically active radiation (PAR) levels of 0, 50, 100, 200, 400, 800, 1600 μmol/m2/s
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provided by red light-emitting diodes; actual PAR levels fluctuated slightly due to additional
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diffuse light entering the cuvette in this field conditions. Measurements were taken using a
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portable photosynthesis system (CI-340, CID, Inc., Camas, WA, USA) in open mode. Ambient
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air in the leaf chamber was maintained at 20°C, relative humidity was 60% and CO2
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concentration of the incoming air was approximately 400 μmol L-1. It was not possible to control
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incoming CO2 concentrations in the field as accurately as in the controlled-growth experiment
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(even though the CID equipment does use a CO2 cartridge to approximately control incoming
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CO2 concentration) and this increased measurement error relative to the laboratory
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measurements. The leaf was allowed to habituate to a given irradiance level for 5 minutes before
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measurements began at each light level. We then took 3 measurements over a 5 minute period at
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this irradiance level. Each measurement consisted of 80 seconds for estimating CO2
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concentration in the incoming air followed by 20 seconds for estimating CO2 concentration in the
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outgoing air. The net photosynthetic rates for a leaf were based on three sets of measurements
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over seven light levels, giving a total of 21measurements per leaf.
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Appendix S1 gives the leaf traits and the three parameters of the Mitscherlich curve for
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each leaf of each species. Table 1 lists the units for all measured variables; these were chosen to
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be comparable with the values in Wright et al. (2004).
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All statistical analyses were done using the R system (R-Development-Core-Team 2008).
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The parameters of the Mitscherlich equation were estimated separately for each leaf using the nls
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(nonlinear least squares regression) function. Mixed model regressions relating parameter values
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to the leaf traits were done using the lme (linear mixed effects) function, in which the random
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component reflected the hierarchical nature of the data (leaves nested within individuals nested
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within species). Variance components (via restricted maximum likelihood) were estimated using
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the lme function.
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Results
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Table 2 gives the variance components of the parameters of the light response curves that
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were attributable to differences between leaves of the same plant, between plants of the same
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species, and between species; this was done separately for the two data sets. For the herbs
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growing under controlled conditions, virtually all variation was attributable to interspecific
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differences. The response was quite different for the woody plants growing in the field. For the
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light compensation point and the quantum yield at this point there was little added variation due
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to differences between individuals of the same species but there existed significant variation at
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this level for Amax. Furthermore, all three parameters exhibited substantial variation between
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leaves from the interior and exterior of the canopy of the same plant. The residual standard
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errors of the fitted curves from each leaf during the gas exchange measurements represent pure
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measurement error. The observed net photosynthetic rates of the herbs grown in controlled
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conditions were much more precisely predicted (median residual SE: 8.2 nmol μmol-1 s-1; range:
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2.3 to 44.4) than were the observed values of the woody species growing in the field (median
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residual SE: 35.7 nmol μmol-1 s-1; range 8.7 to 108.0) and this measurement variation will inflate
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the leaf-level variance component.
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Do the leaves of our species follow the leaf economics spectrum?
Figure 1 shows the bivariate patterns of correlation between LMA, Rd, Amax and N
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(natural logarithmic scale). The grey points show the values from the GLOPNET data set of
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Wright et al. (2004). The upper diagonal (solid dots) shows the values for the 25 herbaceous
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species growing under controlled growth conditions and measured in the laboratory. Species
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means are shown since almost all variation existed at this level. Amax was based on the mean
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fitted value from the Mitscherlich equation. Since Rd is not a fitted parameter of the
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Mitscherlich equation, the reported Rd values are the mean observed values per leaf when the
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light-emitting diodes were turned off since we were able to exclude all other incoming light in
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the laboratory setting and the incoming CO2 concentrations were precisely controlled. The lower
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diagonal shows the values for the 160 leaves from the 40 woody species measured in the field
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(open circles). We report the leaf-level values for these species since a non-trivial amount of
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variation existed at this level. Rd for these species is not shown: diffuse light entering the cuvette
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of the portable field instrument in the high irradiance conditions in the field, even when the light-
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emitting diodes were turned off, and the less precise control over incoming CO2 concentrations,
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prevented us from getting accurate estimates of gas exchange in the dark. With the exception of
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four herbaceous species, whose daytime dark respiration rates are rather low, both the controlled-
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growth herbs and the field-measured trees closely followed the general trends. In fact, the
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relationships displayed by the herbaceous species, grown under controlled conditions and
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measured in the laboratory, were much tighter than in the GLOPNET data set upon which the
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leaf economics spectrum was derived.
