Differential responses of carbon-degrading enzyme activities to warming: Implications for soil respiration

Abstract

Extracellular enzymes catalyze rate‐limiting steps in soil organic matter decomposition, and their activities (EEAs) play a key role in determining soil respiration (SR). Both EEAs and SR are highly sensitive to temperature, but their responses to climate warming remain poorly understood. Here, we present a meta‐analysis on the response of soil cellulase and ligninase activities and SR to warming, synthesizing data from 56 studies. We found that warming significantly enhanced ligninase activity by 21.4% but had no effect on cellulase activity. Increases in ligninase activity were positively correlated with changes in SR, while no such relationship was found for cellulase. The warming response of ligninase activity was more closely related to the responses of SR than a wide range of environmental and experimental methodological factors. Furthermore, warming effects on ligninase activity increased with experiment duration. These results suggest that soil microorganisms sustain long term increases in SR with warming by gradually increasing the degradation of the recalcitrant carbon pool.
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PRIMARY RESEARCH ARTICLE
Differential responses of carbon‐degrading enzyme activities
to warming: Implications for soil respiration
Ji Chen
1,2,3
|
Yiqi Luo
4,5
|
Pablo García‐Palacios
6
|
Junji Cao
2,7
|
Marina Dacal
6
|
Xuhui Zhou
8,9
|
Jianwei Li
10
|
Jianyang Xia
8
|
Shuli Niu
11
|
Huiyi Yang
12
|
Shelby Shelton
13
|
Wei Guo
14
|
Kees Jan van Groenigen
15
1
Center for Ecological and Environmental Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an,
China
2
State Key Laboratory of Loess and Quaternary Geology (SKLLQG), and Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment,
Chinese Academy of Sciences, Xi'an, China
3
Aarhus University Centre for Circular Bioeconomy, Department of Agroecology, Aarhus University, Tjele, Denmark
4
Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
5
Department for Earth System Science, Tsinghua University, Beijing, China
6
Departamento de Biología y Geología, Física y Química Inorgánica y Analítica, Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Móstoles,
Spain
7
Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, China
8
Center for Global Change and Ecological Forecasting, Tiantong National Field Observation Station for Forest Ecosystem, School of Ecological and
Environmental Sciences, East China Normal University, Shanghai, China
9
Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China
10
Department of Agriculture and Environmental Sciences, Tennessee State University, Nashville, Tennessee
11
Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
12
College of Engineering Mathematics and Physical Sciences, University of Exeter, Exeter, UK
13
Department of Emergency Medicine, University of Colorado Denver, Denver, Colorado
14
Department of Earth and Environmental Sciences, Xi'an Jiaotong University, Xi'an, China
15
Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
Correspondence
Ji Chen, Center for Ecological and
Environmental Sciences, Key Laboratory for
Space Bioscience and Biotechnology,
Northwestern Polytechnical University, Xi'an,
China.
Email: chenji@ieecas.cn
Junji Cao, State Key Laboratory of Loess and
Quaternary Geology (SKLLQG), and Key
Laboratory of Aerosol Chemistry and
Physics, Institute of Earth Environment,
Chinese Academy of Sciences, Xi'an, China.
Email: cao@loess.llqg.ac.cn
and
Jianwei Li, Department of Agriculture and
Environmental Sciences, Tennessee State
University, Nashville, TN.
Email: jli2@tnstate.edu
Funding information
US National Science Foundation, Grant/
Award Number: 1137293 and OIA‐1301789;
National Natural Science Foundation of
Abstract
Extracellular enzymes catalyze rate‐limiting steps in soil organic matter decomposi-
tion, and their activities (EEAs) play a key role in determining soil respiration (SR).
