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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.
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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:
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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.
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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).
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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.
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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.
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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;
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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
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... Each factor interferes with the other and eventually forms a coherent and synergistic impact mechanism, as reported by Zhang et al. (2020) too. Hence, the positive effects of climate conditions in Dharamsala were attributed to the higher temperature and radiation, which plausibly enhanced the photosynthesis process (Collalti et al., 2020) and lengthened the growing season. Similar findings were observed in the Tibetan Plateau by Wen et al. (2019). ...
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Over the past few decades, climate change and urban expansion have strongly affected vegetation dynamics and overall carbon sink capacity of Himalayan ecosystem. However, the contribution of these two key factors on varying spatio-temporal scales in Himalayan landscapes still lacks in profound analyses. The present study takes Dharamsala and Pithoragarh urban landscapes as examples and uses the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) as the image fusion technique to generate highly resolved, both spatially (30 m) and temporally (monthly), NDVI images. These are used as inputs for Carnegie-Ames-Stanford Approach (CASA) model for Net Primary Productivity (NPP) estimation over the past two decades (2000–2020). During the study period, the NPP loss due to urbanization was 2065.43 kg C in Dharamsala and 401.07 kg C in Pithoragarh. Climate change, measured in terms of interseasonal changes in temperature, rainfall and solar radiation, had considerably contributed to the NPP variations of vegetated areas in both the study sites, however its contribution ratio was relatively lower than residual factors. There was a significant distribution gradient between how and where urban expansion and climate change influenced the NPP. Urban expansion impacts NPP more in plain areas corresponding to new urban land developments while climate change impacts NPP in high elevation mountainous regions dominated by Oak species. The observed declining trend of NPP under the current threat of urban expansion and climate change in the Himalaya highlights and invites the need of attributing importance to ecological issues. The findings of our study encompassing complex Himalayan landscapes could offer scientific perspectives for the management of ecological environment of highly fragile and vulnerable landscapes of the Himalaya.
... Research on the influencing factors can help direct efforts aimed at improving carbon sequestration efficiency [38]. Thus, existing research can be summarized based on the following perspectives: forestland use including afforestation [11], timber production [39], cutting [40], and sustainable management [41]; natural environment factors including climate conditions [42], forest degradation [43], and forest growth rate [44]; social development factors including population mobility [11], urbanization rates [45], and economic development [46]. ...
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Maximizing the carbon sequestration of forested land is important for achieving carbon neutrality. Although some studies have discussed forest carbon sequestration efficiency (FCSE) from the perspective of total factor production, it is being increasingly recognized that forestland use regulates sequestration and emissions. When viewing forestland use as input and carbon emissions as output, there is a lack of empirical evidence on FCSE and its influencing factors. Here, a superefficiency slacks-based measurement model was applied to estimate FCSE for 66 counties in Zhejiang Province, China. The influencing factors and spatial spillover effects of FCSE were also analyzed using a spatial autocorrelation model. The findings showed that over the sample observation period, county FCSE ranged from 0.199 to 1.258, with considerable gaps. The global Moran’s I index showed that county-level FCSE was markedly spatially autocorrelated. Spatially, forestland use, cutting, pests, and diseases had negative spatial spillover effects on FCSE, whereas average annual temperature and precipitation displayed positive spillover effects. These findings suggest that the overall coordination of forest resource supervision and management among counties should be strengthened. The implementation of forestry management models aimed at consolidating or increasing forest carbon sequestration should be emphasized to improve forest quality, thereby promoting FCSE enhancement.
... Bravo-Oviedo et al. [84] found a better productive performance of maritime pine in warmer sites. Additionally, in another analysis, Collalti et al. [85] stated that forest productivity increases as mean air temperatures increase. ...
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Sustainable forest management requires accurate biometric tools to estimate forest site quality. This is particularly relevant for prescribing adequate silvicultural treatments of forest management planning. The aim of this research was to incorporate topographic and climatic variables into dominant height growth models of patula pine stands to improve the estimation of forest stand productivity. Three generalized algebraic difference approach (GADA) models were fit to a dataset from 66 permanent sampling plots, with six re-measurements and 77 temporary inventory sampling plots established on forest stands of patula pine. The nested iterative approach was used to fit the GADA models, and goodness-of-fit statistics such as the root mean square error, Akaike’s Information Criterion, and Bias were used to assess their performance. A Hossfeld IV GADA equation type that includes altitude, slope percentage, mean annual precipitation, and mean annual minimum temperature produced the best fit and estimation. Forest site productivity was negatively affected by altitude, while increasing the mean annual minimum temperature suggested the fastest-growing rates for dominant tree height.