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Are the light-response parameters related to the leaf economics spectrum?
In the herbaceous species both Amax and q( φ ) were very strongly correlated with N, LMA
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and chlorophyll content. However, the light compensation point was largely independent of the
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other variables (Table 3). The same general pattern was seen with the woody species (Table 3)
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although the correlations were weaker. One exception in the woody species was N, which did
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not correlate strongly with the other leaf properties even though the N values did fall within the
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general patterns of the GLOPNET data (Figure 1). The light compensation point was again
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largely independent of the other variables.
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How accurately can the light-response parameters be predicted from leaf traits?
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Amax is already known to be allometrically related to LMA and N in the GLOPNET data.
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The resulting allometric regressions in our two data sets are given below; Equation 4a is based
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on species means (herbaceous species, controlled conditions) while Equation 4b is a mixed
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model based on values from individual leaves (woody species, field conditions). Values in
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1
parentheses are the standard errors of the regression parameters and the predictor variables are all
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significant (p<0.05). For comparison, we also list the allometric regression obtained from the
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GLOPNET data (Equation 4c). In order to determine if the allometric relationship differed
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between the leaves from inside and outside the canopy (for the woody species only) we added
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this variable as an additional factor in the mixed model. The placement of the leaf had no
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significant affect on Amax, either alone or in interaction with N or LMA, once the values of N and
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LMA are known, indicating that the allometric relationship shown in Equation 4b does not
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change with placement in the canopy. Analysis of covariance revealed that there were no
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significant differences in these multiple regressions between the herbaceous species and the
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GLOPNET data while the only significant difference between our woody species and the
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GLOPNET data was in the partial regression coefficient for Log10(N). The multiple regression
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obtained by combining all data (Eqn. 4d) has a residual standard error of 0.20.
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Log10(Amax) = 4.13 + 0.68Log10(N) -1.19Log10(LMA); r 2 = 0.96
(0.42) (0.31)
(0.17)
4a
Log10(Amax) = 3.11 + 0.31Log10(N)-0.53Log10(LMA); r2=0.53
(0.16) (0.12)
(0.09)
4b
Log10(Amax) = 2.96 + 0.74Log10(N)-0.57Log10(LMA); r2=0.63
(0.16) (0.12)
(0.09)
4c
Log10(Amax) = 3.12 + 0.69Log10(N)-0.64Log10(LMA); r2=0.67
(0.16) (0.12)
(0.09)
4d
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According to Equation 2, the quantum yield at the light compensation point, q( φ ),
should be related to Amax if interspecific variation in the γ parameter is small. The allometric
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1
regression, based on the species-level values from the herbaceous species (Equation 5a, Figure
2
2A) confirms this. Applying stepwise regression (based on AIC values) to the full set of leaf
3
chemical and morphological traits, the best-fitting regression involved only LMA (Equation 6a):
4
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9
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Log10(q( φ )) = -2.88 + 1.36Log10(Amax); r 2 = 0.93 , SEest=0.35
(0.19) (0.08)
5a
Log10(q( φ )) = -1.30 + 0.54Log10(Amax)
(0.21) (0.09)
5b
Log10(q( φ )) = 3.87 – 2.06Log10(LMA); r 2 = 0.86 , SEest=0.21
(0.31) (0.17)
6a
Log10(q( φ )) = 1.60 – 1.00Log10(LMA)
(0.17) (0.11)
6b
Log10(q( φ )) = 1.87 – 1.12Log10(LMA); r 2 = 0.39 , SEest=0.27
(0.18) (0.11)
6c
For the leaf-level measurements of the woody species, the equivalent mixed-model
22
regression of q( φ ) on Amax (Eqn. 5b,) and on LMA (Eqn. 6b) are also given. Including leaf type
23
(outer or inner canopy leaves) in these mixed-model regressions was not significant (p>0.05)
24
either alone or in interaction with the relevant dependent variable (Amax or LMA). Analysis of
25
covariance showed that both the slopes and the intercepts differed in equations 5 and 6 between
26
the herbaceous species (controlled conditions) and the woody species (field conditions). Figure
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2 plots these values. Ignoring these differences, ignoring the nested nature of the data for the
28
woody species, and combining the herbaceous and woody species, we obtained Equation 6c.
