Are absorptive root traits good predictors of ecosystem functioning? A test in a natural temperate forest

Abstract

Trait‐based approaches provide a useful framework to predict ecosystem functions under intensifying global change. However, our current understanding of trait‐functioning relationships mainly relies on aboveground traits. Belowground traits (e.g. absorptive root traits) are rarely studied although these traits are related to important plant functions.
We analyzed four pairs of analogous leaf and absorptive root traits of woody plants in a temperate forest and examined how these traits are coordinated at the community‐level, and to what extent the trait covariation depends on local‐scale environmental conditions. We then quantified the contributions of leaf and absorptive root traits and the environmental conditions in determining two important forest ecosystem functions, aboveground carbon storage, and woody biomass productivity.
The results showed that both morphological trait pairs and chemical trait pairs exhibited positive correlations at the community level. Absorptive root traits show a strong response to environmental conditions compared to leaf traits. We also found that absorptive root traits were better predictors of the two forest ecosystem functions than leaf traits and environmental conditions.
Our study confirms the important role of belowground traits in modulating ecosystem functions and deepens our understanding of belowground responses to changing environmental conditions.

Full text

Are absorptive root traits good predictors of ecosystem
functioning? A test in a natural temperate forest
Rihan Da
1
, Chunyu Fan
1
, Chunyu Zhang
1
, Xiuhai Zhao
1
and Klaus von Gadow
2,3
1
Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China;
2
Faculty of Forestry and Forest
Ecology, Georg-August-University G¨
ottingen, B¨
usgenweg 5, D-37077 G¨
ottingen, Germany;
3
Department of Forest and Wood Science, University of Stellenbosch, Stellenbosch 7600,
South Africa
Authors for correspondence:
Chunyu Zhang
Email: zcy_0520@163.com
Xiuhai Zhao
Email: zhaoxh@bjfu.edu.cn
Received: 1 March 2023
Accepted: 22 March 2023
New Phytologist (2023)
doi: 10.1111/nph.18915
Key words: absorptive root traits, ecosystem
functions, environmental driver, plant
strategies, temperate forest, trait-based
approach.
Summary
Trait-based approaches provide a useful framework to predict ecosystem functions under
intensifying global change. However, our current understanding of trait-functioning relation-
ships mainly relies on aboveground traits. Belowground traits (e.g. absorptive root traits) are
rarely studied although these traits are related to important plant functions.
We analyzed four pairs of analogous leaf and absorptive root traits of woody plants in a
temperate forest and examined how these traits are coordinated at the community-level, and
to what extent the trait covariation depends on local-scale environmental conditions. We then
quantified the contributions of leaf and absorptive root traits and the environmental condi-
tions in determining two important forest ecosystem functions, aboveground carbon storage,
and woody biomass productivity.
The results showed that both morphological trait pairs and chemical trait pairs exhibited
positive correlations at the community level. Absorptive root traits show a strong response to
environmental conditions compared to leaf traits. We also found that absorptive root traits
were better predictors of the two forest ecosystem functions than leaf traits and environmen-
tal conditions.
Our study confirms the important role of belowground traits in modulating ecosystem func-
tions and deepens our understanding of belowground responses to changing environmental
conditions.
Introduction
Understanding how plant communities respond to global change
is one of the main challenges in ecology. Plant functional traits
allowed a more mechanistic and predictive understanding of
community responses to environmental changes and of commu-
nity effects on ecosystem functions (McGill et al., 2006;Dı´az
et al., 2007; Funk et al., 2017; Bruelheide et al., 2018; Gadow
et al., 2021) and that trait syndromes have proven useful proxies
for ecological ‘strategies’ (Wright et al., 2004;Dı´az et al., 2016).
The trait-based approach is built on the assumption that a num-
ber of traits exist that link the environment to the performance of
a plant, for example, growth and survival (Violle et al., 2007) and
hence affect community composition and ecosystem functioning
(Lavorel & Garnier, 2002; Garnier et al., 2016). This approach
can better explain the variation in multiple ecosystem functions
by focusing on the shifts of community functional compositions
(Savage et al., 2007; Enquist et al., 2015), which have been exten-
sively studied using aboveground plant traits (e.g. leaf traits).
However, belowground plant traits (e.g. fine root traits) have
received less attention (Lalibert´
e, 2017; van der Sande
et al., 2018), even these traits regulate a series of ecosystem
processes, such as the cycling and storage of carbon, nutrients,
and water (Bardgett et al., 2014).
