ArticlePDF Available

Abstract and Figures

Forest production efficiency (FPE) metric describes how efficiently the assimilated carbon is partitioned into plants organs (biomass production, BP) or-more generally-for the production of organic matter (net primary production, NPP). We present a global analysis of the relationship of FPE to stand-age and climate, based on a large compilation of data on gross primary production and either BP or NPP. FPE is important for both forest production and atmospheric carbon dioxide uptake. We find that FPE increases with absolute latitude, precipitation and (all else equal) with temperature. Earlier findings-FPE declining with age-are also supported by this analysis. However, the temperature effect is opposite to what would be expected based on the short-term physiological response of respiration rates to temperature, implying a top-down regulation of carbon loss, perhaps reflecting the higher carbon costs of nutrient acquisition in colder climates. Current ecosystem models do not reproduce this phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely to overestimate carbon losses in a warming climate.
Content may be subject to copyright.
Forest production efciency increases with
growth temperature
A. Collalti 1,2, A. Ibrom 3, A. Stockmarr4, A. Cescatti5, R. Alkama 5, M. Fernández-Martínez 6,
G. Matteucci 7, S. Sitch 8, P. Friedlingstein 9, P. Ciais 10, D. S. Goll 11, J. E. M. S. Nabel 12,
J. Pongratz12,13, A. Arneth14, V. Haverd15 & I. C. Prentice16,17,18
Forest production efciency (FPE) metric describes how efciently the assimilated carbon is
partitioned into plants organs (biomass production, BP) ormore generallyfor the pro-
duction of organic matter (net primary production, NPP). We present a global analysis of the
relationship of FPE to stand-age and climate, based on a large compilation of data on gross
primary production and either BP or NPP. FPE is important for both forest production and
atmospheric carbon dioxide uptake. We nd that FPE increases with absolute latitude, pre-
cipitation and (all else equal) with temperature. Earlier ndingsFPE declining with ageare
also supported by this analysis. However, the temperature effect is opposite to what would be
expected based on the short-term physiological response of respiration rates to temperature,
implying a top-down regulation of carbon loss, perhaps reecting the higher carbon costs of
nutrient acquisition in colder climates. Current ecosystem models do not reproduce this
phenomenon. They consistently predict lower FPE in warmer climates, and are therefore likely
to overestimate carbon losses in a warming climate. OPEN
1National Research Council of Italy, Institute for Agriculture and Forestry Systems in the Mediterranean (ISAFOM), 06128 Perugia (PG), Italy. 2University of
Tuscia, Department of Innovation in Biological, Agro-food and Forest Systems (DIBAF), 01100 Viterbo, Italy. 3Technical University of Denmark (DTU),
Department of Environmental Engineering, Lyngby, Denmark. 4Technical University of Denmark (DTU), Department of Applied Mathematics and Computer
Science, Lyngby, Denmark. 5European Commission, Joint Research Centre, Directorate for Sustainable Resources, Ispra, Italy. 6Research group PLECO
(Plants and Ecosystems), Department of Biology, University of Antwerp, 2610 Wilrijk, Belgium. 7National Research Council of Italy, Institute for BioEconomy
(IBE), 50019 Sesto Fiorentino, FI, Italy. 8College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK. 9College of Engineering,
Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK. 10 Laboratoire des Sciences du Climat et delEnvironnement, CEA CNRS
UVSQ, Gif-sur-Yvette 91191, France. 11 Department of Geography, University of Augsburg, Augsburg, Germany. 12 Max Planck Institute for Meteorology,
Hamburg, Germany. 13 Ludwig-Maximilians-Universität München, Luisenstr 37, 80333 Munich, Germany. 14 Karlsruhe Institute of Technology, Institute of
Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany. 15 CSIRO Oceans and Atmosphere,
Canberra, ACT 2601, Australia. 16 Department of Life Sciences, Imperial College London, Silwood Park Campus, London Ascot SL5 7PY, UK. 17 Department of
Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia. 18 Department of Earth System Science, Tsinghua University, 100084
Beijing, China. email:
NATURE COMMUNICATIONS | (2020) 11:5322 |https://doi .org/10.1038/s41467-020-19187-w | 1
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Autotrophic respiration releases to the atmosphere about
half (60 PgC yr1) of the carbon xed annually by
photosynthesis1. Forest ecosystems are the largest carbon
sink on land, taking up about 3.5 ± 1.0 PgC yr1(20082017) on
average2. A small change in the proportion of carbon losses, for
example due to climate change, would strongly affect the net
carbon balance of the biosphere. Predicting the autotrophic
component of the carbon balance of forests under changing cli-
mate requires understanding of how much atmospheric CO
assimilated through photosynthesis (gross primary production,
GPP), how much is released due to plant metabolism (auto-
trophic respiration, R
), how efciently plants use assimilated
carbon for the production of organic matter (net primary pro-
duction, NPP), and how organic carbon is partitioned into plant
organs (biomass production, BP) versus other less stable forms
which include soluble organic compounds exuded to the rhizo-
sphere or stored as reserves, and biogenic volatile organic com-
pounds (BVOCs) emitted to the atmosphere3.
The climate sensitivity of the terrestrial carbon cycle can be
benchmarked using ratios between these uxes across a range of
climates. We focus here on the ratio of NPP to GPP, the so called
carbon use efciency (CUE =NPP/GPP) and of BP to GPP,
called biomass production efciency (BPE =BP/GPP). The two
concepts are close, but not identical4,5. BPE is substantially easier
to obtain, because the additional uxes that constitute NPP are
notoriously difcult to measure. For this reason, there are far
more data available on BPE, while uncertainties associated with
both BP and NPP measurement make it impossible to distinguish
them in large data compilations. Therefore, we assessed estimates
of both BPE and CUE as a single metric, hereafter called forest
production efciency (FPE), but making distinctions between
them when needed and when possible.
Over 20 years ago, the debate about spatial gradients of forest
CUE seemed to be resolved by Waring et al.6, who found CUE to
be nearly constant (0.47 ± 0.04: here and elsewhere, ± denotes one
standard deviation) across temperate and boreal forest stands
(n=12). The assumption of a universal value for CUEimplying
a tight coupling of whole-plant respiration to photosynthesis
has obvious practical convenience, and numerous vegetation
models have adopted it5. Many complex process-based vegetation
models, however, assume decoupling of photosynthesis and
respiration, with the latter driven by temperature7and biomass8
implying that CUE must vary with changing environmental
conditions. There is no general, observationally based consensus
as to which of these two (mutually incompatible) model
assumptions is nearer to the truth. One study found that BPE is
greater at higher soil fertility4, perhaps because less carbon needs
to be allocated for nutrient acquisition. Forest management9,
stand age10 and climate11,12 have also been reported to inuence
CUE and BPE.
Here we revisit the global patterns of forest CUE and BPE
considering multiple controls and the potential effects of meth-
odological uncertainty, based on a large global set of data on
forest CUE and/or BPE (n=244), spanning environments ran-
ging from the tropical lowlands to high latitudes and high alti-
tudes (Supplementary Fig. 1).
Overall, we nd that FPE decreases with age and increases with
site factors such as annual air temperature, total annual pre-
cipitation and absolute latitude.
FPE is not a universal constant. Results show that both CUE
(0.47 ± 0.13; range 0.240.71; n=47) and BPE (0.46 ± 0.12; range
0.220.79; n=197) have large variability; therefore, neither can be
assumed to be uniform (Fig. 1and Supplementary Fig. 2). CUE
and BPE are statistically indistinguishable in our dataset because
of uncertainties associated with both quantities (±0.39 for CUE
and ±0.16 for BPE: see Methods). Overall, the average FPE in our
dataset (0.46 ± 0.12; range 0.220.79; n=244) is statistically
indistinguishable from that provided by Waring et al.6, but its
standard deviation is three times larger (Methods and ref. 5).
Different GPP estimation methods produced slightly different
distributions (Fig. 2a), with median values ranging from 0.42
(scaling; upscaling of chamber-based measurements) through 0.48
(micrometeorological; ecosystem-scale CO
ux measurements) to
0.48 (model; process-based models) (see Methods for denitions).
