A theory of plant function helps to explain leaf-trait and productivity responses to elevation
Yunke Peng1,2, Keith J. Bloomfield2 and Iain Colin Prentice2,3,4
1Masters Programme in Ecosystems and Environmental Change, Imperial College London, Department of Life Sciences, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK2AXA Chair Programme in Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK3Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, A ustralia4Department of Earth System Science, Tsinghua University, Beijing 100084, China
Author for correspondence:
Prof. Iain Colin Prentice
Silwood Park Campus, Imperial College London
Buckhurst Road, Ascot
SL5 7PY, UK
E-mail: [email protected]
Total word count: 4846
Summary: 198
Introduction: 680
Theory: 1450
Data and methods: 1233
Results: 633
Discussion: 850
Acknowledgements: 54
No. of figures: 4
No. of tables: 1
No. of supporting information files: 1 (Tables S1-S5, Figs S1-S10)
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KEYWORDS
Elevation transect, photosynthesis, plant functional traits, acclimation, adaptation, temperature,
primary production, optimality.
SUMMARY
Several publications have examined leaf-trait and carbon-cycling shifts along an Amazon-
Andes transect spanning 3.5 km elevation and 16 mean annual ℃ temperature. Photosynthetic
capacity was previously shown to increase as temperature declines with increasing elevation,
counteracting enzyme-kinetic effects. Primary production declines, nonetheless, due to
decreasing light availability. We aimed to predict leaf-trait and production gradients from
first principles, using published data to test an emerging theory whereby photosynthetic traits
and primary production depend on optimal acclimation and/or adaptation to environment.
We re-analysed published data for 210 species at 25 sites, fitting linear relationships to
elevation for both predicted and observed photosynthetic traits and primary production.
Declining leaf-internal/ambient CO2 ratio (χ) and increasing carboxylation (Vcmax) and
electron-transport (Jmax) capacities with increasing elevation were predicted. Increases in leaf
nitrogen content with elevation were explained by increasing Vcmax and leaf mass-per-area.
Leaf and soil phosphorus covaried, but after controlling for elevation, no nutrient metric
accounted for any additional variance in photosynthetic traits. Primary production was
predicted to decline with elevation.
This analysis unifies leaf and ecosystem observations in a common theoretical framework.
The insensitivity of primary production to temperature is shown to emerge as a consequence
of the optimization of photosynthetic traits.
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INTRODUCTION
Elevation transects provide excellent opportunities for the analysis of variation in plant and
ecosystem function. Many environmental factors covary with elevation. This covariation is a
strength, because it results in strong environmental variations within a limited region; but also a
challenge, because the effects of highly inter-correlated environmental variables cannot be
realistically separated by statistical analysis. Here, we take a novel approach to the study of plant
trait and primary production data on an extensively studied elevation transect. Our approach is to use
theoretical optimality principles to predict plant and ecosystem properties and how they vary with
elevation; to compare the resulting predictions with the data; and, in so far as the data and theory
agree, to use the theory to quantify the contributions of different environmental variables.
We focus on a Peruvian Amazon-Andes transect that has been the subject of many published
papers, based on measurements of plant trait and ecosystem carbon-cycling properties at sites
spanning a range of almost 3500 m in elevation and 16℃ in mean annual temperature (Girardin et
al. 2010; Van der Weg et al. 2009, 2014; Asner et al. 2014a, b, 2016; Bahar et al. 2017; Enquist et
al. 2017; Fyllas et al. 2017; Malhi et al. 2017; Wu et al. 2017). Atmospheric pressure and
temperature decline linearly with increasing elevation at study sites along this transect (Fig. 1). The
remotely sensed fraction of absorbed photosynthetically active radiation (fAPAR), an index of the
absorption of solar radiation by green plant tissues, also declines, as do – although less regularly –
incident photosynthetic photon flux density (PPFD) and vapour pressure deficit (driven primarily by
temperature) (Fig. 1). The decline in PPFD is due to increasing cloud immersion at higher elevations.
Sites at the highest elevations are above the cloud belt, however, and show higher PPFD than sites at
intermediate elevations.
Interpretations of observed gradients in plant traits and primary production along this transect
have sometimes conflicted with one another. For example, according to Malhi et al. (2017) nutrient
limitations do not restrict photosynthesis at any elevation; while Bahar et al. (2017) explained
variations in carboxylation (Vcmax) and electron-transport (Jmax) capacities by variations in leaf and
soil phosphorus, which are highly correlated with elevation. Nonetheless, recent analyses agree on
several key points:
Leaf mass-per-area (MA, the product of leaf thickness and density; an indicator of
leaf longevity) increases with elevation, while carbon isotope discrimination (a proxy for
plant water-use efficiency) decreases (Asner et al. 2014b; Wu et al. 2017).