The coordination and trade-off between plant functional traits
have been proposed to explain ecological strategies for acquiring,
processing, and retaining multiple resources. The leaf economics
spectrum (LES) reflects several plant strategies ranging from fast-
growing, short-lived, and acquisitive leaves to slow-growing but
long-lived and therefore more conservative leaves (Wright
et al., 2004;Dı´az et al., 2016). Since fine roots are considered to
be the belowground equivalent of leaves, the plant economics
spectrum hypothesis postulates that fine root traits follow a simi-
lar one-dimensional fast-slow strategy, aligned with the LES
(Reich, 2014). However, there is growing evidence confirming
that variations in fine or absorptive root traits are multidimen-
sional (Kramer-Walter et al., 2016; Weemstra et al., 2016; Kong
et al., 2019). In a recent study, Bergmann et al.(2020) have
described a conceptual framework of two-dimensional root eco-
nomics space (RES). One of the RES dimensions is defined as
the conservation gradient that represents the ‘classical’ trade-off
between fast and slow return on investment. The other dimen-
sion represents a collaboration gradient of plant–fungal interac-
tions in roots, which varies from a ‘do it yourself’ strategy to an
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‘outsourcing’ strategy. The key traits along this gradient are mean
root diameter (RD) and specific root length (SRL). These traits
reflect that thick-rooted species with low SRL are more readily
colonized by AM fungi as a result of the larger fungal habitat in
the root cortex, other mycorrhizal associations, such as ectomy-
corrhiza (EcM) or ericoid mycorrhiza, tend to colonize moderate
to thin roots with higher SRL (Bergmann et al., 2020). Such
multidimensional patterns of root trait variation also existed
across forest communities (Kramer-Walter et al., 2016; Wang
et al., 2018), which suggested that species-level RES can be extra-
polated to the community level. In addition, previous studies
have examined how the RES is related to the LES at the species
level, but the results of these studies are often contradictory. For
example, Weigelt et al.(2021) found a match between the leaf
and fine root fast-slow strategy, yet this match did not emerge in
the analysis presented by Carmona et al.(2021). Although the
economics spectrums have been gradually unraveled at the species
level, evidence is still lacking to understand the combination of
both LES and RES at the community level.
Assessing trait–environment relationships at the community
level can aid in advancing our ability to estimate how commu-
nities respond to novel environmental conditions. The
community-weighted mean (CWM) values of a given trait shift
via changes in the community composition (relative abundance
of species) and individual trait expression (Chac ´
on-Labella
et al., 2022). It is commonly assumed that intraspecific shifts will
have a smaller effect than abundance shifts on community mean
trait values because interspecific variation exceeds intraspecific
(Albert et al., 2011; Siefert et al., 2015). While accounting for
intraspecific trait variation may strengthen trait–environment
relationships, measuring trait values on a large number of indivi-
duals per species and plot is not feasible, especially for the fine
root traits of woody species. Therefore, neglecting intraspecific
trait variation and representing species by their mean trait values
is a reasonable assumption. There is ample evidence that inte-
grated trait variation and coordination are driven by environmen-
tal conditions. For instance, previous studies have shown that
climatic and soil variables can explain the dimensional spectrum
of both leaf and fine root traits at the global scale (Freschet
et al., 2017; Bruelheide et al., 2018; Laughlin et al., 2021; Joswig
et al., 2022) and regional scale (Wang et al., 2018). However,
such large-scale patterns have failed to account for ecological pro-
cesses and community assemblages operating at local scales
(Pinho et al., 2021). Topography is strongly correlated with the
availability of light, water, and soil nutrients (John et al., 2007;
Weemstra et al., 2016) and thus is expected to play an important
role in driving root trait variations at the local scale (Pierick
et al., 2021).
Forests play an important role in fundamental functions and
services, such as protecting biodiversity, regulating carbon cycle
and biomass production (Gamfeldt et al., 2013). As the domi-
nant carbon reservoir of terrestrial ecosystems, forest ecosystems
comprise >80% of the global terrestrial C pool aboveground
(Pan et al., 2011). However, global change and forest distur-
bances are directly or indirectly causing substantial changes in
forest ecosystem functioning across the world (Zhang &
Liang, 2014). Changes in these functions may be revealed
through the shift of functional traits in forest communities. Con-
sequently, understanding the relationship between tree functional
traits and forest ecosystem functioning (e.g. aboveground carbon
storage and woody biomass productivity) is important for the
study of Earth systems and for natural resource management
(Diaz et al., 2011; Funk et al., 2017). Extensive studies have been
carried out regarding the relationship between community func-
tional composition and aboveground carbon storage or woody
biomass productivity of forests, with a focus on aboveground
traits (Prado J´
unior et al., 2016; Ali et al., 2017; Adair
et al., 2018; Brun et al., 2022). However, despite major progress,
there is still a lack of a mechanistic understanding of how forest
ecosystem functions are related to belowground traits, and about
the relative importance of leaf and root traits for these functions.
Most previous studies have only focused on traits, without con-
sidering the effects of environmental conditions or possible inter-
active effects among traits when assessing trait-functioning
relationships (van der Plas et al., 2020).
This study uses evidence from a local-scale permanent forest
observational infrastructure to evaluate how community-level
traits, and the trade-offs among them, respond to specific envir-
onmental conditions, and their impact on multiple ecosystem
functions of natural temperate forests. Based on 10 yr of observa-
tions of a 21.12 ha forest plot in northeastern China, we focus on
four pairs of analogous leaf and absorptive root traits of woody
plants, and two main forest ecosystem functions, aboveground
carbon storage and woody biomass productivity, which respec-
tively represent system stock and flux. We will specifically address
the following questions:
(1) How are leaf and absorptive root traits coordinated among
the different forest communities?