Stand age had a further effect on FPE, as shown by the differing
median CUE and BPE values of stands in intermediate (in the
forestry sense, i.e. 2060 years) and younger age classes, with FPE
varying from 0.52 (age class <20 years) to 0.42 (age class >60
years) (Fig. 2b). Figure 3shows how the data compare to those
published by Waring et al.6. The small variability of CUE reported
by Waring et al.6was already noted by Medlyn & Dewar13 as
untypical, and articially constrained by the method used to cal-
culate CUE. Medlyn & Dewar13 suggested a 0.310.59 range as
being realistic. Figure 3also indicates systematically lower CUE
than Waring et al.6for forests with GPP < 2000 gC m2yr1,
especially in forests in the old age class; and a tendency to higher
values for forests with GPP> 2000 gC m2yr1and in the
young age class.
Factors controlling FPE variability. We used mixed-effects
multiple linear regression to infer the multiple drivers of the
spatial pattern of FPE. This method separates the contribution of
every predictor variable included in the analysis, even if they are
correlated to some degree (Methods). Four predictorsout of an
initial selection of eleven (listed in Methods)proved to be
important: stand age (age, years), mean annual temperature
(MAT, °C), total annual precipitation (TAP, mm year1) and
absolute latitude (|lat|, °), all included as xed effects (Fig. 4). The
method used to measure GPP (GPP method)was included as a
random effect (Table 1, Eq. (1)).
The use of multiple regression was essential for this analysis.
Simple correlations between FPE and individual predictors
showed no signicant effects, while there were signicant
correlations among the predictors (Supplementary Table 1).
CUE, n = 47
BPE, n = 197
0.2 0.4
Forest production efficiency
0.6 0.8
Fig. 1 Carbon use efciency vs. biomass production efciency. Density
plot of carbon use efciency (CUE, red line, n=47) and biomass
production efciency (BPE, blue line, n=197) data from all available data.
The vertical lines are medians.
2NATURE COMMUNICATIONS | (2020) 11:5322 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
The nal model with four xed effects is:
FPE ¼β0þβ1MAT þβ2age þβ3TAP þβ4lat
þηZGPPMeth þε
where β
is the intercept, β
are the estimated sensitivities of
FPE to MAT, stand age, TAP and |lat|; ηZ
is a random
intercept for two distinguishable GPPmethodclasses and εis the
residual (Table 1). The model could not be further reduced at a
5% test level, i.e. omitting any one of these predictors yielded a
signicantly different model. Supplementary Table 2 lists the
combinations that were tried.
We examined random effects from three methods to estimate
GPP (Methods). The methods biometric and scaling constituted a
single class, while GPP values determined from micrometeor-
ological measurements were systematically higher (thus FPE was
systematically lower). The multiple regression model explained
30% of the variance in the observed FPE values. Given the large
uncertainty in the estimation of NPP and GPP values, and the
structural and physiological diversity of the forests, this value was
unexpectedly high.
We could not t an independent statistical model for CUE,
because there were too few sites with NPP (n=31) measure-
ments. Furthermore, adding a random intercept for the two
categories (CUE or BPE) to Eq. (1) yielded almost identical
values, of 0.47 for CUE and 0.46 for BPE.
We also applied the mixed-effects multiple regression model to
the TRENDY v.7 outputs of eight Dynamic Global Vegetation
Models (DGVMs) to examine whether the multivariate relation-
ships shown for the FPE data could also be seen in the model
simulations, in order to test whether the observed pattern would
also emerge from the processes representations in the models. We
originally aimed to use the same mixed-effects linear model, at
the locations of the data points to t the simulated FPE. However,
because these DGVMs do not consider forest age, we had to alter
the model equation to:
FPE ¼μ0þμ1MAT þμ2TAP þμ3latjjþηZModel þεð2Þ
Neither this model, nor any model that could be derived from
it, fullled the conditions of normally distributed residuals. In
other words, the simulations did not represent a common
emergent relationship consistent with the data. The most likely
explanation is that the models use different parameters (and even
sometimes different functional relationships) for different biomes,
so that no general relationship applying across all forest types can
be expected to emerge. This phenomenon is evident from Fig. 5
where many models show discontinuities in CUE.
CUE outputs from the TRENDY v.7 model ensemble,
produced by the eight DGVMs, consistently showed a negative
relationship with MATopposite to that shown by our analysis.
The slope (CUE/MAT) estimated from data was +0.006 °C1
(see Table 1); the slopes from models ranged from 0.0025 °C1
for LPJ-GUESS to 0.0098 °C1for SDGVM (Fig. 5). The average
slope across the eight models was 0.005 °C1. All models
showed high CUE for boreal forests and low CUE for tropical
forests, but with considerable variation among models (Supple-
mentary Fig. 4). The modelled CUE values agree well with the
data only in temperate regions (MAT 515 °C, n=156), but
differ greatly in boreal (MAT < 5 °C, n=35) and tropical (MAT
> 15 °C, n=40) regions.
Micromet <20 yr
>60 yrs
20–60 yrs
0.2 0.4
0.6 0.8 0.2 0.4
0.6 0.8
Fig. 2 Effects of GPP method and age on FPE variability. a Forest production efciency (FPE) density plots for three subsets of data where the GPP was
estimated with three different methods (micrometeorological, red line, n=98; scaling, blue line, n=73; and models, green line, n=53). The vertical lines
are medians. bDensity plots for different age classes (age < 20 years, light brown line, n=47; 2060 years, green line, n=49; and age > 60 years, blue
line, n=77).
2000 <20 yr
>60 yrs
2–60 yrs
NPP or BP in g C m–2 yr–1
GPP in g C m–2 yr–1
1000 2000 3000 4000 5000
n = 228
Fig. 3 Comparison of the present work dataset vs. Waring et al.6.Scatter
plot of net primary production (NPP, gC m2yr1) or biomass production
(BP, gC m2yr1) versus gross primary production (GPP, gC m2yr1)
(n=228). Open circles: BP, lled circles: NPP. Stars represent data points
from Waring et al.6. The line marked with W98 represents a CUE (i.e. NPP/
GPP) of 0.47. Age classes are marked by colours (see top left of the gure);
NA stands for age not available. The uncertainty (gC m2yr1) of the data
points is indicated by bars (for data uncertainty see Methods).
NATURE COMMUNICATIONS | (2020) 11:5322 | | 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved
The empirical ranges of CUE and BPE and age effects on FPE.
Under some extreme circumstances, the carbon ux to mycor-
rhizae and root exudates can constitute as much as 50% of daily
assimilation14 or as much as 30% of annual NPP15,16. While in
nonstressed conditions BVOCs consume a small fraction (5% or
less) of annual NPP, under stressed conditions and in hot cli-
mates, BVOC emissions can consume 1550% of annual
NPP17,18. Thus CUEif not equal to BPE , should always be
larger than BPE. In our dataset, in those cases where both could
be estimated, CUE was larger than the estimated BPE in seven out
of thirteen cases (Supplementary Information, Supplementary
Table 3). In the remaining cases, the estimated CUE was statis-
tically indistinguishable from BPE. This nding suggests that the
fraction of these unaccounted organic carbon ows varies sub-
stantially among forests.
Statistically tted values of BPE and CUE ranged between 0.27
(0.04) and 0.58 (+0.04). The numbers in parentheses for CUE
and BPE reect our estimates of methodological bias (random
intercepts, see Table 1). Ninety-two percent of BPE and CUE
Predicted FPE
Predicted FPE
0 0 10 20 30
|lat| in °
40 50 60500 1000 1500
TAP in mm per year
2000 2500 3000 3500
Predicted FPE
Predicted FPE
–5 0 100 200 300 400 500
MAT in °C Age in years
20 25
Fig. 4 Predicted FPE vs. single effects of environmental and structural variables. Predictions of the mixed linear model for single xed effects (Eq. (1)),
given the other independent variables constant at their average values for that GPP method category. The dashed lines represent condence intervals at
the 0.05 and 0.95 levels calculated with the function predict Intervalof the R-package merTools.
Table 1 Parameters of the mixed-effects multiple regression model (Eq. (1)).
Estimate Std Error df t value pvalue Signicance
Random intercepts
Scaling |biometric0.14
Intercept (β
) Slopes: 0.19 0.106 24 1.77 0.09 n.s.
MAT (β
) 0.0060 0.0025 136 2.45 0.016 *
age (β
)0.00038 0.000116 136 3.28 0.0013 **
TAP (β
) 6.8E5 2.07E05 136 3.28 0.0014 **
|lat| (β
) 0.0039 0.0016 136 2.45 0.016 *
Parameter estimate of coefcients in Eq. (1) and their standard errors (Std. Error), degrees of freedoms (df), t- and pvalues of the two-sided t-test and the ANOVA (*p< 0.05, **p< 0.01, ***p< 0.001).
