Linking rhizosphere soil microbial activity and plant resource acquisition strategy

Abstract

Plants live in association with a diversity of soil microorganisms, which are extremely important in affecting plant growth and soil biogeochemical cycling.
By adopting plant trait‐based approaches, we explored the linkages between rhizosphere soil microbial activity and plant resource acquisition strategy of above‐ and below‐ground across a range of tree species in a subtropical evergreen mixed forest. The microbial activities were represented by diverse extracellular enzymes relevant to carbon, nitrogen and phosphorus cycling and soil organic carbon (SOC) mineralization.
At the species level, leaf and root traits were mainly represented by two leading dimensions, that is, the ‘fast‐slow’ economics spectrum on which leaf and root traits were well aligned and the orthogonal collaboration gradient in the root.
Both extracellular enzymes and SOC mineralization in the rhizosphere varied greatly across plant species. We found that diverse rhizosphere soil microbial activities positively correlated with the classical ‘fast‐slow’ conservation gradient of plant resource acquisition (especially above‐ground), that is, the rhizosphere soil microbes associated with fast‐growing plant species feature higher metabolism than that of slow‐growing plant species. In comparison, rhizosphere soil microbial activities were independent of the plant collaboration gradient in the root, and it might be an alternative exploitative strategy in foraging soil nutrients for plants.
Synthesis. Our study strengthens the multivariate nature of plant resource acquisition in adapting to above‐ and below‐ground stresses. The findings on the linkages between rhizosphere soil microbial activity and plant resource acquisition strategy have the potential to improve our understanding and prediction of plant species turnover impacts on soil biogeochemical cycles.

Full text

Journal of Ecology. 2023;00:1–14.
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1wileyonlinelibrary.com/journal/jec
Received: 19 July 2022
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Accepted: 6 January 2023
DOI: 10.1111/1365-2745.14067
RESEARCH ARTICLE
Linking rhizosphere soil microbial activity and plant resource
acquisition strategy
Mengguang Han1 | Ying Chen1 | Lijuan Sun2 | Miao Yu1 | Rui Li1 |
Shuaifeng Li3 | Jianrong Su3 | Biao Zhu1
© 2023 The Authors. Journal of Ecology © 2023 British Ecological Society.
1Institute of Ecology, College of Urban
and Environmental Sciences, and Key
Laboratory for Ear th Sur face Processes
of the Ministry of Education, Peking
University, Beijing, China
2State Key Laboratory of Herbage Seeds
and Grassland A gro- ecosystems, and
College of Pastoral Agricultural Science
and Technology, Lanzhou University,
Lanzhou, China
3Institute of Highland Forest Science,
Chinese Academy of Forestry, Kunming,
China
Correspondence
Biao Zhu
Email: biaozhu@pku.edu.cn
Funding information
China Postdoctoral Science Foundation,
Grant/Award Number: BX20220003;
National Natural Science Foundation of
China, Grant/Award Number: 31988102
Handling Editor: Marina Semchenko
Abstract
1. Plants live in association with a diversity of soil microorganisms, which are ex-
tremely important in affecting plant growth and soil biogeochemical cycling.
2. By adopting plant trait- based approaches, we explored the linkages between
rhizosphere soil microbial activity and plant resource acquisition strategy of
above- and below- ground across a range of tree species in a subtropical ever-
green mixed forest. The microbial activities were represented by diverse extra-
cellular enzymes relevant to carbon, nitrogen and phosphorus cycling and soil
organic carbon (SOC) mineralization.
3. At the species level, leaf and root traits were mainly represented by two lead-
ing dimensions, that is, the ‘fast- slow’ economics spectrum on which leaf and
root traits were well aligned and the orthogonal collaboration gradient in the
root.
4. Both extracellular enzymes and SOC mineralization in the rhizosphere varied
greatly across plant species. We found that diverse rhizosphere soil microbial
activities positively correlated with the classical ‘fast- slow’ conservation gradi-
ent of plant resource acquisition (especially above- ground), that is, the rhizos-
phere soil microbes associated with fast- growing plant species feature higher
metabolism than that of slow- growing plant species. In comparison, rhizos-
phere soil microbial activities were independent of the plant collaboration gra-
dient in the root, and it might be an alternative exploitative strategy in foraging
soil nutrients for plants.
5. Synthesis. Our study strengthens the multivariate nature of plant resource acqui-
sition in adapting to above- and below- ground stresses. The findings on the link-
ages between rhizosphere soil microbial activity and plant resource acquisition
strategy have the potential to improve our understanding and prediction of plant
species turnover impacts on soil biogeochemical cycles.
KEYWORDS
decomposition, economics spectrum, extracellular enzyme, microbial activity, plant trait,
rhizosphere, trait space
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HAN et al.
1 | INTRODUC TION
Over time, plant species have evolved a suite of diverse strategies
to survive and compete for resources in natural habitats, and such
strategies have been quantified and interpreted by adopting trait-
based approaches in the light of economics theory (Díaz et al., 2016;
Reich, 2014; Weigelt et al., 2021; Wright et al., 20 04). Following the
trade- off between growth and survival, plant species fall along a
‘fast- slow’ economics spectrum: from fast resource acquisition and
therefore rapid growth to the conservation of acquired resources
but enhanced survival and life span. This classical ‘fast- slow’ eco-
nomics spectrum (or called conservation gradient) is applied ideally
to depict the above- ground plant resource acquisition strategy but is
partly applicable to depict the resource acquisition strategy of plant
below- ground, and the latter has been found to be supplemented
by another trait dimension named the collaboration gradient (from
‘do it yourself’ resource uptake by roots to ‘outsourcing’ of resource
uptake to mycorrhizal partners) (Bergmann et al., 2020; Weigelt
et al., 2021). The multidimensional trait- based plant resource acqui-
sition has been successfully linked to plant performance (Weemstra
et al., 2021), species distribution and community assembly (Laughlin
et al., 2021; Laughlin & Laughlin, 2013), and ecosystem processes
(e.g. soil organic matter turnover) (Bardgett et al., 2014; Han
et al., 2020; Henneron et al., 2020).
Plants live in association with soil microorganisms during their
entire development, and it is well recognized that plant nutrition is
considerably affected by microbial community composition and ac-
tivity in the rhizosphere, the zone of soil under the direct influence of
living roots (Hartmann et al., 2008; Lambers et al., 2009). Thus, co-
operating with a rich diversity of rhizosphere microorganisms might
be an essential way to acquire nutrients for plants, and the func-
tioning of these microbes is extremely important in affecting plant
growth, soil nutrient cycling and soil carbon (C) dynamics (Denison
et al., 2003; Kuzyakov & Razavi, 2019; Lambers et al., 2009). Despite
such intimate connections between plants and microorganisms in
the rhizosphere, and considerable influences of plant species identity
on rhizosphere processes have been identified (Cheng et al., 2014;
Lambers et al., 2009; Lau & Lennon, 2011), the knowledge about
how plant traits and associated resource acquisition strategies influ-
ence rhizosphere soil microbial activity needs further exploration.