(2) To what extent do the major dimensions of community-level
leaf and absorptive root traits depend on local-scale environmen-
tal conditions, and which are the main environmental drivers?
(3) How are the community-level leaf and absorptive root traits
related to aboveground carbon storage and woody biomass pro-
ductivity, and which are the best predictors (leaf and absorptive
root traits, or environmental conditions) of these functions?
Materials and Methods
Study area
This study was carried out in a natural temperate forest located in
the Jiaohe Management Bureau of the Experimental Forest Zone
in Jilin Province, northeastern China (43°57054″–43°58016″N,
127°42047″–127°43019″E). The area is characterized by a tempe-
rate continental climate affected by a monsoon season, which has
a mean annual temperature of 3.8°C and a mean annual precipi-
tation of 695.9 mm. The average monthly temperature ranges
from −18.6°C (January) to 21.7°C (July). The brown forest soil
typical of the area has a rootable depth ranging between 20 and
100 cm.
A permanent forest observational study covering an area of
21.12 ha (660 m ×320 m) was established in a secondary forest
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during the summer of 2009 (Zhang et al., 2012). The last har-
vesting activities took place during the 1960s. The study area was
subdivided into 528 square plots each measuring 20 ×20 m. All
individual woody plants with a diameter at breast height
(DBH) ≥1 cm were tagged, measured, and identified at the spe-
cies level. A total of 23 tree species have been identified within
the study site (Supporting Information Table S1). The average
number of species per plot is 10.15 (ranging from 5 to 17). The
plots were resurveyed twice in 2014 and 2019.
Five topographic variables and four soil variables were assessed
as proxies of the environmental conditions in each subplot. The
topographic variables included elevation, convexity, aspect, and
slope. The convexity of a plot was calculated as the elevation of
the focal plot minus the mean elevation of the eight surrounding
plots (Yamakura et al., 1995). Positive and negative convexity
values indicate convex (ridge) and concave (valley) land surfaces,
respectively (Zhang et al., 2012). Since aspect is a circular vari-
able, we standardized it to reflect north (cosA) and east (sinA)
facing slopes (Zhong et al., 2021). These two variables increase
with increasing northerly and easterly directions of the aspect.
Four soil properties, namely, available nitrogen, available phos-
phorus, available potassium, and soil acidity, were assessed from
soil samples extracted to a depth of 10 cm in each 40 m ×40 m
plot. These soil variables were then interpolated to grids of 20
m×20 m using Ordinary Kriging in the GSTAT package of R.
Estimation of aboveground carbon storage and woody
biomass productivity
Aboveground carbon storage (AGC) was estimated as the carbon
stored in the aboveground biomass of all live trees. To determine
aboveground carbon storage and woody biomass productivity, we
use data from the first and last inventories (from 2009 to 2019).
The aboveground biomass of each individual tree was estimated
from its DBH using a set of species-specific allometric equations
(Table S1). We then multiplied tree biomass by the average bio-
mass carbon content of 50% (Adair et al., 2018). By summing
the AGC of all live trees recorded during the first census scaled
up to one hectare, we calculated the initial aboveground carbon
storage (AGC, Mg ha
−1
)ofeach20×20 m plot. Aboveground
woody biomass productivity (AWP, Mg ha
−1
yr
−1
) was estimated
as the total annual biomass increment of all individual trees that
were present in both censuses plus the annual increment of
recruits in the last census (van der Sande et al., 2018). The annual
increment of recruits was calculated using the actual biomass
minus the biomass of the individual with DBH of at least 1 cm
(the cutoff value for DBH), divided by the average time between
the censuses. Because the initial AGB differed in each plot, which
may affect AWP, we focused on the relative aboveground woody
biomass productivity (RAWP, % yr
−1
), defined as the ratio of
AWP to AGB (Piponiot et al., 2022).
Measurement of leaf and root traits
In this study, we focused on four pairs of functional traits that
reflect the LES (Wright et al., 2004) and the RES (Bergmann
et al., 2020). Pairs of morphological traits were leaf thickness
(LT) and RD; specific leaf area (SLA) and SRL; the chemical
traits included leaf and root nitrogen content (LNC and RNC)
and carbon : nitrogen ratios (LCN and RCN). More details of
these traits can be seen in Table S2. Previous studies have found
that RCN was closely related to root tissue density (RTD) at both
the species and community level (Wang et al., 2018;An
et al., 2022). Therefore, to analogize with leaf traits, we consid-
ered RCN here instead of RTD, even which is the more com-
monly used RES trait. All functional traits were measured from 5
to 10 randomly selected individuals for each woody species pre-
sent in the study area. For each individual tree, the leaf and root
samples were collected according to a common protocol.
At least five fresh, intact, and fully expanded leaf samples were
taken from each sampled individual on the highest part of the
tree crown, which was exposed to direct sunlight or high lateral
light levels (Liu et al., 2013). Specific leaf area was obtained using
the standard methods (Cornelissen et al., 2003). Leaf images were
recorded in gray mode at 300 dpi, and Winfolia software
(Regent, Canada) was used to calculate the projected leaf area.
