Temperature drives the coordination between above‐ground nutrient conservation and below‐ground nutrient acquisition in alpine coniferous forests

Abstract

Above‐ground nutrient conservation via resorption processes and below‐ground nutrient acquisition from soils are two important mechanisms for plants to maintain nutrition and ecosystem functions. However, the mechanism by which plants coordinate these two nutrient strategies, especially for ectomycorrhizal (ECM)‐dominated conifers in alpine forests, remains unclear.
We investigated the relationships between above‐ground nutrient conservation and below‐ground nutrient acquisition and their environmental drivers by measuring leaf nutrient (i.e. nitrogen [N] and phosphorous [P]) resorption efficiency, resource foraging‐ and uptake‐related root morphological (root diameter [RD], specific root length [SRL]/area [SRA]) and physiological (root tissue density [RTD], root N and P concentration) traits, mycorrhizal colonization rate (MCR), rhizosphere effect on soil N and P cycling, and environmental factors of 40 ECM coniferous populations on the eastern Tibetan Plateau, China.
Our results showed that with increasing leaf nutrient (N and P) resorption efficiency, conifers shifted from depending on the ‘outsourcing’ strategy by mycorrhizal fungi (high MCR) to relying on the ‘do‐it‐yourself’ strategy of root mining (high rhizosphere effect on N‐ and P‐mining‐related enzyme activities) rather than on root foraging (high SRL and SRA) and preferred more conservative roots (high RTD and low root N and P concentrations). Temperature was the main factor driving a negative relationship of ECM fungi foraging, root uptake and a positive relationship of root mining with leaf nutrient resorption, while precipitation resulted in a decoupled relationship between root foraging and leaf nutrient resorption.
Our findings demonstrate temperature‐driven and diverse collaborations (e.g. trade‐off or synergy) between below‐ground nutrient acquisition and above‐ground nutrient conservation strategies in alpine ECM conifers and highlight that the preference for below‐ground nutrient acquisition strategies could influence the above‐ground nutrient utilization strategy. This is insightful for a holistic understanding of the adaptation and responses of alpine forests to climatic change.

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Full text

Functional Ecology. 2023;00:1–14.
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1wileyonlinelibrary.com/journal/fec
Received: 31 August 2022
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Accepted: 24 March 2023
DOI : 10.1111/136 5-243 5.14 330
RESEARCH ARTICLE
Temperature drives the coordination between above- ground
nutrient conservation and below- ground nutrient acquisition in
alpine coniferous forests
Junxiang Ding1,2 | Wenjing Ge1 | Qing Liu2 | Qitong Wang2 | Deliang Kong3 |
Huajun Yin2
© 2023 The Authors . Functional Ecology © 2023 British Ecological Society.
1College of Eco logy and Enviro nment,
Zhengzhou Universit y, Zhengzhou, China
2CAS Key L aboratory of M ountain
Ecologic al Restoratio n and Bioresour ce
Utilization & Ecological Restor ation an d
Biodive rsity Conservation Key Lab orator y
of Sichuan Province & China- Croatia
“Belt and Road” Joint Laboratory on
Biodive rsity and Ecosystem Se rvices,
Chengdu Instit ute of Biol ogy, Chinese
Academy of Sciences, Che ngdu, China
3College of Forestry, Henan Agricultural
University, Zhengzhou , China
Correspondence
Huajun Y in
Email: yinhj@cib.ac.cn
Deliang Kong
Email: deliangkong1999@126.com
Funding information
Chinese Acade my of Scien ces (CA S)
Interdisciplin ary In novation Team, Grant/
Award Number: xbzg- zysys- 202112;
Nationa l Natural Scien ce Foundation of
China, G rant/Award Numbe r: 32201517,
32171757 and 3217174 6; Natur al Science
Foundat ion of Sich uan Province, Gr ant/
Award Number: 2022NSFSC0085 and
2022Z YD012 2; The Second Tibetan
Plateau S cientific Expedition and
Research Program, Grant/Award Nu mber:
2019QZKK0301
Handling Editor: Alexandra Wright
Abstract
1. Above- ground nutrient conservation via resorption processes and below- ground
nutrient acquisition from soils are two important mechanisms for plants to main-
tain nutrition and ecosystem functions. However, the mechanism by which plants
coordinate these two nutrient strategies, especially for ectomycorrhizal (ECM)-
dominated conifers in alpine forests, remains unclear.
2. We investigated the relationships between above- ground nutrient conservation
and below- ground nutrient acquisition and their environmental drivers by meas-
uring leaf nutrient (i.e. nitrogen [N] and phosphorous [P]) resorption efficiency,
resource foraging- and uptake- related root morphological (root diameter [RD],
specific root length [SRL]/area [SRA]) and physiological (root tissue density [RTD],
root N and P concentration) traits, mycorrhizal colonization rate (MCR), rhizos-
phere effect on soil N and P cycling, and environmental factors of 40 ECM conif-
erous populations on the eastern Tibetan Plateau, China.
3. Our results showed that with increasing leaf nutrient (N and P) resorption effi-
ciency, conifers shifted from depending on the ‘outsourcing’ strategy by mycor-
rhizal fungi (high MCR) to relying on the ‘do- it- yourself’ strategy of root mining
(high rhizosphere effect on N- and P- mining- related enzyme activities) rather
than on root foraging (high SRL and SRA) and preferred more conservative
roots (high RTD and low root N and P concentrations). Temperature was the
main factor driving a negative relationship of ECM fungi foraging, root uptake
and a positive relationship of root mining with leaf nutrient resorption, while
precipitation resulted in a decoupled relationship between root foraging and
leaf nutrient resorption.
4. Our findings demonstrate temperature- driven and diverse collaborations (e.g.
trade- off or synergy) between below- ground nutrient acquisition and above-
ground nutrient conservation strategies in alpine ECM conifers and highlight that
the preference for below- ground nutrient acquisition strategies could influence
the above- ground nutrient utilization strategy. This is insightful for a holistic un-
derstanding of the adaptation and responses of alpine forests to climatic change.

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DING et al.
1 | INTRODUC TION
Most terrestrial ecosystems are widely limited by essential nutri-
ents, especially nitrogen (N) and phosphorus (P), so it is of great
significance to focus on plant nutrient uptake and utilization ad-
aptations to different environments (Aerts & Chapin, 2000; Du
et al., 2020). Plants have evolved two main strategies, that is, op-
timizing below- ground nutrient acquisition and enhancing above-
ground nutrient conservation to adapt to various nutrient limitations
(Brant & Chen, 2015; Freschet et al., 2010 ). The two nutrient strate-
gies usually interact in nutrient- limited environments and contribute
greatly to whole- plant per formance and ecosystem functioning (Lin
et al., 2020; Ushio et al., 2015). Accordingly, nutrient resorption from
senescing leaves, a key above- ground nutrient conservation strat-
egy, could be coordinated with below- ground nutrient acquisition
strategies by roots and mycorrhizae, but this needs to be tested ur-
gently. As plant per formance ultimately depends on the coordinated
functioning of above- and below- ground compartments, exploring
relationships between leaf nutrient resorption and roots and mycor-
rhizae can advance our understanding of plant adaptations from a
whole- plant perspective.
