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ARTICLE
Fungal composition associated with host tree identity
mediates nutrient addition effects on wood microbial
respiration
Zhenhong Hu
1,2,3
| Marcos Fern
andez-Martínez
3,4
| Qinsi He
1,5
|
Zhiyuan Xu
1,6
| Lin Jiang
7
| Guiyao Zhou
8
| Ji Chen
9,10
|
Ming Nie
11
| Qiang Yu
1
| Hao Feng
1
| Zhiqun Huang
12,13
|
Sean T. Michaletz
14
1
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Soil and Water Conservation Science and Engineering
(Institute of Soil and Water Conservation), Northwest A&F University, Yangling, Shaanxi, China
2
Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong, China
3
CREAF, Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
4
BEECA-UB, Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Catalonia, Spain
5
School of Life Sciences, University of Technology Sydney, Sydney, New South Wales, Australia
6
Key Laboratory of Low-carbon Green Agriculture in Northwestern China, Ministry of Agriculture and Rural Affairs of China, College of Natural
Resources and Environment, Northwest A&F University, Yangling, Shaanxi, China
7
School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
8
Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla,
Spain
9
State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, China
10
Guanzhong Plain Ecological Environment Change and Comprehensive Treatment National Observation and Research Station, Xi’an, China
11
Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland
Ecosystems of the Yangtze Estuary, Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai, China
12
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, Fuzhou, China
13
School of Geographical Science, Fujian Normal University, Fuzhou, China
14
Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
Correspondence
Zhiqun Huang
Email: zhiqunhuang@hotmail.com
Funding information
National Natural Science Foundation of
China, Grant/Award Number: 32271853;
Shenzhen Science and Technology
Program, Grant/Award Number:
JCYJ20230807111402004; Beatriu de Pin
os
Program, Grant/Award Number:
2022BP00059; Spanish National Research
Council, Grant/Award Number: AYUDAS
DE EXCELENCIA RYC-MAX 2023;
European Research Council, Grant/Award
Number: ERC-StG-2022-101076740
Abstract
Fungi are key decomposers of deadwood, but the impact of anthropogenic
changes in nutrients and temperature on fungal community and its conse-
quences for wood microbial respiration are not well understood. Here, we
examined how nitrogen and phosphorus additions (field experiment) and
warming (laboratory experiment) together influence fungal composition and
microbial respiration from decomposing wood of angiosperms and gymno-
sperms in a subtropical forest. Nutrient additions significantly increased wood
microbial respiration via fungal composition, but effects varied with nutrient
types and taxonomic groups. Specifically, phosphorus addition significantly
increased wood microbial respiration (65%) through decreased acid
Received: 18 October 2023 Revised: 3 March 2024 Accepted: 17 May 2024
DOI: 10.1002/ecy.4375
Ecology. 2024;e4375. https://onlinelibrary.wiley.com/r/ecy © 2024 The Ecological Society of America. 1of18
https://doi.org/10.1002/ecy.4375
Handling Editor: Joseph B. Yavitt phosphatase activity and increased abundance of fast-decaying fungi
(e.g., white rot), while nitrogen addition marginally increased it (30%).
Phosphorus addition caused a greater increase in microbial respiration in gym-
nosperms than in angiosperms (83.3% vs. 46.9%), which was associated with
an increase in Basidiomycota:Ascomycota operational taxonomic unit abun-
dance in gymnosperms but a decrease in angiosperms. The temperature
dependencies of microbial respiration were remarkably constant across nutri-
ent levels, consistent with metabolic scaling theory hypotheses. This is because
there was no significant interaction between temperature and wood phospho-
rus availability or fungal composition, or the interaction among the three fac-
tors. Our results highlight the key role of tree identity in regulating nutrient
response of wood microbial respiration through controlling fungal composi-
tion. Given that the range of angiosperm species may expand under climate
warming and forest management, our data suggest that expansion will
decrease nutrient effects on forest carbon cycling in forests previously domi-
nated by gymnosperm species.
KEYWORDS
carbon cycle, metabolic scaling theory, nutrient limitation, phosphorus and nitrogen,
temperature sensitivity, tree species, wood decomposition
INTRODUCTION
Deadwood stores 73 ± 6 petagram (Pg; 10
15
g) carbon
(C) globally, which occupies ~8% of the global forest C
stock (Pan et al., 2011). Meanwhile, wood decomposition
releases 7%–14% of total assimilated CO
2
back into the
atmosphere via respiration (Harmon et al., 2020). The
classical decomposition triangle posits that wood traits,
climate, and decomposer organisms are fundamental
controls on decomposition rates (Bradford et al., 2021).
Under this conceptualization, decomposer organisms are
assumed to directly influence decomposition rates, and
wood traits and climate regulate organism physiological
rates. Nutrient availability is the important factor among
wood traits, and temperature is the important factor
among climate variables that stimulate decomposer activ-
ity (Berg & McClaugherty, 2014; Harmon et al., 2020),
and both are being altered worldwide (IPCC, 2021;
Peñuelas et al., 2020). However, the combined effects of
nutrients and temperature on decomposer community and
functioning in decomposing wood are largely unknown, as
most previous studies have focused on one factor at a time
(Bradford et al., 2021; Rinne et al., 2019). Given that global
changes in nutrients and temperature are occurring concur-
rently, a more thorough understanding of their combined
effects is required to accurately predict the role of deadwood
in global C dynamics (Harmon et al., 2020).
Saprophytic fungi play a major role in initial wood
decomposition and respiration (Boddy & Watkinson,
1995; van der Wal et al., 2014). Decomposition depends
on extracellular enzymes produced by fungi, which break
down all components of deadwood (Boddy & Watkinson,
1995). Different fungal species (primarily Basidiomycete
and Ascomycete) are probably involved in various rates
of wood decomposition (van der Wal et al., 2014). In the
Basidiomycetes, white rot fungi can completely degrade lig-
nin that dominate wood chemistry (Schilling et al., 2015),
and brown rot fungi modify lignin during decomposition of
hemicellulose and cellulose components (van der Wal
et al., 2014). As for the Ascomycetes, soft rot fungi attack
part of the cellulose and utilize simple C compounds,
generating a localized decay (Boddy & Watkinson, 1995).
Due to the high C to nutrient ratios in deadwood, there is
a serious nutrient limitation for fungi during the wood
decomposition. Thus, nutrient availability in deadwood
strongly influences fungal community composition and
the decomposition processes.
Wood nutrient concentrations vary widely across
woody plant species (Cornwell et al., 2009). Angiosperm
wood generally contains higher concentrations of nitro-
gen (N) and phosphorus (P) than gymnosperm wood
(Weedon et al., 2009). Within similar climate conditions,
these differences in wood nutrients correlated with higher
fungal diversity and respirationrateinangiospermwood
(Hu et al., 2020; Purahong et al., 2018). On the other hand,
human activities have substantially increased N and P
inputs to the biosphere. Specifically, N deposition has
increased more rapidly than P deposition, leading to
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increases in plant N:P ratios (Peñuelas et al., 2020). This
has led to increased phosphatase activity and decreased
decay rate of leaf litter (Gora & Lucas, 2019). As the N:P
ratio of wood is much higher than that of leaf litter
(Spohn, 2020), increasing N deposition may aggravate
P-limitation for fungal decomposition, especially in tropi-
cal and subtropical forests where P availability is rela-
tively low. On the other hand, fungi could produce
hydrolytic extracellular enzymes to release nutrients from
decaying wood to alleviate nutrient limitations (Gora &
Lucas, 2019). However, production of hydrolytic enzymes
comes at the cost of reducing abundances of fast
decomposing fungi, such as white rot fungi (Lustenhouwer
et al., 2020). Understanding such trade-offs in key fungal
composition linking C decomposition and nutrient acquisi-
tion greatly advances our ability to predict how nutrients
will drive fungal wood decomposition under future scenar-
ios of altered biogeochemical cycles and climate change.
Recent advances in ecological stoichiometry theory
(EST) explain how the balance of multiple elements
influences organismal metabolic rates (Sterner & Elser,
2002). A prominent hypothesis emerging from EST is the
growth rate hypothesis (GRH), which suggests that orga-
nisms with rapid growth rates have high P:N ratios due
to increased P concentration in their tissues, reflecting
the role of P supply in P-rich ribosomes required for pro-
tein synthesis (Isanta-Navarro et al., 2022). The shift in
plant P:N ratio may influence the functioning of fungi by
altering the relative inputs of either N or P into the sys-
tem, leading to reduced microbial metabolism in
P-limited conditions (Buzzard et al., 2019). However,
much less is known regarding how changes of wood N
and P influence fungal communities and activities, and
whether these effects vary with host tree identity.