Typically, in previous reports, (i) most of our understanding on this
topic centers around the connection of plant above- ground traits
and soil properties (De Vries et al., 2012; Garnier et al., 2004); (ii)
roots and leaves are rarely considered as a unified whole, despite
the fact that plant as a whole influences soils rather than through
isolated organs (Weemstra et al., 2022) and (iii) the linkage of plant
traits and soils is in the context of a one- dimensional resource eco-
nomics spectrum, neglecting the multivariate nature of plant below-
ground resource acquisition (i.e. multidimensional trait space) (Han
et al., 2020; Henneron et al., 2020).
In recent years, it has been proposed that fast- strategy grassland
species promote rapid mineralization of soil organic carbon (SOC),
while the opposite is true for slow- strategy grasses (Henneron
et al., 2020). This result has also been elucidated across a range of
tree species in a temperate hardwood forest (Han et al., 2020). Such
tight correlation between plant ‘fast- slow’ economics spectrum and
SOC mineralization seems to be driven by the plant fixing C above-
ground of which up to 20% is exuded below- ground as, for example,
sugars, organic acids and/or amino acids (i.e. root exudation, Haichar
et al., 2008; Guyonnet et al., 2018; Henneron et al., 2020). Root ex-
udates are the main energy- labile carbohydrates for soil microbiota
and are known to stimulate (and sometimes restrain) their activity,
as pointed out by the ‘microbial activation hypothesis’ (Dijkstra
et al., 2013; Kuzyakov, 2002). Whether in quantity (total amount
of organic C) or quality (e.g. the proportion of nitrogen- rich com-
pounds and the diversity of components), root exudation is higher
for fast- growing than slow- growing plant species (Drake et al., 2013;
Guyonnet et al., 2018). This is probably due to the fact that fast-
growing plants feature larger photosynthetic capacity and higher
below- ground investment of net C fixation than slow- growing plants
(Henneron et al., 2020; Kaštovská et al., 2015). More directly, as an
acquisitive trait, root exudation is more closely correlated with root
nitrogen concentration (positively) and root tissue density (nega-
tively) compared with root morphological traits, and is mainly loaded
onto the below- ground ‘fast- slow’ conservation gradient (Sun
et al., 2021; Wen et al., 2022; Williams et al., 2022).
It needs to be noted that SOC mineralization could not fully rep-
resent microbial activity during the processes of microbial mining
for soil nutrients, for example, phosphorus (P) is mostly released
through hydrolysis of organic phosphate compounds by phospha-
tase without CO2 production (Dijkstra et al., 2013). Nitrogen (N) and
P are deemed as the two most important nutrients that limit plant
productivity in terrestrial ecosystems (Elser et al., 2007). However,
most N and P (sometimes >90%) are contained as complex insol-
uble polymers in soils, such as proteins, chitin, mononucleotides,
phospholipids and nucleic acids. These organic polymers are cleaved
into available N and P by extracellular enzymes produced by soil mi-
crobes before they can be absorbed and used by plants (Schimel &
Bennett, 2004; Turner & Engelbrecht, 2011). Therefore, extracellu-
lar enzymes are also deemed as useful indicators of soil microbial
activities and the capacity of soil nutrient release in the rhizosphere
(Kuzyakov & Razavi, 2019). However, the linkages between plant
traits and these rhizosphere soil extracellular enzymes are still
largely unclear. We know relatively little about how rhizosphere soil
microbial activities associated with C- , N- and P- cycling link to the
different functional dimensions of plant resource acquisition.
By far, the large majority of plant traits (especially root) and
rhizosphere process studies are carried out in temperate ecosys-
tems, and such knowledge lags far behind in the (sub)tropics (Gan
et al., 2021; Guerrero- Ramírez et al., 2021; Kattge et al., 2020). Here,
in this study, the interspecies changes of rhizosphere soil microbial
activities across a range of tree species spanning both the classical
‘fast- slow’ economics spectrum (conservation gradient) and the col-
laboration gradient of plant resource acquisition were explored in a
subtropical evergreen mixed forest. We built up on a previous study
in which root traits and leaf chemical traits have been determined
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Journal of Ecology
HAN et al.
(Han et al., 2022). Rhizosphere soil extracellular enzymes relevant
to C- , N- and P- cycling and SOC mineralization were used as indi-
cators of microbial activities, and the higher enzyme activities and
SOC mineralization rate, the higher microbial activities (Levakov
et al., 2021). We also determined leaf morphological traits and rhi-
zosphere soil physical– chemical properties. Our objectives were to
(i) quantify the effect of tree species on rhizosphere soil microbial
activities, (ii) explore the correlations of rhizosphere soil microbial
activities with leaf traits, root traits and soil properties, and (iii)
unveil how rhizosphere soil microbial activities are linked to plant
resource acquisition strategy in the context of plant traits space.
We hypothesized that rhizosphere soil microbial activities would
be tightly and positively correlated with the ‘fast- slow’ economics
spectrum (from slow to fast) rather than the collaboration gradient of
the multidimensional plant resource acquisition, due to the central
role of root exudation in influencing rhizosphere microbiota and its
significant loading onto the fast side of the conser vation gradient of
plant resource acquisition.
2 | MATERIALS AND METHODS
2.1 | Site description and field sampling
The study was performed in a 30- ha permanent forest plot
(22°34′48″N, 101°70′48″E), which was located at the southern edge
of Wuliang Mountain, a transitional zone between the tropics and
subtropics of Yunnan, China. At an elevation between 1467 m and
1587 m a.s.l., the forest is a mixture of evergreen broadleaved spe-
cies. The mean annual temperature is 17.7°C, and the mean annual
precipitation is 1548 mm (Li et al., 2020).
We sampled 20 co- occurring tree species which comprised: (1)
the most important species in terms of the composition of the local
forest community; (2) as many families and genera from different
clades, to span a long evolutionary history and (3) as much variations
in leaf and root morphology (e.g. leaf area and root diameter) as pos-
sible, to span a wide range of strategies in resource acquisition. In
total, the 20 species were from 13 families and 19 genera. At least
three individuals (3– 5) of each species (leading to a total of 94 indi-
vidual trees) were randomly selected in the permanent forest plot,
and we collected rhizosphere soil, leaf and root samples from each
tree. The basic information of each species could be seen in Table S1.
2.2 | Rhizosphere soil microbial activities and
soil properties
We collected one sample of rhizosphere soil for each tree using the
adhering soil method, which has been widely employed in rhizos-
phere studies. Briefly, we first identified coarse roots that could be
traced back to the trunk of the mother tree and dug soil to a depth
of up to 20 cm along with these coarse roots. Then we picked out
at least 30 root branches of the first three orders attached to the
coarse roots. These root branches were gently shaken to collect the
soil adhering to the root surface, which was defined as rhizosphere
soil. We sampled 94 rhizosphere soil samples matching with the
total number of individual trees for the 20 species (Table S1). The
rhizosphere soil samples were passed through 2 mm sieves and then
used to determine potential activities of extracellular enzymes, SOC
mineralization rate, microbial biomass and other physical– chemical
properties, such as organic C, total N, available P and soil pH.