Both EEAs and SR are highly sensitive to temperature, but their responses to cli-
mate warming remain poorly understood. Here, we present a meta‐analysis on the
response of soil cellulase and ligninase activities and SR to warming, synthesizing
data from 56 studies. We found that warming significantly enhanced ligninase activ-
ity by 21.4% but had no effect on cellulase activity. Increases in ligninase activity
were positively correlated with changes in SR, while no such relationship was found
for cellulase. The warming response of ligninase activity was more closely related to
the responses of SR than a wide range of environmental and experimental method-
ological factors. Furthermore, warming effects on ligninase activity increased with
experiment duration. These results suggest that soil microorganisms sustain long‐
term increases in SR with warming by gradually increasing the degradation of the
recalcitrant carbon pool.
Received: 28 September 2017
|
Accepted: 11 June 2018
DOI: 10.1111/gcb.14394
Glob Change Biol. 2018;1–11. wileyonlinelibrary.com/journal/gcb ©2018 John Wiley & Sons Ltd
|
1

China, Grant/Award Number: 41701292;
United States Department of Energy, Grant/
Award Number: DE‐SC00114085; US
Department of Energy
KEYWORDS
cellulase activity, decomposition, extracellular enzyme activity, global warming, ligninase
activity, recalcitrant carbon pool, soil microorganisms, soil respiration
1
|
INTRODUCTION
The average global surface temperature is predicted to increase
between 1 and 4°C by the end of the twenty‐first century (Collins &
Knutti, 2013; O'neill et al., 2017). Rising temperatures have cascad-
ing impacts on ecosystem carbon (C) budgets, and these can cause
both positive and negative C cycle–climate feedbacks (Carey et al.,
2016; Chen, Sang, Zhang, & Hu, 2016; Chen, Zhou et al., 2017;
Karhu et al., 2014; Paustian et al., 2016; Peñuelas et al., 2017; Yang
et al., 2018). Soil respiration (SR) represents the largest C flux from
soils to the atmosphere (Bradford et al., 2016; Tucker, Bell, Pendall,
& Ogle, 2013) and is primarily driven by the microbial decomposition
of soil organic matter (SOM). However, we know little about the
mechanisms underlying the response of SR to climate warming
(Chen, Luo, Xia, Wilcox et al., 2016; Conant et al., 2011; Van Gestel et
al., 2018). It is specific that there is a lack of information regarding the
degree to which soil extracellular enzymes (EEs), which catalyze the
rate‐limiting step in SOM decomposition (Allison, Wallenstein, & Brad-
ford, 2010; Jing et al., 2014; Sinsabaugh, 2010; Stone et al., 2012), are
affected by warming. These enzymes, primarily produced by microbes,
are considered proximate agents of SR because they lower the activa-
tion energy of key reactions and speed up the breakdown of polymers
(Chen, Luo et al., 2017; Chen et al., 2018; Janssens et al., 2010; Sus-
eela, Tharayil, Xing, & Dukes, 2014). Although the rates at which these
enzymes are produced and degraded are sensitive to temperature (Alli-
son & Treseder, 2008; German, Marcelo, Stone, & Allison, 2012;
Papanikolaou, Britton, Helliwell, & Johnson, 2010; Steinweg, Dukes,
Paul, & Wallenstein, 2013), it is still unclear how warming responses of
enzymes affect SR.
Cellulose and lignin are the two most abundant SOM com-
pounds, and microbially mediated decomposition of these materials
composes a main source of SR (Carreiro, Sinsabaugh, Repert, & Par-
khurst, 2000; Chen et al., 2018; Janssens et al., 2010; Waldrop, Zak,
Sinsabaugh, Gallo, & Lauber, 2004). Cellulose and hemicellulose com-
prise the main composition of primary plant cell walls. Hydrolysis of
cellulose and hemicellulose is mainly catalyzed by cellulase, including
β‐1,4‐glucosidase (BG), β‐1,4‐xylosidase (BX), and β‐D‐cellobiosidase
(CBH) (Carreiro et al., 2000; Chen, Luo et al., 2017; Jian et al.,
2016). The aromatic C polymer lignin is found in secondary plant cell
walls, where it covers and shields cellulose from microbial decay.