Higher temperatures are expected to increase rates of
microbial respiration in predictable ways, based on meta-
bolic scaling theory (MST) that describes how the rate of
most cellular reactions increases exponentially with tem-
perature (Allen et al., 2005; Arrhenius, 1915; Michaletz &
Garen, 2024). Microbial respiration under nutrient lim-
ited conditions may require more enzymatic steps
and possibly yield a greater temperature dependence
(i.e., the activation energy of wood microbial respiration:
E[in electron volts]) under equal conditions of humidity
(Sinsabaugh & Follstad Shah, 2012). However, recent
analyses of global wood decomposition showed that
nutrient limitation instead yielded a lower temperature
dependence, with a range of E≈0.16–0.43 eV (Hu
et al., 2018). This reflects considerable uncertainly in the
magnitude of Efor wood microbial respiration under cli-
mate changes. Thus, further studies are needed to explore
how fungal composition and respiration rate could response
when N inputs are increased more than P inputs from
anthropogenic sources and when forests are warmer due to
climate change (Cross et al., 2015).
We addressed these uncertainties based on insights
from MST and EST and explored the individual and com-
bined influences of nutrients and temperature on fungal
composition and respiration rates from decomposing wood
in a subtropical forest. We hypothesized that (H1) P addi-
tion increases microbial respiration through increasing
abundances of fast decomposing fungi (e.g., white rot) and
decreasing P-acquiring enzyme activity, while N addition
has a reversed effect; (H2) the increased microbial respira-
tion resulting from P addition is larger in the P-poor gym-
nospermwoodthaninP-richangiospermwood;and
(H3) P addition decreases the temperature dependence
of microbial respiration, with a larger decrease in gym-
nosperm wood than in angiosperm wood.
MATERIALS AND METHODS
Site description
The study was conducted at the Tiantong National Station
for Forest Ecosystem Research (29480N, 121470E),
Zhejiang Province, China. It has a subtropical monsoon cli-
mate, with mean (1982–2022) annual temperature of
16.2C and precipitation of 1375 mm. Soils belong to red
and yellow types, and soil texture is mainly sandy to silty
clay loam Ferric Acrisol according to the FAO/UNESCO
classification. The climax vegetation is a subtropical ever-
green broad-leaved forest, and Castanopsis fargesii Franch.,
Schima superba Gardn, and Castanopsis carlesii (Hemsl.)
Hayata are dominant tree species.
Experiment design
We used four nutrient addition treatments that were
based on empirical data for N and P deposition in the
region (Du et al., 2016): 0 kg N ha
−1
year
−1
+ 0 kg P
ha
−1
year
−1
(control); +N: 100 kg N ha
−1
year
−1
; +P:
15 kg P ha
−1
year
−1
; and +NP: 100 kg N ha
−1
year
−1
+15kg P ha
−1
year
−1
. These treatments were applied
to twelve 20 m × 20 m plots within an evergreen
broad-leaved forest. Fertilizer (NH
4
NO
3
or NaH
2
-PO
3
in
20 L of distilled water) was applied monthly over the for-
est floor from January 2017 to October 2020. Meanwhile,
20 L of distilled water was applied to the control treat-
ment to avoid moisture differences among treatments.
Each treatment was replicated in triplicate in a com-
pletely random design. The individual plots were at least
10 m apart. To prevent subsurface and overland flow of
water into the plots, the four sides of each plot were
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enclosed by PVC boards inserted into the soil to a depth
of 1.0 m and height of 0.5 m above the ground.
Deadwood samples were obtained from four angio-
sperms (Michelia maudiae,Liquidambar formosana,
S. superba,andC. fargesii)andfourgymnosperms
(Cunninghamia lanceolata,Pinus massoniana,Pseudolarix
amabilis,andCryptomeria japonica) that are dominant tree
species in subtropical China. All wood samples were
obtained from a common even-aged plantation, except
for Castanopsis, which was from a non-plantation forest.
Twelve stems of each species with a length of 1.5 m were
cut and randomly placed into the 12 experimental plots
in November 2017. In total, the experiment comprised
8×12=96 stem sections. The initial wood density; C
quality; and C, N, and P concentrations were determined
before the experiment. These quantities were higher in
angiosperms than gymnosperms except no significant dif-
ference was found in C concentration (Hu et al., 2020). The
diameters of sampled stem sections did not vary signifi-
cantly across treatments (p< 0.05), with a mean value of
13.2 ± 0.24 cm. This is important since diameter is a key
trait influencing decomposition rates (Hu et al., 2018).
Wood sample preparation
Following Hu et al. (2017), we used a chain saw to
obtain 5-cm long wood discs from each stem in
October 2020. Little bark was left for sampling after
3 years, and thus, we mainly sampled wood sections
for analysis. Two wedge-shaped pieces (approximately
10% of the disc) were cut from each disc and used for
wood density and respiration measurements. For each
disc, sawdust samples were obtained using an electric
drill with an 8 mm drill bit that was sterilized with
ethanol between samples. At least 20 drilled holes were
made in each disc.
Wood physical and chemical properties
and decomposition rates
For each segment, the volume was calculated using
Archimedes’water displacement method. The dry mass
was measured after being dried at 70C for 3 days. The
density of each segment was calculated as dry mass per
unit volume (in grams per cubic centimeter). Moisture
content (in percentage) was calculated as ([wet wood
mass −dry wood mass]/[dry wood mass]) × 100. The C
and N concentrations were determined from the milled
sawdust samples with a LECO CHN-2000 analyzer
(LECO Corp., Italy). Phosphorus was extracted with
H
2
SO
4
-HClO
4
and the concentration measured by a con-
tinuous flow analyzer (San++, Skalar, Netherlands).
We used solid-state
13
C nuclear magnetic resonance
spectroscopy with cross-polarization and magic-angle
spinning to assess wood C components. Then the wood C
quality index was characterized as the ratio of lignin
C (alkyl + N-alkyl + aromatic + phenolic) to cellulose C
(O-alkyl + acetal). Annual decomposition rate constant
k(per year) was estimated by fitting a single exponential
decay model (Olson, 1963):
Xt¼X0e−kt,ð1Þ
where Xt(in grams per cubic centimeter) is the density of
deadwood at time t,t(in years) is time, and X0(in grams
per cubic centimeter) is the initial density.
Molecular work and bioinformatics
We used the MoBio Power Soil Kit (Qiagen, Carlsbad,
CA, USA) to extract DNA according to the manufac-
turer’s protocol. The primers ITS3 (GCA TCG ATG AAG
AAC GCA GC) and ITS4 (TCC TCC GCT TAT TGA TAT
GC) targeted the ITS2 region of fungal genes. All subsam-
ples were amplified in triplicate. Polymerase chain reac-
tion (PCR) amplicons from triplicates were combined
and examined by electrophoresis in a 2% (w/v) agarose
gel. PCR products with a bright band were mixed in
equal density ratios and purified using the Qiagen Gel
Extraction Kit. The purified PCR amplicon products were
sequenced on the Illumina NovaSeq platform (Illumina
Inc., San Diego, CA, USA) at Novogene Bioinformatics
Technology Co., Ltd. (Beijing, China). Paired-end reads
were merged using FLASH v1.2.7. Quality filtering of the
raw tags was performed under specific filtering conditions
to obtain a high-quality clean tag according to the QIIME
v1.9.1 quality control process. Any chimeric sequences were
removed using the UCHIME algorithm. Raw sequences
were demultiplexed and processed using the QIIME and
UPARSE pipelines. Sequences were quality filtered
(maxEE =0.5) with minimum read lengths of 150 and
250 bp for bacteria and fungi, respectively, and chimeric
sequences were removed. The sequences were split into
groups according to their taxonomy and assigned to opera-
tional taxonomic units (OTUs) at a 97% sequence similarity
level using the UPARSE pipeline. After quality filtering, the
remaining 6,077,674 fungal sequences were 3903 OTUs.
Fungal OTUs were assigned to the UNITE and MycoBank
database, and the functional groups of fungi were inferred
using FunGuild.
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Exo-enzyme activities
Assays included three C-acquiring enzymes (β-1,
4-glucosidase [BG], β-1,4-xylosidase [XS] and β-D-
cellobiohydrolase [CB]), one C and N-acquiring enzyme
(β-1,4-N-acetylglucosaminidase [NAG]), one N-acquiring
enzyme (L-leucine aminopeptidase [LAP]), and one
P-acquiring enzyme (acid phosphatases [AP]). Fluorimetric
enzyme assays were performed for these enzyme activities
following the methods as previously described (German
et al., 2011). We prepared sawdust slurry by mixing
125 mL of 50-mM sodium acetate buffer at pH 4.5 (wood
pH in NL-BELT) with 1 g of sawdust from each
reconstructed wood samples. In 96-well black plates, we
mixed 200 μL of sawdust slurry with 50 μL of 400-μM
substrate solution with corresponding sawdust control
(50 μL of 50-mM sodium acetate buffer + 200 μL of saw-
dust slurry), quench control (50 μL of 10-μM standard),
substrate control (50 μL of 400-μM substrate + 200 μLof
50-mM sodium acetate buffer), and standard (50 μLof
10-μM standard + 200μL of 50-mM sodium acetate
buffer). Finally, enzyme activities were expressed as
nanomoles of substrate released per hour per gram of dry
wood compound (in nanomoles per gram per hour).