Specific leaf area was then calculated as the ratio of leaf area to
dry weight. Leaf thickness was measured using an electronic digi-
tal caliper at 5–10 points per blade, avoiding the midrib. Leaf C
and N concentrations were gathered using an elemental analyzer
PE2400 SeriesII (PerkinElmer Inc., Waltham, MA, USA). Leaf
carbon : nitrogen ratio was calculated as the leaf carbon concen-
tration divided by the leaf nitrogen concentration.
Root samples were collected according to the procedure
described by Freschet et al.(2021a). One intact fine root strand
was sampled from each individual tree by tracing coarse roots from
the stem until fine roots were reached. The intact sample was cut
from the main lateral woody roots and then immediately trans-
ported to the laboratory for further morphological and chemical
analyses. The traditional definition of fine roots using a 2 mm dia-
meter threshold has recently been criticized (McCormack
et al., 2015) and may be especially problematic in woody species
(Freschet et al., 2021a). The main limitation is that the pool of
roots ≤2 mm includes several root orders that differ in structure
and function. The functional classification approach can thus be
useful to reconcile traditional single diameter cutoff approaches by
subdividing the single fine-root category into functionally similar
categories of absorptive roots and transport roots (McCormack
et al., 2015; Freschet et al., 2021a). We therefore focused on the
most distal second-order roots that are involved in resource acqui-
sition and often considered most ‘absorptive’ in woody species
(McCormack et al., 2015). In the laboratory, after careful washing
of adhering soil particles, the root strands were sorted into second-
order roots by hand. The root samples were then scanned to
images at 800 dpi using the photo scanner. The images were ana-
lyzed with the software WINRHIZO 2004a (Regent Instruments,
Canada) to measure average RD and total root length. The oven-
dry weight of each root sample was obtained after being dried at
60°C for at least 48 h. Specific root length was then calculated as
the ratio of total root length to oven-dry weight. Root nitrogen
content and root carbon : nitrogen ratios (RCN) were determined
using the same methods as those applied to LNC and LCN.
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Statistical analysis
We calculated the CWM value for each leaf and root trait using
the following equation (Garnier et al., 2004; van der Sande
et al., 2018):
CWM ¼∑
n
i
wiTi,
where w
i
and T
i
are the relative basal area and the trait value of
species i, respectively, and nis the total number of species in the
community. We weighted by basal area which reflects relative
biomass and is a better indicator of ecosystem functioning than
abundance (Prado J´
unior et al., 2016; Wieczynski et al., 2018).
We use community composition data from the first inventory.
All traits were log-transformed before analysis to meet assump-
tions of normality.
We identified the main dimensions of community-level trait
variation by performing principal component analyses (PCAs)
using CWM values of the measured traits for: only the leaf traits
(LPCA); only the root traits (RPCA); and whole set of traits
together. Additionally, Pearson’s correlation analysis was per-
formed to explore the correlations between all leaf and root traits.
We extracted the scores of the first axis (LPC1) from LPCA
because it captured a high proportion of the variation (84.8%);
consequently, its scores can be used as a proxy of the leaf eco-
nomic spectrum, while we found that the RPCA separated along
two dimensions and the first two components jointly explained
97.4% of the total variation. For this reason, the scores of the first
two axes (RPC1 and RPC2) from RPCA were used as the contin-
uous variables defining community-level root economic space.
Principal component analyses were performed using the R pack-
age FACTOMINER(L
ˆ
eet al., 2008), and all the axes of these PCAs
were not rotated.
To test for associations between the principal components of
CWMs and the environmental variables, we applied multiple lin-
ear regression using the lm function in R. The LPC1, RPC1, and
RPC2 scores were used as response variables, and the environ-
mental variables were used as predictors. The possible interac-
tions between these predictors were not considered in our
models. For each model, the variance inflation factors of all pre-
dictors were <5, to avoid multicollinearity. Before fitting these
models, all explanatory variables and response variables were
standardized (to 0 mean and 1 SD) to interpret the relative
importance of these variables on a comparable scale (Gross
et al., 2017). Then, a model selection procedure based on mini-
mizing Akaike’s information criterion (AIC, ΔAIC <2) was used
to select the best predictors (Table S3) of community-level eco-
nomic spectrums (Burnham & Anderson, 2002). The dredge
function in the MUMINpackage in R was used for this procedure
(Barto´
n, 2022). We calculated the relative effects of each factor as
the ratio of the sum of its parameter estimate to the sum of all
parameter estimates in the model, expressed as a percentage.
We clarified the linear relationships between community-level
gradients of trait variations and ecosystem functions using linear
regressions with the AGC and RAWP as the dependent variable,
and each of the extracted PCA axis as the independent variable.
Outliers were removed by dropping the outer 2% (1% highest
and 1% lowest) of initial aboveground carbon stock values.
Moreover, variation partitioning analysis (Borcard et al., 1992)
was used to estimate the independent and shared contributions
of the leaf and root traits to ecosystem functioning, using
adjusted R
2
in redundancy analysis ordination. All the leaf and
root traits were considered here, and we also considered the
topography and soil variables to assess the effects of environ-
mental conditions on ecosystem functioning. We carried out
the variation partitioning analysis using the varpart function of
the VEGAN package in R. All statistical analyses were performed
using R 4.2.1 (R Core Team).