Oxidation and degradation of phenolic‐containing recalcitrant com-
pounds are facilitated by ligninase, that is, peroxidase (PER), phenol
oxidase (PO), and polyphenol oxidase (PPO; Dashtban, Schraft, Syed,
& Qin, 2010; Romero‐Olivares, Allison, & Treseder, 2017; Sinsabaugh
et al., 2008; Zhou et al., 2012). The critical roles of cellulase and
ligninase in mediating SOM decomposition suggest that climate
warming may affect SR through its effects on EEAs, yet we still lack
direct evidence.
Cellulase and ligninase are synthesized by specific groups of
microorganisms (Burns et al., 2013; Carreiro et al., 2000; Wang et
al., 2012), and it may take years for microbial communities to adapt
to environmental changes (Deangelis et al., 2015). Thus, responses
of cellulase and ligninase activities to warming may vary over time.
Because warming methods differ in their effects on soil temperature
and moisture (Chen et al., 2015, Lu et al., 2013), soil microbial com-
munity (Chen et al.,2015), and belowground C allocation (Rustad et
al., 2001; Schindlbacher, Schnecker, Takriti, Borken, & Wanek, 2015),
they may differ in their effects on EEAs as well. Including cellulase
and ligninase activities in soil C models may improve future predic-
tions of soil C stocks (Ali et al., 2015; Luo, Chen, Chen, & Feng,
2017; Moorhead, Sinsabaugh, Hill, & Weintraub, 2016). However,
warming effects on cellulase and ligninase activities, as well as the
underlying mechanisms, are still unclear.
To address this knowledge gap, we conducted a meta‐analysis of
the responses of cellulase and ligninase activities to warming and
their links with SR responses. More specifically, our study seeks (a)
to quantify the effects of warming on cellulase and ligninase activi-
ties, (b) to investigate the factors affecting the responses of cellulase
and ligninase activities to warming, and (c) to test whether the
responses of cellulase and ligninase activities to warming are linked
with changes in SR.
2
|
MATERIALS AND METHODS
2.1
|
Data collection
We extracted results for enzyme activities of ligninase and cellulase
under warming experiments conducted in the field. We used Web of
Science (http://apps.webofknowledge.com/), Google Scholar (http://
scholar.google.com/), and China National Knowledge Infrastructure
(http://www.cnki.net/) for an exhaustive search of journal articles
published before June 2018, using the following key words : (a) “cli-
mate change”or “experimental warming”or “elevated temperature”
and (b) “cellulase,”or “ligninase,”or “glucosidase,”or “xylosidase,”
or “cellobiosidase,”or “peroxidase,”or “phenol oxidase,”or
“polyphenol oxidase,”and (c) “terrestrial”or “soil”or “land.”
To be included in our dataset, experiments had to meet several cri-
teria: (a) the warming treatment lasted at least 1 year; (b) vegetation,
soil physicochemical parameters, and climate were similar between
control and warming treatments; (c) sample size and standard devia-
tions were reported; and (d) warming protocols (i.e., warming method,
2
|
CHEN ET AL.

warming magnitude, warming time, and warming season) were clearly
described. All studies in our dataset measured enzyme activity for
warmed and control soils at the same incubation temperature (i.e.,
temperature differences between treatments occurred only in the field
and not during the incubation). As such, differences in enzyme activity
between warmed and control soils were not related to the tempera-
ture sensitivity of enzymes, but reflect warming effects on enzyme
production by soil microbes. We found 56 articles that met our
requirements (see Supporting information Dataset and Figure S1).