According to the enzyme vector model (Moorhead
et al., 2016; Sinsabaugh & Follstad Shah, 2012), we calcu-
lated the activity vector lengths and angles for all enzyme
data using Microsoft Excel (Office for Windows 2019).
C/(C + N) versus C/(C + P) could quantify relative C ver-
sus nutrient investments as the vector length, and P versus
N investment was determined as the vector angle. The vec-
tor length and degree were respectively expressed as
Vector length ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
x2+y2
pð2Þ
Vector degree ¼atan2 y,xðÞ× 180=π,ð3Þ
where xrepresents the relative C- versus P-acquiring
enzyme activities as (BG + XS + CB)/((BG + XS + CB)
+ AP) and yrepresents the relative C- versus N-acquiring
activities as (BG + XS + CB)/((BG + XS + CB) + (NAG
+ LAP)). The relative investment in C acquisition incr-
eases with vector length, and a vector angle of 45
roughly represents the boundary between the relative
investment in N (<45) and P acquisition (>45).
Wood microbial respiration
We estimated total microbial respiration with the second
segment sample, ~10-g dry mass wood, following Fang
et al. (2005). Each segment sample was adjusted to 60%
water content (under aerobic conditions) by adding
distilled water, which was considered to exceed moisture
limitation of wood decay (less than 43%; Bond-Lamberty
et al., 2002). This controls for confounding effects of vari-
ation in wood moisture on microbial respiration.
Subsamples were incubated in 150-mL jars with four
experimental replicates, and their jars were submerged in
a water bath at 20C to control their temperature for
7 days to activate microorganisms. Given that the mean
monthly temperature of our study site ranged 6–31C
during the recent 40 years, the incubation temperature
was increased from the lowest to the highest between
5 and 30C in continuous steps of 5C and then
decreased. After changing to a new temperature, the jars
containing subsamples were kept in the water bath to
obtain a new equilibrium state to avoid the possible
effects of temperature change on microbial respiration.
As respiration rate was small at the low temperature, and
it may need more time to reach a balance, the equilibra-
tion time was approximately 28 h at 5C, 18 h at 10C,
12 h in 15C, 7 h at 20C, 3 h at 25C, and 1 h at 30C.
During the equilibration period, ambient air was continu-
ously pumped through the headspace of the jars. After
equilibration, gas samples were analyzed using an infra-
red gas analyzer (Model LI-6800, LI-COR Inc., Lincoln,
NE, USA). Data during the first 15 s were discarded to
avoid artifacts resulting from closing the jar. Respiration
rate was calculated according to Fang et al. (2005), taking
net CO
2
flux in the jar divided by the subsample C
weight, and measurement time.
Statistical analyses
To test hypothesis (H1) whether P addition increases
microbial respiration and N addition decreases microbial
respiration through influencing fungal composition, we
used linear mixed models (LMMs) to evaluate the effects
of N and P additions on fungal community composition,
enzyme activity, microbial biomass C, and respiration.
LMMs also were used to determine the effects of nutrient
additions on wood density; C quality; C, N, and P concen-
trations; and N:C, P:C, and P:N ratios. Nutrient treat-
ment, tree taxonomic group, and their interactions were
taken as fixed effects. The random effect was the individ-
ual wood samples (i.e., wood ID) nested within tree spe-
cies. LMMs were subjected to a stepwise selection process
based on Akaike information criterion (AIC), the likeli-
hood ratio test, marginal, and conditional R
2
. The “lme4”
package in R was used to run these LMMs, and the
“lmerTest”package was used to estimate significance
probabilities for each parameter. We then tested the main
effects (treatment and taxonomic group) and intera-
ction effect on fungal community, with a two-way
PERMANOVA as implemented with the Adonis function
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using the “vegan”package. As nutrient additions mainly
influenced deadwood N and P concentration but not
wood C quality index (Appendix S1: Tables S1 and S2,
Figures S1 and S2), we focused the analysis on nutrient
effects of wood microbial respiration. Further, we used
the same procedure to examine the relationship between
microbial respiration and nutrient concentrations, extra-
cellular enzymes, and fungal composition.
We used linear discriminant analysis Effect Size (LefSe)
tests to identify the key OTUs associated with their taxo-
nomic affiliation at phylum and genus levels. The LefSe
analysis employed the Kruskal–Wallis sum-rank test to
detect features with significantly different abundances
between assigned classes (α≤0.05). First, samples were
grouped based on low (0.013–0.062 year
−1
), medium
(0.065–0.104 year
−1
), and high (0.109–0.247 year
−1
)decom-
position rates (see Decomposition_biomarkers_database in
Hu et al., 2023), with an equal number of 32 replicates in
each group. Spearman correlation analysis was then used
to estimate the relationship between the relative distribu-
tion of key fungi and decomposition rate in each group.
The relative distribution of key fungi was calculated as
the relative abundance of a key fungus/sum of the rela-
tive abundance of all key fungi. Finally, random forest
analysis was employed to estimate the relative impor-
tance of key fungi on the prediction of wood decomposi-
tion rate by evaluating the percentage increase in mean
squared error (MSE) for each OTU: higher MSE% values.
The relative distribution of key fungi served as predictors
for the decomposition rate. The significance of each OTU
on the decomposition rate was evaluated using the
rfPermute package (rfPermute function, 5000 trees, 1000
permutations), while the overall model’s explanatory
power (R
2
) for the variables was computed using the
randomForest package (randomForest function, 5000
trees, 1000 permutations).
To test hypothesis (H2) whether the effects of P addi-
tion on the microbial respiration are dependent on wood
traits, we used LMM to evaluate the difference of the
increases of respiration rate between angiosperms and
gymnosperms. The increases of respiration rate were cal-
culated as the difference between the control and nutri-
ent addition treatment. Tree taxonomic group was taken
as a fixed effect, and tree species was taken as a random
effect. We used Tukey’s honestly significant difference
post hoc comparisons to determine the statistical differ-
ence of nutrient sensitivity of respiration rate between
angiosperms and gymnosperms. The slope of the linear
relationship of respiration rate and wood N and P con-
centration (also N:C, P:C, and P:N ratio) was used to
determine nutrient sensitivity of respiration rates.
We used piecewise structural equation models (SEMs)
to identify the direct and indirect effects of N and P addi-
tions on wood microbial respiration. Based on the
rationale of the GRH, P:N ratio and P concentration had
been served as the primary driver of variation in micro-
bial metabolism (Isanta-Navarro et al., 2022). The vari-
ables in the model included N and P additions,
respiration rate, wood P:N ratio and P concentration,
OTU richness, Basidiomycota:Ascomycota ratio, and acid
phosphatase activity. We allowed N and P additions to
affect wood P:N ratio and P concentration, wood P:N
ratio, and P concentration to affect fungal diversity
(i.e., OTU richness), composition (i.e., the
Basidiomycota:Ascomycota ratio), and function (acid
phosphatase), and all variables to affect the respiration
rate. We also allowed fungal diversity affect fungal com-
position and fungal composition affect functions
(Buzzard et al., 2019). To obtain the most parsimonious
model, we assessed the full model versus reduced models
by the goodness-of-fit statistics and AIC. Then, we chose
the final model with the lowest AIC score among alterna-
tive models. Nitrogen and P additions were categorical
variables in the models. We implemented SEMs using the
“piecewiseSEM”package to account for the random
effect of “tree species.”
To test hypothesis (H3) whether P addition decreases
the temperature dependence of microbial respiration and
whether the effect is dependent on wood traits, we used
the linear form of the Arrhenius equation to examine
temperature dependence of respiration rate (Michaletz &
Garen, 2024). The temperature dependence can be char-
acterized by the linearized form of the Arrhenius equa-
tion (Arrhenius, 1915):
lnR¼lnR0+E1
kBT0
−
1
kBT
ð4Þ
where Ris the respiration rate (
—
in micrograms of
CO
2
per gram of wood carbon per hour) at the absolute
temperature T,R
0
is a respiration normalization constant
that implicitly accounts for the influence of other respira-
tion rate drivers such as nutrients, E(in electron volts)
is an apparent activation energy that characterizes
the temperature dependence of respiration rates, k
B
(8.617 × 10
−5
eV K
−1
) is Boltzmann’s constant, Tis tem-
perature (in Kelvin), and T
0
is a standard temperature.
