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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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Tree Physiology 39, 1473–1483
Is NPP proportional to GPP? Waring’s hypothesis 20 years on
A. Collalti 1,2,6 and I.C. Prentice 3,4,5
1National Research Council of Italy–Institute for Agriculture and Forestry Systems in the Mediterranean (CNR-ISAFOM), 87036, Rende, CS, Italy; 2Foundation
Euro-Mediterranean Centre on Climate Change–Impacts on Agriculture, Forests and Ecosystem Services Division (CMCC-IAFES), 01100, Viterbo, Italy; 3Department of Life
Sciences, AXA Chair of Biosphere and Climate Impacts, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK; 4Department of Biological
Sciences, Macquarie University, North Ryde, 2109 NSW, Australia; 5Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science,
Tsinghua University, Beijing 100084, China; 6Corresponding author (
Received September 24, 2018; accepted March 13, 2019; handling Editor Andrea Polle
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;
Net primary production of forests: a constant fraction of gross primary production? Tree Physiol 18:129–134) suggested
that a NPP:GPP ratio of 0.47 ±0.04 (SD) is universal across biomes, tree species and stand ages. Representing NPP
in models as a xed 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 the
Waring et al. paper. Research has conrmed the existence of pervasive acclimation mechanisms that tend to stabilize
the NPP:GPP ratio and indicates that Rashould not be modelled independently of GPP. Nonetheless, studies indicate
that the value of this ratio is inuenced by environmental factors, stand age and management. The average NPP:GPP
ratio in over 200 studies, representing dierent 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
(0.22–0.79) that is too large to be disregarded.
Keywords: autotrophic respiration, carbon-use eciency, forest ecosystem, modelling, primary production.
Forest carbon budgets are dominated by two opposing
uxes: photosynthesis (or gross primary production, GPP)
and autotrophic respiration (Ra). The remainder is net primary
production (NPP), which accrues to tissues (eventually
becoming detritus and respired heterotrophically) and to a
variety of non-structural compounds that help to maintain plant
and rhizosphere function (Chapin et al. 2006). Estimation and
modelling of the net carbon balance of forests requires accurate
estimation of how GPP is partitioned, because a small relative
error in this partitioning could lead to a larger relative error in
thecarbonbalance(DeLucia et al. 2007,Hermle et al. 2010).
However, there is still large uncertainty about how GPP
is partitioned in forests. One school of thought emphasizes
constancy in the ratio of NPP to GPP.
McCree and Troughton (1966) argued that this ratio should
be more-or-less invariant (in plants generally) with respect
to ageing, CO2and temperature. Van Oijen et al. (2010)
suggested, moreover, that it should be stoichiometrically
constrained between 0.55 and 0.6. Giord (1995,2003)
and Van Oijen et al. (2010) emphasized that the substrate
for respiration originates from photosynthesis and so Ra
must inevitably depend on GPP, at least when averaged over
long enough periods; Giord (1995) provided experimental
evidence (from wheat) in support of the idea of a near
invariant of NPP to GPP. Waring et al. (1998) (hereafter W98)
subsequently reported, in this journal, that the NPP:GPP ratio in
a sample of 12 temperate and boreal forest stands was tightly
constrained, with a central value of 0.47 ±0.04 (here and
elsewhere, ‘±’ denotes one standard deviation) and a narrow
range from 0.40 to 0.52. This claim of a constant NPP:GPP
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1474 Collalti and Prentice
Figure 1. Regression of NPP versus GPP forced through the origin for
12 forest sites (from Waring et al. 1998, Table 2).
ratio near 0.5 was re-iterated in the recent review by Waring
and Landsberg (2016). The close relationship of NPP to GPP
in the data analysed by W98 is illustrated in Figure 1.
Giord (1995) and W98 noted that for modelling purposes,
assuming invariance would be more realistic that treating
respiration as if it were independent of GPP. A universal
value of the NPP:GPP ratio would indeed be convenient for
analysis and modelling, because it would allow straightforward
estimation of NPP, whose measurement requires either
destructive sampling or allometric approximations, from GPP.
Gross primary production can be estimated either from remote
sensing using a light-use eciency model, at a time scale
of around a week (Heinsch et al. 2006)oronasub-
daily basis by partitioning the net ecosystem CO2exchanges
measured by the eddy covariance network (Beer et al. 2010).
A number of vegetation models [including all published
versions of 3-PG, Landsberg and Waring 1997; CenW (as one
option), Kirschbaum 2005;PROMOD,Sands et al. 2000;C-Fix,
Veroustraete et al. 2002; early versions of TRIPLEX, Peng et al.
2002; FullCAM, Richards and Ewans 2004;HyLand,Levy et al.
2004; 4C, Lasch et al. 2005;BASFOR,Van Oijen et al. 2005;
TOPS-BGC, Nemani et al. 2009; ForCent, Parton et al. 2010;
G’DAY (as one option), Dezi et al. 2010; Picus, Seidl et al. 2012;
and early versions of 3D-CMCC FEM, Collalti et al. 2014]have
assumed a constant NPP:GPP ratio. W98 has been cited in many
carbon balance studies (in the Web of Science search engine,
the search string ‘Waring et al. 1998’ yielded 422 returns
and in Google Scholar, 596 returns; both accessed 1 March
2019). A constant NPP:GPP ratio has been invoked in local,
regional and global carbon storage assessments (e.g., Lenton
and Huntingford 2003,Magnani et al. 2007,Zhang et al.
2006,Zha et al. 2009,Peichl et al. 2010,Sun et al. 2014),
considered as a benchmark value (e.g., Gris et al. 2004),
described in textbooks (Landsberg and Sands 2010,Ågren and
Andersson 2012) and applied in one version of the MODIS NPP
product (Jay et al. 2016).
A number of empirical and theoretical studies carried out
since W98 have thus converged on the insight that Rashould
not be regarded as a process independent of GPP, but rather
regarded as a relatively conservative fraction of GPP, and
numerous ecosystem models have adopted the approximation
that Rais a constant fraction of GPP. The two uxes are indeed
closely coupled, and the ratio of NPP to GPP is therefore more
nearly constant than one would expect if they were independent.
On the other hand, a growing body of research since W98
has emphasized variation in the NPP:GPP ratio, and focused on
identifying its controls. Responding to W98, Medlyn and Dewar
(1999) argued for a broader range of NPP:GPP ratios (0.31–
0.59) than was reported there. They noted that the methods
used by W98 to calculate NPP may have predetermined the
nding of a near-invariant NPP:GPP ratio. They concluded that
the hypothesis of a xed NPP:GPP ratio, although ‘having some
basis in theory’, was just one option for modelling—and not
a desirable avenue to pursue in the absence of experimental
In this paper we review studies on the partitioning of forest
GPP since the publication of W98. Some of these studies have
pointed to an approximately constant ratio of NPP to GPP, others
to a variable one. To some extent this has been a question of
emphasis (is the glass ‘half full’ or ‘half empty’?). Thus, some
publications have reported negative results (e.g., no eect of
forest type or stand age on the ratio), others have reported
such eects while downplaying their importance, while others
have focused on the importance of considering these eects.
We summarize results of these various studies approximately
chronologically within each of three broad categories of study,
according to their principal focus on eects of forest type and
stand age, eects of climate (temperature and drought) or
eects of site fertility, disturbance and management. We nally
summarize data on NPP:GPP ratios in a new data set compiled
from over 200 studies, representing dierent biomes, species
and stand ages, and draw some general conclusions about the
state of knowledge and research needs.
Denitions of terms
GPP is the balance between carbon xed through photosyn-
thesis and carbon lost through photorespiration, expressed per
unit ground area and time (Wohlfahrt and Lu 2015). NPP is
GPP minus autotrophic respiration (Clark et al. 2001). Biomass
production (BP; Vicca et al. 2012) is the part of NPP that
is used for biomass growth (taken to include litter and fruit
production). Biomass production and NPP dier because part of
NPP can be allocated to organic compounds that are not used for
growth, including non-structural carbohydrates (NSCs; including
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Is NPP proportional to GPP? 1475
starch, sugars and other polysaccharides), labile root exudates
(primarily organic acids) that support biological activity in
the rhizosphere and secondary metabolites (including biogenic
volatile organic compounds) that are involved in signalling,
defence against pathogens and herbivores or protection of
tissues against thermal oxidative stress. Following the denitions
by Roxburgh et al. (2005), BP is constrained to be always zero
or positive (tissues consumed by herbivores, for example, are
considered to be part of BP), while NPP can be potentially
negative (Ra>GPP) for limited periods. Over periods of years,
NPP must equal or exceed BP.
Giord (1995) proposed the term ‘carbon use eciency’
(CUE = 1 Ra/GPP), which is equivalent to the NPP to GPP ratio
as dened by W98. Vicca et al. (2012) more recently introduced
the term ‘biomass production eciency’ (BPE = BP/GPP),
which is equivalent to ‘gross growth eciency’, as dened
in the comprehensive review—across all kinds of organisms,
both autotrophic and heterotrophic—by Manzoni et al. (2018).
Although in general BPE CUE, none of the papers reviewed
here, apart from Vicca et al. (2012),Fernández-Martínez et al.