The extracellular enzymes related to labile C cycling (β- 1 , 4 -
glucosidase [BG] and β- cellobiohydrolase [CB]), recalcitrant C cycling
(Polyphenol oxidase [POX] and Peroxidase [PER]), N cycling (β- 1 , 4 - N-
acetylglucosaminnidase [NAG] and Leucine aminopeptidase [LAP])
and P cycling (Acid phosphatase [AP]) were measured using the 96-
well microplates (German et al., 2011). In brief, the fresh rhizosphere
soil was suspended in the sodium acetate buffer to make slurry, and
the buffer pH was adjusted to 4.6 which was close to the average pH
of soil samples; then we combined soil slurry and enzyme substrate
in each well of the microplates. The plates for hydrolytic enzymes
assay (BG, CB, NAG, LAP and AP) were incubated in the dark at 25°C
for 3 h, and the plates for oxidative enzymes assay (POX and PER)
were incubated for 24 h under the same condition. More details of
the method can be seen in Jing et al. (2017). Afterwards, we mea-
sured the amount of fluorescence for hydrolytic enzymes at 360 nm
excitation and 460 nm emission, and the amount of absorbance for
oxidative enzymes at 450 nm using a microplate reader (Synergy
2; Biotek). Detailed information of functions of these enzymes is
shown in Table 1.
We determined the potential rate of SOC mineralization through
a laboratory incubation experiment. In short, we weighed fresh
soil (8 g on an oven- dry basis) of each rhizosphere soil sample, and
placed it in a plastic jar with NaOH solution (0.5 M, 15 mL), then all
jars were incubated in dark at 25°C for 30 days. After incubation, the
electrical conductivity procedure was used for the measurement of
SOC- derived CO2, which was previously absorbed in alkali (Wollum
II & Gomez, 1970). A standard curve was first established by mixing
known amounts of NaOH and Na2CO3 and reading conductivities,
then we could calculate the quantity of CO2- C in the alkali and fur-
ther the rate of SOC mineralization according to the electrical con-
ductivity of the alkali.
The chloroform fumigation extraction method was performed
to determine soil microbial biomass C (MBC) (Vance et al., 1987).
The MBC wa s calculated as the difference between fumigated and
non- fumigated extracts (in 0.5 M K2SO4 solution) and then mul-
tiplied by a conversion factor (0.45) (Jenkinson et al., 2004). The
organic C in the extracts was measured by a TOC/TN analyser
(multi N/C 3100; Analytik Jena, Germany). The organic C concen-
tration calculated from non- fumigated extracts was also deemed
as soil- dissolved organic C (DOC). Soil organic C (SOC) and soil
total N (STN) were determined using an elemental analyser (Vario
EL III; Elementar). To measure soil total P (STP), a subsample of the
rhizosphere soil was first digested in the concentrated nitric acid–
hydrofluoric acid– perchloric acid (HNO3– H F – H C l O 4, 6:2:1), and
the P content in the extract was determined using an inductively
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HAN et al.
coupled plasma optical emission spectroscopy (iCAP 6000 Series;
Thermo Scientific). After that, we calculated the ratio of SOC to
STN (SCN) and STP (SCP), and the ratio of STN to STP (SNP). Soil
available N (SAN), that is, NH4
+- N and NO3
−- N, was firs t ext racted
using 2 M KCl solution, and then determined by a Continuous
Flow Analyser (AA3; Bran + Luebbe, Germany). To determine soil
available P (SAP), a subsample of the rhizosphere soil was first ex-
tracted using a mixed solution of NH4F- HCl and then determined
through an ultraviolet (UV) spectrophotometer. Soil pH was de-
termined using a laboratory pH meter, and soil water content was
estimated after oven drying at 105°C.
2.3 | Plant traits
For each tree, we collected 10 mature and intact leaves from the
upper canopy (southern exposure). We measured the thickness of
the upper, middle and lower parts of the leaf using a vernier cal-
liper, and leaf thickness (LT) was calculated as the mean value of the
three measurements. Leaf surface area was determined using an LI-
3000C (Li- Cor, Inc.). Afterwards, the leaf sample was dried at 65°C
to constant weight. The specific leaf area (SLA) was then calculated
as leaf surface area divided by leaf dry mass. Leaf N concentration
(LNC) was determined using the elemental analyser. Leaf P concen-
tration (LPC) was determined using inductively coupled plasma opti-
cal emission spectroscopy.
Using the root tracking method, we sampled one intact root
branch of the first three orders of each tree to measure mor-
phological and chemical traits, and several root branches (3– 5)
to determine root mycorrhizal colonization (RMC). These root
branches are different from those used to collect rhizosphere soil.
For root morphology and chemistry, the root branch was cleaned
using deionized water and scanned using an Epson Expression
11000XL scanner at a resolution of 400 dpi (Seiko Epson Corp.).
The scanned images were analysed using WinRHIZO pro 2004b
(Regent Instruments Inc.), to get the average root diameter (RD),
total length, total surface area, total volume and root tip num-
ber. Then all root branches were oven dried at 65°C to constant
weight. Specific root length (SRL) was calculated as total length
divided by root dry mass, specific root area (SRA) was calculated
as total surface area divided by root dr y mass, branching intensity
(BI) was calculated as root tip number divided by total length, and
root tissue density (RTD) was calculated as root dry mass divided
by total volume. Root N concentration (RNC) was determined
using the elemental analyser.
We quantified the RMC of species associated with arbuscular
mycorrhizal (AM) fungi and ectomycorrhizal (EcM) fungi. In short,
for AM trees, root branches were cleared using 10% KOH solution
in a 90°C water bath for 1 h, acidified in 2% HCl solution for 5 min
and were then stained with 0.05% Trypan blue until constantly blue.
Afterwards, roots were rinsed in lactic acid– glycerol– water solution
for decolorization and were then divided into fragments (c. 1 cm).
These root fragments were mounted on slides to quantify the de-
gree of mycorrhizal colonization of root branches, by visualizing
and counting any mycorrhizal fungi structures (e.g. arbuscules and
vesicles) (Trouvelot et al., 1986). For EcM trees, the root tips colo-
nized by EcM fungi can be visualized by the presence of a yellow- to
golden- brown swollen mantle. The RMC was determined based on
counts of EcM root tips and vital non- EcM root tips, that is, the per-
centage of EcM root tips in the total root tips (c. 100– 150) (Teste
et al., 2006). It should be noted that part of these plant traits data
has been published by Han et al. (2022).
Indicators Function Abbr.
β- 1 , 4 - g l u c o s i d a s e Catalyses the hydrolysis of terminal
1 , 4 - l i n k e d β- D- glucose residues
BG
β- cellobiohydrolase Catalyses the hydrolysis of 1,4- β- D -
glucosidic linkages in cellulose and
cellotetraose
CB
Polyphenol oxidase Oxidizes benzenediols to semiquinones
with O2
POX
Peroxidase Catalyses oxidation reactions via the
reduction of H2O2
PER
β- 1 , 4 - N- acetylglucosaminnidase Catalyses the hydrolysis of terminal 1,4
linked N- acetyl- beta- D- glucosaminide
residues
NAG
Leucine aminopeptidase Catalyses the hydrolysis of leucine and other
amino acid residues from the N- terminus
of peptides
LAP
Acid phosphatase Mineralizes organic P into phosphate by
hydrolyzing phosphoric (mono) ester
bonds under acidic conditions
AP
Soil organic carbon
mineralization
Representing the overall decomposition of
carbon substrate
SOC- min
Note: Abbr. represents the abbreviations of these microbial activities.