The squared Pearsons correlation coefcient and the squared Spearmans correlation values are both equal to 0.31.
MAT mean annual temperature, age stand age, TAP total annual precipitation, |lat| absolute latitude.
4NATURE COMMUNICATIONS | (2020) 11:5322 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
values in the dataset lie within the allowable range according to
Amthor19,reecting maximum growth with minimum expendi-
ture (0.65) and minimum growth with maximum maintenance
costs (0.2). The remaining 8% of the data exceed the upper bound
given in ref. 19. However, no values below 0.2 were found, and it
seems likely such values cannot be physiologically sustained by
plants for long periods8. They might be encountered in moribund
stands, unlikely to be sampled (perhaps an example of survivor-
ship bias, or the desk drawer problem20). However, values >0.65
apparently can (temporarily) occur in young, actively growing
Age- or size-related declines in both GPP, NPP and FPE (as
CUE or BPE) have been reported in earlier studies9,11,21,22 but a
decline in FPE is shown unequivocally here (slope FPE/age =
0.0004 yr1), based on a larger dataset than previously
analysed2325 (Fig. 4and Table 1). This decline could have
several contributory causes. First, the longer transport pathway
for water in taller trees can result in more closed stomata (to
avoid xylem cavitation) and therefore reduced GPP26, with no
corresponding reduction in R
, at least in the short-term27.
Second, larger trees may respire more because of their greater
sapwood volume and mass per unit leaf area8,28,29, leading to
increased R
(for the maintenance of living sapwood tissues) and
reduced NPP relative to GPP. Third, soil fertility declines due to
nutrient immobilization as stands age30; this is consistent with
observations of an increased ratio of ne-root-to-leaf-carbon, and
reduced nitrogen concentration in soils10,31. Ontogenetic shifts
from structural biomass to reserve allocation, and structural and
resource limitations in older stands, are also all expected to
decrease production efciency32. Conversely, reducing plant
competition and rejuvenating stands through forest management
should tend to increase both CUE and BPE9,33. At the stand scale,
closing canopies may contribute further to reducing or stabilizing
the GPP34 of individual trees. It is also likely that young trees
allocate more carbon to biomass growth as they compete for light
and nutrients; while older trees invest more in maintenance of
their existing biomass, and prioritize the chemical defence of that
biomass, relative to acquisition of new biomass35,36.
An additional hypothesis37 invokes an increase in nonstruc-
tural carbohydrates (NSC) allocation as trees grow. NSC is a
substantial carbon pool, containing in some cases up to four times
the carbon content of leaves in the canopy and increasing as trees
increase in size38. An increased ux to NSC however would imply
a reduced BPE, but not a reduced CUE.
Environmental effects on FPE. The increase in FPE with
increasing annual precipitation, to our knowledge, has not been
noted previously. Higher TAP results in increasing soil water
availability and greater stomatal openness, which might imply
increased photosynthesis. There is no direct evidence that water
availability inuences autotrophic respiration; on the other
hand, respiration has been found to increase with drought39.
0.9 Slope=–0.0037 ± 0.0 Pvalue=0.0
Slope=–0.0063 ± 0.0001 Pvalue=0.0 Slope=–0.0025 ± 0.0 Pvalue=0.0
Slope=–0.005 ± 0.0001 Pvalue=0.0
Slope=–0.005 ± 0.0004 Pvalue=0.0
Slope=–0.005 ± 0.0001 Pvalue=0.0
Slope=–0.0098 ± 0.0 Pvalue=0.0
Slope=–0.004 ± 0.0 Pvalue=0.0
Slope=–0.0044 ± 0.0001 Pvalue=0.0
–10 0
MAT (°C)
10 20 30 –10 0 10 20 30 –10 0 10 20 30
0.05 0.10 0.15
0.20 0.25 0.30
MAT (°C) MAT (°C)
Fig. 5 Modelled TRENDY v.7 CUE and growth temperature patterns. Density plots (i.e. frequency of forest carbon use efciency (CUE) value divided by
the total number of grid cells of simulated CUE derived from the following TRENDY v.7 process-based models: ISAM, JULES, LPJ-GUESS, CABLE-POP,
ORCHIDEE, ORCHIDEE-CNP, JSBACH and SDGVM, averaged from 1995 to 2015 as function of MAT (°C). In the last (right bottom) density plot, data
points extracted from coordinates and times of observed sites and used to plot the simulated CUE as function of MAT from the eight TRENDY v.7 models.
NATURE COMMUNICATIONS | (2020) 11:5322 | | 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved
With increasing TAP, photosynthesis might be expected to
increase faster than plant respiration, leading to higher CUE
and BPE.
The increase of FPE with absolute latitude has also, to our
knowledge, not been described before. Higher latitudes experi-
ence longer days in summer. The diffuse fraction also increases
with the path-length of radiation through the atmosphere. Both
effects might be expected to increase the radiation use efciency
of GPP. While high irradiances in the tropics leads to saturation
of photosynthesis in the uppermost leaf layers40, they also allow
for higher leaf area to utilize the transmitted radiation in the
relatively short daylight hours. Higher leaf area also implies
higher R
and lower FPE. The latitude effect compensates for the
MAT effect, because these two variables are negatively correlated.
Despite the correlation, the linear mixed model is able to
distinguish between the individual effects of MAT and |lat|. This
can be demonstrated by comparison of high-latitude with high-
elevation sites at the same MAT. The effects of radiation are
incorporated in vegetation models which, therefore, could in
principle represent radiation regime effects on FPE.
Moreover, as far as we know, the observed increase in FPE with
mean annual temperature has not previously reported. This
increase is opposite to what would be expected based on the
instantaneous responses of photosynthesis and plant maintenance
respiration as described in textbooks41 and assumed in many
process-based models (Fig. 5). The instantaneous response of
maintenance respiration to a temperature change is steeper than
that of photosynthesis42. Moreover, under natural conditions
photosynthesis is commonly limited by light, while respiration is
not. However, the instantaneous response of autotrophic
respiration rate is largely irrelevant here because of the longer
time scale. A long line of investigations, starting with Gifford43,
has shown the ubiquity of respiratory thermal acclimation,
whereby the effect of increased growth temperature on enzyme
kinetics is offset by a lowering of the base rate44. This acclimation
takes place on a time scale of days to weeks1. Genetic adaptation
throughout multiple generations is expected to proceed in the
same direction (for denitions and distinctions between acclima-
tion and adaptation see ref. 45). One consequence of these
processes is that observed rates of maintenance respiration vary
with temperature (in both space and time) far less steeply than
would be expected based on the instantaneous response of
enzyme kinetics46. This has been shown comprehensively in
leaves, and is likely to apply to all plant tissues1. Moreover, the
ratio of respiration to carboxylation capacity, assessed at growth
temperature, is slightly but signicantly larger in colder
He et al.47 foundin contrast to our resultsa latitudinal
pattern with higher CUE at high latitudes declining nonlinearly
with increasing MAT and stabilizing at increasing TAP. These
results were obtained using an emergent constraintmethod to
narrow the range of global mean carbon use efciency values
produced by an ensemble of ecosystem models. The observed
correlation between simulated global and site-specic CUE was
used to translate the probability distribution of observed site CUE
into a distribution of global CUE. This methods validity,
however, depends on the models correctly representing the
relationship between site-specic and global CUE. Thus, the
ndings of ref. 47 could simply reect the standard assumption of
models that R
increases with temperature more steeply than
GPP42. We have shown the same patterns here in all of the
TRENDY v.7 ensemble simulations (Fig. 5) but our analysis
shows that the underlying assumption is incorrect.
Adaptive mechanisms, potentially contributing to respiratory
thermal acclimation, include changes in the physiology and
growth of active tissues (i.e. the relation between assimilating and
non-assimilating tissues) and changes in the amount of enzymes
and their activation states to match substrate availability42,48.
Heat tolerance in leaves has also been found to increase linearly
with temperature and to decrease with absolute latitude49.
Therefore, a simple explanation for the increase of FPE with
temperature might be that plants can achieve the same function at
a higher temperature with smaller amounts of enzymes, thereby
decreasing the respiratory losses incurred during the maintenance
of catalytic capacity. Especially low FPE in boreal forests could be
the consequence of greater allocation of assimilates to nutrient
acquisition (via root exudation and exports to mycorrhizae) in
cold soils where microbial activity is much lower than in tropical
forests31,50. Low FPE in cold climates may also reect the need to
repair tissues affected by frost damage51.