(2014) and Campioli et al. (2015), make a clear distinction
between BP and NPP—compelling us, for comparison with
the wider literature, to treat BPE and CUE as if they were
synonymous although they clearly are not.
As data on ‘NPP’ have often been obtained by biometric
measurements, some reported values may be better considered
as estimates of BP rather than NPP (see also Malhi et al. 2015).
However, even if the dierence is usually small, the distinction
is worth making in future research and should allow better
quantication than is possible based on the current literature.
Studies examining eects of forest type and stand age
Law et al. (1999) compared two patches at dierent succes-
sional stages (45 and 250 years old) in a Pinus ponderosa
(Douglas ex C. Lawson) forest in a summer-dry climate in
Oregon, USA. They found the same NPP:GPP ratio (0.45) in
both patches. Cannell and Thornley (2000) and Thornley and
Cannell (2000) showed a relatively stable ratio of Raand GPP
(implying a range of only 0.55–0.65 for the NPP:GPP ratio)
over an age range of 0–60 years. However, they noted that
assuming a single constant value in models might overlook
both dierences among sites and variations from year to year.
Amthor (2000), analysing 30 years of studies on the NPP:GPP
ratio (obtained by subtracting respiration estimates from GPP),
maximum growth rate per unit of photosynthesis and minimum
tissues maintenance costs (0.65) to limited growth (if any)
and maximum maintenance costs (0.2). He reported the lowest
values for moist tropical forests, and the highest value for a
temperate Fraxinus plantation.
Mäkelä and Valentine (2000) applied a process-based model
of forest growth in conjunction with measurements from a Pinus
sylvestris (L.) forest. They found that increasing sapwood mass
with increasing height and age increased the respiring biomass,
and thus Ra. Their model estimated a decline in the NPP:GPP
m. They concluded it was unlikely that Racould be a constant
fraction of photosynthesis over the full time course of stand
development. Later, Vanninen and Mäkelä (2005), also studying
aP. sylvestris (L.) forest, measured a reduction in the NPP:GPP
ratio, from 0.65 to 0.45, with increasing tree height.
Ryan et al. (2004) used data from an experimental Eucalyptus
saligna (Sm.) forest to explicitly challenge the long-standing
forest dynamics paradigm of Kira and Shidei (1967) and Odum
(1969), in which increasing Rawas assumed to drive a decline
in the NPP:GPP ratio with stand age.Ryan et al.found instead
that an age-related decline in GPP was accompanied by a decline
in Ra. The NPP:GPP ratio did vary with stand age, but only
slightly, from 0.66 at age 2 years to 0.62 at age 6 years.
They concluded that NPP:GPP should be considered ‘roughly’
constant and eectively independent of biomass or stand age.
Litton et al. (2007) reviewed the constancy, or otherwise, of
NPP:GPP in the literature available at the time, and their study
was updated with new data by Ise et al. (2010). Litton et al.
conducted a meta-analysis of annual carbon budgets based on
63 forest ecosystems varying in fertility, structure and stand
age. They found a highly signicant correlation between Raand
GPP (R2= 0.95, n= 23, P<0.01). The NPP:GPP ratio was
approximately constant across sites (0.43 ±0.02), but the
total range was from 0.29 to 0.58. Three outliers (two boreal,
0.29–0.34. No eect of either stand age (n= 4) or soil fertility
(n= 7) was found, probably due to the paucity of data.
De Lucia et al. (2007) analysed published data from 60
forest sites with stand ages varying from 5 to 500 years.
They found that the NPP:GPP ratio, largely based on biometric
estimates of BP standing in for NPP, varied substantially among
biomes and age classes. The highest value (0.83) was found
in a 5-year-old plantation of Populus nigra (L.), experimentally
exposed to a high CO2concentration. The lowest (0.22) was
found in a 115-year-old Picea mariana (Mill.) stand. Figure 2,
based on the data analysed by De Lucia et al., shows a clear
dierence among biomes—with low ratios of NPP:GPP in boreal
forests and high ratios in temperate deciduous forests.
Thornley (2011) found the NPP:GPP ratio to vary from 0.5
to 0.6 and argued that this ratio is conservative among ecosys-
tems, but that it varies with stand age depending on growth
eciency and recycling fraction. Goulden et al. (2011),
analysing biometric data from a chronosequence of seven
even-aged boreal forest stands, found that the NPP:GPP ratio
decreased from 0.5 to 0.3 with increasing stand age.
Tang et al. (2014) found variability in the NPP:GPP ratio across
biomes (obtained mostly from biometric BP measurements)
from 0.3 to 0.45, but no signicant correlation between
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1476 Collalti and Prentice
Figure 2. Regressions of NPP versus GPP forced through the origin
for dierent forest types, from data in DeLucia et al. (2007).Circles
represent boreal; squares, temperate deciduous (TD); triangles, temper-
ate coniferous (TC); diamonds, west coast maritime (WCM); crosses,
temperate mixed (TM); plus signs, tropical (T).
NPP:GPP and age—suggesting that declining GPP during
stand development (due to hydraulic limitations) is paralleled
by decreasing Raas proposed by Ryan et al. (1997b) and
Drake et al. (2011). They also speculated that biome-specic
dierences might be due to soil fertility and/or temperature.
Studies examining eects of temperature
Saxe et al. (2000) noted that the short-term relationship of
respiration to temperature, as observed in the laboratory and
driven by enzyme kinetics, is likely to be unrepresentative
of the long-term behaviour of respiration because the crucial
acclimatory response of the base rate to temperature is missing.
They argued, instead, that acclimation should cause NPP to be
proportional to GPP.
Dewar et al. (1999) noted that a short-term increase in
Rawith temperature could be fuelled by non-structural carbon
reserves, whereas in the longer term Ramust be constrained by
the supply of substrates from photosynthesis. They accepted
the idea that NPP:GPP might be conservative. However, they
expressed caution with regard to applying a single value across
a wider range of temperatures (and other environmental factors)
than were considered in W98.
Giord (2003), analysing data (obtained in some cases from
Ra, in others from biometric measurements) from various earlier
works (Giord 2003, Table 3 and references therein), noted
that while the average NPP:GPP ratio for eld-grown trees varies
only slightly (0.47 ±0.05), glasshouse-grown seedlings in
controlled environments generally show higher average values
(0.58 ±0.03). Citing Tjoelker et al. (1999),hesuggested
that (i) species may acclimate to increasing temperature to
dierent extents and (ii) that the overall NPP:GPP ratio is likely
to decrease slightly with increasing temperature.
Piao et al. (2010), analysing the global forest carbon budget
data set (n= 104) compiled by Luyssaert et al. (2007),
considered variations of Ra, obtained by dierent methods, in
relation to mean annual temperature (MAT), NPP, total biomass,
height, maximum Leaf Area index (LAI) and stand age. This
analysis indicated a non-linear relationship of the Ra:GPP ratio
to MAT across latitudes (R2= 0.43, P= 0.03): decreasing at
rst, levelling o around 11 C and thereafter increasing again.
This analysis implies a range in the NPP:GPP ratio from 0.25 to
0.42, with a maximum at 11 C. No relation to forest age was
found outside the MAT range of 8–12 C.
Keith et al. (2010), in common with Dewar et al.(1999),
noted that W98’s methods for estimating GPP and NPP were not
independent, thus potentially biasing W98’s conclusion towards
a constant ratio. They noted that asynchrony between the pro-
duction and utilization of assimilated and stored carbohydrates
must inevitably produce interannual variations in the NPP:GPP
ratio in response to a temporarily variable climate. In a global
sample of 27 forests, they found the NPP:GPP ratio to vary
between 0.29 and 0.61, being non-linearly related to GPP (see
also Malhi et al. 2015), with MAT and solar radiation accounting
for about half of the variation.
Chambers et al. (2004) and Metcalfe et al. (2010), analysing
data from a throughfall exclusion experiment in Amazonian
rainforest, found NPP:GPP ratios ranging from 0.24 ±0.04 for
experimentally drought trees to 0.32 ±0.04 for control trees.
In an assessment of the carbon balance in a Fagus sylvatica
(L.) forest over a 5-year period, Wu et al. (2013) found
that NPP:GPP varied from 0.32 to 0.4, due to asynchronous
interannual variations in GPP and NPP linked to interannual
variability in climate.
Studies examining eects of soil fertility, disturbance and
Malhi et al. (2009,2011,2015) and Malhi (2012) estimated
the NPP:GPP ratio of primary tropical forests to be in the range
from 0.3 to 0.4, varying because of disturbances and across
dierent soil fertility classes. They also argued that their values
were likely underestimated to some extent because of missing
components of NPP, in particular the poorly quantied transfer of
carbon to the rhizosphere through root exudates and transfers
to mycorrhizal symbionts.