TAB LE 1 The indicators of rhizosphere
soil microbial activities in this study.
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HAN et al.
2.4 | Statistical analyses
In the present study, we focused on the activities of microorganisms
and thus adjusted the extracellular enzymes and SOC mineraliza-
tion rate to soil microbial biomass C (per unit MBC). The Kruskal–
Wallis test was performed across all individuals to test the effects
of species on rhizosphere soil extracellular enzymes and SOC min-
eralization rate. Leaf and root trait data and soil properties in the
rhizosphere were also averaged per species. We then assessed the
pairwise relationships between rhizosphere soil extracellular en-
zymes, SOC mineralization rate and leaf traits, root traits and soil
properties at the species level using Spearman's correlation analysis.
Multiple factor analysis (MFA) was then performed to evaluate
the relationships between two matrices, namely plant leaf traits and
root traits, for visualizing and identifying the multiple dimensions of
plant resource acquisition. The core of MFA is based on principal
component analysis and has been nicely used to analyse the correla-
tion of different matrices (i.e. variables are organized into distinct
groups) (Carlson et al., 2010; Dray et al., 2003). Among our target
tree species, the two ericoid mycorrhizal (ErM) species (Vaccinium ex-
aristatum and Lyonia ovalifolia) were not determined for RMC. Thus,
we performed the MFA of 20 species (the root traits matrix did not
contain RMC) and 18 species (the root traits matrix included RMC),
respectively. The first two dimensions which accounted for the most
variation of plant traits were then extracted from the MFA, based
on their significance using the ‘PCAtest’ function in the r package
PCAtest (Camargo, 2022). These two trait dimensions represented
different plant resource acquisition strategies. Linear regression
analysis was performed to quantify the relationships bet ween rhizo-
sphere soil microbial activities and plant resource acquisition strate-
gies (i.e. the first two trait dimensions) across species. The MFA was
conducted using the ‘MFA’ function in the r package FactoMineR (Lê
et al., 2008).
The shared ancestry is important in structuring interspecies
traits variation (especially for roots), due to the fact that evolution-
ary constraint is sometimes stronger than ecological filtering for
plant organs (Ma et al., 2018; Reich et al., 2003; Valverde- Barrantes
et al., 2015). Therefore, it is essential to examine the phylogenetic
conservatism of plant traits, and to clarify how plant phylogeny
would influence trait– trait correlations. We first constructed a phy-
logenetic tree of the target species pruned from the backbone phy-
logeny of Za nne et al . (2014 ), usi ng the ‘phylo.maker ’ function of the r
package V.P hyloMaker (Jin & Qian, 2019). The inf l u e nces of plant phy-
logeny on rhizosphere soil microbial activities and plant traits were
then evaluated by Blomberg's K value, using the ‘phylosig’ function
of the r package Phytools (Revell, 2012). A larger K value, a greater
phylogenetic conservatism of the variable (Blomberg et al., 2003).
Finally, we performed phylogenetic independent contrasts (PICs)
to analyse the influences of plant traits and soil properties on rhi-
zosphere soil extracellular enzymes and SOC mineralization rate by
excluding phylogenetic signals, using the ‘pic’ function in the r pack-
age ape (Paradis et al., 20 04). All statistical analyses were conducted
using R v.4.1.0 (R Core Team, 2021).
3 | RESULTS
3.1 | Interspecific changes of rhizosphere soil
microbial activities
Both soil extracellular enzymes and SOC mineralization rate in the
rhizosphere varied significantly among the 20 target tree species.
At the species level, the hydrolases and oxidases exhibited four- to
ninefold variation, SOC mineralization rate exhibited fivefold varia-
tion, and the coefficient of variations of these rhizosphere soil mi-
crobial activities were up to nearly 70% (Table 2). Interestingly, AP
was much greater than C- and N- cycling soil extracellular enzymes in
the rhizosphere, and this difference was consistent across all species
(please see Table 1 for the functions and abbreviations of different
extracellular enzymes). There were no significant phylogenetic sig-
nals in rhizosphere soil extracellular enzymes and SOC mineraliza-
tion rate (Table S2 and Figure S1), indicating that plant phylogeny
exhibited minor impacts on rhizosphere soil microbial activities.
3.2 | Multivariate ordination of leaf and root traits
The MFA showed that there were two leading dimensions in leaf
and root traits variation, and dim 1 and dim 2 together accounted for
nearly 70% variation of plant traits (Figure 1a,b). Specifically, LNC,
LPC, SLA , LT, RNC and RTD had significant loadings on dim 1 (both
LT and RTD showed negative correlations with LNC, LPC and SLA);
while SRL, SRA, BI, RD and RMC had significant loadings on dim 2
(both RD and RMC showed negative correlations with SRL, SRA and
BI) (Figure 1b; Figure S2a,b). Across all plant traits, only SRL, RD and
RMC exhibited significant phylogenetic signals (Table S2), and the
trait– trait correlations were weakly influenced by plant phylogeny
(Figure S2c).
3.3 | Relationships between rhizosphere soil
microbial activities and plant traits and soil properties
The Spearman's correlation analysis showed that nearly all rhizos-
phere soil extracellular enzymes and SOC mineralization rate were
generally linked to leaf traits and root nutrients (RNC) but were less
impacted by root morphological traits (Figure 2). Specifically, rhizo-
sphere soil extracellular enzymes and SOC mineralization rate in-
creased with LNC, LPC, SLA and RNC and tended to decrease with
LT. Rhizosphere soil nutrients and stoichiometric ratios also showed
significant positive correlations with soil extracellular enzymes (BG,
CB and NAG) and SOC mineralization rate. The results of PICs were
generally consistent with those of Spearman's correlation analysis
using the original data (Figure S3a,b), except that the correlations of
rhizosphere soil microbial activities with RNC were weakened after
considering plant phylogeny.
The regression analysis further demonstrated that rhizosphere
soil extracellular enzymes (BG, CB, NAG, LAP, AP, POX and PER)
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Journal of Ecology
HAN et al.
and SOC mineralization rate significantly increased with the dim 1
of the plant traits space, which generally represents the ‘fast- slow’
economics spectrum (from slow to fast) (Figure 3a– h), whether for 20
species (the root traits matrix did not contain root mycorrhizal colo-
nization, see Figure 1a) or 18 species (excluding V. e xaris tatu m and L.
ovalifolia which are ErM species, and the root traits matrix included
root mycorrhizal colonization, see Figure 1b). Comparatively, inter-
species rhizosphere soil microbial activities were not correlated with
the dim 2 of the plant traits space, which mostly denotes the collab-
oration gradient (from outsourcing to do it yourself) (Figure S 4 a – h ).