While plants enhance above- ground nutrient conservation via
leaf nutrient resorption under nutrient limitations, they can employ
diverse strategies below- ground to optimize nut rient acquisition, in-
cluding alterations in root morphology, physiology and mycorrhizal
symbioses (Lambers et al., 2013; Wen et al., 2022). Recent studies
on the covariation of such a diverse suite of morphological, physio-
logical and symbiotic root traits have revealed a multidimensional
below- ground nutrient acquisition strategy (Bergmann et al., 2020;
Weemstra et al., 2022). For instance, one major trait dimension de-
picts resource foraging by roots and mycorrhizas (Ding et al., 2020;
Ma et al., 2018). This dimension is represented by strong inverse re-
lationships bet ween root diameter (RD) and key root foraging traits
such as specific root length (SRL) and specific root area (SR A); one
end of the dimension is characterized by ‘do- it- yourself’ strategy
with higher SRL, and the other end is characterized by outsourc-
ing’ strategy with lower SRL and higher dependence on mycorrhizas
(Bergmann et al., 2020). The other dimension is represented by the
significant negative relationship between root tissue density (RTD)
and root N content (RN) (Han et al., 2022; Kramer- Walter et al., 2016).
Here, we term this dimension the root uptake dimension because
RTD and RN are closely related to root activity in nutrient uptake.
The rationale is that higher RTD is always associated with higher
root cell wall thickening (Wahl & Ryser, 2000), which greatly reduces
resource allocation to the cytoplast and hence lowers root activity
for nutrient uptake (Taiz & Zeiger, 2015a). Additionally, higher RN
usually indicates higher root respiration, which can provide more
energy to drive nutrient upt ake by roots (Reich et al., 2008; Taiz &
Zeiger, 2015b). This multidimensional root economics space well
summarizes the diverse nutrient acquisition strategies in roots and
provides a powerful co nceptual fr amework for exploring th e coordi-
nation between leaf nutrient resorption and below- ground nutrient
acquisition strategies. Furthermore, mounting evidence has shown
that leaf nutrient levels are significantly related to root traits in
the upt ake dimension but not in the foraging dimension (Fortunel
et al., 2012; Ushio et al., 2015). Given that leaf nutrient resorption is
controlled by leaf nutrient levels, changes in leaf nutrient resorption
may also coordinate with the root uptake dimension but be decou-
pled from the root foraging dimension (Kobe et al., 2005). However,
few studies to date have elucidated whether and how leaf nutrient
resorption is coordinated with below- ground strategies for nutrient
acquisition under the framework of root multidimensionality.
In addition to adjustments in foraging and uptake traits, roots
can also mine organically bound nutrients in rhizosphere soils by
directly secreting extracellular enzymes or indirectly by stimu-
lating rhizosphere microbes to synthesize extracellular enzymes
(i.e. root mining strategy; Lambers et al., 2013; Meier et al., 2017;
Wen et al., 2022). These mining activities by roots usually cause
a greater rhizosphere effect on extracellular enzyme activity and
nutrient mineralization intensity and consequently a higher nu-
trient availability in the rhizosphere compared to bulk soil (Lin
et al., 2020; Phillips & Fahey, 2006; Yin et al., 2013). Thus, root
mining represents an impor tant strategy for plants to improve nu-
trient acquisition, particularly for those growing in alpine conifer-
ous forests where nutrients occur dominantly in organic forms (Lin
et al., 2020; Wen et al., 2022). Additionally, it has been demon-
strated that changes in leaf nutrient resorption result in the input
of litter of different qualities into soil. In particular, plants with
higher leaf nutrient resorption generally return nutrient- poor leaf
litter to the soil (e.g. low N and P), which may further aggravate
soil nutrient deficiency due to slow litter decomposition (Freschet
et al., 2010; Jiang et al., 2023). In such cases, adopting a root min-
ing strategy to mobilize the abundant organic nutrients in the rhi-
zosphere soil seems to be an effective approach for meeting plant
nutrient demand. This is because nutrients in organic forms can-
not be directly accessed by only adjusting foraging- related traits,
such as producing thin roots with high SRL (Han et al., 2022; Lin
et al., 2020; Ushio et al., 2015). Therefore, it would be expected
that root mining could be coordinated with above- ground nutrient
conservation and can be more important for plants with higher leaf
nutrient resorption. Nevertheless, root mining has been largely
ignored when assessing the relationship between below- ground
nutrient acquisition and leaf nutrient resorption. This represents
a vital knowledge gap in the relationships between above- ground
nutrient conservation and below- ground nutrient acquisition
strategies. Therefore, it is necessary to examine how root mining
KEY WORDS
alpine coniferous forest, below- ground nutrient acquisition strategy, ECM fungi, nutrient
resorption, rhizosphere effect, root traits, temperature
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DING et al.
together with root foraging and root uptake is coordinated with
leaf nutrient resorption, especially in alpine forests.
The relationship between above- ground nutrient conservation
and below- ground nutrient acquisition strategies may be shaped
by a range of environmental factors, such as climate and soil fer-
tility (Brant & Chen, 2015). For example, plants growing in warm
and humid environments tend to have lower leaf nutrient (e.g. N
and P) resorption (Oleksyn et al., 2003; Vergut z et al., 2012; Zhang
et al., 2018), and they generally produce thick roots with high N and
P concentrations, supporting greater mycorrhizal colonization and
lower root mining of rhizosphere nutrients (i.e. lower rhizosphere
effect; Brunn et al., 2022; Ostonen et al., 2011). In infertile soils,
plants may improve leaf nutrient resorption above- ground (Yuan &
Chen, 2009) and simultaneously enhance root foraging (high SRL;
Ma et al., 2018) and root mining (high rhizosphere effect on enzyme
activity; Lin et al., 2020) and weaken root uptake (low absorptive
activity) and mycorrhizal colonization due to the limited carbon
allocation below- ground (Ding et al., 2020; Ostonen et al., 2017;
Soudzilovskaia et al., 2015). Therefore, climatic factors such as tem-
perature and precipitation together with soil fertility ultimately drive
the coordination of leaf nutrient resorption with below- ground nu-
trient acquisition strategies. Nevertheless, because leaf nutrient re-
sorption and below- ground nutrient acquisition strategies are of ten
investigated separately across environments, our understanding of
how environmental factors (e.g. climate and soil fer tilit y) drive the
relationships bet ween these two strategies remains unclear.
Here, we selected 40 coniferous populations on the Tibetan
Plateau in southwestern China encompassing great differences in
climate and soil properties. A range of functional traits related to
above- ground nutrient conservation and below- ground nutrient ac-
qu i sit ion were me a sure d , incl udi n g lea f nu trie n t (e. g . N and P) re sorp -
tion efficiency, foraging- related root morphological traits (e.g. root
diameter [RD], specific root length [SRL], specific root area [SRA]),
uptake- related root physiological traits (e.g. root N, P and root tissue
density [RTD]), MCR and rhizosphere effec ts on soil N and P cycling.
For plant nutrients, we mainly focused on N and P in leaves, roots
and soils because these two elements are the main limiting nutrients
to plant growth in most environments and have been the subject of
most studies (Freschet et al., 2010; Yuan & Chen, 2009).
Ou r obj ec tive s wer e to id en tif y the rel at ion shi p bet wee n lea f nu -
trient resorption and the diverse below- ground nutrient acquisition
strategies mentioned above and how climate and soil fac tors struc-
ture these relationships. Accordingly, we proposed two hypotheses
as follows:
1. With increasing leaf nutrient resorption, plants rely more on
‘do- it- yourself’ strategies (e.g. producing foraging- efficient thin
roots and/or enhancing rhizosphere effects on N- and P- mining-
related enzyme activity) than on the ‘outsourcing’ strategy (via
mycorrhizal fungi) to forage nutrients below- ground and si-
multaneously construct denser roots (higher RTD) with low
nutrient concentrations to enhance resource conservation.