Maier et al. (2004) found that experimentally increasing
nutrient availability in a Pinus taeda (L.) plantation had no eect
on the NPP:GPP ratio. In contrast, Giardina et al. (2003) and
Vicca et al. (2012) found that site fertility was a major control
of this ratio to GPP, which was found to range from 0.4 to
0.6. Vicca et al.(2012) described temperate forests as being
usually more fertile than boreal forests and consequently having
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Is NPP proportional to GPP? 1477
Figure 3. Regressions of BP versus GPP forced through the origin at
dierent level of soil fertility, from data in Vicca et al. (2012).Circles,
squares and triangles represent dierent soil nutrient availability classes,
‘H’, ‘M’ and ‘L’ refers to high-, medium- and low-soil fertility, respectively.
a higher BP:GPP ratio. Forest management emerged as the
next best predictor of the BP:GPP ratio, followed by stand age.
Figure 3shows the string eect of site fertility in the data of
Vicca et al. Doughty et al. (2018), analysing 14 tropical forest
sites across dierent rainfall and soil regimes in Amazonia and
the Andes, found no signicant relationship between tempera-
ture and CUE (see also Malhi et al. 2015), but did nd lower
CUE in less fertile sites.
Campioli et al. (2015) analysed ratios of BP to GPP in
131 managed and unmanaged sites, mainly in Europe and
North America. They found a constant ratio (0.46 ±0.01)
in unmanaged forests and a higher ratio in managed forests
(0.53 ±0.03). They described how management can shift
carbon allocation patterns to favour above-ground production,
at least for a certain period after intervention. Figure 4shows
the eect of forest management in the data of Campioli et
al. Recently, Kunert et al. (2019) conrmed that disturbed
forests have a higher NPP:GPP ratio in comparison with the
undisturbed ones.
Variability across measurement methods
Methodological dierences could account for some of the
divergent results obtained in dierent studies. Curtis et al.
(2005) compared independent methods to estimate BP and
eddy covariance for GPP. Working in the transition zone between
temperate deciduous and boreal forests in North America, they
estimated an average NPP:GPP ratio of 0.42 ±0.016 using
biometric methods for both NPP and GPP and 0.54 ±0.04
using biometric methods for NPP against eddy covariance GPP.
Figure 4. Regressions of BP versus GPP forced through the origin
comparing managed versus unmanaged sites, from data in Campioli
et al. (2015). Circles represent managed sites; squares, unmanaged
By comparison with other data they suggested that the eddy
covariance method was overestimating GPP at this site.
Maseyk et al. (2008) analysed the NPP:GPP ratio in a man-
aged plantation of Pinus halepensis (Mill.) in Israel by comparing
chamber, ux and biometric data. They also found rather large
dierences (up to 0.15) in annual NPP:GPP ratios estimated
by the dierent methods (but see also Zha et al. 2007,
Hermle et al. 2010;Wu et al. 2013).
Zanotelli et al. (2013), combining biometric and eddy covari-
ance measurements for an apple orchard, found that NPP:GPP
varied depending on the methodology from 0.79 ±0.13 to
0.64 ±0.10. They attributed the dierences to the large fraction
of NPP allocated to fruit, reducing total respiration and causing
higher values of the NPP:GPP ratio when compared with data
reported in literature for forests.
A further potential complication in the measurement of
NPP:GPP ratios, which was not been studied to our knowledge,
is the possibility that CO2originating from sapwood respiration
could be transported to and re-assimilated by the leaves. This
would essentially lead to systematic underestimation of stem
respiration, and thus, potentially, somewhat lower NPP:GPP
Despite the large number of publications since W98 that have
attempted to clarify and quantify the controls on the NPP:GPP
ratio, no universal picture has emerged. Dierent studies have
come to dierent conclusions about the role of stand age,
climate and soil fertility on this ratio (or on the ratio of BP to
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1478 Collalti and Prentice
GPP, in some recent works that have made the distinction). The
potential role of management is a complicating factor that has
only recently been recognized. The diculty of generalization is
compounded by the fact that dierent measurement techniques
for both NPP (or BP) and GPP have been shown to yield dierent
values. However, the range of variation in the NPP:GPP ratio
appears to be substantially wider than was indicated by W98.
Moreover, there are sucient indications in the recent literature
for eects of forest type (although whether due to species
characteristics, climate or soil properties is unclear), stand age,
site fertility and management to motivate further investigations
of the controls on partitioning of GPP in forests.
Data survey
Data on annual GPP, NPP (or Ra) and NPP:GPP (or BP:GPP)
ratios in forest ecosystems were compiled for this review based
on previously published global data sets (e.g., De Lucia et al.
2007,Vicca et al. 2012,Tang et al. 2014,Campioli et al.
2015), supplemented by missing or more recent data from the
literature (Table S1 available as Supplementary Data at Tr ee
Physiology Online). NPP (or Ra) values obtained by assuming a
xed ratio to GPP were excluded from consideration. In the case
of multi-year estimates, average values across years were used.
Data were cross-checked to avoid repetition. The combined
data set included data from 211 records for more than 100
forest stands between 5 and 500 years, with a worldwide dis-
tribution. Data collected come by diverse methodologies (e.g.,
eddy covariance, chamber and biometric measurements, site-
level modelling) and represent dierent forests, which include
managed and non-managed forests, mixed or pure forests,
disturbed by re or not and with dierent levels of soil nutrients
availability at dierent MAT and precipitation.
The mean value for the NPP:GPP ratio in the data set is
statistically indistinguishable from that given by W98, i.e., 0.46
±0.12 (R2= 0.77, P<0.0001, n= 211) (Figure 5). However,
specic values ranged from 0.22 (Turner et al. 2003)to0.79
(Valentini et al. 2000), and the standard deviation was three
times larger than reported by W98. Including 17 additional sets
of data that included only the ratios of NPP to GPP changed
the slope mean value slightly, to 0.47, but not the standard
deviation (±0.12, n= 228). Net primary production:GPP ratios
by biome were 0.42 (±0.12, n= 48) for boreal sites, 0.48
(±0.12, n= 162) for temperate sites and 0.41 (±0.11,
n= 18) for tropical sites, thus presenting no evidence for a
consistent trend with latitude and (in particular) no evidence
for higher ratios of Ra:GPP in tropical sites, as might have been
expected if Radid not adapt/acclimate to temperature. However,
generally higher values of the NPP:GPP ratio (0.50 ±0.13, n
= 71) were shown by deciduous broad-leaf species than for
evergreen needle leaf forests (0.45 ±0.11, n= 12), evergreen
broad-leaf forests (0.44 ±0.11, n= 127) and mixed forests
(0.43 ±0.09, n= 18). These general ndings are consistent
Figure 5. Regression forced through the origin (R2= 0.77, P<0.0001,
n= 211) based on the present literature survey (red dots) and data from
Waring et al. (1998) (blue triangles).
with Luyssaert et al. (2007,2009),Malhi (2012),Vicca et al.
(2012) and Campioli et al. (2015), who also found that
temperate deciduous forests are slightly more ecient than
boreal and tropical forests in converting photosynthates into
biomass. Cannell and Thornley (2000) (citing Goetz and Prince
1998) speculated that conifers might generally have smaller
NPP:GPP ratios than broad-leaved species because of larger
foliage biomass, which increases maintenance respiration costs
but not necessarily their assimilation capacity. Values lower than
0.22 were not encountered, and it seems likely that values
below 0.2 cannot be physiologically maintained (Amthor 2000;
Keith et al. 2010).
What factors stabilize the NPP:GPP ratio or cause
it to vary?
Twenty years on, it is now possible to answer to the question
‘Net primary production of forests: a constant fraction of gross
primary production?’ (W98). Literature review has revealed
that it is not. There is a compelling body of evidence that
the NPP:GPP ratio is not a universal constant. However, there
are mechanisms at work that tend to stabilize this ratio at a
more constant value than would occur if Raand GPP were
independent. Dierent classes of mechanisms, tending either to
stabilize or to perturb the NPP:GPP ratio, are discussed below.
The role of carbohydrate reserves
To a limited degree, plants can buer the eects of altered
carbon demand—that is, seasonal or interannual variations in
Ra—by tapping into the pool of NSC (Cannell and Thornley
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Is NPP proportional to GPP? 1479
2000,Thornley and Cannell 2000,Trumbore 2006,Sala et al.
2012,Martínez-Vilalta et al. 2016). Over a longer time period
(a few years in trees), however, increased demand for NSC
to fuel increases in Ramust be reected in reduced tissue
growth (Collalti et al. 2018). This is a negative feedback
mechanism, which would be expected to stabilize the NPP:GPP
(Van Oijen et al. 2010), but not necessarily the BP:GPP ratio
(Collalti et al. in review). In a purely ‘active’ view of carbon
storage (i.e., storage has priority in current assimilates allocation
over the structural growth; e.g., Sala et al. 2012,Collalti et al.
2016), NSC increases at the expense of BP and thus would
not be accounted for, while it would be mirrored in a stepwise
reduction (seasonal or for longer periods) on the NPP:GPP
ratio. Conversely, in a ‘passive’ view of carbon storage (e.g.,
Kozlowski 1992), BP would outcompete reserve accumulation
and, thus, reecting narrower range of variations in the NPP:GPP
ratio. There is mounting evidence in support of an, at least
partial, active view of NSC accumulation (Dietze et al. 2014,
Martínez-Vilalta et al. 2016).