... Carbon sequestration is the process of capturing, collecting, and sequestering carbon into carbon pools. Net primary productivity (NPP) is a key link in the carbon feedback between the terrestrial biosphere and the atmosphere , which can effectively characterize the ability of vegetation to absorb carbon and fix it into organic matter (Collalti et al., 2020). Therefore, the carbon sequestration service in this study was represented by net primary productivity (NPP). ...
Ecosystem services (ES) are the important component supporting the United Nations Sustainable Development Goals (SDGs) realization. In recent decades, rapid urbanization has strongly affected the relationship between ES and SDGs, resulting in the decoupling of ES and SDGs. However, the key urbanization factors dominating the relationship between ES and SDGs are still unclear. In this study, a structural equation model was constructed to explore the impact path and its change of urbanization structure and scale factors on the relationship between ES and SDGs. The results showed that the economic urbanization structure indicator (Engel coefficient) under the influence of technology import significantly impacts the relationship between ES and SDGs in different periods. Under the influence of changes in urban and rural population, population urbanization structure indicator (labor force population proportion) had significant impact on the relationship between ES and economic SDGs, which was significantly stronger in the period of 2010–2015 than in the period of 2000–2005. Land urbanization scale indicators (construction land proportion, and protected natural area proportion) also significantly impacted the relationship between ES and SDGs. Especially for ecological SDGs, the negative impact of construction land on protected natural area increased significantly in the period of 2010–2015, which might further weaken the ES's contribution to SDGs. This study highlighted that along with the continuous transformation of China's society, the key impacts on the relationship between ES and SDGs resulted from the urbanization indicators of scale as well as structure, which provided an extensive support for the sustainable development and social transformation of developing countries and regions.
... Forests constitute approximately one-third of the Earth's land surface and store about 90% of terrestrial vegetation carbon [5]. The average annual carbon sequestration of forest ecosystems was about 3.5 ± 1.0 Pg C from 2008 to 2017 [6], suggesting that they are the main carbon pool in terrestrial ecosystems [7]. Moreover, forest ecosystems play an important role in global carbon and water cycles [8,9]. ...
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Carbon sinks in terrestrial ecosystems can be significantly increased by afforestation, which will slow global warming. However, it is still unclear how different plantations influence the carbon sink and how they respond to environmental factors, especially in drylands. In this study, eddy correlation method (EC) was used to measure carbon and water fluxes and environmental factors of two artificial forests (Larix principis-rupprechtii and Pinus tabulaeformis) in the dryland of Northwest China, and the responses of evapotranspiration (ET), net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO) to environmental factors were also assessed. Results showed that the L. principis-rupprechtii forest ecosystem had higher water use efficiency (WUE), light use efficiency (LUE), GPP, and RECO than the P. tabulaeformis forest ecosystem. However, the proportion of net ecosystem production (NEP) to GPP in the P. tabulaeformis forest ecosystem (62.89%) was higher than that in the L. principis-rupprechtii forest ecosystem (47.49%), indicating that the P. tabulaeformis forest ecosystem had the higher carbon sequestration efficiency. In addition, the CO2 and H2O fluxes in the L. principis-rupprechtii forest ecosystem were more sensitive to environmental factors, compared with the P. tabulaeformis forest ecosystem. Further, the RECO of the L. principis-rupprechtii forest ecosystem was more sensitive to temperature changes, which implies that the L. principis-rupprechtii forest ecosystem will release more CO2 than the P. tabulaeformis forest ecosystem with a warming climate. Therefore, the P. tabulaeformis forest ecosystem may have better carbon sequestration potential. These results are important for understanding the effects of climate change on the CO2 and H2O cycles in coniferous plantation ecosystems in drylands.