Results
Covariation in community-level leaf and root traits
The first axis of the LPCA accounted for 84.8% of the total varia-
tion in CWM leaf traits (Fig. 1a). Traits associated with an acqui-
sitive strategy (e.g. high SLA and LNC) had positive loadings on
the first LPCA axis. Conversely, traits linked with a conservative
strategy (e.g. high LT and LCN) had negative loadings on the
first LPCA axis. LPC1 thus represents the leaf resource ‘conserva-
tion’ gradient (leaf economic spectrum). The PCA for absorptive
root traits revealed two major dimensions of root trait covariation
at the community level (Fig. 1b). The first RPCA axis accounted
for 52.1% of the total variability, which represents the root con-
servation gradient from RNC to RCN. The second RPCA axis
(45.3% of total trait variation) represents the root collaboration
gradient from SRL to RD. Together, the root conservation and
collaboration gradients encompass the so-called RES. The results
of the PCA based on all the leaf and root traits showed that the
first two principal components capture 86% of the total variation
(Fig. 1c). The conservation-related root traits (RNC and RCN)
formed a separate PC axis that was orthogonal to the leaf
conservation-associated traits, while the root collaboration PC
axis was closely aligned with the leaf conservation PC axis
(Fig. 1c). We found strong positive relationships between pairs of
leaf and root morphological traits (SLA–SRL and LT–RD), but
only relative weak positive correlations were observed between
pairs of leaf and root chemical traits (LNC–RNC and LCN–
RCN; Figs 1c,S1;df=513).
Explained variation of environmental factors on the
community-level trait dimensions
The variation in the dimensions of CWMs explained by envir-
onmental conditions (Fig. 2) was greatest for the root conserva-
tion gradient (RPC1, adjusted R
2
=0.41), followed by the root
collaboration gradient (RPC2, 0.13), and the leaf conservation
gradient (LPC1, 0.04). Topography factors played a dominant
role in determining the community-level root trait dimensions
(i.e. responsible for 80% and 98% of the explained variation in
RPC1 and RPC2, respectively). However, the relative impor-
tance of soil factors may be equivalent to the topography factors
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for driving the leaf conservation gradient (LPC1). Specifically,
available potassium (AK) and convexity (CON) were both sig-
nificantly associated with the ‘slow’ side of the leaf conservation
gradient (i.e. had negative effects on LPC1; Fig. 3a). Elevation
(ELE) was also associated with ‘fast’ communities, but this
effect was only marginally significant. For the root conservation
gradient, slope (SLO) was significantly associated with ‘slow’
communities, which was the strongest individual predictor
(Fig. 3b). Available potassium (AK) and convexity (CON) were
also significantly associated with the ‘slow’ communities, avail-
able nitrogen (AN), and northerly aspect (cosA), on the con-
trary, were significantly associated with ‘fast’ communities.
Additionally, the slope (SLO) and elevation (ELE) were signifi-
cantly associated with the ‘do-it-yourself’ side of the root ‘colla-
boration’ gradient (Fig. 3c).
Explained variation of leaf and root traits on ecosystem
functioning
The economic gradients have significantly related to both AGC
and RAWP (Fig. 4). Aboveground carbon storage was more
strongly related to the root conservation gradient (R
2
=0.31;
Fig. 4b) than the leaf conservation gradient (R
2
=0.06; Fig. 4a)
and the root collaboration gradient (R
2
=0.02; Fig. 4c). The
impact of the root conservation gradient (R
2
=0.09; Fig. 4e)on
RAWP was also stronger than that of the root collaboration gra-
dient (R
2
=0.03; Fig. 4f) and the leaf conservation gradient (R
2
=0.02; Fig. 4d). The variation partitioning analysis revealed that
leaf traits, root traits, and environmental conditions together
accounted for 46.0% and 25.3% of the total variation of AGC
and RAWP, respectively (Fig. 5). For AGC (Fig. 5a), the highest
proportion of variance explained came from the root traits
(40.2%), followed by the environmental conditions (26.1%),
and then the leaf traits (18.6%). Similarly, the greatest amount of
variation explained was by the root traits (22.2%), followed by
leaf traits (15%), and the environmental conditions (11.4%) in
RAWP (Fig. 5b).
Discussion
Trait relationships and dimensionality of community leaf
and root traits
Identifying the major dimensions of leaf and root trait variation
has become a central focus in ecological studies over the past few
decades (Wright et al., 2004;Dı´az et al., 2016; Bergmann
et al., 2020). Instead of examining the coordination of species
mean traits, this study used community-level traits to account for
the effect of species turnover, which represented the functional
dominance of each community. We found strong coordination
and trade-off among CWMs of leaf traits, which indicated the
existence of a unidimensional community-level LES (Fig. 1a).
This result was consistent with those of previous studies
Fig. 1 Principal components analyses (PCA) of community-level (a) leaf traits, (b) absorptive root traits, and (c) whole set of traits. Abbreviations for traits
are as follows: LCN, leaf carbon : nitrogen ratios; LNC, leaf nitrogen content; LT, leaf thickness; RCN, root carbon : nitrogen ratios; RD, root diameter; RNC,
root nitrogen content; SLA, specific leaf area; SRL, specific root length.