For each study in our dataset, we extracted information on cellu-
lase and ligninase activities (Supporting information Table S1). If a
paper reported multiple warming responses (e.g., in multifactor experi-
ments or studies applying more than one warming protocol), each
experiment was included separately in our dataset. If one paper
reported two or three kinds of cellulase or ligninase, then their sum
values were considered as the overall responses of cellulase and ligni-
nase activities. We also recorded a wide range of environmental vari-
ables, including latitude, longitude, elevation, climatic variables (mean
annual temperature (MAT), mean annual precipitation (MAP)), sam-
pling date, sampling temperature, vegetation type (http://www.worldc
lim.org/), and soil type (http://www.fao.org/about/en/). Regarding the
warming protocols, we recorded the magnitude (i.e., the average tem-
perature difference between the warming and control plots), duration
(in years), and methods (open top chamber (OTC), infrared heater (IH),
green house (GH), heating cable, and curtain). We also recorded SR,
soil C:N, microbial biomass, and the ratio of fungal to bacterial abun-
dance for both control and warming treatments when these data were
reported. When warming responses of SR were not available, we used
responses of heterotrophic respiration or weight loss in litter bag
experiments as proxy values. To extract data from figures, we used
Engauge Digitizer 4.1 (http://digitizer.sourceforge.net). When some
critical information was not reported in the article, we tried to obtain
this information by contacting the corresponding author.
2.2
|
Data analysis
We used meta‐analysis to evaluate the effects of warming on cellu-
lase, ligninase, individual enzyme activity, and other ancillary vari-
ables (García‐Palacios et al., 2014; Hedges, Gurevitch, & Curtis,
1999; Van Groenigen, Qi, Osenberg, Luo, & Hungate, 2014; Zhao et
al., 2017). The effects of warming on EEAs were evaluated using the
natural logarithm of the response ratio (lnR):
ln R¼ln XW
XC

¼lnðXWÞlnðXCÞ;(1)
with XWand XCas the arithmetic mean concentrations in the warm-
ing and control treatments, respectively. The variances (ν)oflnR
were calculated as follows:
ν¼S2
W
nWX2
W
þS2
C
nCX2
C
;(2)
with n
W
and n
C
as the number of replicates and S
W
and S
C
as the
SDs for warming and control treatments, respectively.
The overall effect and the 95% confidence interval were calcu-
lated using the “rma.mv”function in the R package “metafor”
(Viechtbauer, 2010). Because incubation temperature for enzyme
measurements varied among studies, we included “incubation tem-
perature”as a random factor in the meta‐analysis. Because several
papers contributed more than one response ratio, we also included
the variable “paper”as a random factor (Chen et al., 2018; Terrer,
Vicca, Hungate, Phillips, & Prentice, 2016; Van Groenigen et al.,
2017). The effects of warming were considered significant if the
95% confidence interval did not overlap with zero. The results for
the analyses on lnRwere back‐transformed and reported as percent-
age change with warming (i.e., 100 ×(e
lnR
−1)) to ease interpreta-
tion.
The meta‐analytic models were selected using the same
approach as in Chen et al. (2018), Terrer et al. (2016), and Van
Groenigen et al. (2017). In brief, we analyzed all potential combina-
tions of the studied factors in a mixed‐effects metaregression model
using the “glmulti”package in R (Bangert‐Drowns, Hurley, & Wilkin-
son, 2004; Calcagno & De Mazancourt, 2010). The importance of a
specific predictor was expressed as the sum of Akaike weights for
models that included this factor, which can be considered as the
overall support for each variable across all models. A cutoff of 0.8
was set to differentiate between important and nonessential predic-
tors.
3
|
RESULTS
Across the whole dataset, warming significantly enhanced ligninase
activity by an average of 21.4%. It is specific that warming signifi-
cantly increased activities of PER by 18.4%, PO by 13.5%, and PPO
by 28.6%. In contrast, warming had no effect on cellulase activity
(Figure 1a) or any of the individual cellulase enzymes BG, BX, and
CBH. The responses of cellulase and ligninase activities to warming
were normally distributed (Figure 1b,c), and they were independent
of the sample size (Supporting information Figure S2).