Vcmax standardized to 25˚C (Vcmax,25) increases with elevation. This increase
counteracts the decline in the light-saturated photosynthetic rate that would otherwise be
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expected due to the direct effect of temperature on Rubisco kinetics (Bahar et al. 2017;
Enquist et al. 2017).
Gross primary production (GPP) and net primary production (NPP = GPP minus
plant respiration) decline with elevation, but the magnitude of this decline can be fully
accounted for without invoking temperature: either because of declining PPFD and
foliage cover (Asner et al., 2014a; Fyllas et al. 2017; Malhi et al. 2017), or declining
plant biomass (Enquist et al. 2017).
There is little or no change in the ratio of NPP to GPP along the transect (Enquist et
al. 2007; Malhi et al. 2017).
Our analysis builds on recent theoretical developments and empirical analyses on the control
of the leaf-internal/ambient CO2 ratio (χ) (Prentice et al. 2014; Wang et al. 2017a, b; Bloomfield et
al. 2018), Vcmax and Jmax (Wang et al. 2017a, b; Bloomfield et al. 2018; Togashi et al. 2018; Smith et
al. 2019), leaf nitrogen (N) (Dong et al. 2017), GPP (Wang et al. 2017a), and the fraction of GPP
allocated to biomass production (He et al. 2019). The theory makes predictions about both absolute
values and variations with elevation of χ, Vcmax, Jmax, GPP and NPP. Given observed leaf mass-per-
area (MA), the theory also predicts leaf nitrogen (N per unit area, Narea, and per unit mass, Nmass) as
functions of MA and Vcmax,25. Here we examine these various predictions and show that they account
for general trends of leaf-trait and productivity variation along the transect.
THEORY
Environmental controls of the CO2 drawdown from air to leaf
During photosynthesis, the intercellular partial pressure of CO2 inside the leaf (ci) is reduced relative
to the ambient partial pressure (ca) such that their ratio (χ) is maintained within a narrow range,
determined by the growth environment. The least-cost hypothesis (Wright et al. 2004) states that χ is
set by the interaction of photosynthesis and stomatal conductance (gs) in such a way as to minimize
the sum of the costs (per unit assimilation) of maintaining the required capacities for carboxylation
and transpiration. Using the framework provided by the Farquhar et al. (1980) model of
photosynthesis, Prentice et al. (2014) and Wang et al. (2017a) showed that according to the least-cost
hypothesis, the optimal value of χ (denoted χo) is:
χo = Γ*/ca + (1 – Γ*/ca) ξ/(ξ + √D), where ξ = √[β (K + Γ*)/1.6 η*] (1)
Γ* in equation (1) is the photorespiratory compensation point and Κ is the effective Michaelis-
Menten coefficient of Rubisco (Pa; both functions of temperature and atmospheric pressure). D is the
leaf-to-air vapour pressure deficit (Pa), η* is the viscosity of water relative to its value at 25˚C, and β
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is the ratio at 25˚C of the unit costs of maintaining carboxylation and transpiration capacities
(estimated as 146, based on a global compilation of leaf 13C measurements). K is given by:
K = KC (1 + O/KO) (2)
where KC and KO are the Michaelis-Menten coefficients for carboxylation and oxygenation
respectively (Pa, reflecting the twin affinities of Rubisco), and O is the partial pressure of O2 (Pa).
Γ*, ca and O also decline with elevation, in proportion to atmospheric pressure.
Note that our analysis follows the original Farquhar et al. (1980) model, and standard practice in
the literature on plant functional traits, in making no distinction between intercellular (ci) and
chloroplastic (cc) CO2. Mesophyll conductance (gm) in the liquid phase is implicitly assumed to be
infinite. This is an appropriate simplification given the current dearth of information about the biotic
and environmental controls of gm (Rogers et al. 2017). It implies that Γ*, K, Vcmax and Jmax are
represented by “apparent” values, referenced to ci rather than cc (see e.g. Sharkey et al., 2007; von
Caemmerer, 2013; Walker & Ort, 2015; Bahar et al., 2018 for further discussion) – consistent with
the definition of χ as the ratio of ci (rather than cc) to ca.
Optimal χ according to equation (1) increases with increasing growth temperature; decreases
with increasing vapour pressure deficit; and (all else equal) decreases with increasing elevation
because of the decline in atmospheric pressure with elevation. All three environmental dependencies
were corroborated by global leaf carbon isotope (δ13C) measurements, and equation (1) correctly
predicts their signs and magnitudes (Wang et al. 2017a). Wang et al. (2017a) also showed how the
theory could be extended, without loss of realism in fitting observations, to consider finite gm under
the assumption of a fixed ratio between gm and gs.