Mediterranean pine plantations provide several ecosystem services but are vulnerable to climate change. Forest management might play a strategic role in the adaptation of Mediterranean forests, but the joint effect of climate change and diverse management options have seldom been investigated together. Here, we simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate scenarios corresponding to increasing levels of atmospheric CO2 concentration and warming, and six management options with different goals, including wood production and renaturalization. We analysed the effect of climate change on annual carbon fluxes (i.e., gross and net primary production) and stocks (i.e., basal area, standing and harvested carbon woody stocks) of the autotrophic compartment, as well as the impact of different management options compared to a no management baseline. Results show that higher temperatures (+3 to +5°C) and lower precipitation (−20 % to −22 %) will trigger a decrease in net primary productivity in the second half of the century. Compared to no management, the other options had a moderate effect on carbon fluxes over the whole simulation (between −14 % and +11 %). While standing woody biomass was reduced by thinning interventions and the shelterwood system (between −5 % and −41 %), overall carbon stocks including the harvested wood were maximized (between +41 % and +56 %). Results highlight that management exerts greater effects on the carbon budget of Laricio pine plantations than climate change alone, and that climate change and management are largely independent (i.e., no strong interaction effects). Therefore, appropriate sil-vicultural strategies might enhance potential carbon stocks and improve forest conditions, with cascading positive effects on the provision of ecosystem services in Mediterranean pine plantations.
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Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely‐used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models’ performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapor pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe’s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.
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Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly , most studies investigating effects of air pollutants on forests have generally neglected impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend 1) establishment of much denser array of monitoring sites, particularly in the South Hemisphere; 2) further integration of ground and satellite monitoring; 3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; 4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; 5) intensification of experimental studies, addressing combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~ 73,000 tree species on Earth; 6) more experimental focus on phenomics and genomics; 7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and 8) development of models integrating air pollution and climate change data from long-term monitoring programs.
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Mediterranean pine plantations provide several ecosystem services but are particularly sensitive to climate change. Forest management practices might play a strategic role in the long-term adaptation of Mediterranean forests, however the joint effect of climate change and alternative management options together in the near and far future must be investigated. Here, we developed a management options portfolio and simulated the development of a Laricio pine (Pinus nigra subsp. laricio) stand in the Bonis watershed (southern Italy) from its establishment in 1958 up to 2095 using a state-of-the-science process-based forest model. The model was run under three climate change scenarios corresponding to increasing levels of atmospheric CO2 concentration, and seven management options with different goals, including post-disturbance management, wood production and renaturalization purposes. We analyzed the effect of climate change on annual carbon fluxes (i.e., gross and net primary production) and stocks (i.e., basal area and potential carbon woody stocks), as well as the impact of different management options compared to no management. Results show that, while climate change (i.e. warming and enriched atmospheric CO2 concentration) seems to increase carbon fluxes and stocks in the first half of the century, both show a substantial decrease in the second half, along with higher temperatures (+3 to +5 °C) and lower precipitation (–20% to –22%). When compared to no management, alternative options had a moderate effect on carbon fluxes over the whole simulation (between –6% and +7%) but overall carbon stocks were maximized by thinning interventions and the shelterwood system (+54% to +55%). We demonstrate that the choice of management exerts greater effects on the features of Laricio pine plantations than climate change alone. Therefore, silvicultural strategies might enhance potential stocks and improve forest conditions, with cascading positive effects on the provision of ecosystem services in Mediterranean pine plantations.