This is bec ause higher leaf nutrient resorption, usually under
infertile soils with less plant carbon allocation below- ground,
could be accompanied by lower mycorrhizal association and
lower root uptake activity due to their high carbon costs in
nutrient acquisition and maintenance (Brant & Chen, 2015; Ding
et al., 2020).
2. Temperature is a driving factor that governs the coordination of
leaf nutrient resorption with below- ground nutrient acquisition
strategies in alpine ecosystems. Specifically, with decreasing tem-
perature, leaf nutrient resorption, root foraging traits (e.g. SRL,
SRA) and rhizosphere effects on N- and P- mining- related enzyme
activity will increase (Oleksyn et al., 2003; Ostonen et al., 2017;
Zhang et al., 2018), while root uptake traits (e.g. N, P concen-
trations) and mycorrhizal colonization will decrease (Fernandez
et al., 2017; Ostonen et al., 2011).
2 | MATERIALS AND METHODS
2.1 | Study area and field sampling
This study was conducted in the alpine region of the eastern Tibetan
Plateau, which spans a wide geographic range, from latitude 27.37
to 35.27°N, longitude 94.55 to 103.31°E, and with an altitude of
2562.0– 4351.0 m a.s.l. (Figure S1). This area belongs to temperate
and subtropical climate zones and is characterized by drastic varia-
tions in temperature, precipitation and soil properties (e.g. soil types,
clay content and organic horizons), even over short distances and
along altitudinal gradients. The mean annual temperature (MAT)
and mean annual precipitation (MAP) are shown in Table S1, with
values ranging from 1.09 to 11.94°C and 494.99 to 982.83 mm re-
spectively. Alpine coniferous forests dominate most of the forested
areas on the eastern Tibetan Plateau, and spruce forests, as one of
the typical representatives of alpine coniferous forests, constitute
an important part of the forest types in this region. In most areas,
a single Picea species usually accounts for the majority of individu-
als in a stand, thus leading to monodominance in most communities
(Zhang et al., 2018).
In this study, we fo cused on sp ru ce fo rest s and selec te d 40 pure
forest stands for leaf, root and soil collection in July and August
2017– 2018. All necessary permits were gained before the begin-
ning of field investigation. Specifically, we first set up three to five
30 m × 40 m experimental plots in each stand; then, in each plot , at
least three mature and healthy individuals of a target conifer spe-
cies were randomly selected for leaf and root collections. For each
individual, three representative twigs from sun- exposed, first- order
branches were collected from the mid to upper canopy using pole
pruners, and then the fully expanded, current- year needles of each
cohort were immediately detached from the twigs. Accordingly,
freshly fallen leaf litter was collected under the canopy of the same
individual in a similar position to that of the green leaves. Here, only
the terminal two orders of a root branch were sampled because of
their highest absorptive activit y and mycorrhizal colonization in
the root branch (Guo et al., 2008). Root traits in 27 forest stands
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DING et al.
were determined in our previous study (Ding et al., 2020), and root
sam pling and trait me asu remen ts in the oth er 13 forest stand s were
performed using the same protocol as in that study. More impor-
tantly, although some of the root trait data were used from our pre-
vious study (Ding et al., 2020), we focus here on the relationships
between above- and below- ground nutrient strategies, which have
not been addressed in previous studies but urgently need to be
tested, especially in alpine plants.
In brief, we excavated surface soil (0– 20 cm) at the base of a
target tree (approximately 1.5– 2 m away from the trunk) to ex-
pos e th e main lateral root s, and root branch es wit h in ta ct termi nal
branch orders and adhering soil were picked out. After shaking
the root branch gently, we carefully collected the soil adhering to
the root s with forceps and def ined it as rhizo sphere soil (Phillips &
Fah ey, 2006). At the same time, the bulk soil was collected using
a soil auger (5 cm in diameter, 20 cm in length), with five soil cores
(0– 20 cm depth) randomly collected in each plot. Once collec ted,
the leaf, root and soil samples in each plot were separately com-
bined to make one comp osite samp le to minimize any heterogene-
ity caused by sampling positions and immediately placed on ice,
transported to the laboratory, and kept at −20°C prior to further
analyses. In total, 10 species from the genus Picea were collected
across the 40 sampling sites, including Picea aurantiaca, P. n eove-
itchii, P. retroflexa, P. wilsonii, P. asperata, P. purpurea, P. brachytyla
va r. complanate, P. likiangensis, P. likiangensis var. linzhiensis and P.
likiangensis var. balfouriana (Table S2).
2.2 | Root morphology and mycorrhizal
colonization
Root morphological and chemical traits were determined on the
fir s t two r o o t or der s , as th ey ar e ty pi c a l ab so r p t i v e se gm e n t s am on g
fine roots (Comas & Eissenstat, 2009; Guo et al., 2008). Pr io r to the
measurements, the first- and second- order roots in each plot were
cut from rinsed root branches and collected together as a single
sample. Each root sample was then divided into two subsamples,
wit h one subsample used fo r th e measur em en t of root mo rp ho lo gi-
cal and chemic al traits and the other subsample stored at −80°C
for the consequent determination of mycorrhizal colonization. Root
samples for measurement of morphological traits were scanned
on an Epson Expression 10000 XL scanner (Epson Expression
11000XL, Seiko Epson Corp.) at a resolution of 4 00 dpi. The av-
erage diameter, total length, total surface area and total volume
were extracted using WinRhizo software (Regent Instrument, Inc.,
2012). Afterward, all root samples were oven- dried at 65°C for 48 h
and weighed. SRL was calculated using total length divided by dry
mass; SR A was c al cul at ed usin g to tal are a div ide d by dr y mas s; RTD
was calculated as the ratio of root dry mass to root volume. Tree
species from the genus Picea form associations with ECM fungi,
which primarily colonize root tips (Ding et al., 2020). Thu s, we id en-
tified ECM fungi by carefully examining the specific morphologi-
cal characteristics of root tips, such as fungal sheaths, hyphae and
rhizomorphs (de Neergaard et al., 2000). Ultimately, 50 first- order
roots were randomly picked from the second subsample of each
plot for the determination of mycorrhizal colonization. The mycor-
rhizal colonization rate (MCR, %) was calculated as the number of
roots colonized by ECM fungi divided by the total number of roots
examined in each plot (Guo et al., 2008).
2.3 | Leaf, root and soil chemistry
The oven- dried leaf and root samples were ground into fine pow-
der prior to chemic al analysis. Soil samples, including bulk soil and
rhizosphere soil, were sieved (<2 mm) and separated into two sub-
samples. One subsample was air- dried at room temperature for the
determination of total carbon (C), total N, total P and plant- available
P; the other subsample was stored at −20°C for soil moisture and
inorganic N assays. Soil moisture was determined by oven- drying
soil samples at 105°C for 48 h and weighing. Soil pH was measured
with a suspension using a laboratory pH metre (Model PHS- 2, INESA
Instrument). The total C and N concentrations of leaves, roots and
soils were determined using an elemental analyser (Vario Macro
cube, Elementar). Subsamples of plants (i.e. leaves and roots) and
soils were digested in H2O2- H 2SO4 and a mixture of concentrated
H2SO4- H C l O 4, respectively, using a Microwave Digestion System
(CEM MARS 6, CEM Corp.). Then, the concentration of total P was
determined using an inductively coupled plasma- atomic emission
spectrometer (ICP– OES) (Optima 5300 DV, Perkin Elmer). To de-
termine the plant- available P concentration (PAP), subsamples were
first extracted using 0.5 M NaHCO3 and then determined with the
molybdenum- blue colorimetric method. The concentration of soil
inorganic N (IN, the sum of NH4
+- N and NO3
−- N) was determined
colorimetrically using an AutoAnalyser III (SEAL Analy tical) after ex-
traction with 2 M KCl.