Environmental control of photosynthetic capacity
The coordination hypothesis, which has strong experimental support (Maire et al., 2012), predicts
that carboxylation and electron transport tend to co-limit photosynthesis under average daytime
environmental conditions. This hypothesis, in its “strong” form as defined by Togashi et al. (2018),
implies that Vcmax at the leaf scale is adjusted to current growth conditions so that neither the capacity
for carboxylation (Vcmax) nor the rate of electron transport is in excess. Such adjustment can be
considered optimal because a higher Vcmax would incur futile maintenance costs, while a lower Vcmax
would under-use available light. Optimality could be achieved either by intra-seasonal adjustment
(acclimation) of Rubisco activity, or over generations by genetic adaptation. These two processes can
be distinguished by examining seasonal variations in individual plants (testing for acclimation) and
in common garden experiments (testing for adaptation). Kumarathunge et al. (2019) presented
evidence for acclimation as a key, potentially universal, process that shifts the temperature optimum 5
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of photosynthesis changes in the same direction as growth temperature. They showed that this is
achieved in part by acclimation of the temperature optima of both Vcmax and Jmax, ensuring that the
current growth temperature is always lower than the temperature optimum for both quantities (see
Fig. 2 in Kumarathunge et al., 2019).
The ratio of Jmax to Vcmax has also been hypothesized to adjust to current growth conditions so that
the marginal benefit (defined as additional photosynthesis) of an increase in Jmax is equal to its
marginal cost (Wang et al., 2017a). This ratio, too, has been shown to vary intra-seasonally with
growth temperature – thus contributing to the acclimation of the temperature optimum of
photosynthesis – while also having a genetic component (Kumarathunge et al., 2019).
The coordination hypothesis for Vcmax and the cost-benefit hypothesis for Jmax together lead to the
following predicted optimal values, Vcmax,o and Jmax,o (Wang et al. 2017a):
Vcmax,o = φ0 Iabs [(ci + K)/(ci + 2Γ*)] √[1 – (c*/m)2/3] (3)
Jmax,o = 4 φ0 Iabs / √{1/[1 – (c*/m)2/3] – 1] (4)
Here φ0 is the intrinsic quantum efficiency of photosynthesis (mol C mol–1 photons); Iabs is the PPFD
absorbed by the leaf (μmol m–2 s–1); ci = χoca; m is the dimensionless ratio (ci – Γ*)/(ci + 2Γ*); and c*
is a factor proportional to the cost of maintaining electron transport capacity, estimated as 0.41 based
on experimental values of Jmax/Vcmax (Wang et al. 2017a). We model φ0 using the empirical
temperature dependence of electron transport in light-adapted leaves, as reported by Bernacchi et al.
(2003):
φ0 = (0.352 + 0.021 T – 3.4 10–4 T2) / 8 (5)
where T is temperature in ˚C. The factor 8 arises because eight photons are required to fix one
molecule of CO2. Iabs is equated to the incident PPFD for leaf-scale calculations.
The optimal Vcmax and Jmax values defined by equations (3) and (4) can, in principle, be
achieved either by acclimation or by adaptation – processes which cannot be distinguished in data
sets of the kind analysed here. The optimal rates both increase in direct proportion to PPFD. They
also increase with growth temperature. Optimal Vcmax,25 and Jmax,25 however decrease with growth
temperature, because at higher temperatures, less enzymes are required to achieve a given rate of
photosynthesis – and this enzyme kinetic effect is stronger than the predicted increase of (non-
standardized) Vcmax and Jmax. These predictions have been supported by measurements on the same
plants in different seasons (Togashi et al. 2018), and by experimental manipulations of growth
temperature (Scafaro et al. 2017). Optimal values of Vcmax and Jmax also increase with vapour pressure
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deficit and (all else equal) with elevation, because higher photosynthetic capacities are needed to
compensate for lower χ in drier air or at lower atmospheric pressure (Wang et al. 2017b).
Environmental control of leaf nitrogen
One implication of the theory outlined above, supported by extensive observations (Dong et al.
2017), is that the metabolic component of leaf N primarily reflects Vcmax,25 – which, in turn, is
optimally adjusted to the growth environment. On this basis, Narea can be approximated as the sum of
a bulk leaf tissue component, assumed to be proportional to MA, and a metabolic component,
assumed to be proportional to Vcmax,25:
Narea = ns MA + nr Vcmax,25 (6)
where ns and nr are the coefficients of proportionality for the two components. It follows that:
Nmass = ns + nr Vcmax,25/MA (7)
Environmental control of primary production
Another consequence of the theory is that total photosynthesis, when accumulated over time scales of
a week to a month (long enough to allow acclimation of Vcmax and Jmax: Medlyn et al. 2002; Kattge &
Knorr 2007), is proportional to absorbed PPFD (Wang et al. 2017a). This prediction is consistent
with a long-standing body of evidence on crop growth (Monteith, 1977) and with the logic of
empirical “light use efficiency models” for GPP. Leaf-level photosynthesis can therefore be scaled
up linearly to ecosystem-level GPP (Aeco):
Aeco = φ0 Iabs,eco m √[1 – (c*/m)2/3] (8)
where Iabs,eco is the product of incident PPFD and fAPAR. “Light use efficiency” (LUE) is the ratio of
Aeco to Iabs,eco, which is explicitly predicted by equation (8).