None of the variables tested for the effects of warming on cellu-
lase activity reached the threshold value (0.8) of the summed Akaike
weights (Figure 2a). In contrast, effects of warming on ligninase
activity were best explained by warming duration and warming
method (Figure 2b). Linear regression analysis confirmed that lnRof
ligninase activity was positively correlated with warming duration,
while no such relationship was found for cellulase activity (Figure 3a,b).
Regarding warming methods, warming did not affect cellulase activ-
ity for any of the warming methods (Figure 3a). In contrast, OTC,
GH, and IH significantly increased ligninase activity by 15.5%, 31.4%,
and 22.3%, respectively, while cables had no effect on ligninase
activity (Figure 3b).
Warming significantly increased microbial biomass‐specific ligni-
nase activity (i.e., the ratio of ligninase activity to total microbial
abundance) by 40.6% (Supporting information Figure S3a). This
increase was weakly positively correlated with warming‐induced
changes in the ratio of fungal to bacterial abundance (Supporting
information Figure S3b). At last, our analyses suggest that warming
CHEN ET AL.
|
3

had stronger positive effects on biomass‐specific ligninase activity
for long‐term than short‐term studies, while this relationship was not
observed for biomass‐specific cellulase activity (Supporting informa-
tion Figure S4a and S4b).
Warming on average increased SR by 15.8% (95% CI: 6.3%–
26.1%) in our dataset. We found no relationship between the
responses of cellulase activity and the responses of SR to warming
(Figures 4a). However, the warming response of SR was positively cor-
related with the response of ligninase activity and the positive rela-
tionship held when analyzed for PER, PO, and PPO individually
(Figure 4b; Supporting information Figure S5). To compare the relative
importance of cellulase and ligninase activities in explaining the
response of SR to warming, we limited our model selection analysis to
studies that simultaneously reported the effects of warming on cellu-
lase and ligninase activities and SR. Effects of warming on SR were
best predicted by the responses of ligninase activity over a wide range
of ecosystem types, climatic variables, and warming protocols (Fig-
ure 4c). Experiment duration had no significant impact on SR
responses to warming, either in the subset of studies that reported
responses of both enzymes (Figure 4c) or across the entire dataset.
4
|
DISCUSSION
Our results show that warming significantly enhanced ligninase activ-
ity and that warming responses are positively correlated with warm-
ing duration. In contrast, warming does not affect cellulase activity.
55
36
124
172
136
96
168
189
PPO
PO
PER
Ligninase
CBH
BX
BG
Cellulase
–15 0 15 30 45
Effect of warming (%)
Enzyme
(a)
0
1
2
–2 –1 0 1
lnR−−Celllulase
Density
BG BX CBH Cellulase
(b)
0
1
2
3
4
–1012
lnR−−Ligninase
Density
Ligninase PER PO PPO
(c)
FIGURE 1 (a) Effects of warming on
cellulase and ligninase activities indicated
with the mean percentage of change in
warming vs. control plots. Distribution of
the log‐transformed response ratios (lnR)
of (b) cellulase and (c) ligninase activities to
experimental warming. Error bars represent
95% confidence intervals. The sample size
for each variable is shown in the right
column of the figure. PER, peroxidase; PO,
phenol oxidase; PPO, polyphenol oxidase;
BG, β‐1,4‐glucosidase; BX, β‐1,4‐xylosidase;
CBH, β‐D‐cellobiosidase
Soil
Vegetation
Method
Season
Time
Duration
Latitude
Magnitude
MAT
MAP
Sample.T
0.0 0.2 0.4 0.6 0.8 1.0
Sum of Akaike weights
Variable
Cellulase
(a)
Vegetation
Time
Latitude
Sample.T
Magnitude
MAT
MAP
Season
Soil
Method
Duration
0.0 0.2 0.4 0.6 0.8 1.0
Sum of Akaike weights
Variable
Ligninase
(b)
FIGURE 2 Model‐averaged importance
of the predictors of warming effects on
soil (a) cellulase and (b) ligninase activities.