2.4 | Soil enzymes and climate characteristics
The potential extracellular enzyme activit y of bulk and rhizo-
sphere soils involved in the mining of N and P from soil organic
matter was measured using fluorogenic substrates (German
et al., 2011). These enzymes were the N- mining- related β- 1,4- N-
acetylglucosaminidase (NAG, involved in chitin or peptidoglycan
degradation), leucine aminopeptidase (L AP, involved in degradation
of proteins or other peptide substrates) and the P- mining- related
acid phosphatase (AP, involved in organic phosphorus degrada-
tion). Briefly, we prepared soil slurries by homogenizing 1 g soil
with 50 mL of acetate buf fer (50 mM, pH 5.0) using a magnetic stir
plate, and the soil slurry was immediately dispensed into 96- well
microplates with buffer, sample, reference and substrate follow-
ing a strict order and position on the plate (German et al., 2011).
The microplates for NAG, LAP and AP were then incubated for 2,
6 and 2 h, respectively, after which the NAG, LAP and AP activities
were determined fluorometrically (excitation, 365 nm; emission,
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DING et al.
450 nm) using corresponding fluorescent tagged substrates (4- M
UB- N- acetyl- β- D- glucosaminide for NAG, L- leucine- 7- amido- 4-
methylcoumarin for LAP, 4- methylumbelliferyl- phosphate for AP).
Eight replicates for each soil sample were set up in each plate, and
enzyme activity was calculated as nmol of substrate converted per
hour per gram of dr y soil (nmol h−1 g−1 ).
Climate variables, including MAT and MAP, were obtained from
a grid dataset containing monthly records of temperature and pre-
cipitation (1981– 2015). The climate data, with a spatial resolution of
0.5° × 0.5°, were downloaded from the China Meteorological Data
Service Center (http://data.cma.cn/en/).
2.5 | Calculations and statistical analysis
Nutrient resorption variables included N and P resorption efficiencies
(N RE, PRE), whic h wer e def i ned as the am oun t of N or P res o rbe d dur-
ing le af sene sc ence an d cal cu lated as th e di f fe ren ce in the N or P con-
centrations between green and senescent leaves, that is, NRE or PRE
(%) = (N or P in green leaves − N or P in senesced leaves × [MLCF])/N
or P in green leaves × 100. MLCF is the mass loss correction factor,
with a value of 0.745 for conifers (Vergutz et al., 2012). The rhizos-
phere effect on NAG and LAP (RE- [NAG+LAP], %) or AP (RE- AP, %)
was calculated as the percentage differences between rhizosphere
soil and bulk soil of the same plot for (NAG+LAP) or AP, respectively,
that is, [((NAG+LAP) or AP in rhizosphere soil − (NAG+LAP) or AP in
bulk soil)/(NAG+LAP) or AP in bulk soil] × 100. The rhizosphere effect
on IN and PAP was calculated using the same method, thar is, [(IN
or PAP in rhizosphere soil − IN or PAP in bulk soil)/IN or PAP in bulk
soil] × 100 (Lin et al., 2020; Phillips & Fahey, 2006). The rhizosphere
effects on N- mining- and P- mining- related enzyme activity were
positively correlated with the rhizosphere effects on IN and PAP, re-
spectively (Figure S2), suggesting that it can be used to indicate root
mining capacity for plant- available N or P.
The mean values of root traits, nutrient resorption efficiency, rhi-
zosphere effect and environmental factors were calculated using the
experimental plot (30 m × 40 m) as the within- site unit of replication.
Before statistical analyses, all data were tested for normality using
the Shapiro– Wilk test and for variance homogeneity using Levene's
test, and log- 10 transformation was conducted when necessary. For
hypothesis (1), we first performed principal component analysis (PCA)
on six root traits for the 40 sites with the package STATS and selec ted
the first two PCA axes that were represented by the negative RD and
SRL, SRA relationship and by the negative RTD and root nutrient (i.e.
N and P) concentration relationship as proxies for the root foraging
dimension and root uptake dimension respectively (Figure 1a; Ding
et al., 2020; Kong et al., 2019). Then, the relationships of leaf nutri-
ent (i.e. N and P) resorption efficiency with root foraging dimension,
root uptake dimension, MCR, RE- (NAG+LAP) and RE- AP were fitted
by linear regressions using the package STATS. To test the robustness
of the relationships indicated by linear regressions, we also performed
a linear mixed- effect model using the package LME4, where the root
fora ging dimension, root upt ake dimension , MCR , RE- (NAG+LAP) and
RE- AP were treated as fixed factors and conifer species as a random
FIGURE 1 Principal component analysis (PCA) of the six core root trait s that define the root economics space (a) and the relationship
between the first two PCA axes (i.e. the root foraging dimension and the root uptake dimension) and leaf N resorption efficiency (b, c) and
leaf P resorption ef ficiency (d, e). The first PCA axis (45.91%) represents the root foraging dimension, describing from large- diameter roots
that are characterized by low foraging ef ficiency to high specific root length (SRL) and specific root area (SR A) root s that are related to
high foraging efficiency. The second PCA axis (31.71%) represents the root uptake dimension, describing low absorptive activit y roots with
high root tissue density (RTD) to high absorptive activity roots with high N and P concentrations. RD, root diameter; RNC , root nitrogen
concentration; RPC, root phosphorus concentration. *: p < 0.05, **: p < 0.01, ***: p < 0.001.
RD
SRL
SRA
RTD
RNC
RPC
-4 -2 024
-4
-2
0
2
4
Root uptake dimension
Root foraging dimension
40
48
56
64
72
Leaf Nresorptionefficiency(
%)
R2=0.028 R2=0.32***
R2=0.064 R2=0.23**
(b) (d)
(c) (e)
(a)
-3 -2 -1 0123
40
50
60
70
80
Leaf Presorptionefficien cy (%)
Root foraging dimension
-2 024
Root uptakedimension
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6
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Functional Ecology
DING et al.
factor. Additionally, the bivariate relationships among root traits, my-
corrhizal fungi, rhizosphere effect on N- and P- mining- related enzyme
activit y an d le af nu tr ient re so rption ef fici ency (i.e . NRE and PRE) wer e
also assessed by Pearson's correlations.
