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: c.prentice@imperial.ac.uk

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

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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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REFERENCES

Almeida JP, Montúfar R, Anthelme F. 2013. Patterns and origin of intraspecific functional

variability in a tropical alpine species along an altitudinal gradient. Plant Ecology & Diversity

6: 423-433.

Asner G, Anderson C, Martin R, Knapp D, Tupayachi R, Sinca F, Malhi Y. 2014a. Landscape-

scale changes in forest structure and functional traits along an Andes-to-Amazon elevation

gradient. Biogeosciences 11: 843-856.

Asner GP, Knapp DE, Anderson CB, Martin RE, Vaughn N. 2016. Large-scale climatic and

geophysical controls on the leaf economics spectrum. Proceedings of the National Academy of

Sciences 113: E4043-E4051.

Asner GP, Knapp DE, Martin RE, Tupayachi R, Anderson CB, Mascaro J, Sinca F, Chadwick

KD, Higgins M, Farfan W et al. 2014b. Targeted carbon conservation at national scales with

high-resolution monitoring. Proceedings of the National Academy of Sciences 111: E5016-

E5022.

Atkin OK, Bloomfield KJ, Reich PB, Tjoelker MG, Asner GP, Bonal D, Bönisch G, Bradford

MG, Cernusak LA, Cosio EG et al. 2015. Global variability in leaf respiration in relation to

climate, plant functional types and leaf traits. New Phytologist 206: 614-636.

Bahar NH, Ishida FY, Weerasinghe LK, Guerrieri R, O'Sullivan OS, Bloomfield KJ, Asner

GP, Martin RE, Lloyd J, Malhi Y et al. 2017. Leaf‐level photosynthetic capacity in lowland

Amazonian and high‐elevation Andean tropical moist forests of Peru. New Phytologist 214:

1002-1018.

Berberan-Santos MN, Bodunov EN, Pogliani L. 1997. On the barometric formula. American

Journal of Physics 65: 404-412.

Bernacchi C, Pimentel C, Long S. 2003. In vivo temperature response functions of parameters

required to model RuBP‐limited photosynthesis. Plant, Cell & Environment 26: 1419-1430.

Bernacchi C, Singsaas E, Pimentel C, Portis Jr A, Long S. 2001. Improved temperature response

functions for models of Rubisco‐limited photosynthesis. Plant, Cell & Environment 24: 253-

259.

Bloomfield KJ, Farquhar GD, Lloyd J. 2014. Photosynthesis–nitrogen relationships in tropical

forest tree species as affected by soil phosphorus availability: a controlled environment study.

Functional plant biology 41: 820-832.

Bloomfield KJ, Prentice IC, Cernusak LA, Eamus D, Medlyn BE, Rumman R, Wright IJ, Boer

MM, Cale P, Cleverly J et al. 2019. The validity of optimal leaf traits modelled on

environmental conditions. New Phytologist 221: 1409-1423.

18

449

4504514524534544554564574584594604614624634644654664674684694704714724734744754764774784794804814823536

Cernusak LA, Ubierna N, Winter K, Holtum JA, Marshall JD, Farquhar GD. 2013.

Environmental and physiological determinants of carbon isotope discrimination in terrestrial

plants. New Phytologist 200: 950-965.

Collalti A, Ibrom A, Prentice IC. 2019. Forest production efficiency increases with temperature. In

prep.

Collalti A, Prentice IC. 2019. Is NPP proportional to GPP? Waring’s hypothesis 20 years on. Tree

Physiology 39: 1473-1483.

Crous KY, Quentin AG, Lin Y-S, Medlyn BE, Williams DG, Barton CV, Ellsworth DS. 2013.

Photosynthesis of temperate Eucalyptus globulus trees outside their native range has limited

adjustment to elevated CO2 and climate warming. Global Change Biology 19: 3790–3807.

Dong N, Colin Prentice I, Evans B, Caddy-Retalic S, Lowe A, Wright I. 2017. Leaf nitrogen

from first principles: field evidence for adaptive variation with climate. Biogeosciences 14:

481-495.

Dreyer E, Roux XL, Montpied P, Daudet FA, Masson F. 2001. Temperature response of leaf

photosynthetic capacity in seedlings from seven temperate tree species. Tree Physiology 21:

223-232.

Enquist BJ, Bentley LP, Shenkin A, Maitner B, Savage V, Michaletz S, Blonder B, Buzzard V,

Espinoza TEB, Farfan‐Rios W et al. 2017. Assessing trait‐based scaling theory in tropical

forests spanning a broad temperature gradient. Global ecology and biogeography 26: 1357-

1373.

Farquhar GD, Cernusak LA. 2012. Ternary effects on the gas exchange of isotopologues of carbon

dioxide. Plant, Cell & Environment 35: 1221-1231.