We further hypothesize that total plant respiration acclimates closely to growth temperature
(Gifford, 2003; Reich et al., 2016), so that the ratio of NPP to GPP remains within a relatively
narrow range (Waring et al., 1998; Collalti & Prentice, 2019). As the biometric estimates of NPP
considered here are based on actual biomass production (Malhi et al., 2017) and do not explicitly
consider carbon exported to mycorrhizae and the rhizosphere, we assume that it is appropriate to
apply the generic value of 0.41 for the biomass-production to GPP ratio, as recently estimated for
non-agricultural ecosystems by He et al. (2019).
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DATA AND METHODS
Site selection
The data analysed were field measurements at 25 one-hectare plots (Table S1) distributed from the
lowland Amazon basin up to treeline in the eastern Andes (Atkin et al. 2015; Bahar et al. 2017;
Enquist et al. 2017; Fyllas et al. 2017; Malhi et al. 2017; Wu et al. 2017). Plot locations ranged from
13˚ to 3˚S, 69˚ to 72˚W, and 117 to 3537 m elevation. Ten montane plots were located in the
Kosnipata Valley, three submontane plots in the Tono Valley and the Andes Pantiacolla front range,
eight lowland plots in Loreto, North Peru (without a dry season), and another four lowland plots at
Madre de Dios, S Peru (with a dry season) (Malhi et al. 2017). Reported mean annual temperatures
ranged from 10.4 to 24.8˚C among the upland sites and 25.6 to 26.5˚C among the lowland sites.
Separate analyses were based on site-mean trait values (averages across all co-occurring species)
from Bahar et al. (2017), Enquist et al. (2017) and Wu et al. (2017); site-species leaf-trait
measurements from Bahar et al. (2017) and Wu et al. (2017); and site-level GPP and NPP from
Malhi et al. (2017).
Environmental data
Fig. 1 shows environmental variation with elevation across sites. Monthly values of mean daily
maximum (Tmax) and minimum (Tmin) temperature (˚C) and vapour pressure (ea) were extracted from
gridded Climatic Research Unit data (CRU TS4.01: Harris et al. 2014) for 2006–2015 at 0.5˚
resolution, and interpolated to the latitude, longitude and elevation of each site using geographically
weighted regression (GWR), implemented in ArcGIS. Mean daytime growth temperature (Tg) was
estimated by approximating the daily temperature cycle with a sine curve, assuming 12 hours of
daylight as the sites are near to the equator:
Tg = Tmax (1/2 + 1/π) + Tmin (1/2 – 1/π) (9)
CRU does not provide solar radiation or PPFD, therefore monthly incident solar radiation data were
obtained from WATCH Forcing Data ERA-Interim (Weedon et al., 2014) for the same period and
grid resolution and interpolated using GWR. Solar radiation was converted to incident PPFD
assuming a flux-to-energy ratio of 4.6 μmol J–1 and a photosynthetically active fraction of 0.50. Mean
atmospheric pressures (Patm) were calculated using the barometric formula (Berberan-Santos et al.
1997). Values of vapour pressure deficit (D, kPa) were calculated after correcting gridded ea from
CRU to Patm at site elevation:
D = es – ea where es = 0.611 exp [17.27 TC/(TC + 237.3)] (10)
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and TC is temperature in ˚C. This calculation was made separately for Tmin and Tmax and the resulting
values averaged. fAPAR data were obtained from the 0.5 km resolution MODIS FPAR product
(MOD15A2H: Myneni et al. 2015), for the same period, interpolated using GWR.