W98 argued that for every mole of GPP, about half must
be expended on Ra(see also Enquist et al. 2007). However,
biomass produced in one year may be derived from the previous
year’s photosynthates that had been allocated to NSC and
subsequently remobilized and used for growth or metabolism
(Gough et al. 2009,Vargas et al. 2009,Drake et al. 2016,
Solly et al. 2018). Asynchrony between (photosynthetic) source
and (utilization) sink implies some degree of uncoupling of
Ra, and consequently NPP, from GPP. These processes are
controlled in the short term by dierent environmental drivers:
photosynthesis by light, temperature, atmospheric CO2concen-
tration and water supply; respiration primarily by temperature.
Noting that NSC has often been observed to increase with tree
size (Sala and Hoch 2009,Richardson et al. 2013)andthe
potential variability of the NPP:GPP ratio due to the asynchrony
of source and sink might also be expected to increase with
tree size (Sala et al. 2012,Collalti et al. 2019, Collalti et al.
in review), in contrast to the limited variability generally seen in
herbaceous plants. However, how this regulation could occur at
the whole-tree level is not known (Sala et al. 2012). A useful
task for the future would be to compare the NPP:GPP ratio and
the BP:GPP ratio across forests in dierent stages of develop-
ment, allowing quantication of the fraction of GPP allocated
to non-structural components and—we hope—contributing to
an improved understanding of the regulation of the dierent
components of the forest carbon balance.
Responses to disturbance
Plants live in a dynamic environment and are subjected to a
variety of disturbances including ozone damage, re and pest
outbreaks—see, for example, the major eects of pine beetle
outbreaks, as described by Edburg et al. (2011). Disturbances
may force plants to deviate from homoeostasis between GPP and
Ra. The consistently higher BP:GPP ratio found by Campioli et al.
(2015) for managed stands relative to unmanaged ones is
likely to be attributable to thinning practices, which include
the removal of suppressed and moribund trees in order to
encourage the growth of younger and more ecient trees, and
improve their nutrient status (see also Vanninen and Mäkelä
2005,Grant et al. 2007,Manzoni et al. 2018). More generally, it
seems that any practice that leads to rejuvenation of the stands,
through lowering the mean stand age and competition between
individuals, may eectively tend to increase the NPP:GPP ratio
(Collalti et al. 2018,Doughty et al. 2018,Kunert et al. 2019).
Changes during stand development
It is generally agreed that photosynthesis and Raincrease in
parallel during early stand development. But what happens after
canopy closure, when LAI stabilizes (or even slightly decreases)
and GPP cannot be increased any further (Thornley and Cannell
2000)? If the NPP:GPP ratio is constant, respiration must then
become constant after canopy closure (Maier et al. 2004).
This is plausible for leaf respiration (and presumably also
for ne root respiration), because leaf and ne root biomass
are not expected to change substantially. How woody tissue
respiration could remain stable, despite a continuing increase
in woody biomass, is less obvious. There could be constant
turnover from live to dead woody tissues, or specic respiration
rates, and/or tissue nitrogen concentrations, could decrease. But
there is only limited evidence that the maintenance respiration
of woody biomass stabilizes (Hunt et al. 1999,Pruyn et al.
2000,Mäkelä and Valentine 2000,Vanninen and Mäkelä 2005,
Grant et al. 2007,Piao et al. 2010,Goulden et al. 2011), and
woody tissue respiration rates and nitrogen concentrations do
not appear to change signicantly (Machado and Reich 2006,
Reich et al. 2008). Stem respiration may instead continue to
increase, because of the continuing accumulation of sapwood
biomass as trees become taller (Saxe et al. 2000;Reich et al.
2006;Mori et al. 2010), resulting in a decline in the NPP:GPP
ratio over the course of stand development. Malhi (2012),
Malhi et al. (2015) and Doughty et al. (2018) argued that
there might be a link between high mortality rates (at least in dry
tropical and more fertile sites) and high NPP:GPP ratios because
of strategies in favour of inherently shorter life history and with
consequent low residence time (i.e., higher turnover rates) and
maintenance costs—suggesting that demographic traits might
contribute to dierences in NPP:GPP ratios among species.
Responses to temperature
Higher temperatures might be expected to accelerate the
kinetics of all biochemical processes in plants, up to high-
temperature thresholds beyond which enzymes are inactivated
(Saxe et al. 2000). There is also evidence, cited also in
textbooks (e.g., Larcher 2003), that the threshold temperature
for inactivation of respiration is generally higher than that for
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1480 Collalti and Prentice
photosynthesis. So it might be inferred that the NPP:GPP ratio
must decline at high temperatures. However, this reasoning
overlooks the fact that the specic respiration rates of plant
tissues (and the respiration rates of whole plants) acclimate
response to temperature (driven by enzyme kinetics) remains
steeply increasing (Heskel et al.2016), the base rate changes
in such a way that the rate of respiration at the new growth
temperature diers little from the previous rate (Atkin and
Tjoelker 2003,Giord 2003,Medlyn et al. 2005,Smith and
Dukes 2012,Atkin et al.2015,Slot and Katajima 2015,
Vanderwel et al. 2015,Reich et al. 2016). This acclimation
process implies that the NPP:GPP ratio is considerably more
stable with respect to growth temperature than textbook
physiology would suggest. However, a comprehensive, quan-
titative treatment of respiratory acclimation to temperature is
still missing.
Responses to soil fertility and drought
There is strong evidence that the ratio of BP to GPP varies
among sites dependent on their fertility (Vicca et al. 2012,
Fernández-Martínez et al. 2014), even if the mechanisms for
this response are not rmly established. Across biomes, carbon
allocation (below- versus above-ground) varies such that where
either nutrients or water are scarce, total below-ground carbon
allocation is greater (Gill and Finzi 2016). Total below-ground
carbon allocation includes not only allocation to ne roots
(production, respiration and turnover) but also exudation of
low molecular-weight organic compounds that are unaccounted
by classical measurements, which may constitute a substantial
fraction (up to 30%) of NPP (Hobbie 2006,Courty et al.
2010). Vicca et al. (2012) inferred that under conditions of
low nutrient availability (which can be either due to poor soils
or cold climates inhibiting microbial activity) a greater fraction of
assimilates are ‘lost’ through carbon transfer to root symbionts
and the rhizosphere. These ndings are consistent with the idea
that carbon allocation follows an adaptive programme, which
balances the demands of growth with the acquisition of water
and nutrients required to support growth.
There are known mechanisms that of course couple NPP to
GPP because ‘plants cannot respire what they did not photosyn-
thesize before’ (Giord 2003). But there is also now sucient
evidence to reject the hypothesis of a universal, constant or even
tightly variable ratio of NPP to GPP, as it is used as a simplifying
concept in a large number of ecosystem models. Ageing and
biomass accumulation, climate, soil fertility and management
have all been indicated to inuence the ratio of NPP (or BP) to
GPP likely in a non-mutually exclusive way. Currently available
data do not allow straightforward and clear generalizations
about each of these inuences: in part because of inconsistent
and contrasting results among dierent studies, in part because
of a widespread failure to distinguish NPP and BP and in
part because of unresolved methodological issues aecting the
measurement of NPP, Raand GPP. The determination of whole-
tree carbon budgets under dierent environmental conditions,
at dierent developmental stages and even after disturbances
remains a key issue for analysis. Without a clear understanding
of these processes, ecosystem models are likely to continue to
yield highly uncertain projections of forest carbon budgets in a
changing world.
Data accessibility statement
The database used in this analysis is provided in the Supporting
Information and is freely available.
Supplementary Data
Supplementary Data for this article are available at Tree Physiol-
ogy Online.
We are grateful for help and invaluable assistance from A. Ibrom
in conceiving and preparing the manuscript. The authors are also
indebted to A. Mäkelä, G. Matteucci and B.E. Medlyn for early
motivation, constructive comments and thoughtful suggestions,
and to E. Grieco for data compilation and checking. We are also
thankful to two anonymous reviewers for constructive comments
that helped us to improve the manuscript substantially. This work
is a contribution to the AXA Chair Programme in Biosphere
and Climate Impacts and the Imperial College initiative on
Grand Challenges in Ecosystems and the Environment (I.C.P.).
I.C.P. has also received funding from the European Research
Council under the European Union’s Horizon 2020 research
and innovation programme (grant agreement no: 787203
Ågren G, Andersson F(2012) Terrestrial ecosystem ecology: principles
and applications. Cambridge University Press, Cambridge, UK.
Amthor J(2000) The McCree-de wit-Penning de Vries-Thornley respi-
ration paradigms: 30 years later. Ann Bot 86:1–20.
Atkin OA,TjoelkerMG (2003) Thermal acclimation and the dynamic
response of plant respiration to temperature. TRENDS in Plant Science
Atkin OA, Bloomeld KJ,ReichPB et al. (2015) Global variability in
leaf respiration in relation to climate, plant functional types and leaf
traits. New Phytologist 206:614–636.
Beer C, Reichstein M, Tomelleri Eet al. (2010) Terrestrial gross
carbon dioxide uptake: global distribution and covariation with climate.
Science 329:834–838.