For hypothesis (2), we first quantified the relative importance
of climate and soil factors on leaf N and P resorption efficienc y,
root traits, MCR, RE- (NAG+LAP) and RE- AP by adopting the hi-
erarchical partitioning method using the ‘rdacca.hp’ function in
the rdacca.hp package (Lai et al., 2022). Meanwhile, the effec ts
of specific climatic and soil variables on leaf N and P resorption
efficiency, root foraging dimension, root uptake dimension, MCR,
RE- (NAG+LAP) and RE- AP were tested with a linear mixed- effect
model using the package LME4 2014 (Bates et al., 2015). Prior
to the analysis by the linear mixed- effect model, we performed
a PCA on soil nutrient- related variables (TC, TN and TP) to han-
dle multicollinearity between soil variables. The first PCA axis,
which explained the majority of the variation in nutrient fac tors
(76.80%), was used as a combined index for soil nutrient avail-
ability (Figure S3); this axis combined with MAP, MAT and soil pH
was treated as a fixed factor, and conifer species was treated as
a random factor in the linear mixed- effect model. The variance
explained (conditional R2) by the linear mixed- effect models was
determined by the r.squaredLR function in the MuMIn package
(Bartón, 2016). The variation trends of leaf nutrient (i.e. N and P)
resorption efficiency, root foraging dimension, root uptake dimen-
sion, MCR, RE- (NAG+LAP) and RE- AP along the most influential
environmental factors were examined by linear regressions using
the package STATS. Statistical analyses were conducted using
SPSS 19.0 (SPSS Inc.) and R 3.5.3 (R Core Team, 2019).
3 | RESULTS
3.1 | Relationships of leaf nutrient resorption with
below- ground nutrient acquisition strategies
The PCA results showed a multidimensional root economics space
across the 40 coniferous populations, and the first two components
explained more than 70% of the variance in root traits (Figure 1a).
The first dimension (45.91%) was described by negative correlations
between RD and foraging- related traits, that is, SRL and SRA (here-
after termed the root foraging dimension), and a higher score in the
foraging dimension supported a higher root foraging efficiency by a
higher SRL and SR A and smaller RD (Figure 1a). The second dimen-
sion (31.71%) was represented by a negative relationship between
traits related to root uptake, that is, RTD and root nutrient (i.e. N
and P) concentration (hereafter the root uptake dimension) and a
higher score in the uptake dimension represented a higher root N
concentration and smaller RTD, supporting a higher root uptake rate
(Figure 1a).
NRE and PRE showed no significant relationship with the score
in the root foraging dimension but decreased linearly with the score
in the root uptake dimension (Figure 1b– e). Among the root forag-
ing trait s, RD, SRL and SRA had no significant correlations with NRE
and PRE (Figure S4). Among root uptake traits, RTD was positively
correlated with NRE, and root N and P concentrations were nega-
tively correlated with NRE and PRE (Figure S4). Additionally, NRE
and PRE decreased lin early with MCR (Figure 2a,b) but increased lin-
early with RE- (NAG+LAP) and RE- AP respectively (Figure 2c,d). The
results of linear mixed- ef fect models showed that after accounting
FIGURE 2 Relationship between
mycorrhizal colonization rate and leaf
N resorption and leaf P resorption
efficiency (a, b); relationship between
rhizosphere effect on N, P- mining- related
enzyme activity and leaf N resorption
and leaf P resorption efficiency (c, d).
MCR, mycorrhizal colonization rate;
RE- (NAG+LAP), rhizosphere effect on
N- mining- related enzyme activity (i.e.
β- 1,4- N- acetylglucosaminidase [NAG] and
leucine aminopeptidase [L AP]); RE- AP,
rhizosphere effect on P- mining- related
enzyme activity (i.e. acid phosphatase
[AP]). *: p < 0.05, **: p < 0.01, ***: p < 0.001.
24 36 48 60 72
40
48
56
64
72
24 36 48 60 72
60 90 120 150 180 210
40
48
56
64
72
40 80 120 160
Leaf Nresorptionefficiency(%)
MCR(%)
R2=0.31***
MCR(%)
(b)
(c)
40
50
60
70
80
Leaf Presorptionefficiency(%)
R2=0.26***
Leaf Nresorptionefficiency(%)
RE-(NAG+LAP) (%)
R2=0.23** R2=0.17**
RE-AP(%)
(a)
(d)
40
50
60
70
80
Leaf Presorptionefficiency(%)
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|
7
Functional Ecology
DING et al.
for the identity of conifer species, NRE and PRE were also signifi-
cantly influenced by changes in the root uptake dimension, MCR,
RE- (NAG+LAP) and RE- AP (Table S3). Furthermore, RE- (NAG+LAP)
and RE- AP were significantly correlated with MCR and decreased
linearly with MCR (Figure S5).
3.2 | Influence of environmental factors on leaf
nutrient resorption and below- ground nutrient
acquisition strategies
The results of hierarchical partitioning analysis showed that MAT
was the most imp or tant driver of NRE , PRE, root N and P concent ra -
tions, MCR, RE- (NAG+LAP) and RE- AP (Figure 3a,b,g– k), whereas
MAP was the most import ant driver of RD, SRL and SRA (Figure 4c–
e; Table S4). Soil N was the most im port ant driver of RTD (Figure 3f).
Pearson correlation analysis indicated that soil C and N contents also
significantly affected NRE, PRE, RD, SRL, SRA, RTD, root N, root
P and RE- (NAG+LAP), while soil pH had a significant influence on
MCR (Figure 3l). The results of linear mixed- effect models showed
that af ter accounting for the identit y of conifer species, MAT could
still explain variations in NRE, PRE, root uptake dimension, MCR, RE-
(NAG+LAP) and RE- AP (Table S5).
The results of regression analyses showed that with increas-
ing MAT, NRE and PRE decreased linearly, while NRE/PRE had no
significant change (Figure 4a,b; Figure S6). For the below- ground
part of plant s, with increasing MAT, there was no change in RD and
SRL in the foraging dimension but a significant tendency towards
higher root N concentrations in the upt ake dimension (Figure 4c,d).
Additionally, MCR increased linearly, and RE- (NAG+LAP) and RE- AP
decreased linearly with increasing MAT (Figure 5a– c). Furthermore,
with the increase in MAT, the N and P concentrations in green leaves
increased at the same rate as those in senesced leaves (Figure 6a,b).
4 | DISCUSSION
4.1 | Relationships between above- and below-
ground nutrient strategies
Our study showed that SRL and SRA in the foraging dimension did
not change, while MCR tended to decrease with enhanced leaf N
and P resorption (Figures 1a– c and 2a,b). This result conflicts with
our expectation that roots and mycorrhizal fungi may respond in-
versely to changes in leaf nutrient resorption intensit y and suggests
that adjustments in foraging- related root morphological traits are
likely to operate independently from above- ground nutrient con-
servation mechanisms. The complex soil environments experienced
by root s have recently been proposed to explain the weak connec-
tion between foraging- related root traits and leaf resource econom-
ics (Kramer- Walter et al., 2016; Weemstra et al., 2016). However,
other compensation mechanisms derived from root s, such as the
FIGURE 3 Relative importance of six environmental factors on leaf N and P resorption ef ficiency (a, b), root traits (c- h), mycorrhizal
colonization rate (MCR) (i), rhizosphere effect on N- mining- related enz yme activity (j), and rhizosphere effect on P- mining- related
enzyme activity (k) and correlation matrix between the six environmental factors and leaf N and P resorption ef ficiency, root traits, MCR,
rhizosphere effect on N, P- mining- related enzyme activity (l). MAT, mean annual temperature; MAP, mean annual precipitation; Soil C , soil
total carbon; Soil N, soil total nitrogen; Soil P, soil total phosphorus; NRE, leaf N resorption efficiency; PRE, leaf P resorption ef ficiency;
RD, root diameter; SRL, specific root length; SRA specific root area; RTD, root tissue density; Root N, root N concentration; Root P, root
P concentration; RE- (NAG+LAP), rhizosphere effect on N- mining- related enzyme activity (i.e. β- 1,4- N- acetylglucosaminidase [NAG] and
leucine aminopeptidase [L AP]); RE- AP, rhizosphere effect on P- mining- related enzyme activity (i.e. acid phosphatase [AP]). The asterisks
indicate significant correlations at different levels, *: p < 0.05, **: p < 0.01, ***: p < 0.001.