Stable carbon isotope data
Long-term values of χ were estimated from the δ13C measurements reported in Wu et al. (2017), who
also converted these to isotopic discrimination values (Δ: C3 photosynthesis discriminates against the
heavier stable carbon isotope). We applied the “classical” discrimination equation as given in
Farquhar & Cernusak (2012), simplified by disregarding boundary-layer and mesophyll resistances
as well as ternary effects, and assuming no discrimination during mitochondrial respiration (see
Ubierna & Farquhar, 2014 for further discussion of these choices). Thus we solved for χ in the
following expression:
Δ = as (1 – χ) + bχ – f Γ*/ca (11)
where as = 4.4 ‰, b = 30 ‰, f = 16 ‰ (Cernusak et al. 2013). We also tried including the (separate)
effects of finite boundary-layer (gb) and mesophyll (gm) conductance on inferred χ:
Δ = (ab + θs as) (1 – χ)/(1 + θs) + bχ – f Γ*/ca (12)
Δ = (am + θas)(1 – χ)/θ + b [χ – (1 – χ)/θ] – f Γ*/ca (13)
with ab = 2.7 ‰ and am = 1.8‰ (Ubierna & Farquhar, 2014), θs = gb/gs = 13.64 (Seibt, 2008), and θ =
gm/gs = 1.4 (Wang et al. 2017b). Fig. S6 demonstrates that effect of finite boundary-layer
conductance is slight (Table S4) while the effect of finite mesophyll conductance is larger, implying
that lack of knowledge about mesophyll conductance translates into some uncertainty regarding the
variation of χ with elevation.
Predicting photosynthetic traits
By convention, values of Vcmax and Jmax are corrected from the temperature of measurement to a
standard temperature of 25˚C by means of the Arrhenius equation. Values thus standardized are
called Vcmax,25 and Jmax,25. All of the published values used here were given in this form. However,
equations (3) and (4) predict Vcmax and Jmax at the growth temperature. We therefore applied a
correction to standardize the predicted values to 25˚C, using activation energies based on tobacco as
provided by Bernacchi et al. (2001, 2003).
Predictions of χ (equation (1)), Vcmax and Jmax also require baseline values (at 25˚C) and activation
energies to be supplied for the catalytic constants Γ*, KC and KO. We derived baseline values (at 227
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m above sea level) and activation energies of these constants, also based on tobacco, from Bernacchi
et al (2001). We also substituted a range of alternative values measured on other seed plant species
(Galmés et al. 2015, 2016) in order to assess the potential of variations in Rubisco catalytic
properties, and a range of activation energies for Vcmax and Jmax measured on tree species (Dreyer et
al. 2001). Parameters KC, KO and Γ* at 25 were derived from the scaling constant (℃ c) and Ea by the
Arrhenius equation: Parameter = exp (c – Ea /RT). Values of Γ* were converted from the Rubisco
specificity factor (Sc/o): Γ* = 0.5 O/Sc/o (Bernacchi et al. 2001). Solubilities for CO2 and O2 were
assumed to be 0.334 mmol (L MPa)−1 and 0.0126 mmol (L MPa)−1 to convert from concentrations to
partial pressures (Walker et al. 2013). The original sources of these data are indicated in Galmés et
al. (2015, 2016) and Dreyer et al. (2001). See SI for details.
Statistical analysis
Values of the coefficients ns and nr were estimated by ordinary least squares (OLS) multiple linear
regression (without intercept, i.e. forced through the origin) of Narea against species-mean
observations of MA and Vcmax,25, using the lm function in R (R core team, 2018). All sampled species
were included in the regression except for one (high) outlier for Narea and three (high) outliers for
Nmass. OLS linear regression was also used to relate both observed and predicted variables to
elevation (Figs 1-4, Table S4). Table 1 provides the relevant summary statistics. Comparisons
between observed and predicted values are shown in Fig. S1 (Bahar et al. 2017; Enquist et al. 2017),
with summary statistics in Table S4. Robust regression (the Theil-Sen estimator) was applied as an
alternative using the mblm package in R (R core team, 2018), in order to reduce the potential
influence of outliers. Figs S7-S10 and Table S5 present these alternative analyses. They are not
discussed further, however, as they were always consistent with the OLS results. The impacts of
alternative parameters on photosynthesis prediction were set at a range of two standard deviations,
using the geom_ribbon function in R (R core team, 2018). Figs S3-S5 show these predictions.
Where available, individual species’ trait values were analysed using a mixed-effects model with
a common design using the lme4 package in R (R core team, 2018). Elevation was included as the
fixed term, with site and species as random effects. We used a crossed rather than a fully nested
random design because some species were recorded at more than one site.
RESULTS
CO2 drawdown
χ decreases with elevation (z) (Fig. 2a, Fig. S2a, p < 0.001). Predicted values are slightly lower than
observed values, but the rate of decrease in site-mean χ is predicted well; the slope of the observed
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values against elevation did not differ significantly from that of the predicted values (p = 0.113). The
trend is dominated by the effect of temperature; the predicted effects of vapour pressure deficit and
atmospheric pressure are slight (Table 1). In the analysis of all species (Fig. S2a; Table S3), species
identity accounted for 51% of the variation in χ that was not explained by changing elevation. In
contrast, site identity accounted for only 1% of the variance after controlling for elevation.