Tree Physiology Volume 39, 2019
Downloaded from by guest on 21 August 2019
Is NPP proportional to GPP? 1481
Campioli M, Vicca S, Luyssaert Set al. (2015) Biomass production
eciency controlled by management in temperate and boreal ecosys-
tems. Nat Geosci 8:1–7.
Cannell M,ThornleyJ(2000) Modelling the components of plant
respiration: some guiding principles. Ann Bot 85:45–54.
Chambers J,TribuzyE, Toledo Let al. (2004) Respiration from a
tropical forest ecosystem: partitioning of sources and low carbon use
eciency. Ecol Appl 14:72–78.
Chapin F, Woodwell G,RandersonJet al. (2006) Reconciling carbon-
cycle concepts. Ecosystems 20:1041–1050.
Clark DA,BrownS, Kicklighter DW,ChambersJQ et al. (2001)
Measuring net primary production in forests: concepts and eld
methods. Ecol Appl 11:356–370.
Collalti A, Perugini L, Santini Met al. (2014) A process-based model to
simulate growth in forests with complex structure: evaluation and use
of 3D-CMCC Forest ecosystem model in a deciduous forest in Central
Italy. Ecol Model 272:362–378.
Collalti A,MarconiS,IbromAet al. (2016) Validation of 3D-CMCC
Forest ecosystem model (v.5.1) against eddy covariance data for 10
European forest sites. Geosci Model Dev 9:479–504.
Collalti A, Trotta C, Keenan TF,IbromA, Bond-Lamberty B,GroteRet
al. (2018) Thinning can reduce losses in carbon use eciency and
carbon stocks in managed forests under warmer climate. J Adv Model
Earth Syst 10:2427–2452.
Collalti A,ThorntonPE, Cescatti A,RitaA, Borghetti M, Nolè A, Trotta
C, Ciais P, Matteucci G(2019) The sensitivity of the forest carbon
budget shifts across processes along with stand development and
climate change. Ecol Appl 29:1–18.
Collalti A,TjoelkerMG, Hoch Get al. Size matters: biomass accumula-
tion and living wood turnover may dominate the uncertainty in forest
autotrophic carbon balance. New Phytol in review.
Courty P,BuèeM, Diedhiou Aet al. (2010) The role of ectomycorrhizal
communities in forest ecosystem processes: new processes and
emerging concepts. Soil Biol Biochem 42:679–698.
Curtis P,VogelC,GoughC,SchmidH,SuH-B,BovardB(2005)
Respiration carbon losses and the carbon-use eciency of a northern
hardwood forest, 1999-2003. New Phytol 167:437–456.
DeLucia E,DrakeJ,ThomasR, Gonzalez-Meler M(2007) Forest carbon
use eciency: is a respiration a constant fraction of gross primary
production? Glob Chang Biol 13:1157–1167.
Dewar RC, Medlyn BE,McMurtrieRE (1999) Acclimation of the res-
piration/photosynthesis ratio to temperature: insights from a model.
Glob Chang Biol 5:615–622.
Dezi S, Medlyn B, Tonon G, Magnani F(2010) The eect of nitrogen
deposition on forest carbon. Glob Chang Biol 16:1470–1486.
Dietze MC,SalaA,CarboneMS, Czimczik C, Mantooth JA,
Richardson AD,VargasR(2014) Nonstructural carbon in woody
plants. Annual review of plant biology 65:667–687.
Doughty CE, Goldsmith GR,RaabNet al. (2018) What controls
variation in carbon use eciency among Amazonian tropical forests?
Biotropica 0:1–10.
Drake JE, Gallet-Budynek A,HofmockelKS et al. (2011) Increases in
the ux of carbon belowground stimulate nitrogen uptake and sustain
the long-term enhancement of forest productivity under elevated CO2.
Ecol Lett 14:349–357.
Drake JE,TjoelkerMG,AspinwallMJ et al. (2016) Does physiological
acclimation to climate warming stabilize the ratio of canopy respiration
to photosynthesis? New Phytol 211:850–863.
Edburg S,HickeJ, Lawrence D,ThorntonP(2011) Simulating coupled
carbon and nitrogen dynamics following. J Geophys Res 116:1–15.
Enquist BJ,KerkhoAJ,HuxmanTE,EconomoEP (2007)
Adaptive dierences in plant physiology and ecosystem para-
doxes: insights from metabolic scaling theory. Glob Chang Biol
Fernández-Martínez M, Vicca S, Janssens Iet al. (2014) Nutrient
availability as the key regulator of global forest carbon balance. Nat
Clim Chang 4:471–476.
Galbraith D,LevyP,SitchSet al. (2006) Multiple mechanisms of
Amazonian forest biomass losses in three dynamic global vegetation
models under climate change. New Phytol 187:647–665.
Giardina C,RyanM,BinkleyD, Fownes J(2003) Primary production
and carbon allocation in relation to nutrient supply in a tropical
experimental forest. Glob Chang Biol 9:1438–1450.
Giord RM (1995) Whole plant respiration and photosynthesis of
wheat under increased CO2concentration and temperature: long-
term vs. short-term distinctions for modelling. Glob Chang Biol 1:
Giord R(2003) Plant respiration in productivity models: conceptual-
isation, representation and issues for global terrestrial carbon-cycle
research. Funct Plant Biol 30:171–186.
Gill AL,FinziAC (2016) Belowground carbon ux links biogeochemical
cycles and resource-use eciency at the global scale. Ecol Lett
Girardin M,RaulierFet al. (2008) Response of tree growth to a
changing climate in boreal Central Canada: a comparison of empir-
ical, process-based, and hybrid modelling approaches. Ecol Model
Goetz SJ,PrinceSD (1998) Variability in carbon exchange and light
utilization among boreal forest stands: implications for remote sensing
and net primary production. Can J For Res 28:375–389.
Gough CM,FlowerCE et al. (2009) Whole-ecosystem labile carbon
production in a north temperate deciduous forest. Agric For Meteorol
Goulden M, McMillan A,WinstonG,RochaA,ManiesK,HardenJ,Bond-
Lamebrty B(2011) Patterns of NPP, GPP, respiration, and NEP during
boreal forest succession. Glob Chang Biol 17:855–871.
Grant RF,BarrAG,BlackTA,GaumontDet al. (2007) Net ecosystem
productivity of boreal jack pine stands regenerating from clearcut-
ting under current and future climates. Glob Chang Biol 13:
Gris T,BlackTet al. (2004) Seasonal variation and partitioning of
ecosystem respiration in a southern boreal aspen forest. Agric For
Meteorol 125:207–223.
Heinsch F, Zhao M, Running Set al. (2006) Evaluation of remote
sensing based Terrestrial productivity from MODIS using regional
tower Eddy ux network observations. IEEE Trans Geosci Remote Sens
Hermle S,LavigneMB, Bernier PY, Bergeron O,ParèD(2010) Com-
ponent respiration, ecosystem respiration and net primary production
of a mature black spruce forest in northern Quebec. Tree Physiol
Heskel MA, O’Sullivan O,ReichPB et al. (2016) Convergence in the
temperature response of leaf respiration across biomes and plant
functional types. Proc Natl Acad Sci 113:3832–3837.
Hobbie EA (2006) Carbon allocation to ectomycorrhizal fungi cor-
relates with belowground allocation in culture studies. Ecology
Huang J, Hammerbacher A, Weinhold Aet al. (2018) Eyes on the
future—evidence for trade-os between growth, storage and defense
in Norway spruce. New Phytol 222:144–158.
Hunt E,LavigneM,FranklinS(1999) Factors controlling the decline
of net primary production with stand age for balsam r in New-
foundland assessed using an ecosystem simulation model. Ecol Model
Ise T, Litton Cet al. (2010) Comparison of modeling approaches
for carbon partitioning: impact on estimates of global net primary
production and equilibrium biomass of woody vegetation from MODIS
GPP. J Geophys Res 115:1–11.
Tree Physiology Online at
Downloaded from by guest on 21 August 2019
1482 Collalti and Prentice
Jay S, Potter C,CrabtreeRet al. (2016) Evaluation of modelled net
primary production using MODIS and landsat satellite data fusion.
Carbon Balance Manag 11:1–13.
Keith H,MackeyBet al. (2010) Estimating carbon carrying capacity in
natural forest ecosystems across heterogeneous landscapes: address-
ing sources of error. Glob Chang Biol 16:2971–2989.
Kira T,ShideiT(1967) Primary production and turnover of organic
matter in dierent forest ecosystems of the western pacic. Jpn J Ecol
Kirschbaum M(2005) A model analysis of the interaction between
forest age and forest responsiveness to increasing CO2 concentration.
Tree Physiol 25:953–963.
Kozlowski TT (1992) Carbohydrate sources and sinks in woody plants.
Bot Rev 58:107–222.
Kunert N, El-Madany TS, Aparecido LMT, Wolf S, Potvin C(2019)
Understanding the controls over forest carbon use eciency on small
spatial scales: eects of forest disturbance and tree diversity. Agric
For Meteorol 269–270:136–144.