MA
T
M
AP
pH
STC
STN
STP
NRE
PRE
RD
SRL
SRA
RTD
RNC
RPC
MCR
RE-(NAG+LAP)
RE-AP
**** *** *** **
********** *
*** ******
*** ******
*** ** *
** *** *
***
** **** ** **
*******
**********
****
**
*
pH
MAP
Soil P
Soil C
Soil N
MAT
010203040
NRE
Relativeimportance(%)
Soil P
pH
MAP
Soil C
Soil N
MAT
01020304050
PREpH
MAT
Soil P
Soil N
Soil C
MAP
01428425670
RD pH
MAT
Soil P
Soil N
Soil C
MAP
01428425670
SRL MAT
pH
Soil P
Soil N
Soil C
MAP
01428425670
SRApH
MAP
Soil P
MAT
Soil C
Soil N
015304560
RTD
(b) (c) (d)
Soil P
MAP
Soil C
pH
Soil N
MAT
0918 27 36 45
RNC
(a)
MAP
pH
Soil N
Soil P
Soil C
MAT
010203040
RPC
(k) (l)
Soil N
Soil P
Soil C
MAP
pH
MAT
015304560
MCR pH
Soil P
MAP
Soil N
Soil C
MAT
01428425670
RE-(NAG+LAP)Soil C
Soil N
pH
Soil P
MAP
MAT
01428425670
RE-AP
Relativeimportance(%) Relative importance (%)Relativeimportance(%) Relative importance(%) Relativeimportance (%)
0
0.5
1
Relative importance (%)
(e) (f)
Relative importance (%)Relativeimportance(%) Rela tiveimportance(%) Relative importance(%)
(g) (h) (i) (j)
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8
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Functional Ecology
DING et al.
FIGURE 4 Changes in leaf N and P
resorption efficiency (a, b), root foraging
dimension (c) and root uptake dimension
(d) with mean annual temperature. MAT,
mean annual temperature.
40
48
56
64
72
36912
0.0
1.5
3.0
36912
Leaf Nresorptionefficiency(%)
R2=0.26***
Leaf Presorptionefficiency(
%)
R2=0.35***
40
50
60
70
80
Root foraging dimension
R2=0.19**
0
2
4
Root uptake dimension
(a) (b)
(c) (d)
FIGURE 5 Changes in mycorrhizal colonization rate (a), rhizosphere ef fect on N- mining- related enzyme activity (b), and rhizosphere
effect on P- mining- related enzyme activity (c) with mean annual temperature. MCR, mycorrhizal colonization rate; RE- (NAG+LAP),
rhizosphere effect on N- mining- related enzyme activity (i.e. β- 1,4- N- acetylglucosaminidase [NAG] and leucine aminopeptidase [LAP]); RE-
AP, rhizosphere effect on P- mining- related enzyme activity (i.e. acid phosphatase [AP]); MAT, mean annual temperature.
036912
40
80
120
160
200
036912
30
60
90
120
150
036912
24
36
48
60
72
RE-(NAG+LAP) (%)
R2=0.36***
(b)
RE-AP (%)
R2=0.29***
(c)
MCR (%)
(a) R2=0.28***
FIGURE 6 Changes in N and P concentrations in green leaves and senesced leaves with mean annual temperature (MAT). Panels (a) and
(b) refer to the current conifers, and panel (c) illustrates the comparison of the conifers (solid green and orange lines) with the global and local
patterns (Ren et al., 2018; Yuan & Chen, 2009). With the increase in MAT from T1 to T2, the N or P concentrations in the green leaves of the
current conifers changed from a1 to b1, and the nutrients in senesced leaves changed from a3 to b3 at the same rate as those in green leaves,
while global and local studies predicted that nutrients in senesced leaves varied from a2 to b2.
036912
0
4
12
16
036912
0.0
0.6
1.2
1.8
2.4
Nutrient (N or P) concentration
Green leaf
Observed senesced leaf
Expected senesced leaf
(c)(b)
(a)
T1T2
Slope:p=0.33
Intercept: p<0.010
a1
a2
a3
b1
b2
b3
Green leaf
Senesced leaf
Nconcentration(g/kg)
R2=0.20**
R2=0.30***
R2=0.22**
Pconcentration(g/kg)
Slope: p=0.14
Intercept: p<0.010
R2=0.48***
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|
9
Functional Ecology
DING et al.
rhizosphere effect on nutrient mineralization driven by extracellu-
lar enz ymes (i.e. root mining strategy), may also cause root forag-
ing traits to be independent of above- ground nutrient conser vation
mechanisms (Deng et al., 2016; Lambers et al., 2013). For example,
we observed that when more nutrients were resorbed from senesc-
ing leaves, there were more nutrient s available to roots through
nutrient mining in the rhizosphere (Figure S7a,b). This nutrient
benefit from root mining could partially offset that from adjusting
foraging- related root traits, thus leaving more freedom for root for-
aging traits to adapt to other environmental constraints (Kramer-
Walter et al., 2016; Weemstra et al., 2016). Therefore, we suggest
that in addition to complex external constraints, the diverse nutrient
strategies of the roots themselves may also contribute to the weak
association of root foraging strategies with above- ground nutrient
conservation mechanisms.
In addition, we noted that there was a negative relationship be-
tween MCR and RE- (NAG+LAP) and RE- AP (Figure S5). This finding
reinforces recent observational studies in ECM- dominant trees and
indicates that ECM fungi foraging may work in a trade- of f manner
with root mining strategy in response to changes in leaf nutrient re-
sorption intensity (Lin et al., 2020). Furthermore, we also found that
as leaf nutrient resorption increased, MCR decreased significantly
(Figure 2a,b), wh ile RE - ( NAG +LAP) and RE- AP increased significantly
(Figure 2c,d). This suggests that with enhanced above- ground nutri-
ent conservation, plants shift from relying on ECM fungi foraging (i.e.
the ‘outsourcing’ strategy) to depending on root mining represented
by RE- (NAG+LAP) and RE- AP (i.e. the ‘do- it- yourself’ strateg y) to fa-
cilitate nutrient acquisition below- ground. Such a shift reflects the
divergence in below- ground nutrient acquisition strategies among
different coniferous populations and may be related to changes in
below- ground nutrient demands and carbon allocation under the
influence of leaf nutrient resorption (Aerts & Chapin, 2000; Zhao
et al., 2020). Plant s with lower leaf nutrient resorption inevitably al-
locate more carbon below- ground so that sufficient nutrients can
be acquired from soils and supplied to above- ground parts (Brant
& Chen, 2015; Zhao et al., 2020). In such cases, employing ECM
fungi seems to be a wise choice because ECM fungi can access a
large amount of nutrient s (e.g. N and P) from inorganic and organic
sources beyond the root- depletion zone through the vast hyphal
network and explore micropores that roots or even root hairs can-
not enter (Stock et al., 2021). In contrast, root mining is mainly to
mine and absorb the abundant organically bound nutrients in the
rhizosphere by roots themselves, thus representing a relatively cost-
efficient strategy for nutrient acquisition (Kuzyakov & Razavi, 2019;
Wen et al., 2022). Accordingly, it is reasonable to understand why
plants with higher leaf nutrient resorption are more likely to work
cooperatively with the root mining strategy to ensure plant nutrient
needs (Deng et al., 2023; Ushio et al., 2015).