Photosynthetic capacity
Site-mean Vcmax,25 and Jmax,25 increase with elevation (Figs 2b, c, p < 0.001) and in both cases the
predicted and observed slopes are similar (p = 0.367 for Vcmax,25 and p = 0.533 for Jmax,25), although
predicted values for Jmax,25 generally underestimate the true values. The predicted increasing trends
are dominated by temperature effects. Declining PPFD and, to a lesser extent, χ offset the steepness
of the predicted trends. In the analysis of all species (Figs S2b, c) Jmax,25 was unrelated to elevation,
although here site accounted for much more variation than species identity (Table S3).
Figs S3-S5 illustrate the impact of alternative parameter choices on the predictions of Vcmax,25 and
Jmax,25. It is shown that the patterns of increase in both parameters with increasing elevation are
robustly predicted, with similar slopes to those calculated in the main analysis.
Leaf nitrogen
Narea increases with elevation (Fig. 2d, Fig. S2d, p < 0.01) and this increase is predicted by equation
(6). Predicted values are generally lower than observed, but the slope of the predicted relationship
shows no significant difference from the observed relationship (p = 0.321). The predicted increase in
Narea is due to the increase of both MA and Vcmax,25, in accordance with equation (6). Nmass (being the
ratio of Narea to MA) does not change systematically with elevation (Figs 2e, S2e, p = 0.460). From
equation (7), the sign of the response of Nmass to elevation depends on the relative magnitude of the
responses of Vcmax,25 and MA. The observed variations of Vcmax,25 and MA with elevation are opposite
and of similar magnitude, and therefore cancel one another.
Leaf and soil nutrients
The increase of leaf P with elevation (Fig. 2f) parallels that of soil total P (Fig. 3b, p < 0.001 for both
leaf and soil P). The increase of leaf N (Fig. 2d) likewise appears to parallel soil N (Fig. 3a, p <
0.001) but is accompanied by an increasing soil C:N ratio (Fig. 3a, p < 0.05), indicating increasing
total soil organic matter with elevation. Leaf N:P ratio declines with elevation (Fig. 3c, p < 0.01), but
soil N:P ratio does not (Fig. 3d). When added to multiple regression models fitted to observed
photosynthetic traits, no leaf nutrient, soil nutrient or structural component contributed any
significant additional explanatory power.11
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Primary production
GPP decreases with increasing elevation (p < 0.01). GPP is slightly over-predicted overall, but the
predicted (negative) slope of GPP against elevation is similar to the observed slope (Fig. 4a, p =
0.463). The predicted decrease is a consequence of a decline in absorbed light, partly due to
declining incident PPFD and partly to declining vegetation cover, i.e. a smaller fraction of incident
PPFD is absorbed at higher elevations (Table 1). LUE is predicted to show a slight increase with
elevation, because photorespiration is reduced at lower temperatures.
NPP also decreases with elevation (Fig. 4b, p < 0.001). Predicted values of NPP are
somewhat too high in general, but the variation of predicted NPP with elevation is similar to the
variation of observed NPP with elevation (p = 0.957).
Fig. 1 Site-mean values of climatic and environmental variables in relation to elevation: mean
daytime air temperature (a), incident photosynthetic photon flux density (PPFD) (b), atmospheric
pressure (c), fractional absorbed photosynthetically active radiation (d) and vapour pressure deficit
(e) at study sites along the elevation transect (n = 25, Atkin et al. 2015; Bahar et al. 2017; Malhi et
al. 2017; Enquist et al. 2017; Wu et al. 2017; Fyllas et al. 2017). Coefficients for the fitted lines are
given in Table S4. Linear fits are poor for PPFD and vpd. A linear regression is shown for PPFD
nonethless, because the declining trend is significant. The trend is non-significant for vpd, but high
elevations show only low values whereas low elevations show a large range of values.
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Fig. 2 Site-mean values of photosynthetic capacity and leaf traits in relation to elevation. Sources of
observational data (black) are given in Table S2 (Bahar et al. 2017; Enquist et al. 2017). Predictions
(red) were derived from equations (1), (3), (4), (6) and (7) respectively with Narea = 0.0121 MA +
0.0102 Vcmax,25 (d) and Nmass = 0.0121 + 0.0102 Vcmax,25/MA (e). The coefficients in these equations were
derived from the individual-species observations of Vcmax,25 and MA. Coefficients for the fitted lines
are given in Table S4. Equivalent plots based on all species are shown in Fig. S2.
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Fig. 3 Site-mean values of leaf and soil nutrients in relation to elevation. Sources of data are given in
Table S2 (Bahar et al. 2017; Enquist et al. 2017). Total soil nitrogen per unit mass (black dots) and
the soil C: N ratio (grey dots) (a); total soil phosphorus per unit mass (b); nitrogen-phosphorus ratio
(by mass) in leaves (c) and soil (d). Coefficients for the fitted lines are given in Table S4.