Landsberg J, Sands P(2010) Physiological ecology of forest produc-
tion: principles, processes and models. Academic Press, San Diego,
Landsberg J,WaringR(1997) A generalised model of forest pro-
ductivity using simplied concepts of radiation-use eciency, carbon
balance and partitioning. For Ecol Manage 95:209–228.
Larcher W(2003) Physiological plant ecology. Springer, Berlin
Lasch P,BadeckF-W,SuckowFet al. (2005) Model-based analysis of
management alternatives at stand and regional level in Brandenburg
(Germany). For Ecol Manage 207:59–74.
Law B,RyanM, Anthony P(1999) Seasonal and annual respiration of
a ponderosa pine ecosystem. Glob Chang Biol 5:169–182.
Lenton T,HuntingfordC(2003) Global terrestrial carbon storage and
uncertainties in its temperature sensitivity examined with a simple
model. Glob Chang Biol 9:1333–1352.
Levy P, Cannell M, Friend A(2004) Modelling the impact of future
changes in climate, CO 2 concentration and land use on natural
ecosystems and the terrestrial carbon sink. Glob Environ Chang
Litton C,RaichJ,RyanM(2007) Carbon allocation in forest ecosys-
tems. Glob Chang Biol 13:2089–2109.
Luyssaert Set al. (2007) CO2balance of boreal, temperate and
tropical forests derived from global database. Glob Chang Biol
Luyssaert S, Reichstein M, Schulze E-D, Janssens IA,Law,BE,PapaleD,
Dragoni D, Goulden ML, Granier A, Kutsch WL (2009) Toward a
consistency cross-check of eddy covariance ux–based and biometric
estimates of ecosystem carbon balance. Global Biogeochem Cycles
23, doi: 10.1029/2008GB003377.
Machado J-L,ReichPB (2006) Dark respiration rate increases with
plant size in saplings of three temperate tree species despite decreas-
ing tissue nitrogen and nonstructural carbohydrates. Tree Physiol
Magnani F, Mencuccini M, Borghetti Met al. (2007) The human
footprint in the carbon cycle of temperate and boreal forests. Nature
Maier C, Albaugh Tet al. (2004) Respiratory carbon use and carbon
storage in mid-rotation loblolly pine (Pinus taeda L.) plantations: the
eect of site resources on the stand carbon balance. Glob Chang Biol
Mäkelä A, Valentine H(2000) The ration of NPP to GPP: evidence
of change over the course of stand development. Tree Physiol
Malhi Y(2012) The productivity, metabolism and carbon cycle of
tropical forest vegetation. J Ecol 100:65–75.
Malhi Y,DoughtyC, Gabraight D(2011) The allocation of ecosystem
net primary productivity in tropical forest. Philos Trans R Soc Lond B
Biol Sci 366:3225–3245.
Malhi Y,DoughtyCE et al. (2015) The linkages between photosynthe-
sis, productivity, growth and biomass in lowland Amazonian forests.
Glob Chang Biol 21:2283–2295.
Malhi Y,AragãoLEOC, Metcalfe DB et al. (2009) Comprehensive
assessment of carbon productivity, allocation and storage in three
Amazonian forests. Glob Chang Biol 15:1255–1274.
Manzoni S,ˇ
Capek P, Porada P, Thurner Met al. (2018) Reviews
and syntheses: carbon use eciency from organisms to ecosys-
tems—denitions, theories, and empirical evidence. Biogeosciences
Martìnez-Vilalta J,SalaA, Asensio D,GalianoL, Hoch G,PalacioS,
Piper FI,LloretF(2016) Dynamics of non-structural carbohy-
drates in terrestrial plants: a global synthesis. Ecol Monogr 86:
Maseyk K, Grünzweig JM et al. (2008) Respiration acclimation con-
tributes to high carbon-use eciency in a seasonally dry pine forest.
Glob Chang Biol 14:1553–1567.
McCree K(1970) An equation for the rate of respiration of white clover
plants grown under controlled conditions. In: S. I. (ed) Prediction and
measurement of photosynthetic productivity. Centre for Agricultural
Publishing and Documentation, Wageningen, The Netherlands, pp
McCree K, Troughton J(1966) Prediction of growth rate at dierent
light levels from measured photosynthesis and respiration rates. Plant
Physiol 41:559–566.
Medlyn B,DewarR(1999) Comment on the article by R. H. Waring, J.
J. Landsberg and M. Williams relating net primary production to gross
primary production. Tree Physiol 19:137–138.
Medlyn BE, Robinson AP, Clement R,McMurtrieRE (2005) On the
validation of models of forest CO2exchange using eddy covariance
data: some perils and pitfalls. Tree Physiol 25:839–857.
Metcalfe D, Meir Pet al. (2010) Shifts in plant respiration and carbon
use eciency at a large-scale drought experiment in the eastern
Amazon. New Phytol 187:608–621.
Mori S, Yamaji K, Ishida Aet al. (2010) Mixed-power scaling of whole-
plant respiration from seedlings to giant trees. PNAS. doi: 10.1073/
Nemani R, Hashimoto Het al. (2009) Monitoring and forecasting pro-
tected area ecosystem dynamics using the terrestrial observation and
prediction system (TOPS). Remote Sens Environ 113:1497–1509.
Odum E(1969) The strategy of ecosystem development. Science
Parton WJ,HansonPJ,SwanstonC, Torn M, Trumbore SE,RileyR,
Kelly R(2010) ForCent model development and testing using
the enriched background isotope study experiment. J Geophys Res
Peichl M,BrodeurJet al. (2010) Biometric and eddy-covariance based
estimates of carbon uxes in an age-sequence of temperate pine
forests. Agric For Meteorol 150:952–965.
Peng C,LiuJ, Dang Qet al. (2002) TRIPLEX: a generic hybrid model
for predicting forest growth and carbon and nitrogen dynamics. Ecol
Model 153:109–130.
Penning de Vries F(1972) Respiration and growth. In: Rees A,
Cockshull K,HandD,HurdR(eds) Crop processes in controlled
environments. Academic Press, London, pp 327–347.
Piao S, Luyssaert S, Ciais Pet al. (2010) Forest annual carbon
cost: a global-scale analysis of autotrophic respiration. Ecology
Pruyn M, Gartner B,HarminM(2000) Respiratory potential in sapwood
of old versus young coniferous trees. The Ecological Society of
America, Washington DC, p 180.
Tree Physiology Volume 39, 2019
Downloaded from by guest on 21 August 2019
Is NPP proportional to GPP? 1483
Reich PB,TjoelkerMG,MachadoJ-L,OleksynJ(2006) Universal
scaling of respiratory metabolism, size and nitrogen in plants. Nature
Reich PB,TjoelkerMG et al. (2008) Scaling of respiration to nitrogen in
leaves, stems and roots of higher land plants. Ecol Lett 11:793–801.
Reich PB et al. (2016) Boreal and temperate trees show strong
acclimation of respiration to warming. Nature 531:633–636.
Richards G,EwansD(2004) Development of a carbon accounting
model (FullCAM Vers. 1.0) for the Australian continent. Aust For
Richardson AD,CarboneMS et al. (2013) Seasonal dynamics and age
of stemwood nonstructural carbohydrates in temperate forest trees.
New Phytol 197:850–861.
Roxburgh SH, Berry SL,BuckleyTN, Barnes B, Roderick ML
(2005) What is NPP? Inconsistent accounting of respiratory
uxes in the denition of net primary production. Funct Ecol 19:
Ryan M(1991) Eects of climate change on plant respiration. Ecol Appl
Ryan M,GowerS,HubbardRet al. (1995) Woody tissue mainte-
nance respiration of four conifers in contrasting climate. Oecologia
Ryan M,BinkleyD, Fowners J(1997a) Age-related decline in forest
productivity: pattern and process. Adv Ecol Res 27:213–262.
Ryan M,LavigneM,GowerS(1997b) Annual carbon cost of autotrophic
respiration in boreal forest ecosystems in relation to species and
climate. J Geophys Res 102:871–884.
Ryan M,BinkleyD, Fownes J, Giardina C, Senock R(2004) An
experimental test of the causes of forest growth decline with stand
age. Ecol Monogr 74:393–414.
Ryan M, Philips N, Bond B(2006) The hydraulic limitation hypothesis
revisited. Plant Cell Environ 29:367–381.
Sala A, Hoch G(2009) Height-related growth declines in ponderosa
pine are not due to carbon limitation. Plant Cell Environ 32:22–30.
Sala A, Woodru DR, Meinzer FC (2012) Carbon dynamics in trees:
feast or famine? Tree Physiol 32:764–775.
Sands P, Battaglia M, Mummery D(2000) Application of process-based
models to forest management: experience with PROMOD, a simple
plantation productivity model. Tree Physiol 20:383–392.
Saxe H, Cannell Met al. (2000) Tree and forest functioning in response
to global warming. New Phytol 149:369–400.
Seidl R, Rammer W, Scheller R,SpiesT(2012) An individual-based
process model to simulate landscape-scale forest ecosystem dynam-
ics. Ecol Model 213:87–100.
Slot M, Katajima K(2015) General patterns of acclimation of leaf
respiration to elevated temperatures across biomes and plant types.
Oecologia 177:885–900.