The importance is based on the sum of
Akaike weights derived from model
selection using corrected Akaike's
information criteria. Cutoff is set at 0.8 to
differentiate between important and
nonessential predictors. MAT, mean annual
temperature; MAP, mean annual
precipitation; Sample.T, sampling
temperature; time, daily warming regime
(i.e., day, night, or diurnal warming);
season, annual warming regime (i.e.,
growing season, nongrowing season, or
whole‐year warming)
4
|
CHEN ET AL.

Why does warming have differential effects on cellulase and ligni-
nase activities? We propose three possible mechanisms. First, the
enzyme responses reflect warming‐induced changes in substrate
availability. Enzyme activity can be described by the Michaelis–Men-
ten relationship, which primarily depends on substrate availability
(Davidson & Janssens, 2006; Sinsabaugh et al., 2008). Initial
stimulation of SR by warming depletes easily hydrolyzable substrates
(Allison, Mcguire, & Treseder, 2010; Luo, Wan, Hui, & Wallace,
2001), limiting the positive response of cellulase activity to increas-
ing temperatures (Davidson & Janssens, 2006; Stone et al., 2012;
Weedon, Aerts, Kowalchuk, & Van Bodegom, 2014). At the same
time, warming‐induced declines in easily hydrolyzable C pools can
–1.0
–0.5
0.0
0.5
0 5 10 15 20
Duration (year)
lnR−−Cellulase
Cellulase
(a)
0.0
0.5
0 5 10 15 20
Duration (year)
lnR−−Ligninase
Ligninase
(b)
27
5
22
103
32
Cables
Curtain
GH
IH
OTC
–20 0 20
Effect of warming (%)
Method
(c)
20
16
83
53
Cables
GH
IH
OTC
02040
Effect of warming (%)
Method
(d)
FIGURE 3 Relationships between
warming‐induced changes in (a) cellulase
and (b) ligninase activities and warming
duration. Effects of warming on (c)
cellulase and (d) ligninase activities for
various warming methods. The response of
ligninase activity was positively correlated
with warming duration (y= 0.016
x+ 0.113, R
2
= 0.117, p<0.001,
F= 22.590, n= 172). Error bars represent
95% confidence intervals. OTC, open top
chamber; IH, infrared heater; GH, green
house. The sample size for each variable is
shown in the right column of the figure
–1.0
–0.5
0.0
0.5
1.0
–1 0 1
lnR−−Cellulase
lnR−−SR
(a)
–0.5
0.0
0.5
1.0
–0.5 0.0 0.5 1.0
lnR−−Ligninase
lnR−−SR
(b)
Soil
Method
Vegetation
Elevation
Time
MAP
Season
MAT
Magnitude
Sample.T
lnR−Cellulase
Duration
lnR−Ligninase
0.0 0.2 0.4 0.6 0.8 1.0
Sum of Akaike weights
(c)
FIGURE 4 Relationships between the effect of warming (lnR) on soil respiration (SR) and lnR of (a) cellulase and (b) ligninase activities. (c)
Model‐averaged importance of the predictors of warming effects on SR. The warming response of SR was positively correlated with the
warming response of ligninase activity (y= 0.528x+ 0.108, R
2
= 0.467, p<0.001, F= 61.260, n= 72). Model selection analysis is limited to
studies that simultaneously reported the responses of ligninase, cellulase, and SR. The importance is based on the sum of Akaike weights
derived from model selection using corrected Akaike's information criteria. Cutoff is set at 0.8 to differentiate between important and
nonessential predictors. MAT, mean annual temperature; MAP, mean annual precipitation; time, daily warming regime (i.e., day, night, or diurnal
warming); season, annual warming regime (i.e., growing season, nongrowing season, or whole‐year warming); Sample.T, sampling temperature
CHEN ET AL.
|
5

lead to microbial C starvation (Crowther & Bradford, 2013; Fenner
et al., 2006; Melillo et al., 2017; Metcalfe, 2017). Under these cir-
cumstances, soil microbial communities may adapt to utilize previ-
ously inaccessible recalcitrant C pools to fuel their metabolic
activities. Microbial utilization of recalcitrant substrates such as phe-
nol requires depolymerization, a process catalyzed by ligninase (De
Gonzalo, Colpa, Habib, & Fraaije, 2016; Jassey, Chiapusio, Gilbert,
Toussaint, & Binet, 2012; Sinsabaugh, 2010).