Whole-plant constraints and consequences for modelling.
Amthor19 derived an upper bound of 0.65 for CUE, based on a
rough quantication of the minimum respiratory costs for plants
to function. His lower bound of 0.2 was based on the need for a
sufciently positive carbon balance to have minimum photo-
synthesis to survive and to allow trees to compensate for tissue
turnover, reproduction and mortality. However, most CUE values
lie within narrower bounds, suggesting the existence of additional
regulatory mechanisms at the whole-plant scale. Gifford43 noted
that autotrophic respiration and primary production are inter-
dependent, because carbon must be assimilated before it is
respired, while respiration is required for the growth and main-
tenance of tissues. He opined that: Plant respiratory regulation is
too complex for a mechanistic representation in current terres-
trial productivity models for carbon accounting and global
change researchand indicated a preference for simpler approa-
ches that capture the essence of the process. The opposite view
was expressed by Thornley52, who argued that: attempting to
grasp and pin down complexity is often the rst step to nding a
way through a labyrinth. Without taking a position on this
controversy, we note that the standard approach in most of
todays land ecosystem models, or more generally in vegetation
modelswhere maintenance respiration per unit of respiring
tissue is typically determined as a xed basal rate at a standard
temperature (commonly 15 or 20 C°), increasing with the sub-
strate and temperature according to a xed Q
factor or
Arrhenius-type equationcannot generate the positive response
of CUE or BPE to growth temperature observed in our study.
Moreover, as shown in Fig. 5, the presence of discontinuities in
CUE probably represents an attempt to sidestep an inevitable
consequence of this incorrect approach. Unless plant functional
types from warmer environments are assigned lower basal
maintenance respiration rates, modelled CUE becomes implau-
sibly low in warm climates. However, the idea of assigning xed
basal maintenance respiration rates to plant types has no obser-
vational or experimental basis.
In contrast, the use of production efciency concepts in
models seems well motivated53, provided they are not assumed
to be constant across different stands and environments.
Production efciency is a valuable unifying concept for the
analysis of forest carbon budgets. Although more variable than
was once thought, FPE appears to be a relatively conservative
quantity, subject to inherent biological constraints, that has
demonstrable relationships to stand development, latitude and
climate. The possible explanations for the observed global multi-
factorial pattern in FPE give rise to hypotheses on how
vegetation models might incorporate whole-plant regulation
mechanisms of the carbon losses for a given stand. The
demonstrated empirical pattern should then be used to constrain
new model developments.
6NATURE COMMUNICATIONS | (2020) 11:5322 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Denitions of terms. GPP is dened here as the sum of gross carbon xation
(carboxylation minus photorespiration) by autotrophic carbon-xing tissues per
unit area and time54. GPP is expressed as mass of organic carbon produced per unit
area and time, over at least one year. NPP consists of all organic carbon that is
xed, but not respired over a given time period54:
with all terms expressed in unit of mass of carbon per unit area and time. R
autotrophic respiration (composed of growth and maintenance respiration com-
ponents); ΔBis the annual change in standing biomass carbon; litter production
(roots, leaves and woody debris) is L; fruit production is F; the loss to herbivores is
H, which was not accounted here because of the very limited number of obser-
vations available. BP is biomass production4. Symbol Orepresents occult, carbon
ows, i.e. all other allocations of assimilated carbon, including changes in the
nonstructural carbohydrate pool, root exudates, carbon subsidies to symbiotic
fungi (mycorrhizae) or bacteria (e.g. nitrogen xers), and BVOCs emissions
(Supplementary Fig. 1). These occultcomponents are often ignored or unac-
counted when estimating NPP, hence this bias is necessarily propagated into the R
estimate when R
is calculated as the difference between GPP and NPP55.
Estimation methods. We grouped the methodsinto four categories:
biometric: direct tree stock measurements, or proxy data together with
biomass expansion factors, allometric equations and the stock change as a BP
component. If not otherwise stated, we assumed that the values included both
above- and below-ground plant parts (n=13 for GPP; n=200 for NPP
or BP).
micrometeorological: micrometeorological ux measurements using the eddy-
covariance technique to measure CO
ux and partitioning methods to
estimate ecosystem respiration and GPP (n=98 for GPP; n=4 for NPP
or BP).
model: model applications ranging from single mathematical equations (for
canopy photosynthesis and whole-tree respiration) to more complex
mechanistic process-based models to estimate GPP and R
, with NPP as the
net difference between them (n=53 for GPP; n=24 for NPP or BP).
scaling: upscaling of chamber-based measurements of assimilation and
respiration (GPP and R
)uxes at the organ scale, or the entire stand (n=
73 for GPP; n=9 for NPP or BP).
The difference between scalingand modellinglies in the data used. In the case
of scalingthe data were derived from measurements at the site. Modelmeans
that a dynamic process-based model was used, but with parameters calibrated and
optimized at the site, based on either biometric or micrometeorological
Data selection. The data were obtained from more than 300 peer-reviewed articles
(see also ref. 5), adding, merging and extending published works worldwide on
CUE or BPE4,9,11,23,25,56,57. Data were extracted from the text, Tables or directly
from Figures using the Unix software g3data (version 1.5.2, Jonas Frantz). In most
studies, NPP, BP and GPP were estimated for the tree stand only. However, GPP
estimated from CO
ux by micrometeorological methods applies to the entire
stand including ground vegetation. We therefore included only those micro-
meteorological studies where the forest stand was the dominant primary producer.
The database contains 244 records (197 for BPE and 47 for CUE) from >100 forest
sites (including planted, managed, recently burned, N-fertilized, irrigated and
articially CO
-fertilized forests; Supplementary Information, Supplementary Fig. 3
and online Materials;, representing 89
different tree species. Globally, 170 records out of the total data are from temperate
sites, 51 from boreal, and 23 for tropical sites, corresponding to 79 deciduous
broad-leaf (DBF), 14 evergreen broad-leaf (EBF), 132 evergreen needle-leaf (ENF)
and 19 mixed-forests records (MX). The majority of the data (93%) cover the
time-span from 1995 to 2015. We assume that when productivity data came from
biometric measurements the reported NPP would have to be considered as BP
because occult, nonstructural and secondary carbon compounds (e.g. BVOCs or
exudates) are not included. In some cases, multiple datasets from the same site
were included, covering different years or published by different authors. We
considered only those values where either NPP (or BP) and GPP referred to the
same year. From studies where data were available from more than 1 year, mean
values across years were calculated. When the same reference for data was found in
different papers or collected in different databases, where possible, we used data
from the original source. When different authors described the same values for the
same site, one single reference (and value) was used (in principle the oldest one).
By using only commonly available environmental drivers to analyse the spatial
variability in CUE and BPE, we were able to include almost all of the data that we
found in the literature. We examined as potential predictors site-level effects of:
average stand age (n=204; range from 5 to 500 years), mean annual temperature
(MAT; n=230; range 6.5 to 27.1 °C) and total annual precipitation (TAP; n=
232; range from 125 to 3500 mm yr1), method of determination (n=237),
geographic location (latitude and longitude; n=241, 64°07Nto42°52S and 155°
70Wto173°28E), elevation (n=217; 52800 m, above sea level), leaf area index
(LAI, n=117; range from 0.4 to 13 m2m2), treatment (e.g.: ambient or articially
increased atmospheric CO
concentration; n=34), disturbance type (e.g.: re n=
6; management n=55), and the International Geosphere-Biosphere Programme
(IGBP) vegetation classication and biomes (n=244), as reported in the published
articles (online Materials). The methods by which GPP, NPP, BP (and R
) were
determined were included as random effects in a number of possible mixed-effects
linear regression models (Supplementary Table 4).
We excluded from statistical analysis all data where GPP and NPP were
determined based on assumptions (e.g. data obtained using xed fractions of NPP
or R
of GPP). In just one case GPP was estimated as the sum of upscaled R
NPP58; however, this study was excluded from the statistical analysis. NPP or R
estimates obtained by process-based models (n=23) were also not included in the
statistical analysis. No information was available on prior natural disturbance
events (biotic and abiotic, e.g. insect herbivore and pathogen outbreaks, and
drought) that could in principle modify production efciency, apart from re. The
occurrence of re was reported by only a few studies5961. These data were
included in the database but re, as an explanatory factor, was not considered due
to the small number of samples in which it was reported (n=6).