4 | DISCUSSION
Our study showed considerable interspecific differences in rhizos-
phere soil extracellular enzymes and SOC mineralization rate. The
interspecific variations of rhizosphere soil microbial activities were
significantly influenced by both plant traits and rhizosphere soil
nutrient conditions, but not by plant phylogeny. Furthermore, we
demonstrated that there was a tight linkage between the ‘fast- slow’
economics spectrum of plant resource acquisition (especially the
above- ground part of the plant) and rhizosphere soil microbial ac-
tivities. Specifically, rhizosphere soil extracellular enzymes and SOC
mineralization rate were higher among fast- than slow- growing tree
species, while these microbial activities were independent of the
collaboration gradient of plant below- ground resource acquisition.
For plants, this suggests that relying on rhizosphere soil microbial
metabolism might be an alternative exploitive strategy to acquire
soil resources, in addition to depending on the fine root itself and
mycorrhizal partner.
4.1 | Soil microbial activity in the rhizosphere
varies across tree species
Over the 20 target tree species, the microbial secreted hydrolases,
oxidases and SOC mineralization rate in the rhizosphere varied up to
sixfold, 10- fold and fivefold, respectively (Table 2). Such a high de-
gree of interspecific variation of rhizosphere soil microbial activities
was also revealed in grasslands, shrublands and temperate hardwood
forests (Cui et al., 2018; Han et al., 2020; Henneron et al., 2020).
This indicates high variability in the capacity of supplying restricted
TAB LE 2 Summary of the rhizosphere soil microbial activities across the 20 tree species. Means ± standard error.
Species BG CB NAG LAP AP POX PER SOC- min
Lithocarpus fenestratus (LiF) 318 ± 83 66 ± 14 392 ± 82 57 ± 13 2527 ± 581 129 ± 32 205 ± 46 0.36 ± 0.12
Castanopsis echinocarpa (CaE) 123 ± 30 33 ± 4 171 ± 16 22 ± 3 980 ± 167 57 ± 6 53 ± 5 0.13 ± 0.02
Lithocarpus truncatus (LiT) 133 ± 33 41 ± 10 144 ± 20 29 ± 1 989 ± 108 47 ± 8 90 ± 5 0.13 ± 0.02
Myrica esculenta (MyE) 106 ± 22 23 ± 4 119 ± 21 24 ± 3 877 ± 170 65 ± 21 85 ± 20 0.12 ± 0.01
Pygeum henryi (PyH) 157 ± 28 53 ± 16 149 ± 48 29 ± 5 1504 ± 335 98 ± 25 99 ± 15 0.23 ± 0.07
Pithecellobium clypearia (PiC) 396 ± 47 78 ± 6 332 ± 51 74 ± 4 3937 ± 413 262 ± 92 336 ± 13 0.34 ± 0.12
Elaeocarpus fleuryi (ElF) 125 ± 31 46 ± 10 205 ± 17 27 ± 4 1267 ± 184 59 ± 9 70 ± 13 0.11 ± 0.02
Glochidion lanceolarium (GlL) 97 ± 22 14 ± 3 127 ± 27 27 ± 6 988 ± 168 56 ± 12 130 ± 30 0.25 ± 0.19
Decaspermum fruticosum (DeF) 131 ± 15 32 ± 3 127 ± 9 39 ± 4 1146 ± 137 76 ± 15 116 ± 14 0.13 ± 0.02
Anneslea fragrans (AnF) 91 ± 16 21 ± 4 118 ± 11 15 ± 1 868 ± 88 27 ± 4 72 ± 9 0.08 ± 0.01
Eurya grof fii (EuG) 114 ± 18 44 ± 6 147 ± 17 41 ± 6 1300 ± 261 51 ± 7 175 ± 31 0.08 ± 0.02
Schima wallichii (ScW) 120 ± 19 19 ± 5 69 ± 18 17 ± 3 814 ± 47 38 ± 6 62 ± 4 0.08 ± 0.01
Vaccinium exaristatum (VaE) 97 ± 31 37 ± 15 186 ± 59 17 ± 4 747 ± 145 47 ± 14 58 ± 13 0.07 ± 0.02
Lyonia ovalifolia (LyO) 130 ± 18 37 ± 9 180 ± 25 20 ± 2 985 ± 67 50 ± 3 52 ± 8 0.14 ± 0.02
Diplospora mollissima (DiM) 191 ± 18 48 ± 3 150 ± 34 36 ± 3 1455 ± 169 74 ± 9 99 ± 10 0.18 ± 0.02
Wendlandia tinctoria (WeT) 138 ± 40 37 ± 15 220 ± 70 30 ± 11 1027 ± 383 56 ± 18 130 ± 59 0.28 ± 0.26
Olea rosea (OlR) 133 ± 22 41 ± 6 207 ± 22 29 ± 3 987 ± 97 55 ± 7 66 ± 11 0.14 ± 0.01
Machilus rufipes (MaR) 113 ± 24 32 ± 8 159 ± 38 20 ± 2 959 ± 60 56 ± 6 56 ± 13 0.15 ± 0.04
Litsea rubescens (LiR) 137 ± 24 48 ± 10 241 ± 43 26 ± 2 1198 ± 116 60 ± 12 71 ± 10 0.16 ± 0.03
Paramichelia baillonii (PaB) 162 ± 59 53 ± 12 205 ± 46 33 ± 6 1394 ± 326 81 ± 21 64 ± 11 0.19 ± 0.04
Coefficient of variation (CV)50% 39% 41% 47% 56% 69% 66% 50%
Kruskal– Wallis test Chi- squared 29.76 37.0 6 40.80 55.87 35.09 38.91 54.79 35. 55
p value 0.054 0.008 0.003 <0.001 0.013 0.005 <0.001 0.012
Note: The abbreviations of rhizosphere soil microbial activities are shown in Ta b le 1. The units for hydrolases (BG, CB, NAG, L AP and AP), oxidases
(POX and PER) and SOC- min are nmol mg−1 MBC h−1 , μmol mg−1 MBC h−1, g CO2- C g−1 MBC day−1 . The Kruskal– Wallis test is used to analyse the
effect of species on rhizosphere soil microbial activities. The bold statistics denote there are significant differences among tree species.
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HAN et al.
nutrients (e.g. N and P) through rhizosphere microbial metabolism
among different plant species. In part, the high interspecific varia-
tion of rhizosphere soil microbial activities might be related to the
species- specific root released C resources (Guyonnet et al., 2018;
Sun et al., 2021), because these labile root exudates can greatly ac-
celerate or decelerate microbial decomposition on soil organic mat-
ter (Phillips et al., 2011; Wang et al., 2016). Moreover, root traits that
directly represent resource foraging capacity (e.g. SRL , SRA, BI and
RMC) also varied significantly among species (p < 0.001, Figure S1
and Table S3). The high diversity that exists in root morphology, the
degree of association with mycorrhizal fungi, and rhizosphere soil
microbial activities suggest that plant species might have varying
dependence on different ways to explore soil resources (Denison
et al., 2003; Lambers et al., 2009). Such diverse organization of root
traits and rhizosphere microbial metabolism among plant species
could be conducive to plant coexistence in the local subtropical for-
est community.