Smith N,DukesJ(2012) Plant respiration and photosynthesis in a
global-scale models: incorporating acclimation to temperature and
CO2. Glob Chang Biol 19:1–19.
Solly EF, Brunner I, Helmisaari H-S,HerzogC, Leppälammi-Kujansuu J,
Schöning I, Schrumpf M, Schweingruber FH, Trumbore SE,
Hagedorn F(2018) Unravelling the age of ne roots of temperate
and boreal forests. Nat Commun 9:1–8.
Sun Y,GuLet al. (2014) Impact of mesophyll diusion on esti-
mated global land CO2 fertilization. Proc Natl Acad Sci USA
Tang J, Luyssaert S, Richardson Aet al. (2014) Steeper declines in
forest photosynthesis that respiration explain age-driven decreases in
forest growth. Proc Natl Acad Sci USA 111:8856–8860.
Thornley J(1970) Respiration, growth and maintenance in plants.
Nature 227:304–305.
Thornley J(2011) Plant growth and respiration re-visited: maintenance
respiration dened it is an emergent property of, not a separate
process within, the system and why the respiration: photosynthesis
ratio is conservative. Ann Bot 108:1365–1380.
Thornley JH, Cannell M(2000) Modelling the components of plant
respiration: representation and realism. Ann Bot 85:55–67.
Tjoelker M,OleskynJ,ReichP(1999) Acclimation of respiration to
temperature and CO2in seedlings of boreal tree species in relation
to plant size and relative growth rate. Glob Chang Biol 49:679–691.
Trumbore S(2006) Carbon respired by terrestrial ecosystems—recent
progress and challenges. Glob Chang Biol 12:141–153.
Turner D, Ritts W, Cohen Wet al. (2003) Scaling gross primary
production (GPP) over boreal and deciduous forest landscape in
support of MODIS GPP product validation. Remote Sens Environ
Valentini R, Matteucci G, Dolman AJ, Schulze ED, Rebmann C, Moors
EJ et al. (2000) Respiration as the main determinant of carbon
balance in European forests. Nature 404:861–865.
Vargas R, Trumbore SE, Allen MF (2009) Evidence of old carbon
used to grow new ne roots in a tropical forest. New Phytol
Vanderwel MC,SlotM, Lichstein JW,ReichPB, Kattge J,AtkinOK,
Bloomeld KJ (2015) Global convergence in leaf respiration from
estimates of thermal acclimation across time and space. New Phytol
Van Oijen M,RougierJ,SmithR(2005) Bayesian calibration of process-
based models: bridging the gap between models and data. Tree
Physiol 25:915–927.
Van Oijen M, Schapendonk A, Hoglind M(2010) On the relative
magnitudes of photosynthesis, respiration, growth and carbon storage
in vegetation. Ann Bot 105:739–797.
Vanninen P,MäkeläA(2005) Carbon budget for scots pine trees:
eects of size, competition and site fertility on growth allocation and
production. Tree Physiol 25:17–30.
Veroustraete F,SabbeH, Eerens H(2002) Estimation of carbon mass
uxes over Europe using C-x model and Euroux data. Remote Sens
Environ 83:376–399.
Vicca S, Luyssaert Set al. (2012) Fertile forests produce biomass more
eciently. Ecol Lett 15:520–526.
Waring R, Landsberg JL (2016) Tamm review: insights gained from light
use and leaf growth eciency. For Ecol Manage 379:232–242.
Waring RH, Landsberg J, Williams M(1998) Net primary production of
forests: a constant fraction of gross primary production? Tree Physiol
Wohlfahrt G,LuL(2015) The many meanings of gross photosynthesis
and their implication for photosynthesis research from leaf to globe.
Plant Cell Environ 38:2500–2507.
Wu J,LarsenK, van der, Linden L, Beier C, Pileegard K,IbromA(2013)
Synthesis on the carbon budget and cycling in a Danish, temperate
deciduous forest. Agric For Meteorol 181:94–107.
Zanotelli D, Montagnani L,MancaG, Tagliavini M(2013) Net primary
productivity, allocation pattern and carbon use eciency in an apple
orchard assessed by integrating eddy covariance, biometric and con-
tinuous chamber measurements. Biogeosciences 10:3089–3108.
Zha T,XingZ,WangK-Y, Kellomäki S,BarrAG (2007) Total and
component carbon uxes of a scots pine ecosystem from chamber
measurement and Eddy covariance. Ann Bot 99:345–353.
Zha T,BarrAG,BlackAet al. (2009) Carbon sequestration in
boreal jack pine stands following harvesting. Glob Chang Biol
Zhang J-H,HanS-J et al. (2006) Seasonal variation in carbon dioxide
exchange over a 200-year-old Chinese broad-leaved Korean pine
mixed forest. Agric For Meteorol 137:150–165.
Zhang Y,YuG,YangJ,WimberlyMC, Zhang XC,TaoJ,JiangY, Zhu J
(2013) Climate-driven global changes in carbon use eciency. Glob
Ecol Biogeogr 23:144–155.
Tree Physiology Online at
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... T min,→ and VPD → refer to the upper (right-hand) bounds of minimum temperature and VPD; the right-hand bound is the temperature (VPD) at which photosynthesis is completely unlimited (limited) by temperature (VPD). 10.1029/2023JG007457 8 of 18 NPP:GPP ratios for each land-cover type were expected to be close to 0.46 (Collalti & Prentice, 2019) and much lower for EBF (Malhi, 2012). During NPP calibration, to ensure realistic parameter values and the simulated, global NPP:GPP ratios, we rejected some of the mean a posteriori estimates after calibration. ...
... In terms of agreement with the MsTMIP ensemble, the updates improve the NPP:GPP ratio for all land-cover types except DNF, SAV, and GRS ( Figure S6 in Supporting Information S1). When compared to the measured NPP:GPP ratios compiled by Collalti and Prentice (2019) for woody plants, the updates improve simulated NPP:GPP ratios for all land-cover types except EBF ( Figure S7 in Supporting Information S1), for which the median value is 0.49 (0.40 in MsTMIP ensemble, 0.44 in C61, and 0.37 in the update). ...
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The NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA‐20 and NOAA‐21 satellites enable potential continuity of long‐term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model‐data fusion, we combined 18 years of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from the future MOD17 Collection 7 product and new VNP17 product for 2012‐2018 is 127 ± 2.8 Pg C year ⁻¹ (58 ± 1.1 Pg C year ⁻¹ ), which compares well with independent top‐down and bottom‐up estimates. MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000‐2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year ⁻² but slowing to 0.35‐0.44 (0.11‐0.13) Pg C year ⁻² over 2012‐2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these estimates of global, terrestrial primary productivity beyond 2030.
... Together, these new pieces of physiological evidence suggest that g sn may be actively regulated by respiration both at dawn and, more importantly, overnight. As~46% of antecedent photosynthetic products are respired for maintenance and growth (Collalti and Prentice, 2019), g sn likely coordinates several photosynthetic traits (R n , g sd and A) continuously, such that g sn responds to the trend of the previous R n and sets the trend for the following g sd and A (Fricke, 2019). Little is known about the role of g sn in rice plants. ...
... The proposed 'coordinated leaf physiological trait' hypothesis is consistent with current literature findings. In this study, we used R n as a proxy of prior assimilation, based on evidence showing that the magnitude of nocturnal respiration is proportional to and constrained by antecedent photosynthesis (through the law of mass conservation), with a homoeostatic R n -to-photosynthesis ratio of 0.4-0.5 (Collalti & Prentice, 2019;Van Oijen et al., 2010). A recent metaanalysis also reported that changes in g sn are positively related to photosynthesis, see Figure 2 in Chowdhury et al. (2021). ...
The ecological mechanism underlying nocturnal stomatal conductance (gsn ) in C3 and C4 plants remains elusive. In this study, we proposed a 'coordinated leaf trait' hypothesis to explain gsn in rice plants. We conducted an open-field experiment by applying drought, nutrient stress and the combined drought-nutrient stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, gsn exhibited a strong positive correlation with dark respiration (Rn ). Notably, gsn primed early morning (5:00-7:00) photosynthesis through faster stomatal response time. This photosynthesis priming effect diminished after mid-morning (9:00). Leaves were cooled by gsn -derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of gsn .
... The copyright holder for this preprint this version posted November 22, 2023. ; doi: bioRxiv preprint (Collalti and Prentice, 2019), and ignoring intraspecific and leaf age variation in LUE, this would straightforwardly translate in a 8% difference in tree growth. This potential 8% difference is much smaller than the variability observed in basal area increment of tree growth we compute, e.g. at the Barbeau (FR-Fon) forest (Table 1) for which we could access individual tree growth data (coefficient of variation of tree basal area increment, normalized by crown area was 35% among dominant trees). ...