We centered our data using the approximate mean incu-
bation temperature (T
0
=15C=288.15 K) of our wood
samples. For the comparisons of temperature dependence
among treatments × taxonomic groups, we first regrouped
the data into eight groups (four nutrient additions × two
taxonomic groups). Tukey’s post hoc comparisons were
also used to test the differences of temperature depen-
dence among treatments and taxonomic groups. Based
on insights from MST and EST (Cross et al., 2015), we
further estimated the interaction effects of wood nutri-
ents and temperature on microbial respiration rates. We
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unpacked R
0
to reveal the GRH for the potential influ-
ence of wood P:N ratio and fungal composition on micro-
bial respiration:
lnR¼ln R1
ðÞ+E1
kBT0
−
1
kBT
+α1ln PNðÞ+α2ln Fc
ðÞ
+α31
kBT0
−
1
kBT
×lnPNðÞ+α41
kBT0
−
1
kBT
×lnFc
ðÞ+α5ln PNðÞ×lnFc
ðÞ+α61
kBT0
−
1
kBT
×lnPNðÞ×lnFc
ðÞ,
ð5Þ
where R
1
is another respiration normalization constant,
PN is wood P:N ratio, and F
c
is fungal composition
(i.e., the Basidiomycota:Ascomycota in terms of OTU
abundance). α
1
,α
2
,α
3
,α
4
,α
5
, and α
6
are scaling expo-
nents for different variables. Equation (5) predicts that
variations in wood P:N ratio and fungal composition
should influence the temperature dependence Eof micro-
bial respiration. Efor microbial exoenzymes that degrade
organic macromolecules, including lignin and cellulose,
are between ~0.31 and 0.56 eV (Sinsabaugh & Follstad
Shah, 2012; Wang et al., 2012). Wood P concentration
(in milligrams of phosphorus per gram of dry wood mass)
and P:C ratio can be used as an additional line of evi-
dence for wood P availability effects on microbial respira-
tion in the models (Appendix S1: Equations S1 and S2).
Furthermore, wood N concentration is also served as a
nutrient predictor in the model to calculate the indepen-
dent effects of wood N on microbial respiration (see
details in the Appendix S1:SectionS1). We also took the
temperature dependence of respiration rate for specific
wood temperatures (WT) and for an increase in tempera-
ture of 10C(WT
+10
)tocalculateQ
10
values as
R
W
(WT
+10
)/R
W
(WT), which enabled comparison of our
temperature dependence results with those reported as
Q
10
in previous studies. All statistical analyses were
conducted in R v4.1.2 (R Development Core Team, 2021).
RESULTS
Wood fungal community and metabolic
limitations
The fungal community was mainly affected by tree species
rather than by nutrient additions (Figure 1a). Nutrient addi-
tions explained only 7% of the variation, while tree species
and species-by-nutrient-addition interactions together
accounted for 41% of the variation (Appendix S1:
Table S4). Nutrient additions increased Ascomycota abun-
dances and decreased Basidiomycota abundances in angio-
sperms (both p< 0.001; Figure 1b). A contrasting pattern
was observed in gymnosperms (both p< 0.001). Fungal
OTU richness increased due to nutrient additions in angio-
sperms, but it decreased in gymnosperms (both p< 0.001;
Figure 1c). Microbial biomass C increased mostly due to P
addition (p< 0.05), with a higher mean value in angio-
sperms (p< 0.001; Figure 1d). Microbial metabolism of
nutrient acquisition was influenced more by relative P
investment compared with relative C and N investment
(Figure 1e). The relative P investment was significantly
higher in gymnosperms than in angiosperms, and it was
significantly decreased by P addition in both groups
(p< 0.05). Meanwhile, P addition decreased the activity
of P-acquiring enzyme of acid phosphatase (p< 0.01;
Figure 1f), while N addition had a less effect on the activity
of N-acquiring enzymes (i.e., β-1,4-N-acetylglucosaminidase
and L-leucine aminopeptidase; Appendix S1:FigureS3).
Fungal key OTUs
Most key OTUs in groups of high and medium decompo-
sition rates belonged to Basidiomycota, and their all iden-
tified functional traits were white rot fungi (Figure 2).
However, most key OTUs in the group of low decomposi-
tion rates belonged to Ascomycota, and most identified
functional traits were soft rot fungi. The relative distribu-
tions of OTUs in groups of high and medium decomposi-
tion rates had a positive relationship with decomposition
rates, while the relationship was negative in the group of
low decomposition rates (Figure 2a). Random forest
analysis showed that Trechispora was the most
important OTU in the group of high decomposition rates,
Peniophorella was the most important OTU in the group
of medium decomposition rates, and Elmerina was the
most important OTU in the group of low decomposition
rates (Figure 2b). Except Gerronema, all OTUs presented
in groups of high and medium decomposition rates were
more abundant in angiosperms than in gymnosperms,
while all OTUs presented in the group of low decomposi-
tion rates were predominantly detected in gymnosperms
(Figure 2c). Four out of six OTUs presented in groups of
high decomposition rates were more abundant in the P
addition, four out of six OTUs presented in groups of
medium decomposition rates were more abundant in the
N addition, and four out of seven OTUs presented in
the group of low decomposition rates were predomi-
nantly detected in the control (Figure 2d).
Wood microbial respiration
There were positive correlations between wood microbial
respirations and decomposition rates (p< 0.001; Figure 3a).
Microbial respiration rate was increased by nutrient
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FIGURE 1 Effects of nutrient addition on fungal community composition and enzymatic stoichiometry. Nonmetric multidimensional
scaling (NMDS) of fungal community composition (a), mean % of reads from each fungal phyla (b), operational taxonomic unit (OTU)
richness (c), microbial biomass C (d), the vector angles of extracellular enzyme stoichiometry (e), and acid phosphatase activity (f) of
angiosperm and gymnosperm wood within nutrient addition treatments. In panel (b), “Others”are sequences that do not belong to the two
most abundant phyla for fungi. Vector angle represents relative P versus N limitations. Bars represent mean values of all samples per
treatment (n=12) and error bars represent 1 SE. * Indicates a significant difference at α=0.05, ** indicates a significant difference at
α=0.01, and *** indicates a significant difference at α=0.001. ns, no significant. Different lower case letters indicate significant difference
at α=0.05.
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FIGURE 2 Legend on next page.
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FIGURE 2 Relative distribution of specific fungi according to high (left), medium (middle), and low (right) decomposition rates and
their correlations. The relationship between specific fungal relative distribution and decomposition rate (a). The relative importance of
specific fungal relative distribution on the prediction of wood decomposition rate (b). The distribution of specific fungi under different tree
species taxonomic groups (c) and nutrient addition treatments (d). The relationship in panel (a) is represented by spearmen correlation r.
Proportion (in percentage) refers to the relative distribution of each fungus, calculated as the relative abundance of a key fungus divided by
the sum of the relative abundances of all key fungi. The taxonomy of each specific fungus at the phylum (A for Ascomycota, B for
Basidiomycota) and genus levels (# represents taxonomic assignments of operational taxonomic unit (OTUs) were based on the MycoBank
reference databases), and their functional traits are represented by different shapes (star, white rot; square, soft rot; circle, unknown). Their
colors reflect a significant (p< 0.05) correlation between each fungus and decomposition rate (yellow, positive; blue, negative; black, no
significant). OTU taxonomic affiliation at phylum, class, order, family, genus, and species levels is listed in https://doi.org/10.6084/m9.
figshare.24342550.v2, [Wood_identified_fungi_biomarkers]. * Indicates a significant difference at α=0.05, ** indicates a significant
difference at α=0.01, and *** indicates a significant difference at α=0.001. ns, no significant; Ang., angiosperms; Gym., gymnosperms;
MSE, mean squared error.
FIGURE 3 The effects of nutrient addition treatments on microbial respiration rate from decomposing wood of angiosperms and
gymnosperms. The relationship between decomposition rate and microbial respiration (a), and microbial respiration rate (b) and the
increased respiration (c) of angiosperm and gymnosperm wood within nutrient addition treatments. Bars represent mean values of all
samples per treatment (n=12) and error bars represent 1 SE. The increased respiration was calculated as the difference between the control
and nutrient addition treatments. * Indicates a significant difference at α=0.05, ** indicates a significant difference at α=0.01, and
*** indicates a significant difference at α=0.001. Different lower case letters indicate significant difference at α=0.05.
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additions (p< 0.05; Figure 3b), with a higher mean value
in angiosperms than in gymnosperms (p<0.05).
However, the amounts of increased respiration depended
on nutrient types and taxonomic groups. Specifically, micro-
bial respiration was increased more by P addition than by
N addition (p< 0.05; Figure 3c), and the increases were
larger in gymnosperms than in angiosperms (p<0.05).