Data uncertainty. Uncertainties of GPP, NPP and BP data were all computed
following the method based on expert judgment as described in Luyssaert et al.55.
First, grossuncertainty in GPP (gC m2yr1) was calculated as 500 +7.1 × (70|
lat|) gC m2yr1and gross uncertainties in NPP and BP (gC m2yr1) were
calculated as 350 +2.9 × (70|lat|). The absolute value of uncertainty thus
decreases linearly with increasing latitude for GPP and for NPP and BP, because we
assumed that the uncertainty is relative to the magnitude of the ux, which also
decreases with increasing |lat|. Subsequently, as in Luyssaert et al.55, uncertainty
was further reduced considering the methodology used to obtain each variable, by a
method-specic factor (from 0 to 1, nal uncertainty (δ)=gross uncertainty ×
method-specic factor). Luyssaert et al.55 reported for GPP-Micromet a method-
specic factor of 0.3 (i.e. gross uncertainty is reduced by 70% for micro-
meteorological measurements); and for GPP-Model, 0.6. GPP-Scaling and GPP-
Biometric were not explicitly considered in ref. 55 for GPP. We we used values of
0.8 and 0.3, respectively. For BP-Biometric and NPP-Micromet we used a reduc-
tion factor of 0.3; for NPP-Model, 0.6; and for NPP-Scaling (as obtained from
chamber-based R
measurements), 0.8. When GPP and/or NPP or BP methods
were not known (n=7), a factor of 1 (i.e. no reduction of uncertainty for methods
used, hence maximum uncertainty) was used. The absolute uncertainties on CUE
(δCUE) and BPE (δBPE) were considered as the weighted means62 by error pro-
pagation of each single variable (δNPP or δBP and δGPP) as follows:
δCUE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
and similarly for δBPE, by substituting NPP with BP and CUE with BPE.
Data and model selection. The CUE and BPE data were combined into a single
variable, as sites for which both types of estimates existed did not show any
signicant differences between these entities (Supplementary Fig. 2). CUE values
based on modelling were excluded (in our database we do not have BPE data from
modelling). Tests showed that the CUE value was systematically higher when GPP
was estimated with micrometeorological methods, compared to values based on
biometric or scaling methods. Only data with complete information on CUE,
MAT, age, TAP, and latitude were used. Altogether, 142 observations were selected.
In order to use the most complete information possible, a full additive model
was constructed rst (Eq. (1)). The method used for estimation of GPP (GPPmeth)
was specied as a random effect on the intercept, as visual inspection suggested
that CUE values were smaller where scalingwas used to estimate GPP compared
to cases where micrometwas used to estimate GPP.
In Eq. (1) the variable agerepresents the development status of the vegetation,
i.e. either average age of the canopy forming trees or the period since the last major
disturbance. The other three parameters represent different aspects of the climate.
The absolute latitude, |lat|, was chosen as a proxy of radiation climate, i.e. day
length and the seasonality of daily radiation. The term ηZ
represents the
random effect on the intercept due to the different methods of estimating GPP.
These variables were not independent (Supplementary Table 1). If the different
driver variables contain information that is not included in any of the other driver
variables, multiple linear regression is nonetheless able to separate the individual
effects. If, on the contrary, two variables exert essentially the same effect on the
response variable (CUE) this can be seen in an ANOVA based model comparison.
These considerations led us to the selection procedure in which we started with the
full model (Eq. (1)) and compared it with all possible reduced models
(Supplementary Table 2). The result of this analysis is the model with the smallest
number of parameters that does not signicantly differ from the full model.
We also examined, whether there were any signicant interactions of predictor
variables. There were not.
We used the Rfunction lmer from the R-package lme463 to t the mixed and
ordinary multiple linear models to the data. We checked for potential problems of
multicollinearity using the variance ination factor (VIF)64. All predictors had VIF
< 5 (between 1.1 and 3.8).The model residuals were also tested for normality (using
NATURE COMMUNICATIONS | (2020) 11:5322 | | 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved
the Anderson-Darling test of non-normality, in the R-package nortest65). For
models that did not take a random intercept regarding GPPmethinto account
(1630 in Supplementary Table 2) the Anderson-Darling test found signicant
deviation from normality of the model residuals, hence these models were excluded
from the analysis. The remaining models were compared with one another using
the function ANOVA of the R-package lmerTest66. This resulted in a 15 × 15
matrix of model comparisons in which the full model turned out to be signicantly
different from all other models.
The same analysis was also performed with a log-transformed version of Eq. (1):
log FPEðÞ¼β0
1ln MAT þ7:5ðÞþβ0
2ln ageðÞ
3ln TAPðÞþβ0
4ln lat
ðÞþη0ZGPPmeth þεð5Þ
where 7.5 °C was added to MAT in order to make its minimum 1 °C. Note that the
linear model from the log-transformed variables differs from the untransformed
linear model. The coefcients, here noted with a prime, can be interpreted on the
basis of the back-transformed model. Contrary to the untransformed linear model
where effects are additive, the back-transformed model is a multiplicative effect
model, with the slope parameters as exponents for each variable and the intercept
0as power of e). As with the untransformed model, negative slope parameter
values lower CUE, positive increase it with increasing driver variable values.
The results from this analysis were, as with the original additive model (Eq. (1)),
(i) the full model could not be reduced any further and (ii) the directions of the
effects were the same as with the additive model, i.e. the predicted CUE increased
with increasing MAT, TAP and |lat| but decreased with increasing age.
The AIC and BIC values were lower for the log-transformed model compared to
the untransformed model, with AIC values of 169.7 and 157.2 and BIC values
of 149.0 and 136.5 for the log-transformed and untransformed models,
respectively. The coefcients and model performance parameters of the
untransformed and the log-transformed models are shown in Table 1and
Supplementary Table 4. The adjusted squared correlation coefcients were similar:
0.306 for the untransformed and 0.321 for the log-transformed model. Despite
considerable uncertainty of the CUE values, it was possible to derive signicant,
systematic, linear relationships between the four driver variables and CUE or ln
(CUE). Both model variants showed the same direction and similar magnitudes of
the effects. It can be concluded that CUE (or ln (CUE)) from a global dataset of a
large variety of forests is signicantly positively affected by MAT, TAP and |lat|,
and signicantly negatively affected by age. Even excluding from the analysis the
ve tropical forest data with |lat| < 20 degrees did not alter signicantly the
empirical relationship (Supplementary Table 5).
Because the parameters of the untransformed, additive model are much easier
to interpret, we use the additive model in the main text and use the log-
transformed model only as a conrmation of trends found in the additive model.
Outputs from TRENDY v.7. We used the simulations from eight Dynamic Global
Vegetation Models (DGVMs) performed in the framework of the TRENDY v.7
project2,67 (; data downloaded 27 November 2019).
Models that did not provide NPP and GPP at plant functional type level were
excluded because of the need to analyse CUE in forests without signicant con-
tributions from shrubs, grassland or crops. The selection comprises the following
JSBACH and SDGVM (for references on models see refs. 2,67 and Supplementary
Table 6). All the models represent the surface uxes of CO
, water and the
dynamics of carbon pools in response to changes in climate, atmospheric CO
concentration, and land-use change across a global grid. However, processes
underlying the exchanges of water and carbon are based on different formulations
in different models.
In the TRENDY protocol all DGVMs were forced with common historical
climate elds and atmospheric CO
concentrations over the period from 1700 to
2017. Climate elds were taken from the CRU-JRA55 dataset2, whereas the time
series of atmospheric CO
concentrations were derived from the combination of ice
core records and atmospheric observations. Land-use change was taken into
account in the simulations (S3). However, similar simulations without land-use
change (S2) were also tested, showing no differences. CUE was estimated as NPP/
GPP (where NPP is commonly obtained in models by subtracting R
from GPP)
for the forest plant functional types simulated to be present in each grid cell. The
model outputs refer to the mean from 1995 to 2015 for comparability with the
records used when showing global land analysis (Fig. 5and Supplementary Fig. 4).
At site level, the same dates as the observations were chosen from the model
Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.
Data availability
All data supporting this study are available in the supplementary materials and are
publicly available at theZenodo repository (
Correspondence and requests for additional materials should be addressed to A.C. and A.
I. Source data are provided with this paper.
Code availability
There is no particular custom code or mathematical algorithm that is deemed central to
the conclusions. All relevant R-functions that were used are referred to in the method
section (see package vignettes for details).