Soil enzymes are the most potent bioactive components in forest ecosystems. Cellulases and ligninases are vital carbon (C)-degrading enzymes that target different C pools. The ratio of ligninase-to-cellulase activity is good indicator for microbial soil C preference, play an important role in soil C cycling. However, our understanding of enzyme ratios and their drivers across forest ecosystems remains unclear. In this study, we hypothesized that (i) the ligninase-to-cellulase ratio increased from temperate forests to tropical forest ecosystems, and (ii) the dominant factors would be microbial abundances. About 2–3 kg of topsoil (0–10 cm) from each of the ten forest ecosystems were collected across a 3425 km gradient in China between July and August 2019. We analyzed the biogeographic patterns of ligninase and cellulase activities and the ratio of ligninase-to-cellulase activities to determine how this ratio responded to climatic factors, soil properties and substrates, and microbial abundances across the forest ecosystems along the latitudinal gradient. Our findings showed that the average soil ligninase activity was 3.49 nmol h⁻¹ g⁻¹, whereas the average soil cellulase activity was 525.26 nmol h⁻¹ g⁻¹ across the forest ecosystems sampled. The average activity ratio of ligninase-to-cellulase in tropical forest ecosystems was 27.9% higher than that in subtropical forests and 64.2% higher than that in temperate forest ecosystems. The partial least squares path model demonstrated that the ligninase-to-cellulase activity ratio was significantly negatively correlated with soil substrates (r = -0.94, p < 0.001) and significantly positively correlated with microbial abundances (r = 0.38, p < 0.01). The variation partitioning analysis further revealed that soil substrates explained 19.4% variation regarding ligninase-to-cellulase ratio, whereas microbial abundance (fungal abundance) contributed 2.8%. This study provides crucial information about the distribution of enzyme ratios along the latitude gradient, highlights the microbial utilization of recalcitrant C pools in tropical forests, and provides an insight into the response of the global C cycle under a changing climate.
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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.
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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.
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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.
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Accurate assessment of anthropogenic carbon dioxide (<span classCombining double low line"inline-formula">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 <span classCombining double low line"inline-formula">CO2 emissions (<span classCombining double low line"inline-formula"> E FF ) are based on energy statistics and cement production data, while emissions from land use and land-use change (<span classCombining double low line"inline-formula"> E LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric <span classCombining double low line"inline-formula">CO2 concentration is measured directly and its growth rate (<span classCombining double low line"inline-formula"> G ATM ) is computed from the annual changes in concentration. The ocean <span classCombining double low line"inline-formula">CO2 sink (<span classCombining double low line"inline-formula"> S OCEAN ) and terrestrial <span classCombining double low line"inline-formula">CO2 sink (<span classCombining double low line"inline-formula"> S LAND ) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (<span classCombining double low line"inline-formula"> B IM ), 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 <span classCombining double low line"inline-formula">±1 σ . For the last decade available (2008-2017), <span classCombining double low line"inline-formula"> E FF was <span classCombining double low line"inline-formula">9.4±0.5 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , <span classCombining double low line"inline-formula"> E LUC <span classCombining double low line"inline-formula">1.5±0.7 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , <span classCombining double low line"inline-formula"> G ATM <span classCombining double low line"inline-formula">4.7±0.02 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , <span classCombining double low line"inline-formula"> S OCEAN <span classCombining double low line"inline-formula">2.4±0.5 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , and <span classCombining double low line"inline-formula"> S LAND <span classCombining double low line"inline-formula">3.2±0.8 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , with a budget imbalance <span classCombining double low line"inline-formula"> B IM of 0.5 GtC yr<span classCombining double low line"inline-formula">ĝ'1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in <span classCombining double low line"inline-formula"> E FF was about 1.6 % and emissions increased to <span classCombining double low line"inline-formula">9.9±0.5 GtC yr<span classCombining double low line"inline-formula">ĝ'1 . Also for 2017, <span classCombining double low line"inline-formula"> E LUC was <span classCombining double low line"inline-formula">1.4±0.7 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , <span classCombining double low line"inline-formula"> G ATM was <span classCombining double low line"inline-formula">4.6±0.2 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , <span classCombining double low line"inline-formula"> S OCEAN was <span classCombining double low line"inline-formula">2.5±0.5 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , and <span classCombining double low line"inline-formula"> S LAND was <span classCombining double low line"inline-formula">3.8±0.8 GtC yr<span classCombining double low line"inline-formula">ĝ'1 , with a <span classCombining double low line"inline-formula"> B IM of 0.3 GtC. The global atmospheric <span classCombining double low line"inline-formula">CO2 concentration reached <span classCombining double low line"inline-formula">405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6-9 months indicate a renewed growth in <span classCombining double low line"inline-formula"> E FF of <span classCombining double low line"inline-formula">+ 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<span classCombining double low line"inline-formula">ĝ'1 persist for the representation of semi-decadal variability in <span classCombining double low line"inline-formula">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 <span classCombining double low line"inline-formula">CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the <span classCombining double low line"inline-formula">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).
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).