Fig. 4 Site values of gross primary production (GPP, left) and net primary production (NPP, right)
(Mg ha–1 yr–1). Data are from Malhi et al. (2017). Predictions (red) were based on equation (8) for
GPP and NPP = 0.41 GPP (He et al., 2019). Coefficients for the fitted lines are given in Table S4.
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Table 1 Left-hand columns: observed (Obs) and predicted (Pre) slopes of the fitted linear
relationships of χ, Vcmax,25, Jmax,25, Narea, Nmass to elevation (km) as shown in Fig. 2, and GPP and NPP
as shown in Fig. 4. Right-hand columns: predicted slopes due to a single variable at a time, with all
others held constant. Error ranges are ± 1 standard error. ns = non-significant.
χ Obs Pre Temperature vpd Pressure
[–] –0.047 ±
0.004
–0.054 ± 0.002 –0.046 ±
0.0006
0.007 ±
0.002
–0.003 ±
0.0001
Vcmax,25 Obs Pre Temperature χ PPFD
μmol m–2 s–1 7.2 ± 1.6 8.6 ± 1.0 12.5 ± 0.2 0.7 ± 0.1 –1.6 ± 0.5
Jmax,25 Obs Pre Temperature χ PPFD
μmol m–2 s–1 13.0 ± 2.8 14.8 ± 1.4 28.3 ± 0.4 –4.4 ± 0.2 –5.9 ± 0.6
Narea Obs Pre Vcmax,25 MA
g m–2 0.26 ± 0.06 0.20 ± 0.05 0.07 ± 0.02 0.11 ± 0.01
Nmass Obs Pre Vcmax,25 MA
mg g–1 ns 0.39 ± 0.07 0.41 ± 0.06 –0.12 ± 0.04
GPP Obs Pre fAPAR PPFD LUE
Mg ha–1 yr–1 –4.8 ± 1.2 –3.8 ± 2.1 –2.8 ± 2.4 –2.5 ± 1.2 1.7 ± 0.7
NPP Obs Pre
Mg ha–1 yr–1 –1.92 ± 0.39 –1.94 ± 0.41
DISCUSSION
Our transect analysis has revealed that a series of leaf traits show patterns of change with elevation
which, despite considerable scatter around the fitted relationships of trait values to elevation, are
broadly consistent with the predictions of a theoretical model. In agreement with the interpretations
of Enquist et al. (2017) and Fyllas et al. (2017), our results indicate that temperature is a key factor
determining leaf-trait values while not being a principal control on primary production. In other
words, the adjustment of leaf traits to environmental conditions (including temperature) that covary
with elevation permits plants to function efficiently at a range of elevations. For example, Vcmax,25 and
Jmax,25 increase along the gradient, as noted by Bahar et al. (2017), and these increases are predicted
here. The predicted slopes of the relationships between photosynthetic capacity and elevation in the
main analysis similar to the observed slopes, and these predicted slopes are shown to be robust with
respect to alternative choices of parameter values reflecting the natural variability of photosynthetic
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constants and temperature dependencies across species and under different growing conditions.
Increasing photosynthetic capacities counter the potential reduction in primary production that would
otherwise be expected, if these quantities remained constant. We therefore interpret these increases in
terms of optimal acclimation and/or adaptation to temperature. These can be considered as
compensatory mechanisms that allow the full utilization of absorbed light for photosynthesis, at both
the species and community levels.
Enquist et al. (2017) interpreted productivity variations along the elevation gradient in terms
of metabolic scaling theory, which treats biomass as a key biotic control – by contrast our theory
assigns a primary role to absorbed light, and therefore implicitly to leaf area index (LAI) via Beer’s
law. Metabolic theory suggests that primary production should be proportional to the 3/5 power of
total biomass, so our analysis is consistent with the general expectation that leaf area should be less
than isometrically related to total biomass: that is, the relationship of leaf area to biomass should
have an exponent < 1. A further major conclusion of Enquist et al. (2017) was that a previously
proposed, universal temperature response of primary production was contradicted by the data from
this transect. The data were instead interpreted as supporting a model in which any such temperature
response is fully counteracted by acclimation and adaptation of traits. Similarly, Fyllas et al. (2017)
showed that inclusion of a conventional, generic response of photosynthesis to temperature
substantially degraded model performance.