Fig. 2 Explained variation and relative effect of each environmental factor
on the dimensions of community-level leaf and root traits. We show the
adjusted R
2
and the value of Pof the averaged models; LPC1, the scores of
the first axis from LPCA; RPC1 and RPC2, the scores of the first two axes
from RPCA, respectively.
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Fig. 3 Effects of environmental variables on (a) leaf conservation gradient, (b) root conservation gradient, and (c) root collaboration gradient. We show the
averaged parameter estimates (standardized regression coefficients) of model predictors and the associated 95% confidence intervals. The P-value of each
predictor is given: (.), P<0.1; *,P<0.05; **,P<0.01; ***,P<0.001. AK, available potassium; AN, available nitrogen; AP, available phosphorus; CON,
convexity; cosA, North aspect; ELE, elevation; PH, soil acidity; sinA, East aspect; SLO, slope.
Fig. 4 Relationships between leaf conservation gradient, root conservation gradient, and root collaboration gradient to AGC (a–c) and RAWP (d–f). AGC,
aboveground carbon storage (Mg ha
−1
); RAWP, relative aboveground woody biomass productivity (% yr
−1
). Shown are the R
2
and Pvalues of the
regressions, and shading areas associated with the lines represent the 95% confidence interval.
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conducted at the species level in that plant strategy ranging from
fast-growing, short-lived, and acquisitive leaves to slow-growing
but long-lived and therefore more conservative leaves (Reich
et al., 1992; Wright et al., 2004). The leaf ‘conservation’ gradient
is the central component of the LES, which represent the trade-
off in leaf traits related to a ‘slow’ vs ‘fast’ return on resource
investment (Weigelt et al., 2021). At the community level, this
leaf ‘conservation’ gradient ranged from ‘slow’ communities with
high LT and high LCN to ‘fast’ communities with high SLA and
LNC.
However, in contrast to the unidimensional leaf economic
spectrum, our study demonstrated that covariation in CWMs of
absorptive root traits separated along two dimensions that closely
corresponded to the two ecological gradients recently identified
for variation among species (McCormack & Iversen, 2019; Berg-
mann et al., 2020). We found that RNC and RCN, respectively,
represent the ‘fast’ and ‘slow’ end of the classic ‘conservation’ gra-
dient, forming a trade-off relation, which is orthogonal to the
‘collaboration’ gradient from RD to SRL among communities
(Fig. 1b). Henceforth, these gradients suggest the existence of the
community-level RES (Kramer-Walter et al., 2016; Wang
et al., 2018). The decoupled pattern of absorptive root traits may
be a consequence of different resource uptake abilities for roots
compared to leaves. Leaves are adapted for maximizing light and
CO
2
capture while reducing resource loss by herbivores (Poorter
et al., 2009), thus clearly associated with one trait syndrome.
Nevertheless, roots face a more complex soil environment and
selective pressures (Bardgett et al., 2014; Weemstra et al., 2016),
resulting in multiple resource acquisition strategies.
Understanding the covariation of leaf and root traits may
explain certain ecological strategies of whole plants (Craine
et al., 2005). In this study, we selected four common pairs of ana-
logous leaf and absorptive root traits and found that both mor-
phological trait pairs (i.e. SRL and SLA, RD, and LT) and
chemical trait pairs (LNC–RNC and LCN–RCN) exhibited
positive correlations at the community level (Fig. S1). The posi-
tive relations between trait pairs identified here and in other stu-
dies suggest that leaf and root compartments are functionally
bonded (Freschet et al., 2015; de la Riva et al., 2016). However,
we also found a relatively weak coordination between chemical
trait pairs compared with the morphological trait pairs (Figs 1c,
S1). The leaf conservation gradient was orthogonal to the root
conservation gradient, but closely aligned with the root collabora-
tion gradient (Fig. 1c). This result is in contrast to a previous
study (Weigelt et al., 2021). It shows that leaf and root conserva-
tion gradients align as a whole-plant fast–slow gradient while the
root collaboration gradient showed is independent. Our result is
not a special case; the strong correlation between community-
level SRL and leaf conservation gradient was consistent with the
findings of studies of Mediterranean forests, shrublands, and sub-
tropical evergreen broad-leaved forests (de la Riva et al., 2016;
Delpiano et al., 2020; Shen et al., 2022).