Farquhar GD, von Caemmerer Sv, Berry J. 1980. A biochemical model of photosynthetic CO 2

assimilation in leaves of C 3 species. Planta 149: 78-90.

Fisher JB, Malhi Y, Torres IC, Metcalfe DB, van de Weg MJ, Meir P, Silva-Espejo JE, Huasco

WH. 2013. Nutrient limitation in rainforests and cloud forests along a 3,000-m elevation

gradient in the Peruvian Andes. Oecologia 172: 889-902.

Fyllas NM, Bentley LP, Shenkin A, Asner GP, Atkin OK, Díaz S, Enquist BJ, Farfan‐Rios W,

Gloor E, Guerrieri R et al. 2017. Solar radiation and functional traits explain the decline of

forest primary productivity along a tropical elevation gradient. Ecology letters 20: 730-740.

Galmés J, Hermida-Carrera C, Laanisto L, Niinemets U. 2016. A compendium of temperature

responses of Rubisco kinetic traits: variability among and within photosynthetic groups and

impacts on photosynthesis modeling. Journal of Experimental Botany 67: 5067-5091.

19

483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515

3738

Galmés J, Kapralov MV, Copolovici LO, Hermida-Carrera C, Ninnemets U. 2015. Temperature

responses of the Rubisco maximum carboxylase activity across domains of life: phylogenetic signals,

trade-offs, and importance for carbon gain. Photosynthesis Research 123: 183-201.

Gifford RM. 2003. Plant respiration in productivity models: conceptualisation, representation and

issues for global terrestrial carbon-cycle research. Functional Plant Biology 30:171-186.

Girardin CAJ, Malhi Y, Aragao L, Mamani M, Huaraca Huasco W, Durand L, Feeley K,

Rapp J, Silva‐Espejo J, Silman M et al. 2010. Net primary productivity allocation and

cycling of carbon along a tropical forest elevational transect in the Peruvian Andes. Global

Change Biology 16: 3176-3192.

Harris I, Jones PD, Osborn TJ, Lister DH. 2014. Updated high‐resolution grids of monthly

climatic observations–the CRU TS3. 10 Dataset. International journal of climatology 34: 623-

642.

He Y, Peng S, Liu Y, Li X, Wang K, Ciais P, Arain MA, Fang Y, Fisher JB, Goll D et al. 2019.

Global vegetation biomass production efficiency constrained by models and observations.

Global Change Biology DOI: https://doi.org/10.1111/gcb.14816

Kattge J, Knorr W. 2007. Temperature acclimation in a biochemical model of photosynthesis: a

reanalysis of data from 36 species. Plant, cell & environment 30: 1176-1190.

Kumarathunge DP, Medlyn BE, Drake JE, Tjoelker MG, Aspinwall MJ, Battaglia M, Cano

FJ, Carter KR, Cavaleri MA, Cernusak LA et al. 2019. Acclimation and adaptation

components of the temperature dependence of plant photosynthesis at the global scale. New

Phytologist 222: 768-784.

Maire V, Wright IJ, Prentice IC, Batjes NH, Bhaskar R, van Bodegom PM, Cornwell WK,

Ellsworth D, Niinemets Ü, Ordonez A et al. 2015. Global effects of soil and climate on leaf

photosynthetic traits and rates. Global Ecology and Biogeography 24: 706-717.

Malhi Y, Girardin CA, Goldsmith GR, Doughty CE, Salinas N, Metcalfe DB, Huasco WH,

Silva‐Espejo JE, del Aguilla‐Pasquell J, Amézquita FF et al. 2017. The variation of

productivity and its allocation along a tropical elevation gradient: a whole carbon budget

perspective. New Phytologist 214: 1019-1032.

Medlyn B, Dreyer E, Ellsworth D, Forstreuter M, Harley P, Kirschbaum M, Le Roux X,

Montpied P, Strassemeyer J, Walcroft A et al. 2002. Temperature response of parameters of

a biochemically based model of photosynthesis. II. A review of experimental data. Plant, Cell

& Environment 25: 1167-1179.

Monteith JL. 1977. Climate and the efficiency of crop production in Britain. Philosophical

Transactions of the Royal Society of London. Biological Sciences 281: 277-294.

20

516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549

3940

Myneni R, Knyazikhin Y, Park T. 2015. MOD15A2H MODIS/terra leaf area index/FPAR 8-day

L4 global 500 m SIN grid V006. NASA EOSDIS Land Processes DAAC.

Norby RJ, Gu L, Haworth IC, Jensen AM, Turner BL, Walker AP, Warren JM, Weston DJ,

Xu C, Winter K. 2017. Informing models through empirical relationships between foliar

phosphorus, nitrogen and photosynthesis across diverse woody species in tropical forests of

Panama. New Phytologist 215: 1425-1437.