High above- ground nutrient conservation alone may not be
adaptive if the individual plant has roots with high met abolic activit y
and fast turnover (Brant & Chen, 2015; Ushio et al., 2015). Here,
the observed tendency towards higher RTD in the uptake dimension
with the increase in leaf nutrient resorption suppor ted this argument
(Figure 1d,e) and indicates that nutrient conservation mechanisms
in leaves and roots work synergistically in maintaining conifer nu-
trition (Ushio et al., 2015). This is because RTD, which is at the
resource- conservative end of the root uptake dimension, and nutri-
ent resorption are both typical traits that are conducive to nutrient
conservation (Brant & Chen, 2015; Kong et al., 2019). Additionally,
similar to previous studies, nutrient resorption here was found to
increase with the reduction in green leaf nutrients (Figure S8a,b;
Kobe et al., 2005; Ushio et al., 2015). This implies that in regions with
higher leaf nutrient resorption, more low- quality litter will be input
into the soil, which will further lead to slower litter decomposition
and nutrient mineralization and thus lower soil nutrient availability
(Deng et al., 2016; Jiang et al., 2023; Zhang et al., 2018). In such
cases, producing dense roots with lower N and P concentrations that
can facilitate nutrient conservation is believed to be more adaptive;
moreover, such low- uptake- activity roots may be well adapted to
the nutrients released from low- quality litter via root mining (Fort &
Freschet, 2020; Kong et al., 20 19; Reich, 2014). Thus, the synergis-
tic relationship between roots and leaves in nutrient conservation
could result from root adaptation to the ecological effects induced
by nutrient resorption. Overall, our results significantly extend pre-
vious findings that leaf nutrient resorption and mycorrhizal fungi,
together with traits related to root mining and root uptake, shape
pl an t nut r ie nt st r at egi es th rou gh a ra nge of tr a de- offs and sy n erg ies .
4.2 | Factors driving the relationship between
above- and below- ground nutrient strategies
Our results revealed that temperature played a critical role in driving
the relationship between above- ground nutrient conservation mecha-
nisms and below- ground nutrient acquisition strategies (Figure 3;
Table S4). In general, MAT exer ted a strong influence on ECM fungi
and leaf nutrient resorption (Figure 3a,b,i); with increasing MAT, there
was a significant increase in the MCR and a sig nif icant de crease in leaf
resorption of N and P (Figures 4a,b and 5a). These results are consist-
ent with global decreasing trends in nutrient resorption and increas-
ing trends in MCR with MAT (Ostonen et al., 2017; Soudzilovskaia
et al., 2015; Vergutz et al., 2012). Previous studies also reported that
in cold alpine regions, an increase in temperature, even at a slight rate,
coul d cause a con si der abl e in cre as e in the grow th an d me tab oli c ac t iv -
ity of mycorrhizal fungi (Fernandez et al., 2017; Morgado et al., 2015).
Additionally, our results also show that the increase in MAT improves
root uptake by increasing root nutrient (N and P) concentrations
(Figure 4d), which may be due to the direct effect of temperature
on root metabolic activit y or the indirect effect of temperature on
soil nutrient availabilit y by changing moisture (Ding et al., 2020). For
root mining, our study found that temperature is also a key driving
factor (Figure 3j,k; Table S4), which is consistent with recent studies
showing that the rhizosphere effect on plant- available nutrients (e.g.
inorganic N) tends to be higher in cold environments (Bastida et al.,
2019; Nakayama & Tateno, 2022). In addition, temperature is still an
important environmental factor affecting leaf N and P resorption,
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10
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Functional Ecology
DING et al.
root uptake dimension, MCR, RE- (NAG+LAP) and RE- AP, even when
we take into account conifer species effects (Table S5). Therefore,
the coordination between above- ground nutrient conservation and
below- ground nutrient acquisition could be dominantly driven by tem-
perature rather than species identity.
Despite the determinant role of temperature in controlling leaf
nutrient resorption, the ratio of NRE to PRE (i.e. NRE/PRE) showed
a static response to changes in MAT, and the NRE/PRE ratio at most
sites was lower than 1 (Figure S6). This indicated that conifers on
the Tibetan Plateau had a relatively constant resorption ratio of N
versus P from senesced leaves and experienced relatively high P
limitation (Cui et al., 2022; Du et al., 2020). In addition, we noted
that soil properties (e.g. pH and soil nutrients) were less import-
ant than temperature in driving the coordination between above-
ground nutrient conservation and below- ground nutrient acquisition
(Figure 3). For instance, both MCR and foraging- related trait s were
found to be insensitive to change in soil nutrient availability and soil
pH (Table S5). This contrasts with some loc al- scale studies showing
a dominant role of soil fertility in driving root trait variations (Sun
et al., 2021; Yan et al., 2022). One explanation may be due to the fact
that temperature in alpine environments is a key regulator of soil mi-
crobial activity, soil processes and subsequent root and mycorrhizal
traits. On the other hand, we could not exclude the possibility that
some unmeasured soil variables such as soil texture, organic matter
and even microbial properties could also contribute to the coordi-
nation of above- and below- ground nutrient strategies (Bardgett
et al., 2014); and future studies may concentrate on these variables.
Notably, we also observed that with the increase in MAT, N
or P concentrations in green leaves increased at the same rate as
those in senesced leaves (Figure 6a,b). This contrasts with previ-
ous local and global studies reporting that nutrient concentrations
in senesced leaves usually increase at a higher rate than those in
green leaves with increasing MAT (see the dashed line in Figure 6c;
Ren et al., 2018; Yuan & Chen, 2009). For simplicity, we present a
conceptual diagram (Figure 6c) showing the importance and impli-
cation of this parallel nutrient concentration change in green (a1,
b1) and senesced leaves (a2, b2) with MAT increasing from T1 to T2.
In this diagram, b3 is clearly lower than b
2, the expected nutrient
concentration in senesced leaves from the local and global pat terns.
This suggests that nutrient resorption in the conifers in our study
is significantly higher than that expected from the local and global
patterns. Although more empirical evidence is needed, the relatively
higher than expected nutrient resorption in our conifers seems rea-
sonable, and the reason is as follows: in alpine environments with
relatively higher MAT, plants increase their dependence on the ECM
pathway for nutrients, which requires higher energy investment (up
to 20% of plant photosynthetic production; Hobbie & Hobbie, 2008).
From a cost– benefit perspective, it may be uneconomical and non-
adaptive for plants if soil nutrients are acquired through a costly
energy pathway, such as ECM, without adequate resorption and uti-
lization before leaves fall as litter. Therefore, the pattern of nutrient
resorption efficiency with MAT in ECM conifers may represent an
adaptation of ECM conifers to alpine environments with high carbon
and nutrient limitation. Moreover, this also implies that the prefer-
ence of plant strategies for below- ground nutrient acquisition could
influence the above- ground nutrient resorption strategy, reflecting
the coordination of below- and above- ground plant strategies in the
changing environment. Further studies confirming the coordination
relationship between above- ground nutrient resorption strategies
and mycorrhizal dependence could be conducted by using isotope
labelling technology, and this represents a promising field.
5 | CONCLUSIONS
The present study provides a holistic perspec tive of how plants coor-
dinate below- ground nutrient acquisition with above- ground nutrient
resorption in ECM- dominated conifers. We show that leaf nutrient
resorption exhibits a trade- off with root uptake, ECM fungi and a
synerg y with root mining represented by the rhizosphere effect on
extracellular enzyme activities but no relationship with root foraging.