There are two possible reasons explaining the mixed results of
the coordination between leaf and root traits. One reason may be
that these studies were conducted at different spatial scales and
locations. For example, studies undertaken at the regional to glo-
bal scale often include many different taxonomic groups from
several biomes. However, local scale studies seemed to be more
prone to biases due to species pool limitations (Wigley
et al., 2016). In addition, the absolute constraints (e.g. biophysi-
cal laws) are ubiquitous across all taxa and scales, whereas the
variable constraints can change across environmental conditions
(Messier et al., 2017). Consequently, relationships between leaf
and root traits are expected to be less consistent across spatial
scales and locations. This is also why we found that the
community-level LES and RES are consistent with comparisons
at the global scale, but not in the relationship between leaf and
root resource acquisition gradients. Our findings suggest that the
local-scale community leaf and root trait covariations may not be
generalized. Another reason may be that the relationships
between leaf and root traits differed between woody and non-
woody species. For example, a previous study found significant
correlations between SLA and SRL for woody species but not for
nonwoody species (Wang et al., 2017). The different root branch
systems and root functional classifications between woody and
herbaceous species may lead to the disparity of leaf–root trait
relationships (McCormack et al., 2015; Roumet et al., 2016;
Freschet & Roumet, 2017). In this study, we only focused on the
Fig. 5 Explained variation of leaf traits, root traits, environmental conditions, and their interaction with the (a) aboveground carbon storage (AGC) and (b)
relative aboveground woody biomass productivity (RAWP). The total percentage of the explained variation of each factor is shown. Significant values were
tested by 999 permutations with repetitions: *,P<0.05; ***,P<0.001.
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leaf and root traits of woody species in the temperate forest. This
is also the reason why we found a stronger coordination between
morphological trait pairs than chemical trait pairs compared with
other studies comprising both woody and herbaceous species or
studies only focusing on herbaceous species (Bergmann
et al., 2017; Weigelt et al., 2021). Overall, our study provides
empirical evidence of community-level leaf-root trait relation-
ships across forest communities at the local scale.
Contrasting responses of community-level leaf and root
trait dimensions to environmental conditions
Unraveling the relative effects of environmental factors on leaf
and root resource acquisition strategies of communities remains
an important challenge. In this study, we found that the
community-level leaf and root resource acquisition strategies
were all significantly affected by local-scale environmental condi-
tions. However, the variation in these gradients explained by each
environmental factor, and their relative importance varied greatly
between leaf and root traits (Fig. 2). In general, the explained var-
iation was higher for root traits (i.e. root ‘conservation’ and root
‘collaboration’ gradients) than for leaf traits (leaf ‘conservation’
gradient). These results suggest that environmental conditions
(topography and soil factors) were the primary drivers of commu-
nity root economic space but not of community leaf economic
spectrum. The contrasting strategies adopted by leaves and roots
may give plant communities greater flexibility to cope with mul-
tiple and complex environmental constraints (Freschet
et al., 2013; Weemstra et al., 2016; Isaac et al., 2017). In addi-
tion, our results demonstrate that topographic factors play a
dominant role in determining the shift in the covariation of
community-level root traits.
Our finding is consistent with a previous study conducted in
tropical forests (Pierick et al., 2021), highlighting the possibility
that topography acts as an environmental filter for promoting the
different belowground strategies. This is because topography as a
comprehensive proxy of several environmental conditions, not
only affected the soil moisture and nutrient availability (John
et al., 2007; McLaughlin et al., 2017), but also influenced other
environmental components, such as soil texture and chemistry
(Weemstra et al., 2016), thus creating a mosaic of heterogeneous
micro-habitats that structure tree communities at the local scale.
More specifically, we found that slope is the strongest individual
driver of the root ‘conservation’ gradient and is significantly asso-
ciated with the ‘slow’ strategy (Fig. 3b). This indicates that roots
exhibited more acquisitive root traits at the fertile lower slopes to
more conservative root traits at the nutrient-limited upper slopes.
Consistent with both the theoretical framework and several
empirical studies (Kong et al., 2014; Reich, 2014; Roumet
et al., 2016; de la Riva et al., 2018), our results support the
hypothesis that absorptive roots in more resource-limited envir-
onments tend to follow a more conservative resource-use strategy.
We also found no clear relationship between the root ‘collabora-
tion’ gradient and soil factors (Fig. 3c). This finding is consistent
with previous studies (Kramer-Walter et al., 2016; Ding
et al., 2020), which suggests a decoupling of diameter-related
root morphological traits from soil nutrient gradients. The root
‘collaboration’ gradient related to the intensity of plant-fungal
interactions, either thick roots with a high degree of colonization
with mycorrhizas (e.g. ‘outsourcing’ strategy) or thin roots acting
more independently from them (e.g. ‘do-it-yourself’ strategy),
would both support a root system to function in an acquisitive
way (Bergmann et al., 2020; Stock et al., 2021; Weigelt
et al., 2021). Such a mechanism may offset the effect of soil fac-
tors on the diameter-related root morphological traits. Overall,
the community-level root trait covariation is likely to be caused
by the environmental filtering processes, especially the effects of
topographic factors.
Root traits are better predictors of ecosystem functions
than leaf traits
We found consistent relationships between leaf and root ‘conser-
vation’ gradients with ecosystem functions, and that root traits
exhibited higher intensity compared with leaf traits (Fig. 4). Tree
communities with resource acquisition traits (‘fast’ communities)
showed higher relative aboveground woody biomass productivity
(RAWP) but lower aboveground carbon stocks (AGC). This pat-
tern might be attributable to the trade-offs between tree growth
and survival (Wunder et al., 2008; Reich, 2014). The commu-
nities dominated by species with acquisitive traits (growing fast
but dying young) would associate with greater transient produc-
tivity, whereas communities dominated by species with conserva-
tive traits (slow growth and long life) would eventually have
greater carbon stock (Dı´az et al., 2009; Wright et al., 2010; Hao
et al., 2020). Therefore, we propose that the plant economics
spectrum determines the trade-off between tree performance,
while the community economics spectrum determines the trade-
off between ecosystem properties.