Prentice IC, Dong N, Gleason SM, Maire V, Wright IJ. 2014. Balancing the costs of carbon gain

and water transport: testing a new theoretical framework for plant functional ecology. Ecology

Letters 17: 82-91.

R Core Team 2018. R: A language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. [WWW document] URL https://www.R-project.org/.

Reich PB, Sendall KM, Stefanski A, Wei X, Rich RL, Montgomery RA. 2016. Boreal and

temperate trees show strong acclimation of respiration to warming. Nature 531: 633-636.

Rogers A, Medlyn BE, Dukes JS, Bonan G, Von Caemmerer S, Dietze MC, Kattge J, Leakey

ADB, Mercado LM, Niinemets U et al. 2017. A roadmap for improving the representation of

photosynthesis in Earth system models. New Phytologist 213: 22-42.

Scafaro AP, Xiang S, Long BM, Bahar NH, Weerasinghe LK, Creek D, Evans JR, Reich PB,

Atkin OK. 2017. Strong thermal acclimation of photosynthesis in tropical and temperate wet‐

forest tree species: the importance of altered Rubisco content. Global Change Biology 23:

2783-2800.

Seibt U, Rajabi A, Griffiths H, Berry JA. 2008. Carbon isotopes and water use efficiency: sense

and sensitivity. Oecologia 155: 441-454.

Sharkey TD, Bernacchi CJ, Farquhar GD, Singsaas EL. 2007. Fitting photosynthetic carbon

dioxide response curves for C3 leaves. Plant, cell & environment 30: 1035-1040.

Smith NG, Keenan TF, Colin Prentice I, Wang H, Wright IJ, Niinemets Ü, Crous KY,

Domingues TF, Guerrieri R, Yoko Ishida F et al. 2019. Global photosynthetic capacity is

optimized to the environment. Ecology letters 22: 506-517.

Togashi HF, Prentice IC, Atkin OK, Macfarlane C, Prober SM, Bloomfield KJ, Evans BJ.

2018. Thermal acclimation of leaf photosynthetic traits in an evergreen woodland, consistent

with the coordination hypothesis. Biogeosciences 15: 3461-3474.

Ubierna N, Farquhar GD. 2014. Advances in measurements and models of photosynthetic carbon

isotope discrimination in C3 plants Plant, cell & environment 37: 1494-1498.

van de Weg MJ, Meir P, Grace J, Atkin OK. 2009. Altitudinal variation in leaf mass per unit area,

leaf tissue density and foliar nitrogen and phosphorus content along an Amazon-Andes

gradient in Peru. Plant Ecology & Diversity 2: 243-254.

21

550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584

4142

van de Weg MJ, Meir P, Williams M, Girardin C, Malhi Y, Silva-Espejo J, Grace J. 2014.

Gross primary productivity of a high elevation tropical montane cloud forest. Ecosystems 17:

751-764.

Von Caemmerer S. 2013. Steady‐state models of photosynthesis. Plant, Cell & Environment

36:1617-1630.

Walker BJ, Ariza LS, Kaines S, Badger MR, Cousins AB. 2013. Temperature response of in vivo

Rubisco kinetics and mesophyll conductance in Arabidopsis thaliana: comparisons to

Nicotiana tabacum. Plant Cell and Environment 36: 2108-2119.

Walker BJ, Ort DR. 2015. Improved method for measuring the apparent CO2 photocompensation

point resolves the impact of multiple internal conductances to CO2 to net gas exchange. Plant,

cell & environment 38: 2462-2474.

Wang H, Prentice IC, Davis TW, Keenan TF, Wright IJ, Peng C. 2017a. Photosynthetic

responses to altitude: an explanation based on optimality principles. New Phytologist 213: 976-

982.

Wang H, Prentice IC, Keenan TF, Davis TW, Wright IJ, Cornwell WK, Evans BJ, Peng C.

2017b. Towards a universal model for carbon dioxide uptake by plants. Nature plants 3: 734-

741.

Waring RH, Landsberg J, Williams M. 1998. Net primary production of forests: a constant

fraction of gross primary production? Tree Physiology 18:129–134.

Weedon GP, Balsamo G, Bellouin N, Gomes S, Best MJ, Viterbo P. 2014. The WFDEI

meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim

reanalysis data. Water Resources Research 50: 7505-7514.

Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J,

Chapin T, Cornelissen JH, Diemer M et al. 2004. The worldwide leaf economics spectrum.

Nature 428: 821-827.

Wu MS, Feakins SJ, Martin RE, Shenkin A, Bentley LP, Blonder B, Salinas N, Asner GP,

Malhi Y. 2017. Altitude effect on leaf wax carbon isotopic composition in humid tropical

forests. Geochimica et Cosmochimica Acta 206: 1-17.

22

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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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