Received: 4 April 2020; Accepted: 18 September 2020;
1. Reich, P. B. et al. Boreal and temperate trees show strong acclimation of
respiration to warming. Nature 531, 633636 (2016).
2. Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10,
21412194 (2018).
3. Clark, D. A. et al. Measuring net primary production in forests: concepts and
eld methods. Ecol. Appl. 11, 356370 (2001).
4. Vicca, S. et al. Fertile forests produce biomass more efciently. Ecol. Lett. 15,
520526 (2012).
5. Collalti, A. & Prentice, I. C. Is NPP proportional to GPP? Warings hypothesis
20 years on. Tree Physiol. 39, 14731483 (2019).
6. Waring, R. H., Landsberg, J. J. & Williams, M. Net primary production of
forests: a constant fraction of gross primary production? Tree Physiol. 18,
129134 (1998).
7. Cannell, M. G. R. & Thornley, J. H. M. Modelling the components of plant
respiration: some guiding principles. Ann. Bot. 85,4554 (2000).
8. Collalti, A. et al. Plant respiration: controlled by photosynthesis or biomass?
Glob. Chang. Biol. 26, 17391753 (2020).
9. Campioli, M. et al. Biomass production efciency controlled by management
in temperate and boreal ecosystems. Nat. Geosci. 8, 843846 (2015).
10. Fernández-Martínez, M. et al. Nutrient availability as the key regulator of
global forest carbon balance. Nat. Clim. Chang. 4, 471476 (2014).
11. DeLucia, E. H., Drake, J. E., Thomas, R. B. & Gonzalez-meler, M. A. Forest
carbon use efciency: is respiration a constant fraction of gross primary
production? Glob. Chang. Biol. 13, 11571167 (2007).
12. He, Y. et al. Global vegetation biomass production efciency constrained by
models and observations. Glob. Chang. Biol. 26, 14741484 (2020).
13. Medlyn, B. E. & Dewar, R. C. Comment on the article by R. H. Waring, J. J.
Landsberg and M. Williams relating net primar production to gross primary
production. Tree Physiol. 19, 137138 (1999).
14. van Dam, N. M. & Bouwmeester, H. J. Metabolomics in the Rhizosphere:
tapping into belowground chemical communication. Trends Plant Sci. 21,
256265 (2016).
15. Heinemeyer, A. et al. Exploring the overow taptheory: linking forest soil
CO2 uxes and individual mycorrhizosphere components to photosynthesis.
Biogeosciences 9,7995 (2012).
16. Preece, C., Farré-Armengol, G., Llusià, J. & Peñuelas, J. Thirsty tree roots
exude more carbon. Tree Physiol. 38, 690695 (2018).
17. Guenther, A. The contribution of reactive carbon emissions from vegetation
to the carbon balance of terrestrial ecosystems. Chemosphere 49, 837844
18. Kuhn, U. et al. Strong correlation between isoprene emission and gross
photosynthetic capacity during leaf phenology of the tropical tree species
Hymenaea courbaril with fundamental changes in volatile organic compounds
emission composition during early leaf development. Plant. Cell Environ. 27,
14691485 (2004).
19. Amthor, J. S. The McCreede WitPenning de VriesThornley Respiration
Paradigms: 30 years later. Ann. Bot. 86,120 (2000).
20. Medlyn, B. E. et al. Effects of elevated [CO2] on photosynthesis in European
forest species: a meta-analysis of model parameters. Plant. Cell Environ. 22,
14751495 (1999).
21. Drake, J. E., Davis, S. C., Raetz, L. M. & DeLucia, E. H. Mechanisms of age-
related changes in forest production: the inuence of physiological and
successional changes. Glob. Chang. Biol. 17, 15221535 (2011).
22. Collalti, A. et al. The sensitivity of the forest carbon budget shifts across
processes along with stand development and climate change. Ecol. Appl. 29,
118 (2019).
23. Litton, C. M., Raich, J. W. & Ryan, M. G. Carbon allocation in forest
ecosystems. Glob. Chang. Biol. 13, 20892109 (2007).
24. Piao, S. et al. Forest annual carbon cost: a global-scale analysis of autotrophic
respiration. Ecology 91, 652661 (2010).
25. Tang, J., Luyssaert, S., Richardson, A. D., Kutsch, W. & Janssens, I. A. Steeper
declines in forest photosynthesis than respiration explain age-driven decreases
in forest growth. Proc. Natl Acad. Sci. USA 111, 88568860 (2014).
26. Ryan, M. G., Phillips, N. & Bond, B. J. The hydraulic limitation hypothesis
revisited. Plant, Cell Environ. 29, 367381 (2006).
8NATURE COMMUNICATIONS | (2020) 11:5322 | |
Content courtesy of Springer Nature, terms of use apply. Rights reserved
27. Ryan, M. G., Binkley, D., Fownes, J. H., Giardina, C. P. & Senock, R. S. An
experimental test of the causes of forest growth decline with stand age. Ecol.
Monogr. 74, 393414 (2004).
28. Reich, P. B. et al. Scaling of respiration to nitrogen in leaves, stems and roots
of higher land plants. Ecol. Lett. 11, 793801 (2008).
29. Mori, S. et al. Mixed-power scaling of whole-plant respiration from seedlings
to giant trees. Proc. Natl Acad. Sci. USA 107, 14471451 (2010).
30. Johnson, D. W. Progressive N limitation in forests: review and implications
for long-term responses to elevated CO2. Ecology 87,6475 (2006).
31. Gill, A. L. & Finzi, A. C. Belowground carbon ux links biogeochemical cycles
and resource-use efciency at the global scale. Ecol. Lett. 19, 14191428
32. Way, D. A. & Sage, R. F. Elevated growth temperatures reduce the carbon gain
of black spruce [Picea mariana (Mill.) B.S.P.]. Glob. Chang. Biol 14, 624636
33. Collalti, A. et al. Thinning can reduce losses in carbon use efciency and
carbon stocks in managed forests under warmer climate. J. Adv. Model. Earth
Syst. 10, 24272452 (2018).
34. Michaletz, S. T., Cheng, D., Kerkhoff, A. J. & Enquist, B. J. Convergence of
terrestrial plant production across global climate gradients. Nature 512,3943
35. Malhi, Y. The productivity, metabolism and carbon cycle of tropical forest
vegetation. J. Ecol. 100,6575 (2012).
36. Merganičová, K. et al. Forest carbon allocation modelling under climate
change. Tree Physiol. 39, 19371960 (2019).
37. Sala, A. & Hoch, G. Height-related growth declines in ponderosa pine are not
due to carbon limitation. Plant. Cell Environ. 32,2230 (2009).
38. Dietze, M. C. et al. Nonstructural carbon in woody plants. Annu. Rev. Plant
Biol. 65, 667687 (2014).
39. Metcalfe, D. B. et al. Shifts in plant respiration and carbon use efciency at a
large-scale drought experiment in the eastern Amazon. N. Phytol. 187,
608621 (2010).
40. Ibrom, A. et al. Variation in photosynthetic light-use efciency in a
mountainous tropical rain forest in Indonesia. Tree Physiol. 28, 499508
41. Larcher, W. Physiological Plant Ecology (Springer-Verlag Berlin Heidelberg,
42. Drake, J. E. et al. Does physiological acclimation to climate warming stabilize
the ratio of canopy respiration to photosynthesis? N. Phytol. 211, 850863
43. Gifford, R. M. Plant respiration in productivity models: conceptualisation,
representation and issues for global terrestrial carbon-cycle research. Funct.
Plant Biol. 30, 171186 (2003).
44. OLeary, B. M., Asao, S., Millar, A. H. & Atkin, O. K. Core principles which
explain variation in respiration across biological scales. N. Phytol. 222,
670686 (2019).
45. Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-
scale models: incorporating acclimation to temperature and CO2. Glob.
Chang. Biol. 19,4563 (2013).
46. Wang, H. et al. Acclimation of leaf respiration consistent with optimal
photosynthetic capacity. Glob. Chang. Biol. 26, 25732583 (2020).
47. He, Y., Piao, S., Li, X., Chen, A. & Qin, D. Global patterns of vegetation
carbon use efciency and their climate drivers deduced from MODIS satellite
data and process-based models. Agric. Forest. Meteorol. 256257, 150158
48. Grifn, K. L. & Prager, C. M. Where does the carbon go? Thermal acclimation
of respiration and increased photosynthesis in trees at the temperate-boreal
ecotone. Tree Physiol. 37, 281284 (2017).