Microbial respiration was better predicted by wood P con-
centration compared with wood N concentration or P:N
ratio (based on r
2
;Figure4a,b). The P sensitivity of micro-
bial respiration was higher in gymnosperms than in angio-
sperms (p< 0.001). Microbial respiration rate had a
negative relationship with acid phosphatase activity
(Figure 4c). Microbial respiration rate was positively corre-
lated with the Basidiomycota:Ascomycota ratio in
gymnosperms, while the relationship was negative in angio-
sperms (Figure 4d).
Direct and indirect effects of nutrient
addition on microbial respiration
Nitrogen addition may mainly indirectly affect micro-
bial respiration through changing wood P:N ratio and
OTU richness in angiosperms (Figure 5a). For gymno-
sperms, N addition could directly and positively influence
microbial respiration and indirectly affect microbial
respiration through changing wood P:N and Basidiomycota:
Ascomycota ratios (Figure 5b). Phosphorus addition had
direct and positive effects on microbial respiration in both
FIGURE 4 Relationships between wood microbial respiration and wood N concentration (a), P concentration (b), acid phosphatase
activity (c), and Basidiomycota:Ascomycota ratio (d) by linear mixed models (n=96). Slopes of relationships were significantly higher in
gymnosperms than in angiosperms in both panel (b) (p< 0.01, F=6.93) and panel (d) ( p< 0.001, F=12.97). * Indicates a significant
correlation at p=0.05, ** indicates a significant correlation at p=0.01, and *** indicates a significant correlation at p=0.001.
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angiosperms and gymnosperms. However, the indirect
effects of P addition on the fungal-driven dynamic of micro-
bial respiration were different between angiosperms and
gymnosperms. Specifically, P addition had positive effects
on OTU richness and negative effects on Basidiomycota:
Ascomycota ratio in angiosperms, and then
Basidiomycota:Ascomycota ratio negatively influenced
microbial respiration. In contrast, these paths were
reversed in gymnosperms.
Temperature dependencies of wood
microbial respiration
Microbial respiration rate increased with temperature
(i.e., 1/kT
c
−1/kT; Figure 6). The temperature depen-
dence of microbial respiration was statistically indistin-
guishable across nutrient addition treatments or
taxonomic groups (ANCOVA; i.e., no difference in slopes
among substrates, all p> 0.05). The estimated Evalue
FIGURE 5 Structural equation models fitted with range-standardized coefficients. Angiosperm wood (a) and gymnosperm wood (b).
Solid lines represent positive, linear associations, while dashed lines indicate negative, linear associations. Dotted lines indicate no detectable
influence of the driver (p> 0.1). The red lines are used to highlight the different effects between angiosperm wood and gymnosperm wood.
Standardized coefficients with significant effects are presented in bold for each path. Fisher’sC=25.5, p=0.60, Akaike information
criterion (AIC) =87.5 in panel (a), and Fisher’sC=23.0, p=0.52, AIC =89.0 in panel (b). R
2
values for marginal (fixed-effects only) and
conditional (fixed + random effects) are also reported for the response variables. * Indicates a significant correlation at p=0.05, ** indicates
a significant correlation at p=0.01, and *** indicates a significant correlation at p=0.001.
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was 0.51 eV (Q
10
=2.01; Appendix S1: Table S9) with a
95% CI of 0.48–0.54 eV. Wood P availability (i.e., P:N and
P:C ratio and P concentration), temperature, and
Basidiomycota:Ascomycota ratio (except gymnosperms)
significantly influenced microbial respiration in the
theoretical models (all p< 0.001; Tables 1and 2;
Appendix S1: Table S10). The interaction effect between
wood P availability and Basidiomycota:Ascomycota ratio
on microbial respiration was statistically significant in
both angiosperms and gymnosperms. However, the inter-
action effects between temperature and P availability or
fungal composition, or the interaction among three fac-
tors, were not significant in any taxonomic group
(all p> 0.05).
DISCUSSION
Our results indicated that N addition slightly increased
wood microbial respiration, while P addition strongly
increased microbial respiration through increasing abun-
dance of fast-decaying fungi (i.e., white rot) and decreasing
acid phosphatase activity in a subtropical forest, which
partly supports our first hypothesis (H1). Phosphorus addi-
tion increased the Basidiomycota:Ascomycota ratio in
gymnosperms but decreased it in angiosperms, and con-
sequently produced larger increases of microbial respira-
tion in gymnosperms (as expected under our second
hypothesis [H2]). This conclusion was also supported by
FIGURE 6 Modified Arrhenius plot showing linear mixed
model fits of Equation (4) to data for temperature and microbial
respiration subjected to four nutrient addition treatments
(n=576). Estimated activation energies E(shown by slopes) are
statistically indistinguishable across taxonomic groups (p=0.20;
F=1.61) and treatments (p=0.23; F=1.45). The shaded areas
indicate 95% CI.
TABLE 1 Fixed-effect parameter estimates, the SE of each estimate, and associated df (for ttests using Satterthwaite approximation),
t-values, p-values, and 95% CI from the linear mixed models (LMMs) for P:N ratio effects on microbial respiration for angiosperm and
gymnosperm wood.
Group Parameter Estimate SE df tp 95% CI
Angiosperms Intercept 4.604 0.094 220.8 48.5 <0.001 4.413 to 4.784
P:N 0.192 0.035 257.0 5.45 <0.001 0.120 to 0.259
T0.466 0.079 277.1 5.87 <0.001 0.312 to 0.620
B:A −0.154 0.034 258.2 −4.46 <0.001 −0.219 to −0.086
P:N × T−0.018 0.030 277.1 −0.620 0.536 −0.076 to 0.039
P:N × B:A −0.044 0.011 255.1 −3.88 <0.001 −0.066 to −0.022
T× B:A 0.010 0.029 277.1 0.350 0.727 −0.046 to 0.066
P:N × T× B:A 0.004 0.010 277.1 0.391 0.696 −0.015 to 0.022
Gymnosperms Intercept 2.895 0.101 7.519 28.7 <0.001 2.808 to 3.785
P:N 6.155 0.084 278.3 7.66 <0.001 −0.152 to 0.172
T0.658 0.197 277.0 3.35 <0.001 0.257 to 1.018
B:A −0.003 0.002 279.7 −1.57 0.117 0.184 to 0.574
P:N × T0.054 0.068 277.0 0.801 0.424 −0.084 to 0.178
P:N × B:A −0.054 0.007 279.9 −7.43 <0.001 0.017 to 0.151
T× B:A −0.034 0.082 277.0 −0.415 0.679 −0.184 to 0.134
P:N × T× B:A −0.012 0.028 277.0 −0.423 0.673 −0.063 to 0.046
Note: The marginal-R
2
was 0.90 and conditional-R
2
was 0.90 in angiosperms, and the marginal-R
2
was 0.81 and conditional-R
2
was 0.85 in gymnosperms with
Equation (5). Estimates of parameters with CI that exclude zero. Bold p-values are significant.
Abbreviations: A, Ascomycota; B, Basidiomycota; N, nitrogen; P, phosphorus; T, temperature.
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the finding that P sensitivity of microbial respiration was
higher in gymnosperms. Nutrients and temperature had
no synergistic effects on wood microbial respiration, and
these two factors independently and additively increased
wood microbial respiration, which did not support our
hypothesis (H3). Finally, our findings indicated that het-
erotrophic microbial respiration from decomposing wood
of angiosperm and gymnosperm could respond similarly
to temperature increases, but differently to nutrient addi-
tions (especially P addition), with a larger increase in
microbial respiration in gymnosperms.
Host tree identity mediates nutrient
addition effects on wood microbial
respiration
Microbial respiration from nutrient-poor gymnosperm
wood exhibited greater increases in response to nutrient
additions than the nutrient-rich angiosperm wood
(Figure 3c). This likely occurs because the sensitivity to
nutrient limitations tends to be most prevalent in microbes
feeding on food resources with low nutrient concentrations
(Berg & McClaugherty, 2014). Due to the lower N and P
concentrations in gymnosperms than in angiosperms, the
nutrient sensitivity of microbial respiration was higher in
gymnosperms (Figure 4a,b). This finding was also demon-
strated by the higher nutrient limitation of microbial
metabolism in gymnosperms (Figure 1e), which was more
relieved by nutrient addition treatments (Figure 1f). Thus,
although gymnosperm wood was associated with a lower
respiration rate than angiosperm wood, nutrient additions
stimulated its microbial respiration in a higher degree.