29
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The light compensation point was not significantly related to any other leaf trait in the
herbaceous species. In the woody species the only leaf trait that showed a significant
16
1
relationship to the light compensation point was N but this was a very weak relationship and the
2
significance was entirely dependent on a few leaves having very low nitrogen contents.
3
4
How well can photosynthetic light responses be predicted from leaf traits?
5
6
Since two of the three parameters of the Mitscherlich equation (Amax and q( φ )) can be
7
predicted from LMA and N, since LMA and N have been measured on thousands of species, and
8
since the relationship between LMA and N is a very general one (Figure 1), we next attempted to
9
predict the entire photosynthetic light responses of our species using the most general allometric
10
relationships available (Eqns. 4d and 6c); i.e. without distinguishing between herbaceous or
11
woody species or between field or laboratory measurements. Since light compensation points
12
could not be predicted from plant traits we used the median value (13.42 μmol m-2 s-1); 75% of
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all light compensation points were within the range 3.89 to 24.12. Figure 3 plots the resulting
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prediction equation (Eqn. 7). The overall correlation between observed and predicted values
15
was 0.81, as was the correlation using only the woody species (open circles). For the herbaceous
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species the correlation was 0.92 but the predicted values were clearly underestimated at the
17
higher values of net photosynthesis:
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19
⎛ 1318.26 N 0.69
A( I ) = ⎜⎜
0.57
⎝ LMA
⎞
⎛
−0.056
⎟ ( I −13.42 ) ⎞
⎜⎜
⎞⎛⎜
0.55 0.69 ⎟
⎟
⎟ 1 − e ⎝ LMA N ⎠
⎟⎜⎜
⎟⎟
⎠⎝
⎠
(7)
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Discussion
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1
With the exception of four species whose daytime leaf dark respiration rates were low,
2
the 65 species in this study closely followed the leaf economics spectrum described by Wright et
3
al. (2004). This is not surprising given to the demonstrated generality of this spectrum. Perhaps
4
more surprising is the strength of the measured correlations in the herbaceous species; these
5
plants, grown and measured under controlled conditions, had much less scatter than in the
6
original GLOPNET data. The scatter in the field-measured woody species was comparable to
7
the GLOPNET data. Since the GLOPNET data were field-based measurements this suggests
8
that much of the residual variation in the published leaf economics spectrum is due to plastic
9
responses to environmental differences and measurement error rather than to basic biological
10
11
constraints.
Because the published leaf economics spectrum only involves the two extremes of the
12
photosynthetic light response (Rd and Amax) it does not involve photosynthetic responses to
13
intermediate light intensities. Based on our results, it appears that the apparent quantum yield is
14
also constrained by the leaf economics spectrum such that it increases with decreasing LMA
15
(Eqn. 9). The light compensation point ( φ ) was not related to any of the leaf traits implicated in
16
the leaf economics spectrum and interspecific variation in φ might be primarily determined by
17
the deviations of species from the general leaf economics spectrum (i.e. the residuals of the Amax
18
- Rd relationship). This is because φ is determined by the balance between Rd and q( φ ). For a
19
constant Rd, increasing q( φ ) decreases φ . For a constant q( φ ), increasing Rd increases φ .
20
However Rd, q( φ ) and Amax are all positively correlated; the allometric correlation between q( φ )
21
and Rd was 0.69. Due to the difficulty in accurately estimating Rd, this explanation is only
22
hypothetical. However, because variation in φ has only modest effects on the form of the
18
1
photosynthetic light response curve, we were able to obtain approximate predictions of net
2
photosynthesis over the entire range of irradiance and over all leaves from 65 different species
3
(Figure 4). The underestimation of net photosynthesis in the herbs was due to the fact that the
4
quantum yield predicted from the general allometric equation (7c) had a lower slope than the one
5
obtained using only these species. Until more empirical data is obtained we cannot know if such
6
differences are systematic between plant types, or between field vs. laboratory measurements, or
7
if they are unique to our data.