49. Osullivan, O. S. et al. Thermal limits of leaf metabolism across biomes. Glob.
Chang. Biol. 23, 209223 (2017).
50. VOGEL, J. G. et al. Carbon allocation in boreal black spruce forests across
regions varying in soil temperature and precipitation. Glob. Chang. Biol. 14,
15031516 (2008).
51. Sperling, O., Earles, J. M., Secchi, F., Godfrey, J. & Zwieniecki, M. A. Frost
induces respiration and accelerates carbon depletion in trees. PLoS ONE 10,
e0144124e0144124 (2015).
52. Thornley, J. H. M. Plant growth and respiration re-visited: maintenance
respiration denedit is an emergent property of, not a separate process
within, the systemand why the respiration: photosynthesis ratio is
conservative. Ann. Bot. 108, 13651380 (2011).
53. Landsberg, J. J., Waring, R. H. & Williams, M. Commentary on the assessment
of NPP/GPP ratio. Tree Physiol.
54. Chapin, F. S. et al. Reconciling carbon-cycle concepts, terminology, and
methods. Ecosystems 9, 10411050 (2006).
55. Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests
derived from a global database. Glob. Chang. Biol. 13, 25092537 (2007).
56. Campioli, M. et al. Evaluating the convergence between eddy-covariance and
biometric methods for assessing carbon budgets of forests. Nat. Commun. 7,
13717 (2016).
57. Collalti, A. et al. Forest production efciency increases with growth
temperaturedataset. BioRxiv (2020).
58. Curtis, P. S. et al. Respiratory carbon losses and the carbon-use efciency of a
northern hardwood forest, 19992003. N. Phytol. 167, 437456 (2005).
59. Law, B. E., Thornton, P. E., Irvine, J., Anthoni, P. M. & Van Tuyl, S. Carbon
storage and uxes in ponderosa pine forests at different developmental stages.
Glob. Chang. Biol. 7, 755777 (2001).
60. Dore, S. et al. Carbon and water uxes from ponderosa pine forests disturbed
by wildre and thinning. Ecol. Appl. 20, 663683 (2010).
61. Goulden, M. L. et al. Patterns of NPP, GPP, respiration, and NEP during
boreal forest succession. Glob. Chang. Biol. 17, 855871 (2011).
62. Slob, W. Uncertainty analysis in multiplicative models. Risk Anal. 14, 571576
63. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects
models using lme4. J. Stat. Softw. 67,148 (2015).
64. Kumar, K. N. R. Econometrics (Narendra Publishing House, 2020).
65. Gross, J. & Ligges, U. nortest: tests for normality. R package version 1.0-4. (2015).
66. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package:
tests in linear mixed effects models. J. Stat. Softw.82,126 (2017).
67. Sitch, S. et al. Recent trends and drivers of regional sources and sinks of
carbon dioxide. Biogeosciences 12, 653679 (2015).
We thank R.H. Waring, S. Vicca, M. Campioli, F. Pagani and E. Grieco for early con-
structive comments and thoughtful suggestions; S. Noce for the map of data points. We
thank efforts from all site investigators and their funding agencies. This paper contributes
to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial
College initiative Grand Challenges in Ecosystems and the Environment. A.C. and G.M.
are partially supported by resources available from the Ministry of University and
Research (FOE-2019), under the project Climate Change(CNR DTA.AD003.474);
M.F.-M. is a postdoctoral fellow of the Research FoundationFlanders (FWO);
Author contributions
A.C., A.I. and I.C.P. conceived the paper. A.Co., A.S., A.I., A.Ce. and R.A. analysed data.
A.Co., A.I., A.Ce., R.A., M.F.-M. and I.C.P. wrote the manuscript. All authors contributed
substantially to discussions and revisions.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at
Correspondence and requests for materials should be addressed to A.I.
Peer review information Nature Communications thanks Creighton Litton, Akihiko Ito
and the other, anonymous, reviewer for their contribution to the peer review of this
work. Peer reviewer reports are available.
Reprints and permission information is available at
Publishers note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional afliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made. The images or other third party
material in this article are included in the articles Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not included in the
articles Creative Commons license and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this license, visit
© The Author(s) 2020
NATURE COMMUNICATIONS | (2020) 11:5322 | | 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
... However, the impacts of permafrost degradation on tree growth (i.e., indirect effects) with climate change remain elusive yet. Temperature rising is beneficial for the extension of tree growing season Rossi et al., 2014), and enhances forest productivity in temperature-limited boreal forests (Collalti et al., 2020;Kelly and Goulden, 2008;Nemani et al., 2003;Piao et al., 2020), but also cause permafrost degradation and associated soil moisture change. In contrast to the temperature-limited boreal forests, tree at the overlapping area of the southern margin of permafrost and boreal forest have experienced a trend of declining growth and forest browning has been attributed to complex interactions between the drought and permafrost landforms (Carpino et al., 2018;Li et al., 2023;Sulla-Menashe et al., 2018;Wu et al., 2012). ...
... Satellite remote sensing is an effective mean to detect vegetation characteristics from space, which is free from social interference and geographical constraints, and can obtain a large range of real-time observation data [10,11]. The common indicators, which were widely used to measure the characteristics of vegetation change, include vegetation index (VI) [1][2][3]5], leaf area index (LAI) [8,12,13], fractional vegetation coverage (FVC) [2,14], vegetation carbon storage [6,9], gross primary production (GPP), and net primary productivity (NPP) [15][16][17]. For example, Myneni et al. reported the increasing trend of vegetation growth in the Northern Hemisphere by using the remote sensing normalized difference vegetation index (NDVI) data from 1981 to 1991 [18]. ...
Full-text available
Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349.
... The reason for these differences lies in poorly constrained ecosystem processes that have a large impact on GPP. One of these differences arises from the uncertainty in the sensitivity of GPP to environmental drivers (Ahlström et al., 2015;Jung et al., 2017;Beer et al., 2010;Piao et al., 2020;Collalti et al., 2020). The sensitivity of GPP to temperature and precipitation varies among studies, leading to ongoing discussion concerning the dominant driver of global carbon fluxes (Piao et al., 2020). ...
Full-text available
The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) in terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers in (i) the patterns of GPP IAV and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, whereas it is soil moisture in the ESM developed by the Institute Pierre Simon Laplace (IPSL-CM6A-LR) and in the low-resolution configuration of the Max Planck Earth System Model (MPI-ESM-LR), revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable 1 year ahead, with four out of six ESMs showing values of between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, whereas only 7 % to 13 % of the GPP IAV induced by radiation is predictable. The results show that, while ESMs are fairly similar in their ability to predict their own carbon flux variability, these predicted contributions to the atmospheric CO2 variability originate from different regions and are caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties in the GPP sensitivity to soil moisture and by accurate observational products for GPP IAV.
... dynamic global vegetation model, DGVM) and crop growth model (de Wit et al., 2005;Sitch et al., 2008). The process of estimating factor importance for these models is analogous to control experiment, which change one or several parameters while fixing others to see the variation of the output (Choudhury, 1987;Collalti et al., 2020;Deng et al., 2019;Huber et al., 2018;Woodward and Rochefort, 1991;Wu et al., 2022). The advantage of processbased model is the reliable explanation based on more explicit depiction of ecological processes. ...
The strength and persistence of the tropical carbon sink hinges on the long‐term responses of woody growth to climatic variations and increasing CO 2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2‐hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual‐level woody growth responses to historical climate variability and increases in atmospheric CO 2 (C a ). When forced with historical C a , ED2.2‐hydro reproduced the magnitude of increases in intercellular CO 2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing C a based on model‐data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi‐year mature‐forest CO 2 fertilization experiment. In addition, we found that ED2.2‐hydro generally overestimated climatic sensitivity of woody growth, especially for late‐successional plant functional types. The model‐data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree‐level growth sensitivity to C a and climate against tropical tree‐ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing C a . More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by C a rise predicted by biosphere models.
Full-text available
Leaf dark respiration ( R d ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that R d and Rubisco carboxylation capacity ( V cmax ) at 25°C ( R d,25 , V cmax,25 ) are coordinated so that R d,25 variations support V cmax,25 at a level allowing full light use, with V cmax,25 reflecting daytime conditions (for photosynthesis), and R d,25 / V cmax,25 reflecting night‐time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5‐yr warming experiment, and spatially using an extensive field‐measurement data set. We compared the results to three published alternatives: R d,25 declines linearly with daily average prior temperature; R d at average prior night temperatures tends towards a constant value; and R d,25 / V cmax,25 is constant. Our hypothesis accounted for more variation in observed R d,25 over time ( R ² = 0.74) and space ( R ² = 0.68) than the alternatives. Night‐time temperature dominated the seasonal time‐course of R d , with an apparent response time scale of c. 2 wk. V cmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global R d in response to rising CO 2 and warming than is projected by the two of three alternative hypotheses, and by current models.