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Aim. To quantify the intra-community variability of leaf-out (ICVLo) among dominant trees in temperate deciduous forests, assess its links with specific and phylogenetic diversity, identify its environmental drivers, and deduce its ecological consequences with regard to radiation received and exposure to late frost. Location. Eastern North America (ENA) and Europe (EUR). Time period. 2009-2022 Major taxa studied. Temperate deciduous forest trees. Methods. We developed an approach to quantify ICVLo through the analysis of RGB images taken from phenological cameras. We related ICVLo to species richness, phylogenetic diversity and environmental conditions. We quantified the intra-community variability of the amount of radiation received and of exposure to late frost. Results. Leaf-out occurred over a longer time interval in ENA than in EUR. The sensitivity of leaf-out to temperature was identical in both regions (-3.4 days per°C). The distributions of ICVLo were similar in EUR and ENA forests, despite the latter being more species-rich and phylogenetically diverse. In both regions, cooler conditions and an earlier occurrence of leaf-out resulted in higher ICVLo. ICVLo resulted in a ca. 8% difference of radiation absorption over spring among individual trees. Forest communities in ENA had shorter safety margins as regards the exposure to late frosts, and were actually more frequently exposed to late frosts. Main conclusions. We conducted the first intercontinental analysis of the variability of leaf-out at the scale of tree communities. North American and European forests showed similar ICVLo, in spite of their differences in terms of species richness and phylogenetic diversity, highlighting the relevance of environmental controls on ICVLo. We quantified two ecological implications of ICVLo
... The gross primary production (GPP) was derived from the net primary production (NPP) using the NPP/GPP ratio (Luyssaert et al., 2007;Jia et al., 2012). The NPP/GPP ratio of 0.52 was deduced from the MODIS GPP and NPP product (Cheng et al., 2000;Zhang et al., 2009;Collalti and Prentice, 2019). NPP refers to aboveground biomass plus root biomass. ...
... However, the combined effects of multiple global change factors (e.g., increased CO 2 , temperature, soil water and nutrient limitations) can complicate C allocation processes (Chen et al., 2013), creating a more uncertain trajectory of C allocation (Mathias and Trugman, 2022). Thus, the assumed constancy of C allocation makes most land-surface models of C cycling likely to be inaccurate (Collalti and Prentice, 2019;Marshall et al., 2018). To date, most studies using in-situ measurements have focused on the magnitude, spatial pattern, and controls of C allocation (Chen et al., 2013;Fernández-Martínez et al., 2014;Hofhansl et al., 2015), but not on how it changes through time due to interacting climate changes. ...
... Like ΔC/GPP, carbon use efficiency is commonly used by the academic community to characterize the carbon sequestration potential of plants and the distribution relationship between growth and respiration. Carbon use efficiency is defined as the ratio of net primary productivity to gross primary productivity (NPP/GPP) (Collalti and Prentice, 2019;He et al., 2018;Zhang et al., 2014). However, unlike carbon use efficiency, which includes the biomass of the whole plant, ΔC/GPP in this study did not account for belowground biomass. ...
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.
The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.
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The future trajectory of atmospheric CO2 concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in-depth understanding of model sensitivities and uncertainties in non steady-state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO2-enrichment. Here, we systematically assessed if a bio-geochemical process-based model (3D-CMCC-CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest-ageing and-structuring as well as under climate-change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 years of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon-and nitrogen-pools early in forest simulation (i.e. for NPP up to ~37%, 256 g C m-2 yr-1 and for SWB up to ~90%, 65 t C ha-1 , when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium-to longer-time scales, and under climate-change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e. for NPP up to ~30%, 167 g C m-2 yr-1 and for SWB up to ~24%, 64 t C ha-1 , when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate-change (i.e. warming and changes in atmospheric CO2 concentration) itself although model sensitivities were generally higher under climate-change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.
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The cycling of carbon (C) between the Earth surface and the atmosphere is controlled by biological and abiotic processes that regulate C storage in biogeochemical compartments and release to the atmosphere. This partitioning is quantified using various forms of C-use efficiency (CUE) – the ratio of C remaining in a system to C entering that system. Biological CUE is the fraction of C taken up allocated to biosynthesis. In soils and sediments, C storage depends also on abiotic processes, so the term C-storage efficiency (CSE) can be used. Here we first review and reconcile CUE and CSE definitions proposed for autotrophic and heterotrophic organisms and communities, food webs, whole ecosystems and watersheds, and soils and sediments using a common mathematical framework. Second, we identify general CUE patterns; for example, the actual CUE increases with improving growth conditions, and apparent CUE decreases with increasing turnover. We then synthesize > 5000 CUE estimates showing that CUE decreases with increasing biological and ecological organization – from unicellular to multicellular organisms and from individuals to ecosystems. We conclude that CUE is an emergent property of coupled biological–abiotic systems, and it should be regarded as a flexible and scale-dependent index of the capacity of a given system to effectively retain C.
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Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here, we assess the potential impact of thinning on projected carbon-cycle dynamics and implications for forest CUE and its components (i.e. gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process-based forest-ecosystem-model forced by climate outputs of five Earth System Models under four Representative- climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO 2 concentration and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations, lead the forests to grow faster mature earlier but also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody-pools and reduce physiological-climate-induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest-based mitigation strategies and should be carefully considered within a portfolio of mitigation options.
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Fine roots support the water and nutrient demands of plants and supply carbon to soils. Quantifying turnover times of fine roots is crucial for modeling soil organic matter dynamics and constraining carbon cycle-climate feedbacks. Here we challenge widely used isotope-based estimates suggesting the turnover of fine roots of trees to be as slow as a decade. By recording annual growth rings of roots from woody plant species, we show that mean chronological ages of fine roots vary from <1 to 12 years in temperate, boreal and sub-arctic forests. Radiocarbon dating reveals the same roots to be constructed from 10 ± 1 year (mean ± 1 SE) older carbon. This dramatic difference provides evidence for a time lag between plant carbon assimilation and production of fine roots, most likely due to internal carbon storage. The high root turnover documented here implies greater carbon inputs into soils than previously thought which has wide-ranging implications for quantifying ecosystem carbon allocation.
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The cycling of carbon (C) between the Earth surface and the atmosphere is controlled by biological and abiotic processes that regulate C storage in biogeochemical compartments and release to the atmosphere. This partitioning is quantified using various forms of C-use efficiency (CUE) – the ratio of C remaining in a system over C entering that system. Biological CUE is the fraction of C taken up allocated to new biomass. In soils and sediments C storage depends also on abiotic processes, so the term C-storage efficiency (CSE) can be used. Here we first review and reconcile CUE and CSE definitions proposed for autotrophic and heterotrophic organisms and communities, food webs, whole ecosystems, and soils and sediments using a common mathematical framework. Second, we identify general CUE patterns, such as the CUE increase with improving growing conditions, and apparent decrease due to turnover. We then synthesize > 6000 CUE estimates showing that CUE decreases with increasing biological and ecological organization – from unicellular to multicellular organisms, and from individuals to ecosystems. We conclude that CUE is an emergent property of coupled biological-abiotic systems, and it should be regarded as a flexible and scale-dependent index of the capacity of a given system to effectively retain C.
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Why do some forests produce biomass more efficiently than others? Variations in Carbon Use Efficiency (CUE: total Net Primary Production (NPP)/ Gross Primary Production (GPP)) may be due to changes in wood residence time (Biomass/NPPwood), temperature, or soil nutrient status. We tested these hypotheses in 14, one ha plots across Amazonian and Andean forests where we measured most key components of net primary production (NPP: wood, fine roots, and leaves) and autotrophic respiration (Ra; wood, rhizosphere, and leaf respiration). We found that lower fertility sites were less efficient at producing biomass and had higher rhizosphere respiration, indicating increased carbon allocation to belowground components. We then compared wood respiration to wood growth and rhizosphere respiration to fine root growth and found that forests with residence times <40 yrs had significantly lower maintenance respiration for both wood and fine roots than forests with residence times >40 yrs. A comparison of rhizosphere respiration to fine root growth showed that rhizosphere growth respiration was significantly greater at low fertility sites. Overall, we found that Amazonian forests produce biomass less efficiently in stands with residence times >40 yrs and in stands with lower fertility, but changes to long-term mean annual temperatures do not impact CUE.
Carbon (C) allocation plays a central role in tree responses to environmental changes. Yet, fundamental questions remain about how trees allocate C to different sinks, for example, growth vs storage and defense. In order to elucidate allocation priorities, we manipulated the whole‐tree C balance by modifying atmospheric CO 2 concentrations [ CO 2 ] to create two distinct gradients of declining C availability, and compared how C was allocated among fluxes (respiration and volatile monoterpenes) and biomass C pools (total biomass, nonstructural carbohydrates ( NSC ) and secondary metabolites ( SM )) in well‐watered Norway spruce ( Picea abies ) saplings. Continuous isotope labelling was used to trace the fate of newly‐assimilated C. Reducing [ CO 2 ] to 120 ppm caused an aboveground C compensation point (i.e. net C balance was zero) and resulted in decreases in growth and respiration. By contrast, soluble sugars and SM remained relatively constant in aboveground young organs and were partially maintained with a constant allocation of newly‐assimilated C, even at expense of root death from C exhaustion. We conclude that spruce trees have a conservative allocation strategy under source limitation: growth and respiration can be downregulated to maintain ‘operational’ concentrations of NSC while investing newly‐assimilated C into future survival by producing SM .