The contribution of declining vegetation cover to declining GPP and NPP with elevation on
this transect is somewhat equivocal, because different sources of data have shown different rates of
decline with elevation. For example Malhi et al. (2017) indicated only a slight decline in LAI, which
could not account for the steep decrease in fAPAR that we derived from remote-sensing data. If our
estimated fAPAR decline with elevation were too steep, this could account for our slight (although
non-significant) overestimation of the rate of decline of GPP. The close correspondence between the
variations of observed GPP and NPP with elevation indicate that CUE is not greatly affected by
temperature (Malhi et al. 2017), in agreement with recent global analyses (Collalti & Prentice, 2019;
He et al., 2019).
Fyllas et al. (2017) suggested that Nmass is determined by temperature. On the other hand,
Almeida et al. (2012) attributed the control of leaf N to soil N. Our analysis supports neither of these
interpretations, but it is consistent with the conclusion by Dong et al. (2017) that area- and mass-
based leaf N content are primarily determined by MA and Vcmax,25 – the latter optimally adjusted to the
environment. We attribute the increase in Narea with elevation to the combined effect of thicker leaves
and faster Vcmax,25. Nmass shows no significant response to elevation because the positive effect of
increasing Vcmax,25 is cancelled by the dilution effect of increasing MA.
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We did not try to predict MA or leaf P. Asner et al. (2016) proposed that leaf P is controlled
by soil P. Fisher et al. (2013) also noted an increase in leaf Pmass with elevation. Bahar et al. (2017)
used the covariation of leaf and soil P with elevation as a basis for predicting Vcmax,25 and Jmax,25. Our
models do not require information on either leaf or soil P to predict the elevation gradient in Vcmax,25
or Jmax,25. Norby et al. (2017) showed that the P-based equations from Bahar et al. (2017) had very
little predictive power for Vcmax or Jmax in an independent data set of photosynthetic traits in tropical
forests. However, our finding that nutrient metrics do not confer any additional explanatory power
should be interpreted with caution, because the strong covariation of both climatic and soil-chemical
variations with elevation implies that either could independently account for the observed patterns.
Moreover, Bloomfield et al. (2014) showed experimentally that depletion of soil P led to reduced
photosynthetic rates, and reduced the slope of the relationship between Narea and photosynthesis.
More definitive separation of physical and chemical environmental effects in field-measured data
will require the analysis of leaf-trait variations along multiple, independent environmental gradients.
STATEMENT OF AUTHORSHIP
ICP proposed the topic and supervised the research. YP carried out the analyses and created the
graphics. KJB oversaw the statistical analyses. ICP, KJB and YP interpreted the results, and wrote
the paper, together.
DATA ACCESSIBILITY STATEMENT
No new data were used in the analysis presented here. The observed leaf traits and photosynthetic
data were drawn from the cited publications. The observed and predicted data, and ancillary
information including climate values, are available from the corresponding author upon request.
ACKNOWLEDGMENTS
This research has received funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (grant agreement No: 787203 REALM).
It is a contribution to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial
College initiative on Grand Challenges in Ecosystems and the Environment.
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Supporting Information
Table. S1 Description of the study sites.
Table. S2 Data source and figure descriptions of leaf and soil traits.
Table. S3 Partitioning of trait variation unexplained by elevation.
Table. S4 Relationships between leaf functional traits and elevation; and goodness of fit
(observed ~ predicted values)
Table. S5 Relationships between leaf functional traits and elevation from robust model analysis
using Theil-Sen estimator.
Fig. S1 Observed versus predicted photosynthetic traits.
Fig. S2 Site-Species mean values of photosynthetic capacity and leaf functional traits in relation
to elevation.
Fig. S3 Sensitivity analysis of the growth-temperature dependencies of predicted carboxylation
capacity (Vcmax,25) using alternative prediction of non-tobacco species.
Fig. S4 Sensitivity analysis of the growth-temperature dependencies of electron-transport
capacity (Jmax,25) using alternative prediction of non-tobacco species.
Fig. S5 Sensitivity analysis of temperature-dependencies of photosynthetic capacity using
alternative prediction of non-tobacco species.
Fig. S6 Sensitivity analysis of observed χ from carbon isotope discrimination.
Fig. S7 Comparisons of robust regression (blue; by Theil-Sen estimator) and linear regression
(black) for site-mean values of climatic and environmental variables in relation to elevation.
Fig. S8 Comparisons of robust regression (blue; by Theil-Sen estimator) and linear regression
(black) for site-mean values of photosynthetic capacity and leaf functional traits in relation to
elevation.
Fig. S9 Comparisons of robust regression (blue and red; by Theil-Sen estimator) and linear
regression (black) for site-mean values of climatic and environmental variables in relation to
elevation.
Fig. S10 Comparisons of robust regression (blue and red; by Theil-Sen estimator) and linear
regression (black) for site values of gross primary production (left) and net primary production
(Mg ha–1 yr–1).
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