Plant biomass production (BP), nitrogen uptake ( N up ) and their ratio, and nitrogen use efficiency (NUE) must be quantified to understand how nitrogen (N) cycling constrains terrestrial carbon (C) uptake. But the controls of key plant processes determining N up and NUE, including BP, C and N allocation, tissue C:N ratios and N resorption efficiency (NRE), remain poorly known. We compiled measurements from 804 forest and grassland sites and derived regression models for each of these processes with growth temperature, vapour pressure deficit, stand age, soil C:N ratio, fAPAR (remotely sensed fraction of photosynthetically active radiation absorbed by green vegetation) and growing‐season average daily incident photosynthetic photon flux density (gPPFD; effectively the seasonal concentration of light availability, which increases polewards) as predictors. An empirical model for leaf N was based on optimal photosynthetic capacity (a function of gPPFD and climate) and observed leaf mass per area. The models were used to produce global maps of N up and NUE. Global BP was estimated as 72 Pg C/year; N up as 950 Tg N/year; and NUE as 76 g C/g N. Forest BP was found to increase with growth temperature and fAPAR and to decrease with stand age, soil C:N ratio and gPPFD. Forest NUE is controlled primarily by climate through its effect on C allocation—especially to leaves, being richer in N than other tissues. NUE is greater in colder climates, where N is less readily available, because below‐ground allocation is increased. NUE is also greater in drier climates because leaf allocation is reduced. NRE is enhanced (further promoting NUE) in both cold and dry climates. Synthesis . These findings can provide observationally based benchmarks for model representations of C–N cycle coupling. State‐of‐the‐art vegetation models in the TRENDY ensemble showed variable performance against these benchmarks, and models including coupled C–N cycling produced relatively poor simulations of N up and NUE.
Topography plays a crucial role in determining the structure of alpine forests, as it restricts the availability of nutrients and water necessary for plant growth. Nevertheless, our information on how variations in forest carbon allocation patterns driven by fine-scale topography are influenced by broader-scale environmental contexts is limited. In the northern Tibetan Plateau, we combined field data from 89 forest plots with a high-resolution (1 m2) digital elevation model (DEM) and utilized a linear mixed-effects model to investigate how microtopography (characterized by slope, aspect, and topographic wetness index (TWI)) and broader-scale environmental context (characterized by elevation) and their interactions affect the carbon allocation patterns of alpine forest. Our results revealed that at low and high elevations with pronounced subsurface resource limitations, plants tend to allocate a higher proportion of carbon to the root system and have lower aboveground carbon stocks (ACS). Microtopographic heterogeneity significantly influenced the carbon allocation patterns of forest, with the intensity and direction of these effects varying across the environmental gradient. At low elevations, topographically wetter and northerly microhabitats had higher ACS and lower ratios of below- and aboveground carbon stocks (RBA); however, at high elevations, topographically drier and southerly microhabitats had higher ACS and lower RBA. TWI and aspect had the weakest effect on ACS and RBA in the mid-elevations. The relationship between slope and ACS and RBA was significantly positive but not evidently related to the broader-scale environmental gradient.
Full-text available
Forest age results from population dynamics, disturbance regimes, and silviculture playing an important role in the global Carbon (C) cycle. To examine the shaping effects of forest age on the forest productivity under current and future climate conditions, we used a biogeochemical-based model in three managed forest stands and then modeled their development as undisturbed systems. The model was forced with climate outputs of five Earth System Models under four representative climate scenarios plus one baseline scenario, over a matrix of 11 age classes for each stand. We find that the Net Primary Production (NPP) peak was reached in the young and middle-aged class (16- to-50-year-old) regardless of the climate scenario, while total C-woody stocks (tCWS) increased with age with different trajectories in the three sites, but not linearly as expected. Under climate change scenarios, the beech forest shows an expected increasing NPP with increasing atmospheric CO2 and temperature as much for younger age classes as for older ones. Conversely, in the spruce and Scots pine-dominated sites NPP and tCWS decrease under climate change scenarios. A factorial ANOVA and Tukey’s Post hoc test (p = 0.05) were performed to verify if differences between age groups, climate scenarios, and their interactions during the simulation period were statistically significant and not just the result of different forcing in the model configuration. The ANOVA test showed that both age and, only for some sites, climate scenarios have effects with differences on the forest carbon balance only when taken individually, while when analyzed in combination, the analysis showed no effects due to the interaction of the two factors showing, then, a pervasive buffering effect of age on climate but also vice–versa. The consistent influence of age remains the primary determining factor across all sites for both NPP and tCWS. This calls for tailored management strategies to enhance the resilience and adaptability of forests in the face of changing climatic conditions, reflecting the different species and age-dependent response to climate.
Full-text available
Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial ('dark') respiration (Rd ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperature via a link to Vcmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd and Vcmax accessed at growth temperature (Rd,tg and Vcmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax respectively. Data-fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both Rd and Vcmax assessed at 25°C (Rd,25 and Vcmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler-and potentially more reliable-than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.
Full-text available
Carbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions.
Full-text available
Gross primary production (GPP) is partitioned to autotrophic respiration (Ra) and net primary production (NPP), the latter being used to build plant tissues and synthesize non-structural and secondary compounds. Waring et al. (1998) suggested that a NPP:GPP ratio of 0.47 ± 0.04 (s.d.) is universal across biomes, tree species and stand ages. Representing NPP in models as a fixed fraction of GPP, they argued, would be both simpler and more accurate than trying to simulate Ra mechanistically. This paper reviews progress in understanding the NPP:GPP ratio in forests during the 20 years since Waring et al.. Research has confirmed the existence of pervasive acclimation mechanisms that tend to stabilize the NPP:GPP ratio, and indicates that Ra should not be modelled independently of GPP. Nonetheless, studies indicate that the value of this ratio is influenced by environmental factors, stand age and management. The average NPP:GPP ratio in over 200 studies, representing different biomes, species and forest stand ages, was found to be 0.46, consistent with the central value that Waring et al. proposed but with a much larger standard deviation (± 0.12) and a total range (0.22 to 0.79) that is too large to be disregarded.
Full-text available
Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use and land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2008–2017), EFF was 9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1, SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of 0.5 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017, ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from
The present dataset belongs the paper: Collalti A., Ibrom A., Stockmarr A., Cescatti A., Alkama R., Fernández-Martínez M., Matteucci G., Sitch S., Friedlingstein P., Ciais P., Goll D.S., Nabel J.E.M.S., Pongratz J., Arneth A., Haverd V., Prentice I.C.. “Forest production efficiency increases with growth temperature", Nature Communications, 11, 5322 (2020)., and can be downloaded at:
Two simplifying hypotheses have been proposed for whole‐plant respiration. One links respiration to photosynthesis; the other to biomass. Using a first‐principles carbon balance model with a prescribed live woody biomass turnover, applied at a forest research site where multidecadal measurements are available for comparison, we show that if turnover is fast the accumulation of respiring biomass is low and respiration depends primarily on photosynthesis; while if turnover is slow the accumulation of respiring biomass is high and respiration depends primarily on biomass. But the first scenario is inconsistent with evidence for substantial carryover of fixed carbon between years, while the second implies far too great an increase in respiration during stand development – leading to depleted carbohydrate reserves and an unrealistically high mortality risk. These two mutually incompatible hypotheses are thus both incorrect. Respiration is not linearly related either to photosynthesis or to biomass, but it is more strongly controlled by recent photosynthates (and reserve availability) than by total biomass.
Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon–nitrogen interactions tend to be more realistic. Using observation‐based estimates of global photosynthesis, we quantify the global BP of non‐cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model‐estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C). We quantify the global value of biomass production efficiency (BPE) by a number of field measurements with the results of terrestrial carbon cycle models, via an emergent‐constraint approach. We found that carbon cycle models overestimate global BPE, and therefore overestimate global biomass production, although models with carbon–nitrogen interactions show less model–data mismatch. Correcting models for this bias while leaving modeled carbon turnover times unchanged, the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C/year).