8
The final prediction equation (9) also allows one to explore how changes in LMA and N
9
will affect the light response curve (Figure 4). Holding LMA constant, the effect of decreasing
10
N on net photosynthesis is seen primarily at the higher irradiance levels (>500 μmol m-2 s-1),
11
both decreasing Amax and also decreasing the irradiance level beyond which photosynthesis
12
becomes saturated. Decreasing N had little effect on the curve at lower irradiance levels.
13
Holding N constant, the effect of increasing LMA on net photosynthesis is seen over all levels of
14
irradiance. The more realistic scenario, in which LMA and N are negatively correlated, reflects
15
both of these tendencies.
16
On a more speculative level, our results hold out the possibility that easily measured leaf
17
traits, such as those in the GLOPNET data and in other large data sets now being assembled, can
18
predict entire photosynthetic light response curves. Obviously this would have to be more
19
extensively tested in the field and using a much more diverse set of species than included in our
20
study but, if this is generally true then stand, ecosystem or even global models of gas exchange
21
and vegetation productivity might be able to include such information in order to better capture
22
processes involving photosynthesis under changing irradiance levels in multispecies vegetation.
19
1
This would, of course, require knowledge of the degree of plasticity in LMA and N per species at
2
least at the level of shade vs. sun leaves.
3
The allometric approach taken in this paper is rather different from the more detailed and
4
mechanistic models of photosynthetic light response used by physiologists and crop modellers
5
(Farquhar et al. 1980, Farquhar & Von Caemmerer 1982, Thornley 2002). These two
6
approaches are not contradictory; rather, they should be viewed as complementary but applicable
7
to different scales of organisation. What the more mechanistic models gain in realism and
8
explanatory power, when applied to single leaves, to a monoculture or perhaps to a few species,
9
they lose in applicability when applied to multispecies assemblages since it is not possible in
10
practice to parameterise separate curves for each species and environmental condition. Our
11
allometric approach will be less precise for any single species but holds out the possibility that
12
easily measured leaf traits, such as those in the GLOPNET data and in other large data sets now
13
being assembled, can predict entire photosynthetic light response curves that are still relatively
14
accurate and still applicable over many species. Obviously this would have to be more
15
extensively tested in the field and using a much more diverse set of species than included in our
16
study but, if this is generally true then stand, ecosystem or even global models of gas exchange
17
and vegetation productivity might be able to include such information in order to better capture
18
processes involving photosynthesis under changing irradiance levels in multispecies vegetation.
19
This would, of course, require knowledge of the degree of plasticity in LMA and N per species at
20
least at the level of shade vs. sun leaves.
21
22
For example, Ollinger et al. (In press) develop a model of CO2 uptake in temperate and
boreal forests in which a Michaelis-Menten light response curve is used in a “big-leaf” model but
20
1
in which separate parameter estimates must be estimated for each site. In principle, given our
2
results, one could use equation 7 and simply look up SLM and N values for the different species
3
in each site; presumably the predicted net photosynthesis values for each species in the site
4
would then have to be weighted by the relative abundance of the species to give a “community-
5
aggregated” value (Garnier et al. 2007). Whether or not this is possible in practice requires
6
further empirical evaluation.
7
8
9
10
Acknowledgements
This study was financially supported by the Natural Sciences and Engineering Research
Council of Canada (NSERC) and the Université de Sherbrooke.
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Supporting Information
The following Supporting Information is available for this article:
Appendix S1 Species used and leaf-level trait values.
25
1
Table 1. Measured variables and their units.
2
Variable
Units
Leaf mass per area, LMA
g m-2
Leaf nitrogen content, N
mg g-1
Leaf chlorophyll
μmol g-1
concentration, Chl
Irradiance
μmol m-2 s-1
(photosynthetically active
radiation), I
light compensation point, φ
μmol m-2 s-1
apparent quantum yield at
nmoles m2 g-1 μmole-1
light compensation point,
q(φ)=
dAmax
dI
net photosynthetic rate, Amax nmol g-1 s-1
26
1
Table 2. Variance components (standard deviation and % of total variance) of the estimated
2
parameters of the Mitscherlich photosynthetic light response curves. Values are based on 100
3
individual leaves from 50 individuals belonging to 25 species of herbaceous angiosperms grown
4
under controlled growth conditions, and 160 individual leaves from 80 individuals belonging to
5
40 species of trees and shrubs growing in the field.