The different responses of microbial respiration under
nutrient additions between angiosperms and gymnosperms
could further be explained by variation of fungal composi-
tion and functioning. Basidiomycota and Ascomycota were
the dominant fungal species, and they have different decay
abilities (Boddy & Watkinson, 1995). Basidiomycota could
produce oxidative enzymes (especially manganese peroxi-
dase) more strongly than Ascomycota, which is impor-
tant for degrading lignin that dominate wood chemistry
(Schilling et al., 2015). Instead, Ascomycota mostly utilize
simple C compounds of deadwood, and they do not alter
wood structure (Boddy & Watkinson, 1995). As wood
nutrient concentrations increased by nutrient additions,
we found that microbial respiration rates may be mainly
controlled by Ascomycota abundance in angiosperms
while they may be more influenced by Basidiomycota
abundance in gymnosperms (Figure 4d). Additionally,
there was a positive interaction between fungal composi-
tion (i.e., Basidiomycota:Ascomycota) and wood P
TABLE 2 Fixed-effect parameter estimates, the SE of each estimate, and associated df (for ttests using Satterthwaite approximation),
t-values, p-values, and 95% CI from the linear mixed models for P concentration effects on microbial respiration for angiosperm and
gymnosperm wood.
Group Parameter Estimate SE df tp 95% CI
Angiosperms Intercept 4.361 0.050 33.30 86.7 <0.001 4.258 to 4.459
P 0.255 0.036 211.8 7.13 <0.001 0.176 to 0.324
T0.496 0.036 276.9 13.9 <0.001 0.426 to 0.565
B:A −0.184 0.036 277.7 −5.225 <0.001 −0.250 to −0.112
P×T−0.015 0.028 276.9 −0.536 0.593 −0.069 to 0.039
P × B:A −0.084 0.025 278.2 −3.384 <0.001 −0.131 to −0.033
T× B:A 0.019 0.029 276.9 0.64 0.522 −0.038 to 0.075
P×T× B:A 0.015 0.021 276.9 0.722 0.471 −0.025 to 0.055
Gymnosperms Intercept 2.774 0.111 27.9 24.8 <0.001 2.562 to 2.986
P 3.456 0.598 277.7 5.777 <0.001 2.298 to 4.621
T0.393 0.078 277.0 5.022 <0.001 0.241 to 0.546
B:A 0.222 0.035 278.0 6.256 <0.001 0.152 to 0.289
P×T0.728 0.504 277.0 1.445 0.150 −0.250 to 1.706
P × B:A −0.640 0.217 277.7 −2.950 <0.01 −1.060 to −0.217
T× B:A 0.036 0.030 277.0 1.235 0.218 −0.021 to 0.094
P×T× B:A −0.241 0.182 277.0 −1.320 0.188 −0.595 to 0.113
Note: The marginal-R
2
was 0.90 and conditional-R
2
was 0.91 in angiosperms, and the marginal-R
2
was 0.83 and conditional-R
2
was 0.86 in gymnosperms with
Appendix S1: Equation S1. Bold p-values are significant.
Abbreviations: A, Ascomycota; B, Basidiomycota; P, phosphorus; T, temperature.
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availability on microbial respiration (Table 1;
Appendix S1: Table S10). Together, these results indi-
cated that host tree identity regulated nutrient additions
effects on wood microbial respiration through the influ-
ence of fungal composition. Furthermore, wood micro-
bial respiration rates closely mirrored wood
decomposition rates in the field (Figure 3a; Appendix S1:
Figure S4), which indicates microbial respiration may be
a good surrogate of decomposition rate.
At present, 72% of plantations in subtropical China
are coniferous forests, and these fast growth tree species
(e.g., C. lanceolata and P. massoniana) have caused a
decrease in soil fertility (Sheng, 2018). Meanwhile, his-
torical forest management since preindustrial times in
Europe has converted 27% of deciduous forest into conif-
erous forest (Naudts et al., 2016). To mitigate climate
change in the future, parts of coniferous forests in China
and Europe might have to be converted to broad-leaved
forests in the future (Naudts et al., 2016; Sheng, 2018).
Global vegetation models also predict an increase in
deciduous forests particularly at the southern edge of the
boreal region previously dominated by coniferous forests
(IPCC, 2021). Meanwhile, the increased disturbances of
wildfire have resulted in pioneer deciduous forest instead
of coniferous forest in the first several decades in
Canadian forests (IPCC, 2021). Given an increased range
of angiosperm species in the future climate, if we do not
understand tree identity mediation of nutrient input
effects on wood microbial respiration, it will produce seri-
ous biases in modeling wood C fluxes.
Phosphorus addition enhances wood
microbial respiration more than N addition
Phosphorus addition strongly increased wood microbial
respiration, while N addition slightly increased rather
than decreased microbial respiration (Figure 3b,c). This
result was also consistent with the GRH predictions that
soil microbes require overall higher capacities for P than
N use (Buzzard et al., 2019). As wood decomposition was
more limited by wood P than wood N (Figure 1e), P addi-
tion may result in higher rates of microbial respiration
in the P-limited system such as subtropical forests.
Accordingly, P addition increased the abundance of
fast-decaying fungi, but N addition mainly increased the
abundance of medium decaying fungi (Figure 2). Due to
low wood P availability, it may provide a strong incentive
for microbial community to invest more in P acquisition
rather than N acquisition via enzymes (Figure 1f;
Lustenhouwer et al., 2020). Thus, P addition could relieve
P-limitation of microbial metabolism and stimulate
decomposition to a higher degree than N addition (Chen
et al., 2016). Meanwhile, acid phosphatase had a clearly
negative relationship with wood microbial respiration
(Figure 4c), indicating increasing investment in P acquisi-
tion may decrease wood decay by fungi. While our study
could not explicitly evaluate the effect of P on enzyme
dynamics, the role of P availability in the regulation of
wood microbial decomposition requires further study.
It should be noted that the imbalanced atmospheric
N and P deposition has led to a global decrease in wood
P:N ratios (Peñuelas et al., 2020) that may accelerate
P-limitation to wood decomposition in the low-latitude
forests (Gora & Lucas, 2019). Nitrogen addition generally
results in a negative effect on the microbial decomposi-
tion of soil C and leaf litter in temperate forests (Bonner
et al., 2019; Knorr et al., 2005). However, we found that
N addition did not inhibit enzyme activities of C, N, and
P acquisition (Appendix S1: Figure S3). In contrast, it
slightly increased microbial respiration and the activity of
C-acquiring enzyme of β-1,4-glucosidase. Our result was
consistent with previous wood decomposition studies
(Chen et al., 2016; Gora & Lucas, 2019). Instead, it
slightly increased wood microbial respiration in the sub-
tropical forest, with a significant effect in nutrient-poor
gymnosperm wood (Figure 3b). Nitrogen is expected to
be limiting during wood decomposition because the C:N
ratio of the degradative enzymes is far narrower than
that of deadwood (Cornwell et al., 2009;Sinsabaugh
et al., 1993). Given that the data of P concentrations in
wood biomass are scarce, wood N concentration has
previously been reported to be the most important pre-
dictor of wood decomposability (Cornwell et al., 2009;
Hu et al., 2018). Our results highlight that wood micro-
bial respiration was primarily controlled by wood P
availability in a subtropical forest, and N addition did
not decrease microbial respiration as previous studies
suggested (Bonner et al., 2019;Knorretal.,2005).
Consistent temperature dependence of
wood microbial respiration
Our results suggested that nutrients and temperature
had no synergistic effects on wood microbial respiration
(Tables 1and 2; Figure 6). The empirically estimated
Evalues across taxonomic groups and nutrient additions
were similar, with a mean value of 0.51 eV, although they
included different fungal community composition. It
indicated that kinetic effects of temperature on biochemi-
cal reaction rates may be well conserved among diverse
fungal taxa (Allen et al., 2005; Michaletz & Garen, 2024).
Our estimate was similar to the average Eof microbial
exoenzyme activity associated with the acquisition of
N and P and the degradation of lignin and cellulose
ECOLOGY 15 of 18
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(0.31–0.56 eV; Sinsabaugh & Follstad Shah, 2012; Wang
et al., 2012). The comparable temperature dependences
were also consistent with other studies of wood
decay around the global forests (Manning et al., 2018;
Rinne et al., 2019). However, our results were opposite to
those reported for decomposition studies of nutrient-rich
substrates such as soil (Conant et al., 2011), leaf litter
(Fierer et al., 2005), and roots (Allen et al., 2005), which
suggested that temperature dependences of microbial res-
piration decreased with nutrient increases.