It is worth mentioning that the activity of acid phosphatase is
much higher than that of C- and N- cycling- related extracellular en-
zymes in the rhizosphere. At the same time, the ratio of leaf N:P
(>22) is larger than the threshold value of 14– 16 at which plant
growth is believed to be constrained by P across all target tree
species (Reich & Oleksyn, 2004). Accordingly, both plants and soil
microbiota are facing relatively high P limitation compared with N
limitation in the subtropical forest ecosystem. This is because highly
weathered soils which cover vast regions of (sub)tropics contain low
FIGURE 1 The multiple factor analysis
(MFA) for plant leaf traits and root traits
across 20 tree species (the root traits
matrix does not contain RMC, [a]) and
18 tree species (excluding Vaccinium
exaristatum and Lyonia ovalifolia which are
associated with ericoid mycorrhizal fungi,
and the root traits matrix includes RMC,
[b]), which visualizes the plant resource
acquisition strategies. The grey circles
represent species, and the abbreviations
of species are shown in Table 2. LNC is
leaf nitrogen concentration, LPC is leaf
phosphorus concentration, SLA is specific
leaf area, and LT is leaf thickness; RNC
is root nitrogen concentration, RTD is
root tissue density, SRL is specific root
length, SRA is specific root area, RD is
root diameter, RMC is root mycorrhizal
colonization, and BI is branching intensity.
The visual display of the two- dimensional
plant resource acquisition (above: the
‘fast- slow’ economics spectrum; right: the
collaboration gradient) refers to Bergmann
et al. (2020) and Weigelt et al. (2021).
VaE
LyO
EuG
MyE
CaE
DeF
LiT
LiF
PyH
ScW
WeT
GlL
AnF
ElF
PiC
DiM
PaB
LiR
OlRMaR
EuG
MyE
CaE
DeF
LiTLiF
PyH
ScW
WeT
GlL
AnFElFPiC
DiM
PaB
LiR
OlR
MaR
LNC
LPC
SLA
LT
RNC
RTD
SRL
SRA
RD
BI
RMC
LNC
LPC
SLA
LT
RNC
RTD
SRLSRA
RD
BI
(a)
Fast-slow economics spectrum
Collaborationgradient Collaborationgradient
(b)
-1 -0.5 00.5 1
-1
-0.5
0
0.5
1
MFA dim 2(32.4%)
MFAdim 1(37.0%)
-1 -0.5 00.5 1
-1
-0.5
0
0.5
1
MFAdim 2(32.0%)
MFAdim 1(38.4%)
Fast-slow economics spectrum
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8
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Journal of Ecology
HAN et al.
FIGURE 2 The Spearman's correlations between rhizosphere soil microbial activities (extracellular enzymes and soil organic carbon
mineralization rate) with leaf traits, root traits and rhizosphere soil properties across 20 tree species. The asterisk denotes that the
correlation of rhizosphere soil microbial activities with plant traits and soil properties is significant (*, p < 0.05), and a narrower ellipse
represents a stronger correlation. The abbreviations of microbial activities are shown in Table 1. LNC is leaf nitrogen concentration, LPC
is leaf phosphorus concentration, SLA is specific leaf area, and LT is leaf thickness; RNC is root nitrogen concentration, RTD is root tissue
density, SRL is specific root length, SRA is specific root area, RD is root diameter, RMC is root mycorrhizal colonization, and BI is branching
intensity; SOC is soil organic carbon, STN is soil total nitrogen, STP is soil total phosphorus, SCN is the ratio of soil carbon to nitrogen, SCP is
the ratio of soil carbon to phosphorus, SNP is the ratio of soil nitrogen to phosphorus, DOC is dissolved organic carbon, SAN is soil available
nitrogen, SAP is soil available phosphorus, pH is soil pH, and soil water content is soil water content.
**** ** **
******
****** *
***
**** *
***** *
*****
** ****
BG
CB
NAG
LAP
AP
POX
PER
SOC-min
-1
-0.5
0
0.5
1
LNCRNC
LPCSLA LT RTDSRLSRA RD BI SOCSTNSTP SCNSCP SNPDOCSANSAP pH SWC
Leaf Root Soil
RMC
FIGURE 3 The relationships between species scores along the first dimension of the multiple factor analysis (dim 1) representing plant
‘fast- slow’ economics spectrum (from slow to fast) and rhizosphere soil microbial activities (extracellular enzymes and soil organic carbon
mineralization rate). The dark circles denote the result of 20 tree species (for root traits excluding root mycorrhizal colonization). The
grey circles denote the result of 18 tree species excluding Vaccinium exaristatum and Lyonia ovalifolia, which are associated with ericoid
mycorrhizal fungi (for root traits including root mycorrhizal colonization). *p < 0.05, †p < 0.10. The abbreviations of microbial activities are
shown in Table 1. The unit for BG, CB, NAG, LAP and AP is nmol mg−1 MBC h−1 , the unit for POX and PER is μmol mg−1 MBC h−1, and the unit
for soil organic carbon mineralization is g CO2- C g−1 MBC day−1.
100
200
300
400
20
40
60
80
100
200
300
400
20
40
60
80
-2 -1 0123
1000
2000
3000
4000
-2 -1 0123
0
100
200
300
-2 -1 0123
80
160
240
320
-2 -1 01
23
0.1
0.2
0.3
0.4
BG
R2=0.34*
R2=0.38*
(a)
CB
R2=0.48*
R2=0.43*
(b)
NAG
R2=0.17
R2=0.13
(c)
LAP
R2=0.40*
R2=0.40*
(d)
AP
R2=0.39*
R2=0.33*
(e)
POX
R2=0.40*
R2=0.36*
Dim1of planttraits
(f)
Dim1of planttraits
PER
R2= 0.18 R2= 0.18
(g)
SOC-min
R2=0.24*
R2=0.17
(h)
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Journal of Ecology
HAN et al.
concentration of readily exchangeable inorganic phosphate, much of
soil total P exists in various organic forms with low biological avail-
ability (Turner, 2008; Turner & Engelbrecht, 20 11), and this organic
P needs a series of enzymatic attack (hydrolysis) prior to being ex-
ploited by plants and microorganisms.
4.2 | Rhizosphere soil microbial activity is linked
to the plant fast- slow economics spectrum
By connecting the leaf and root at the species level, we found an
integrated leaf and root traits space, which was constituted by two
nearly orthogonal trait axes (Figure 1 and Figure S2), that is, the ‘fast-
slow’ economics spectrum on which leaf (the negative correlations
of LNC, LPC, SLA with LT) and root traits (the negative correlation
of RNC with RTD) were well aligned, and the collaboration gradi-
ent in the root (the negative correlations of RD, RMC with SRL, SRA
and BI). This finding confirms the recent syntheses based on a sub-
set of our traits at the global scale (Bergmann et al., 2020; Weigelt
et al., 2021) and the multidimensional coordination of leaf and root
tr ait s als o emp hasizes the multi ple combinati ons of re sou rce acquisi-
tion strategies adapting to above- ground and below- ground stresses
(Carmona et al., 2021; Kramer- Walter et al., 2016; Weemstra
et al., 2016).