Second, warming may increase ligninase activity through its
effect on soil N availability. Warming‐induced redistribution of N
from soils to vegetation could progressively lead to microbial N limi-
tation, particularly in high C:N regions (Bai et al., 2013; Beier et al.,
2008; Melillo et al., 2011). In that case, soil microorganisms are
expected to invest C and energy to acquire N through decomposi-
tion of N‐containing molecules (Chen, Luo et al., 2017; Sinsabaugh
et al., 2008), which are often physically or chemically protected by
other aromatic macromolecules such as lignin (Hobbie, 2008; Wee-
don et al., 2012; Zhao et al., 2014). This explanation is supported by
the positive correlation between warming effects on ligninase activ-
ity and soil C:N, while no clear relationship is found for the
responses of cellulase activity (Supporting information Figure S6). At
last, warming‐induced changes in soil microclimate (Domínguez,
Holthof, Smith, Koller, & Emmett, 2017; Zhou et al., 2013), fresh C
input (Bhattacharyya, Roy, Neogi, Dash et al., 2013; Xue et al., 2016;
Yin et al., 2013), and plant community composition (Kardol, Cregger,
Campany, & Classen, 2010; Steinauer et al., 2015) can all cause sub-
stantial changes in microbial communities as well.
Increased ligninase production with warming might reflect shifts
in the microbial community composition. Indeed, several studies sug-
gest that warming‐induced changes in soil microbial community com-
position cause differential responses of cellulase and ligninase
activities (Deangelis et al., 2015; Pold, Grandy, Melillo, & Deangelis,
2017). This explanation is also consistent with studies showing that
fungi are main contributors to ligninase production (De Gonzalo et
al., 2016; Kinnunen, Maijala, Jarvinen, & Hatakka, 2017) and that
experimental warming increases fungal abundance (A'bear, Jones,
Kandeler, & Boddy, 2014; Delarue et al., 2015). However, warming
may also directly or indirectly cause physiological adaptation of soil
microorganisms to increase enzyme production (Manzoni, Taylor,
Richter, Porporato, & Gren, 2012; Nie et al., 2013; Schindlbacher et
al., 2015), even when warming decreases total microbial biomass
(Pold et al., 2017; Sistla & Schimel, 2013; Sorensen et al., 2018). This
is consistent with recent findings that experimental warming tends
to decrease microbial C use efficiency (Manzoni et al., 2012; Tucker
et al., 2013).
Why does the effect of warming on ligninase activity increase
over time? Soil microorganisms can adjust their community composi-
tion or alter their C utilization strategies to adapt to warming, but it
requires several years or even decades for significant changes in
their community composition to occur (Deangelis et al., 2015; Feng
et al., 2017; Rousk, Smith, & Jones, 2013). Furthermore, warming‐in-
duced N limitation may take several years to manifest (Bai et al.,
2013; Melillo et al., 2011). In addition, long‐term warming could also
restructure plant community and alter litter quality toward decay
resistance (e.g., high lignin content) (Melillo et al., 2011; Talbot, Yelle,
Nowick, & Treseder, 2012), thereby promoting the microbial produc-
tion of ligninase.