The similar effects of taxonomic groups and nutrient
additions on the temperature dependence of microbial
respiration also strongly supported the GRH (providing
more resources to make P-rich ribosomes for protein syn-
thesis). Compared with C and N availability in wood,
wood P availability is expected to be much lower than
that required for microbial biomass (Spohn, 2020;Xu
et al., 2013); for example, the wood C:N ratio and C:P is
nearly ~80- and ~180-fold larger than that of microbial
biomass, respectively. In that case, microbes may prefer-
entially invest in nutrient acquiring enzymes to gain P at
the expense of decreasing biomass growth and metabo-
lism (Lustenhouwer et al., 2020). We indeed observed a
negative relationship between acid phosphatase and
microbial respiration (Figure 4). Our P concentrations
were low compared to studies of soil respiration and litter
decomposition (Conant et al., 2011; Fierer et al., 2005),
indicating that the low P concentrations may have been
away from a theoretical optimum for eliciting a synergistic
effect (Table 1; Manning et al., 2018). It thereby supported
our results that P addition only increased the intercepts
(but not the slopes) of the relationships between tempera-
ture and microbial respiration (Figure 6).
Caveats
Our analysis has a few limitations that should be con-
sidered when interpreting the results. It is worth noting
that the influence of wood nutrient traits on microbial
decomposition changes over the period of wood decom-
position (Oberle et al., 2020). Our short study (3 years
in duration) is unlikely to capture the long-term influ-
ence of nutrients on C emissions. While the short-term
data are sufficient for testing hypotheses about factors
that influence initial decay rate variation, and they
could accurately reflect microbial response to climate
changes (Manning et al., 2018). Meanwhile, moisture
content is an important variable controlling respiration
rates in deadwood and it differs among tree species (Hu
et al., 2020). With moisture content being artificially
increased and equalized among substrates, we could
not provide information whether the temperature
dependence of wood microbial respiration was mediated
by wood moisture. Additionally, the effect of tempera-
ture on wood microbial respiration was assessed with a
laboratory experiment, which may limit the application
of our study compared with field experiments.
Furthermore, invertebrates have been thought to contrib-
ute directly and indirectly to wood decomposition and C
release (Bradford et al., 2021). It needs to be noted that
our study only captures a subcomponent of decomposi-
tion processes because it excludes the activity of inverte-
brates. Although some limitations may exist in our
study, considering the huge storage of deadwood existing
in forests (Pan et al., 2011), the key control of host tree
identity regulating fungal response of wood respiration
to nutrient enrichment will ultimately be valuable for
benchmarking and parameterizing modeling efforts that
aim to predict the changes in forest C cycling under
global change scenarios.
AUTHOR CONTRIBUTIONS
Zhenhong Hu and Zhiqun Huang designed the study.
Zhenhong Hu and Sean T. Michaletz developed the the-
ory. Zhenhong Hu and Qinsi He performed measure-
ments. Zhenhong Hu analyzed data and wrote the paper
with significant input from all the other authors.
ACKNOWLEDGMENTS
We thank one anonymous reviewer and the subject-matter
editor for constructive comments that improved the manu-
script and Kai Yue and Enzai Du for helpful suggestions
on earlier drafts. Zhenhong Hu was supported by
the National Natural Science Foundation of China
(32271853), the Shenzhen Science and Technology
Program (JCYJ20230807111402004), the Beatriu de Pin
os
Program (2022BP00059), and the Chinese Universities
Scientific Fund (2452022127). Marcos Fern
andez-Martínez
was supported by the European Research Council project
ERC-StG-2022-101076740 STOIKOS and by a Ram
on y
Cajal fellowship (RYC2021-031511-I) funded by the
Spanish Ministry of Science and Innovation, the
NextGenerationEU program of the European Union,
the Spanish plan of recovery, transformation and resil-
ience, and the Spanish Agency of Research. Guiyao Zhou
was supported by AYUDAS DE EXCELENCIA RYC-MAX
2023 project from Spanish National Research Council.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Data (Hu et al., 2023) are available in Figshare at https://
doi.org/10.6084/m9.figshare.24342550.v2. The DNA
genomic sequences are deposited into the NCBI-SRA
16 of 18 HU ET AL.
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under BioProject ID PRJNA997106 at https://www.ncbi.
nlm.nih.gov/bioproject/997106.
ORCID
Zhenhong Hu https://orcid.org/0000-0001-9505-8253
Marcos Fern
andez-Martínez https://orcid.org/0000-
0002-5661-3610
Lin Jiang https://orcid.org/0000-0002-7114-0794
Guiyao Zhou https://orcid.org/0000-0002-1385-3913
Ji Chen https://orcid.org/0000-0001-7026-6312
Ming Nie https://orcid.org/0000-0003-0702-8009
Sean T. Michaletz https://orcid.org/0000-0003-2158-
6525
REFERENCES
Allen, A. P., J. F. Gillooly, and J. H. Brown. 2005. “Linking the
Global Carbon Cycle to Individual Metabolism.”Functional
Ecology 19(2): 202–213.
Arrhenius, S. 1915. “Quantitative Laws in Biological Chemistry.”
Science 43: 172–73.
Berg, B., and C. McClaugherty. 2014. Decomposition of Fine Root
and Woody Litter, Plant Litter: Decomposition, Humus
Formation, Carbon Sequestration. Heidelberg, Berlin: Springer
Berlin Heidelberg.
Boddy, L., and S. C. Watkinson. 1995. “Wood Decomposition,
Higher Fungi, and Their Role in Nutrient Redistribution.”
Canadian Journal of Botany 73(S1): 1377–83.
Bond-Lamberty, B., C. Wang, and S. T. Gower. 2002. “Annual
Carbon Flux from Woody Debris for a Boreal Black Spruce
Fire Chronosequence.”Journal of Geophysical Research:
Atmospheres 107(D23): 8220.
Bonner, M. T. L., D. Castro, A. N. Schneider, G. Sundström,
V. Hurry, N. R. Street, and T. Näsholm. 2019. “Why Does
Nitrogen Addition to Forest Soils Inhibit Decomposition?”Soil
Biology and Biochemistry 137: 107570.
Bradford,M.A.,D.S.Maynard,T.W.Crowther,P.T.Frankson,J.E.
Mohan, C. Steinrueck, G. F. Veen, et al. 2021. “Belowground
Community Turnover Accelerates the Decomposition of Standing
Dead Wood.”Ecology 102(11): e03484.
Buzzard, V., S. T. Michaletz, Y. Deng, Z. He, D. Ning, L. Shen,
Q. Tu, et al. 2019. “Continental Scale Structuring of Forest and
Soil Diversity Via Functional Traits.”Nature Ecology &
Evolution 3: 1298–1308.
Chen,Y.,E.J.Sayer,Z.Li,Q.Mo,Y.Li,Y.Ding,J.Wang,X.Lu,
J. Tang, and F. Wang. 2016. “Nutrient Limitation of Woody
Debris Decomposition in a Tropical Forest: Contrasting
Effects of N and P Addition.”Functional Ecology 30(2):
295–304.
Conant, R. T., M. G. Ryan, G. I. Ågren, H. E. Birge, E. A. Davidson,
P. E. Eliasson, S. E. Evans, et al. 2011. “Temperature and Soil
Organic Matter Decomposition Rates –Synthesis of Current
Knowledge and a Way Forward.”Global Change Biology
17(11): 3392–3404.
Cornwell, W. K., J. H. C. Cornelissen, S. D. Allison, J. Bauhus,
P. Eggleton, C. M. Preston, F. Scarff, J. T. Weedon, C. Wirth,
and A. E. Zanne. 2009. “Plant Traits and Wood Fates across
the Globe: Rotted, Burned, or Consumed?”Global Change
Biology 15(10): 2431–49.
Cross, W. F., J. M. Hood, J. P. Benstead, A. D. Huryn, and
D. Nelson. 2015. “Interactions between Temperature and
Nutrients across Levels of Ecological Organization.”Global
Change Biology 21(3): 1025–40.
Du, E., W. de Vries, W. Han, X. Liu, Z. Yan, and Y. Jiang. 2016.
“Imbalanced Phosphorus and Nitrogen Deposition in
China’sForests.”Atmospheric Chemistry and Physics 16(13):
8571–79.
Fang, C., P. Smith, J. B. Moncrieff, and J. U. Smith. 2005. “Similar
Response of Labile and Resistant Soil Organic Matter Pools to
Changes in Temperature.”Nature 433(7021): 57–59.
Fierer, N., J. M. Craine, K. McLauchlan, and J. P. Schimel. 2005.
“Litter Quality and the Temperature Sensitivity of
Decomposition.”Ecology 86(2): 320–26.
German, D. P., M. N. Weintraub, A. S. Grandy, C. L. Lauber, Z. L.
Rinkes, and S. D. Allison. 2011. “Optimization of Hydrolytic
and Oxidative Enzyme Methods for Ecosystem Studies.”Soil
Biology and Biochemistry 43(7): 1387–97.
Gora, E. M., and J. M. Lucas. 2019. “Dispersal and Nutrient
Limitations of Decomposition above the Forest Floor:
Evidence from Experimental Manipulations of Epiphytes and
Macronutrients.”Functional Ecology 33(12): 2417–29.