Consistent with our hypothesis, the C- , N- and P- cycling extra-
cellular enzymes and SOC mineralization rate in the rhizosphere
were positively linked to the ‘fast- slow’ economics spectrum, but in-
dependent of the collaboration gradient of the plant traits space. By
combining leaf traits, root traits and diverse extracellular enzymes
and SOC mineralization, we proposed a conceptual framework that
links plant resource acquisition strategy and soil microbial metabo-
lism in the rhizosphere (Figure 4). For plants, along with the tradi-
tional economics spectrum that depicts the trade- off between fast
and slow return on investment, fast- growing species feature higher
rhizosphere soil microbial activities than those of slow- growing
species (Figure 4a). This might be driven by root exudates, which
are of central importance in connecting plant and soil microbiota in
the rhizosphere. First, rhizosphere soil enzyme activities have been
found to increase with the amount of energy- labile carbohydrates
exuded from roots (Phillips et al., 2011). Microorganisms use energy
derived from these C- rich exudates to synthesize enzymes to release
N and P from soil organic matter (Drake et al., 2013). Fast- growing
species can exude more C than slow- growing species because the
former has larger photosynthetic capacity (Henneron et al., 2020),
which could be well represented by relevant leaf economics traits
(e.g. LNC, LPC and SLA; Figure 4a). More directly, root- exuded C
was proved to be positively associated with the fast side of the
conservation gradient in the root (RTD- RNC axis) (Sun et al., 2021).
Conservative roots of high construction cost and low nonstructural
C concentration tend to have low C flux of exudation, while it is con-
trary for acquisitive roots of low construction cost and high N con-
centration (Karst et al., 2017; Sun et al., 2021). Second, in addition
to root exudation amount, the composition of root- exuded primary
metabolites is different among species (Guyonnet et al., 2017; Herz
et al., 2018), and the more diverse metabolites (various sugars and
organic acids, for example, sucrose and fructose, fumaric and citric
acid) exuded by fast- growing species could stimulate microbial ac-
tivity more strongly than those of slow- growing species (Guyonnet
et al., 2018). Third, the contrasting quality of root exudation and root
nutrients of the fast- and slow- strategy plants shape differentially
C- , N- and P- cycling microbial communities in the rhizosphere. In
comparison with fast- growing species, slow- growing species attract
more oligotrophic bacteria, and fungi are relatively more abundant
than bacteria in their rhizosphere (Guyonnet et al., 2018; Spitzer
et al., 2021), which contributes to the relatively low microbial activ-
ities (Guyonnet et al., 2017). Through these different mechanisms,
plant resource acquisition on the ‘fast- slow’ economics spectrum,
which influences root exudation amount and composition, can im-
pact rhizosphere microbial activities.
In comparison, rhizosphere soil extracellular enzymes and SOC
mineralization rate were independent of the collaboration gradient
of plant resource acquisition in the root (Figure 4b and Figure S4).
The collaboration gradient shows that plant below- ground strate-
gies of resource uptake range from do it yourself by forming more
efficient root morphology (high SRL, SRA and BI) to outsourcing of
resource uptake to mycorrhizal fungal partners by forming larger
cortex tissue (high RD) and higher colonization of mycorrhizal fungi
(RMC) (Bergmann et al., 2020). Therefore, root exudation, together
with rhizosphere soil microbial activities that are tightly linked to
root- exuded carbohydrates, may constitute an alternative exploit-
ative strategy in foraging soil resources, in addition to depending
on the fine root itself and mycorrhizal partner (Sun et al., 2021; Wen
et al., 2022). In comparison with previous findings on the ecolog-
ical connection of above- ground and below- ground biota (Wardle
et al., 2004), this conceptual framework portrays clearly the specific
linkages between diverse rhizosphere soil microbial activities and
different facets of the multidimensional plant resource acquisition in
the context of integrating leaf and root as a whole.
It should be noted that rhizosphere soil microbial activities
might be more closely related to the ‘fast- slow’ economics spec-
trum of leaves than that of roots, as shown by the relatively weak
correlations of rhizosphere soil microbial activities with RNC and
RTD (Figure 2). In roots, some tissue N is metabolically inactive and
is associated with the transportive capacity and chemical defence
(Moles et al., 2013; Trocha et al., 2017). On the other hand, a sig-
nificant part of plant below- ground C investment is assigned to the
mycorrhizal partner rather than the root itself (Hobbie, 2006). The
mycorrhizal fungi tissue has also been shown to enhance root tissue
N concentration as the former is relatively richer in N than fine root
itself (Langley et al., 2006). All these facts might thus confound the
trade- off between fast and slow return on investment in the root
(i.e. the economics spectrum), highlighting important aspects of
root tissue components that are not captured by the conservation
gradient.
An interesting result was that rhizosphere soil microbial bio-
mass C was negatively linked to the plant ‘fast- slow’ economics
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10
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Journal of Ecology
HAN et al.
spectrum (from slow to fast) (Figure S5), implicating that fast-
growing plant species feature relatively small biomass of rhizo-
sphere soil microorganisms compared with slow- growing plant
species. This might be related to the shift in rhizosphere soil mi-
crobial community composition, that is, the soil fungal to bacterial
ratio decreases with the conservation gradient of plant resource
acquisition (from slow to fast) (Spitzer et al., 2021). Also, the stron-
ger root resource uptake in the rhizosphere of the fast- relative to
slow- strategy plants may result in soil microbes allocating more en-
ergy to mineralization activities (e.g. synthesis of exoenzymes) at
the expense of growth (Drake et al., 2013; Henneron et al., 2020).
Different from rhizosphere soil acid phosphatase activity aligned
wi th the ‘fa s t- sl ow’ cons e r vati on grad ient , root phos phat ase ac tiv-
ity was found to be positively associated with the do it yourself side
of the collaboration gradient (Han et al., 2022). This suggests that
th e re mig ht be com ple ment arit y in acqu irin g soi l phosp horus (and/
or other nutrients) through fine root itself, mycorrhizal fungi and
microbial metabolism in the rhizosphere. It is not surprising that
rhizosphere microbial activities were also linked to soil properties,
especially the nutrient conditions (Figure 1). Various substrates
containing C, N and P are the action objects for microorganisms,
and the release of soil nutrients (i.e. N and P) could be enhanced
by increasing various extracellular enzymes related to C- , N- and
P- cycling, and soil organic matter decomposition (Allison, 2006;
Burns et al., 2013).