An in creas e in leaf nutrie nt (i .e. N and P) resorpti on is accomp anied by
FIGURE 7 A conceptual diagram
showing the coordination bet ween
above- ground nutrient conservation
and below- ground nutrient acquisition
in ECM (ectomycorrhizal)- dominated
alpine coniferous forests. In response to
temperature- induced enhancement of leaf
nutrient resorption, conifers shift from
depending on mycorrhizal fungi (i.e. the
‘outsourcing’ strategy) to relying on root
mining (i.e. the ‘do- it- yourself ’ strategy)
to facilitate nutrient acquisition below-
ground while simultaneously maintaining
more resource- conservative roots.
Organic
matter
Hyphae
Root
IN
Organic
matter
Hyphae
Root
IN
Nutrient resorption
Temperature
Root foraging
Root uptake
ECM foraging
Root mining
N-mining related enzyme
P-mining related enzyme
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|
11
Functional Ecology
DING et al.
a shif t in below- ground strategies from depending on the ‘outsourc-
ing’ strateg y by mycorrhizal fungi to relying on the do- it- yourself ’
strateg y of root mining rather than of root foraging and accompanied
by conservative roots (Figure 7). Temperature played a dominant role
in shaping the relationship of leaf nutrient resorption with root min-
ing, root uptake and ECM fungi foraging but not with precipitation-
controlled root foraging (Figure 7). Overall, the coordinat ion between
above- ground nutrient conservation and below- ground nutrient ac-
quisition strategies may represent the unique adaptation of conifer-
ous forests to alpine environments; this is particularly important for
our understanding and prediction of alpine plant community struc-
ture, dynamics and responses to global change.
AUTHOR CONTRIBUTIONS
Huajun Yin, Deliang Kong and Junxiang Ding discussed and con-
ceived the idea. Junxiang Ding and Qitong Wang collected the data,
and Junxiang Ding, Wenjing Ge and K.D. performed the statistical
analyses. Junxiang Ding, Deliang Kong and Huajun Yin wrote the
first draft of the manuscript with significant help from Qing Liu,
Wenjing Ge and Qitong Wang.
ACKNO WLE DGE MENTS
We thank Qin Cai, Jun Hu, Li Li, Zheng Jiang, Mingzhen Yin
and Yizhong Lin for their kind help with field and laborator y
work. This study was supported jointly by the Second Tibetan
Plateau Scientific Expedition and Research Program (STEP)
(2019QZKK0301), the National Natural Science Foundation of
China (No. 32201517, 32171757, 32171746), the Chinese Academy
of Sciences (CAS) Interdisciplinary Innovation Team (no. xbzg-
zysys- 202112) and the Natural Science Foundation of Sichuan
Province (2022NSFSC0085, 2022ZYD0122). We also thank the
editor Emma Sayer, Frank Harris and other two anonymous re-
viewers for helpful feedback.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVA ILAB ILITY STATE MEN T
Data are available from the Dryad Digital Repository ht t p s : //d oi .
org/10.5061/dryad.83bk3 j9wj (Ding et al., 2023).
ORCID
Junxiang Ding https://orcid.org/0000-0003-3782-9966
Qing Liu https://orcid.org/0000-0002-7046-0307
Deliang Kong https://orcid.org/0000-0002-3418-3787
Huajun Yin https://orcid.org/0000-0001-9202-8286
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Suppor ting Information section at the end of this article.
Figure S1. Locations of the 40 Picea populations in alpine forests on
the Tibetan Plateau, China.
Figure S2. Relationship between RE- (NAG+LAP), RE- AP and
rhizosphere effect on soil inorganic N (a) and available P (b). RE-
(NAG+LAP), rhizosphere effect on N- mining- related enzyme activity
(i.e., β- 1,4- N- acetylglucosaminidase and leucine aminopeptidase);
RE- AP, rhizosphere effect on P- mining- related enzyme activity (i.e.,
acid phosphatase). The asterisks indicate significant relationships at
different levels, *: p < 0.05, **: p < 0.01, ***: p < 0.001.
Figure S3. Principal component analysis (PC A) of soil nutrient-
related variables.
Figure S4. Matrix of the Pearson's correlation coefficients among
leaf nutrient resorption ef ficiency, root traits, mycorrhizal fungi,
rhizosphere effect on N- and P- mining related enzyme activity.
The asterisks indicate significant correlations at dif ferent levels, *:
p < 0.05, **: p < 0.01, ***: p < 0.001. NRE, leaf N resorption efficiency;
PRE, leaf P resorption efficiency; RD, root diameter; SRL, specific
root length; SRA specific root area; RTD, root tissue density; RNC,
root nitrogen concentration; RPC, root phosphorus concentration;
RE- NL, rhizosphere effect on N- mining related enzyme activity (i.e.,
β- 1,4- N- acetylglucosaminidase and leucine aminopeptidase); RE- AP,
rhizosphere effect on P- mining related enzyme activity (i.e., acid
phosphatase).
Figure S5. Relationship between MCR and RE- (NAG+LAP) (a) and
RE- AP (b). MCR , mycorrhizal colonization rate; RE- (NAG+LAP),
rhizosphere effect on N- mining- related enzyme activity (i.e., β- 1,4- N-
acetylglucosaminidase and leucine aminopeptidase); RE- AP, rhizosphere
effect on P- mining- related enz yme activity (i.e., acid phosphatase).
Figure S6. Changes of the leaf resorption ratio of N versus P with
MAT. MAT, mean annual temperature; NRE, leaf nitrogen resorption
efficiency; PRE, leaf phosphorus resorption efficiency.
Figure S7. Relationship between rhizosphere effect on soil inorganic
N, available P and leaf N resorption efficiency (a) and leaf P resorption
efficiency (b).
Figure S8. Relationship bet ween green- leaf N concentration, green-
leaf P concentration and leaf nutrient resorption of N (a) and P (b).
Table S1. Summary of climatic and soil nutrient information at the
40 Picea forest sites.
Table S2. Basic information on the species sampled at each site.
Table S3. Results of linear mixed- effect s models for leaf N and P
resorption efficiency, with root foraging dimension, root uptake
dimension, MCR, RE- (NAG+LAP), and RE- AP as fixed factors and
conifer species as a random factor.
13652435, 0, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2435.14330 by Zhengzhou University, Wiley Online Library on [18/04/2023]. 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

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Functional Ecology
DING et al.
Table S4. Results of the relative importance analysis of six
environmental factors on leaf N and P resorption efficiency, root
traits, MCR, RE- (NAG+L AP), and RE- AP.
Table S5. Results of linear mixed- effects models for leaf N and
P resorption efficiency, root foraging dimension, root uptake
dimension, MCR, RE- (NAG+LAP), and RE- AP, with climate and soil
variables treated as fixed factors and conifer species as a random
fa cto r.
How to cite this article: Ding, J., Ge, W., Liu, Q., Wang, Q.,
Kong, D., & Yin, H. (2023). Temperature drives the
coordination between above- ground nutrient conservation
and below- ground nutrient acquisition in alpine coniferous
forests. Functional Ecology, 00, 1–14. h t t p s: //d o i .
org /10.1111/13 65-2435 .143 30
13652435, 0, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2435.14330 by Zhengzhou University, Wiley Online Library on [18/04/2023]. 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