Regardless of the mechanism underlying the differential warming
response of ligninase and cellulase, our results suggest that warming‐
induced shifts in cellulase and ligninase activities could help to sus-
tain long‐term increases in SR with warming (Lin, Zhu, & Cheng,
2015; Romero‐Olivares et al., 2017; Souza et al., 2017). This is
because warming responses of ligninase activity exert far larger con-
trol over SR than a broad range of environmental and experimental
variables. These results suggest that responses of SR to warming are
largely modulated by a single group of lignin‐modifying enzymes,
which contributes to sustained positive responses of SR to long‐term
climate warming.
Warming methods constituted the second important predictor of
the warming effects on ligninase activity. Cables only warm soils and
are reported to have negative effects on microbial biomass, litter
inputs, and root exudates (Rustad et al., 2001; Schindlbacher et al.,
2015). It is similar that a recent meta‐analysis shows that cables gen-
erally decrease total microbial, fungal, and bacterial abundance, while
other warming methods increase microbial abundance (Chen et al.,
2015). We hypothesize that these negative responses suppressed
microbial activity and microbial enzymatic production (Chen et al.,
2015, Hanson et al., 2017). In addition, high warming magnitude and
large reductions in soil moisture in cable experiments may decrease
microbial C use efficiency (Schindlbacher et al., 2011, 2012), which
could potentially suppress microbial cellulase and ligninase produc-
tion.
Model projections of soil C dynamics often lack representation
of EEA‐regulated SOM decomposition (Davidson & Janssens, 2006;
Luo et al., 2016; Wieder, Bonan, & Allison, 2013). However, our
finding that warming‐induced shifts in cellulase and ligninase activi-
ties may facilitate sustained increases in SR under long‐term climate
warming highlights the need for a closer integration of enzymatic
decomposition into soil biogeochemical models. It is unfortunate that
responses of SR and EEAs to long‐term climate warming remain
understudied, as experiment duration is often constrained by funding
availability. If the relationship between ligninase and warming dura-
tion holds across a wide range of land ecosystems, our results sug-
gest that ecosystem climate–carbon feedbacks could be stronger
than previously assumed.
ACKNOWLEDGEMENTS
First, we would like to thank the authors whose work is included in
this meta‐analysis, especially those who supplied us with additional
data. Second, we would like to thank Robert Sinsabaugh and three
anonymous reviewers for their valuable comments on an earlier ver-
sion of this manuscript. This study was supported by the National
Natural Science Foundation of China (41701292), China Postdoctoral
Science Foundation (2017M610647, 2018T111091), the Natural
Science Basic Research Plan in Shaanxi Province (2017JQ3041), the
6
|
CHEN ET AL.

State Key Laboratory of Loess and Quaternary Geology
(SKLLQG1602), the Key Laboratory of Aerosol Chemistry and Phy-
sics (KLACP‐17‐02), Institute of Earth Environment, and Chinese
Academy of Sciences. Contributions from Dr. Luo's Eco‐lab to this
study were financially supported by US Department of Energy grant
DE‐SC00114085 and US National Science Foundation grants EF
1137293 and OIA‐1301789. This work was also supported by
NSFC‐Yunnan United Fund (U1302267) and the National Science
Fund for Distinguished Young Scholars (31325005).
DATA ACCESSIBILITY
The data associated with this paper are available from the online
supplementary file.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ORCID
Ji Chen http://orcid.org/0000-0001-7026-6312
Pablo García‐Palacios http://orcid.org/0000-0002-6367-4761
Xuhui Zhou http://orcid.org/0000-0002-2038-9901
Jianwei Li http://orcid.org/0000-0002-0429-3627
Jianyang Xia http://orcid.org/0000-0001-5923-6665
Shuli Niu http://orcid.org/0000-0002-2394-2864
Kees Jan van Groenigen http://orcid.org/0000-0002-9165-3925
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
How to cite this article: Chen J, Luo Y, García‐Palacios P,
et al. Differential responses of carbon‐degrading enzyme
activities to warming: Implications for soil respiration. Glob
Change Biol. 2018;00:1–11. https://doi.org/10.1111/
gcb.14394
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