Harmon, M. E., B. G. Fasth, M. Yatskov, D. Kastendick, J. Rock,
and C. W. Woodall. 2020. “Release of Coarse Woody
Detritus-Related Carbon: A Synthesis across Forest Biomes.”
Carbon Balance and Management 15(1): 1.
Hu, Z., H. Y. H. Chen, C. Yue, X. Y. Gong, J. Shao, G. Zhou, J.
Wang, et al. 2020. “Traits Mediate Drought Effects on Wood
Carbon Fluxes.”Global Change Biology 26(6): 3429–42.
Hu, Z., M. Fern
andez-Martínez, Q. He, Z. Xu, L. Jiang, G. Zhou,
J. Chen, et al. 2023. “Fungal Composition Associated with
Host Tree Identity Mediates Nutrient Addition Effects on
Wood Microbial Respiration.”Figshare. Dataset. https://doi.
org/10.6084/m9.figshare.24342550.v2.
Hu, Z., S. T. Michaletz, D. J. Johnson, N. G. McDowell, Z. Huang,
X. Zhou, and C. Xu. 2018. “Traits Drive Global Wood
Decomposition Rates More than Climate.”Global Change
Biology 24(11): 5259–69.
Hu, Z., C. Xu, N. G. McDowell, D. J. Johnson, M. Wang, Y. Luo, X.
Zhou, and Z. Huang. 2017. “Linking Microbial Community
Composition to C Loss Rates during Wood Decomposition.”
Soil Biology and Biochemistry 104: 108–116.
IPCC. 2021. “Climate Change 2021: The Physical Science Basis.”In
Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by
V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors,
C. Péan, Y. Chen, L. Goldfarb, M. I. Gomis, J. B. Robin
Matthews, S. Berger, M. Huang, O. Yelekçi, R. Yu, B. Zhou,
E. Lonnoy, T. K. Maycock, T. Waterfield, K. Leitzell, N. Caud,
578–582. Cambridge: Cambridge University Press.
Isanta-Navarro, J., C. Prater, L. M. Peoples, I. Loladze, T. Phan,
P. D. Jeyasingh, M. J. Church, Y. Kuang, and J. J. Elser. 2022.
“Revisiting the Growth Rate Hypothesis: Towards a Holistic
Stoichiometric Understanding of Growth.”Ecology Letters
25(10): 2324–39.
Knorr, M., S. D. Frey, and P. S. Curtis. 2005. “Nitrogen Additions
and Litter Decomposition: A Meta-Analysis.”Ecology 86:
3252–57.
Lustenhouwer, N., D. S. Maynard, M. A. Bradford, D. L. Lindner,
B. Oberle, A. E. Zanne, and T. W. Crowther. 2020. “ATrait-Based
ECOLOGY 17 of 18
19399170, 0, Downloaded from https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.4375 by Csic Organización Central Om (Oficialia Mayor) (Urici), Wiley Online Library on [26/06/2024]. 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
Understanding of Wood Decomposition by Fungi.”Proceedings of
the National Academy of Sciences 117(21): 11551–58.
Manning, D. W. P., A. D. Rosemond, V. Gulis, J. P. Benstead, and
J. S. Kominoski. 2018. “Nutrients and Temperature Additively
Increase Stream Microbial Respiration.”Global Change
Biology 24(1): e233–e247.
Michaletz, S. T., and J. C. Garen. 2024. “Hotter Is Not (Always)
Better: Embracing Unimodal Scaling of Biological Rates with
Temperature.”Ecology Letters 27: e14381.
Moorhead, D. L., R. L. Sinsabaugh, B. H. Hill, and M. N.
Weintraub. 2016. “Vector Analysis of Ecoenzyme Activities
Reveal Constraints on Coupled C, N and P Dynamics.”Soil
Biology and Biochemistry 93: 1–7.
Naudts,K.,Y.Chen,M.J.McGrath,J.Ryder,A.Valade,J.Otto,
and S. Luyssaert. 2016. “Europe’s Forest Management
Did Not Mitigate Climate Warming.”Science 351(6273):
597–600.
Oberle, B., M. R. Lee, J. A. Myers, O. L. Osazuwa-Peters, M. J.
Spasojevic, M. L. Walton, D. F. Young, and A. E. Zanne. 2020.
“Accurate Forest Projections Require Long-Term Wood Decay
Experiments Because Plant Trait Effects Change through
Time.”Global Change Biology 26: 864–875.
Olson, J. S. 1963. “Energy Storage and the Balance of Producers and
Decomposers in Ecological Systems.”Ecology 44(2): 322–331.
Pan,Y.,R.A.Birdsey,J.Fang,R.Houghton,P.E.Kauppi,W.A.Kurz,
O. L. Phillips, et al. 2011. “A Large and Persistent Carbon Sink in
the World’sForests.”Science 333(6045): 988–993.
Peñuelas, J., I. A. Janssens, P. Ciais, M. Obersteiner, and J. Sardans.
2020. “Anthropogenic Global Shifts in Biospheric N and
P Concentrations and Ratios and Their Impacts on
Biodiversity, Ecosystem Productivity, Food Security, and
Human Health.”Global Change Biology 26: 1962–85.
Purahong, W., T. Wubet, D. Krüger, and F. Buscot. 2018.
“Molecular Evidence Strongly Supports Deadwood-Inhabiting
Fungi Exhibiting Unexpected Tree Species Preferences in
Temperate Forests.”ISME Journal 12: 289–295.
R Development Core Team. 2021. R: A Language and Environment
for Statistical Computing. Vienna: R Foundation for Statistical
Computing.
Rinne, K. T., K. Peltoniemi, J. Chen, M. Peltoniemi, H. Fritze, and
R. Mäkipää. 2019. “Carbon Flux from Decomposing Wood
and its Dependency on Temperature, Wood N2 Fixation Rate,
Moisture and Fungal Composition in a Norway Spruce
Forest.”Global Change Biology 25(5): 1852–67.
Schilling, J. S., A. Ayres, J. T. Kaffenberger, and J. S. Powers.
2015. “Initial White Rot Type Dominance of Wood
Decomposition and its Functional Consequences in a
Regenerating Tropical Dry Forest.”Soil Biology and
Biochemistry 88: 58–68.
Sheng, W. 2018. “On the Maintence of Long-Term Productivity of
Plantation in China.”Forest Research 31(1): 1–14.
Sinsabaugh, R. L., R. Antibus, A. Linkins, C. McClaugherty,
L. Rayburn, D. Repert, and T. Weiland. 1993. “Wood
Decomposition: Nitrogen and Phosphorus Dynamics in Relation
to Extracellular Enzyme Activity.”Ecology 74(5): 1586–93.
Sinsabaugh, R. L., and J. J. Follstad Shah. 2012. “Ecoenzymatic
Stoichiometry and Ecological Theory.”Annual Review of
Ecology, Evolution, and Systematics 43: 313–343.
Spohn, M. 2020. “Increasing the Organic Carbon Stocks in Mineral
Soils Sequesters Large Amounts of Phosphorus.”Global
Change Biology 26(8): 4169–77.
Sterner, R. W., and J. J. Elser. 2002. Ecological Stoichiometry: The
Biology of Elements from Molecules to the Biosphere. Princeton,
NJ: Princeton University Press.
van der Wal, A., E. Ottosson, and W. de Boer. 2014. “Neglected
Role of Fungal Community Composition in Explaining
Variation in Wood Decay Rates.”Ecology 96(1): 124–133.
Wang, G., W. M. Post, M. A. Mayes, J. T. Frerichs, and J. Sindhu. 2012.
“Parameter Estimation for Models of Ligninolytic and Cellulolytic
Enzyme Kinetics.”Soil Biology and Biochemistry 48: 28–38.
Weedon, J. T., W. K. Cornwell, J. H. C. Cornelissen, A. E. Zanne, C.
Wirth, and D. A. Coomes. 2009. “Global Meta-Analysis of
Wood Decomposition Rates: A Role for Trait Variation among
Tree Species?”Ecology Letters 12(1): 45–56.
Xu, X., P. E. Thornton, and W. M. Post. 2013. “A Global Analysis of Soil
Microbial Biomass Carbon, Nitrogen and Phosphorus in Terrestrial
Ecosystems.”Global Ecology and Biogeography 22(6): 737–749.
SUPPORTING INFORMATION
Additional supporting information can be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Hu, Zhenhong,
Marcos Fern
andez-Martínez, Qinsi He,
Zhiyuan Xu, Lin Jiang, Guiyao Zhou, Ji Chen,
et al. 2024. “Fungal Composition Associated with
Host Tree Identity Mediates Nutrient Addition
Effects on Wood Microbial Respiration.”Ecology
e4375. https://doi.org/10.1002/ecy.4375
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