We also found that the root ‘conservation’ gradient could bet-
ter predict AGC and RAWP than the leaf ‘conservation’ gradient.
Our results suggest that the root ‘conservation’ gradient may be
more important for affecting specific ecosystem functions. How-
ever, the root ‘collaboration’ gradient showed only a weak rela-
tion with AGC and RAWP. Additional analyses of mycorrhizal
fungi-related root traits may lead to better estimates of tree per-
formance, carbon cycling, and storage at the ecosystem level
(Weemstra et al., 2022). In addition, this study only focused on
the LES traits, without including other important leaf trait
dimensions, for example, leaf hydraulics, which is orthogonal to
LES (Li et al., 2015). Therefore, incorporating more leaf trait
dimensions apart from the LES may improve the power of leaves
in linking ecosystem functions. Overall, we found clear evidence
that root traits play a critical role in modulating both AGC and
RAWP (Fig. 5). Our results reveal the contributions of root traits
on specific ecosystem functions, which emphasizes the need to
further extend the trait-based approach underground.
Most previous studies have only considered trait–functioning
relationships, without testing for additional effects of environ-
mental conditions and the interaction with traits (van der Plas
et al., 2020). York et al.(2013) found that traits and environment
together significantly influence functioning. Our findings
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suggested that contrasting feedbacks of leaf and root traits with
ecosystem functions might be attributable to very specific
decoupled responses to multiple environmental factors (Freschet
et al., 2013). In addition to the effects of plant traits and environ-
mental conditions, the effects of species richness (SR) on many
ecosystem functions and especially aboveground productivity are
well studied (Liang et al., 2016; Weisser et al., 2017). We there-
fore inspect the importance of SR on these two ecosystem func-
tions (Fig. S2). Results showed that SR has an insignificant
relation with AGC and significant positive relation with RAWP.
The positive biodiversity–productivity relationship (BPR) found
here is consistent with previous studies conducted at the local
scales (Chisholm et al., 2013; Thompson et al., 2018).
Future studies should consider the intraspecific variation in the
trait–functioning relationships. Our analysis only focused on the
interspecific variation in leaf and root traits, ignoring the possible
contribution of intraspecific trait variation. Intraspecific variation
was found to contribute considerably to trait variation and can
thus influence community composition and ecosystem processes
(Messier et al., 2010; Westerband et al., 2021). Recent studies
demonstrate that intraspecific rather than interspecific root trait
variability plays an important role in driving plant root commu-
nity responses to long-term climatic changes (Spitzer et al.,
2022). In addition, this study only focused on the linear relations
between traits and ecosystem functions without considering the
potential nonlinear response, which may provide a better fit for
the data. Developing a new methodology for quantifying
community-level root traits is also essential to better incorporate
root traits into ecological processes (Wang et al., 2021). For
instance, the novel framework of ‘trait-based productivity’
appears to improve the understanding of productivity-related
ecosystem functions from leaf community traits and environmen-
tal conditions (He et al., 2022). Such new concepts involving
root traits can help to resolve the difficulties of traditional
scaling-up approaches (Freschet et al., 2021b), and better link the
root traits with ecosystem functioning.
Acknowledgements
This study was supported by the Key Project of National Key
Research and Development Plan (2022YFD2201003), and Beij-
ing Forestry University Outstanding Young Talent Cultivation
Project (2019JQ03001).
Competing interests
None declared.
Author contributions
RD and CZ conceived the idea of this study. RD, CF, CZ and
XZ collected and extracted data. RD analyzed the data and wrote
the first draft of the manuscript. CF, CZ, XZ and KvG contribu-
ted to the writing via multiple rounds of revision. All authors
contributed substantially to manuscript revisions and the devel-
opment of the conceptual framework.
ORCID
Rihan Da https://orcid.org/0000-0003-0552-5108
Chunyu Fan https://orcid.org/0000-0002-3360-2919
Klaus von Gadow https://orcid.org/0000-0003-3641-0397
Chunyu Zhang https://orcid.org/0000-0003-3091-5060
Xiuhai Zhao https://orcid.org/0000-0003-0879-4063
Data availability
The data that support the findings of this study can be accessed
on Figshare (doi: 10.6084/m9.figshare.22193479).
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Supporting Information
Additional Supporting Information may be found online in the
Supporting Information section at the end of the article.
Fig. S1 Pairwise correlation of all traits used in the analysis.
Fig. S2 Relationships between species richness to AGC and
RAWP.
Table S1 Region-specific allometric equations for the calculation
of aboveground biomass for each tree species (in kg).
Table S2 Descriptive statistics of the functional traits used in this
study.
Table S3 Results of multiple regression models for the dimen-
sions of community-level leaf and root traits.
Please note: Wiley is not responsible for the content or function-
ality of any Supporting Information supplied by the authors. Any
queries (other than missing material) should be directed to the
New Phytologist Central Office.
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