4.3 | Limitations and implications
It is important to note that our study has some limitations. First,
across the 20 species, RTD was not mostly loaded onto the first
dimension based on the multivariate analysis of leaf and root
FIGURE 4 (a) The schematic diagram of the linkage between the ‘fast- slow’ plant resource acquisition strategy (represented by both leaf
and root traits) and rhizosphere soil microbial activities. The ‘fast- slow’ economics spectrum of the leaf (LNC, LPC, SLA and LT) and root
(RNC and RTD) are coordinated, forming the plant conservation gradient. The unidirectional arrow from above to below- ground denotes
the quantity (the size of the arrow) and quality (the colour of the arrow) of plant rhizosphere deposited carbon. The larger the arrow size,
the higher amount of root exudates; the lighter the arrow colour, the higher quality and diversity of root exudates. The gear and broad
bean- shaped icon denote rhizosphere soil microbial activities (extracellular enzymes and soil organic carbon mineralization rate). The larger
the gear, the higher microbial activity. The solid lines that connect plant resource acquisition strategy and leaf traits denote their strong
correlations with rhizosphere soil microbial activities, while the dashed lines that connect plant resource acquisition strategy and root traits
denote their relatively weak correlations with rhizosphere soil microbial activities. (b) The three strategies of plant resource acquisition and
their relationships. The tradeoff between efficient root morphology (represented by SRL, SRA and BI) and symbiosis with mycorrhizal fungi
(represented by RD and RMC) denotes the collaboration gradient of plant resource acquisition (Bergmann et al., 2020; Weigelt et al., 2021).
The rhizosphere soil microbial activities (iii) are relatively independent of the plant collaboration gradient (from ‘do it yourself’ resource
uptake by roots [i] to ‘outsourcing’ of resource uptake to mycorrhizal fungi [ii]). The graphical presentation (panel b) is inspired by Bergmann
et al. (2020) and Wen et al. (2022). LNC is leaf nitrogen concentration, LPC is leaf phosphorus concentration, SLA is specific leaf area, and LT
is leaf thickness; RNC is root nitrogen concentration, RTD is root tissue density, SRL is specific root length, SR A is specific root area, RD is
root diameter, RMC is root mycorrhizal colonization, and BI is branching intensity; RMA is rhizosphere soil microbial activity. The graph was
created with BioRe nder.com.
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11
Journal of Ecology
HAN et al.
traits (Figure 1a). This might relate to the fact that there are dif-
ferences in root tissue components between ericoid- (ErM) and
other mycorrhizal trees (i.e. AM and EcM). At the whole tree level,
the combined leaf and root traits matrix could clearly sharpen
the ‘fast- slow’ economics spectrum of plant below- ground (RTD-
RNC axis) when excluding the two ErM species (V. e xa ris ta tu m and
L. ovalifolia), even if the RMC was not in consideration (Figure S6).
In compa riso n with AM an d EcM sp ecie s, it rem ains un cle ar what is
th e mul tiv a riat e nature of th e roo t economi c s sp ace of Er M spe cie s
(Bergmann et al., 2020), which needs to be resolved in the future
stud y. Second, in natu ral habitats, we can not ide ntify th e cau salit y
between changes in plant traits and soil fertility, and to accurately
distinguish their confounding effects on microbial activity. It has
been shown that fertile soils favour fast- growing species, while
slow- growing species dominate infertile soils associated with slow
nutrient cycling (Kramer- Walter et al., 2016). However, plant re-
source acquisition strategies can also significantly influence soil
fertility. For example, fast- growing species accelerate soil nutri-
ent cycling by stimulating microbial decomposition through the
investment of C sources (e.g. root exudates), compared with con-
servative plants (Phillips et al., 2013; Wardle et al., 2004). A com-
mon garden experiment might be a potential method to clearly
distinguish the confounding effects of plant traits and soil fertility
on rhizosphere soil microbial activities. Despite these limitations,
the specific correlations of rhizosphere soil microbial activities
with plant resource acquisition strategies are helpful to develop
better representations of plant– soil feedback.
Taken together, our study links rhizosphere soil microbial ac-
tivities and plant resource acquisition strategies, in the context
of a multidimensional space of integrated leaf and root traits. The
diverse soil microbial activities in the rhizosphere representing
carbon and nutrient cycling were positively related to the plant
‘fast- slow’ economics spectrum (from slow to fast, and especially
ab ove - gro und) . The expa nsion of th e fast- rela tive to slow - gr owin g
plants at the global scale due to continuous atmospheric nutrient
deposition might therefore lead to a higher risk of SOC loss and
CO2 emission. From the perspective of plants, complementarity
might exist among the ways of acquiring soil resources through
microbial metabolism in the rhizosphere, the fine root itself and
their mycorrhizal partners, as the diverse rhizosphere soil micro-
bial activities were independent of the below- ground collabora-
tion gradient. The use of easily measurable plant traits as well as
their intimate relationships with microbial activities in the rhizo-
sphere has the potential to improve modelling soil carbon dynam-
ics and nutrient cycling. In sum, these novel insights contribute
to a better understanding of the ecological connections between
above- ground and below- ground biotic and abiotic components of
terrestrial ecosystems.
AUTHOR CONTRIBUTIONS
Biao Zhu and Mengguang Han conceived the idea; Mengguang Han,
Miao Yu and Rui Li performed field sampling and laboratory analyses;
Ying Chen and Lijuan Sun contributed to statistical analysis and data
interpretation; Shuaifeng Li and Jianrong Su developed the field plot
(30 ha) for sampling. Mengguang Han and Biao Zhu led the writing
of the manuscript, and all authors contributed critically to the drafts.
ACKNO WLE DGE MENTS
This study was financially supported by the National Natural Science
Foundation of China (31988102) and the China Postdoctoral Science
Foundation (BX20220003). We are very grateful for the construc-
tive comments and insightful suggestions from Marina Semchenko
(the Associate editor), Monique Weemstra and an anonymous re-
viewer that greatly improved the manuscript. We thank the staff
at Taiyanghe Natural Reserve for access permission and logistical
support. We thank Prof. Zeqing Ma for assisting in scanning root
samples. We also thank the Plant Science Facility of the Institute
of Botany, Chinese Academy of Sciences, for technical assistance in
laboratory analyses. We also acknowledge assistance from the labo-
ratory of Prof. Chengjun Ji and Yuxin Li in determining root mycor-
rhizal colonization.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest. Biao Zhu is an Associate
Editor for the Journal of Ecology but took no part in the peer review
or decision- making process for this paper.
DATA AVA ILAB ILITY STATE MEN T
The data that support the findings of the study are available in fig-
share at http://doi.org/10.6084/m9.figsh are.19750666 (Han, 2023).
ORCID
Mengguang Han https://orcid.org/0000-0003-4020-991X
Ying Chen https://orcid.org/0000-0002-1519-5029
Lijuan Sun https://orcid.org/0000-0003-4915-525X
Mia o Yu https://orcid.org/0000-0002-1788-0169
Shuaifeng Li https://orcid.org/0000-0002-2555-1808
Jianrong Su https://orcid.org/0000-0001-5667-5670
Biao Zhu https://orcid.org/0000-0001-9858-7943
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
Appendix S1: Supplementary methods.
How to cite this article: Han, M., Chen, Y., Sun, L., Yu, M., Li,
R., Li, S., Su, J., & Zhu, B. (2023). Linking rhizosphere soil
microbial activity and plant resource acquisition strategy.
Journal of Ecology, 00, 1–14. ht tps://doi.org/10 .1111/1365-
2745.14067
13652745, 0, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2745.14067 by Peking University, Wiley Online Library on [29/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License