Source
φ
q( φ ) (quantum
Amax (maximum net
of
(light compensation
yield at light
photosynthetic rate)
variation
point)
compensation)
Controlled,
Field,
Controlled,
Field,
Controlled,
Field,
herbs
woody
herbs
woody
herbs
woody
Between
12.53
10.53
3.81
0.46
243.38
44.19
species
(99%)
(34.7%)
(91.9%)
(52%)
(~100%)
(18.6%)
0.17 (~0%)
0.001
0.17 (1.3%) 0.16
0.43 (~0%)
51.35
Between
individuals
(~0%)
(6.3%)
(25.1%)
within
species
Between leaves
0.93 (1%)
within
14.41
0.04 (1.1%) 0.41
(65.3%)
1.37 (~0%)
(41.5%)
76.96
(56.3%)
Individuals
Total SD
12.56
17.84
3.81
0.64
243.38
102.53
27
1
Table 3. Pearson correlation coefficients between three measured leaf traits and the three
2
parameters of the Mitscherlich photosynthetic light-response curve. Correlations
3
between the 25 herbaceous species are based on species’ means. Correlations between
4
the 40 woody species are based on leaf-level values. See Table 1 for names and units.
Herbaceous species, controlled conditions
log10(N)
log10(LMA)
log10(Amax)
log10(q( φ ))
log10( φ )
log10(Chl)
1.00
-0.93
0.94
0.87
-0.05
0.93
-0.93
1.00
-0.98
-0.93
0.12
-1.00
0.94
-0.98
1.00
0.96
-0.19
0.98
0.87
-0.93
0.96
1.00
-0.27
0.92
-0.05
0.12
-0.19
-0.27
1.00
-0.11
0.93
-1.00
0.98
0.92
-0.11
1.00
Woody species, field conditions
1.00
-0.10
0.25
0.01
-0.31
0.26
-0.10
1.00
-0.49
-0.66
-0.04
-0.80
0.25
-0.49
1.00
0.47
-0.21
0.47
0.01
-0.66
0.47
1.00
0.06
0.50
-0.31
-0.04
-0.21
0.06
1.00
-0.07
0.26
-0.80
0.47
0.50
-0.07
1.00
28
1
Figure captions.
2
3
Figure 1. Bivariate relationships (log10 transformed) between leaf mass per area (LMA, g
4
m-2), daytime dark leaf respiration rate (Rd, nmol g-1 s-1), maximum leaf net
5
photosynthetic rate (Amax, nmol g-1 s-1) and leaf nitrogen concentration (N, % dry mass) in
6
the worldwide Glopnet data set (gray points), in the species’ mean data of the 25
7
herbaceous species grown under controlled growth and measured in the laboratory (upper
8
diagonal, dark circles) and in the 160 leaves from 40 woody species measured in the field
9
(lower diagonal, open circles).
10
11
Figure 2. Relationships between (left) quantum yield at the light compensation point vs.
12
maximum net photosynthetic rate (Amax) and (right) between quantum yield at the light
13
compensation point vs. leaf mass per area (LMA). Dark circles are species means from
14
25 herbaceous species grown in controlled-growth conditions and values measured in the
15
laboratory and open circles are leaf-level values from 40 woody species measured in the
16
field.
17
18
Figure 3. Observed versus predicted net photosynthetic rates of 25 species of herbaceous
19
species grown and measured under controlled conditions (100 leaves, from 50
20
individuals) and 40 woody species growing and measured in the field (160 leaves from
21
80 individuals), measured over a range of irradiances from 0 to 1600 μmol m-2 s-1 PAR.
22
Predicted values were based on the Mitscherlich equation in which maximum net
23
photosynthetic rate and quantum yield at the light compensation point were estimated
24
from allometric relationships with leaf mass per area and leaf nitrogen content; modelled
25
light compensation points were constant at 13.42.
26
27
Figure 4. Simulation results based on Equation 9. Left: Leaf nitrogen (N, % dry mass) is
28
constant and only leaf mass per area (LMA) changes. Centre: LMA is constant and only
29
N changes. Right: Both LMA and N change as found in empirical interspecific trends.
30
29
1
Figure 1
2
30
1
Figure 2
2
31
1
Figure 3.
2
32
1
Figure 4.
2
3
4
5
6
7
8
33