Global Change Effects on Carbon Cycling in Terrestrial

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Global Change Effects on Carbon Cycling in Terrestrial Global Change Effects on Carbon Cycling in Terrestrial

Ecosystems Ecosystems

Guopeng Liang Utah State University

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GLOBAL CHANGE EFFECTS ON CARBON CYCLING IN TERRESTRIAL

ECOSYSTEMS

by

Guopeng Liang

A dissertation submitted in partial fulfillment

of the requirements for the degree

of

DOCTOR OF PHILOSOPHY

in

Ecology

Approved:

___________________________ ___________________________

John M. Stark, Ph.D. Bonnie G. Waring, Ph.D.

Major Professor Committee Member

___________________________ ___________________________

Jeanette M. Norton, Ph.D. Andrew Kulmatiski, Ph.D.

Committee Member Committee Member

___________________________ ___________________________

William D. Pearse, Ph.D. D. Richard Cutler, Ph.D.

Committee Member Interim Vice Provost

of Graduate Studies

UTAH STATE UNIVERSITY

Logan, Utah

2022

ii

Copyright © Guopeng Liang 2022

All Rights Reserved

iii

ABSTRACT

Global Change Effects on Carbon Cycling in Terrestrial Ecosystems

by

Guopeng Liang, Doctor of Philosophy in Biology

Utah State University, 2022

Major Professor: Dr. John M. Stark

Department: Biology

Terrestrial ecosystems play a significant role in global warming by releasing or

absorbing CO2. Given that terrestrial ecosystems store approximately 3 times more

carbon than the atmosphere, small decreases in terrestrial carbon caused by climate

change can significantly increase CO2 concentration in the atmosphere, which in turn

exacerbates global warming. However, unknowns still remain regarding the underlying

mechanisms of global change effects on terrestrial C cycling.

In chapter 2, a meta-analysis study was conducted to determine whether nitrogen

deposition effects on plant productivity in terrestrial ecosystems change over time. 44%

of studies showed evidence of a consistent directional change in the strength of the

impacts of nitrogen additions over time. The direction of this change varied with biome

type (forests, decrease; grasslands and shrublands, increase). The results indicate that

temporally dynamic impacts of long-term nitrogen addition on plant productivity are

generally observed in terrestrial ecosystems.

iv

In chapter 3, the effects of multiple global change factors on soil respiration in

dryland soils were examined in an incubation experiment. The experiment included four

factors: previous warming, carbon availability, soil moisture content, and soil moisture

variability. The individual effect of each of the four global change factors on soil

respiration was significant, although carbon availability caused the most increase in soil

respiration. Soil respiration under variable soil moisture was lower than that under stable

soil moisture. This study suggests that the effect of one global change factor on soil

carbon cycling in dryland soils is affected by other factors.

In chapter 4, synthetic root/soil systems were used to quantify the effects of

carbon input quality, root exudates, soil clay activity, and soil microbial community on

soil organic carbon cycling. Greater positive effects of root exudates on soil respiration

were found in low than high activity clays. Root exudates decreased mineral-associated

organic carbon when clay activity was low and medium but increased it in soils with high

clay activity. These suggest that root exudates and soil minerology interactions play a

significant role in soil carbon dynamics, which should be studied more in the future.

(213 pages)

v

PUBLIC ABSTRACT

Global Change Effects on Carbon Cycling in Terrestrial Ecosystems

by

Guopeng Liang

Since terrestrial ecosystems store approximately 3 times more carbon (C) than the

atmosphere, they have a significant effect on the atmospheric CO2 concentration.

Although many studies have been conducted to determine global change effects on C

cycling in terrestrial ecosystems, the underlying mechanisms remain uncertain. To

address this knowledge gap, I utilized meta-analysis, laboratory experiments, and soil

microbial community analysis.

In chapter 2, I conducted a meta-analysis to examine whether effects of long-term

N addition on plant productivity can shift over time. I found that 44% of studies showed a

marked trend (increase or decrease) in the strength of N impacts over time. The temporal

trend of N impacts on plant productivity was mainly explained by climate variables (e.g.

mean annual temperature and precipitation). This chapter suggests that, to estimate N

impacts on terrestrial ecosystem more accurately, not only the magnitude of N impacts on

plant productivity, but also their temporal pattern should be considered in future studies.

In chapter 3, I determined the responses of dryland soil C cycling to multiple

global change factors (e.g. previous warming, C availability, soil moisture content, and

soil moisture variability) by conducting a laboratory incubation experiment. I found that

interactive effects of multiple global change factors were ubiquitous in drylands. For

example, effects of soil moisture and previous warming on soil respiration were

vi

insignificant without C addition. However, higher soil respiration was found under high

soil moisture and prior warming in soils with C additions. This chapter indicates that

future experiments should include multiple global change factors to assess their

interactive effects on soil C cycling and to unravel underlying mechanisms.

In chapter 4, I quantified roles of plant-microbe-soil interactions in soil C cycling

by utilizing synthetic root/soil systems. The treatments consisted of C input quality, root

exudates, soil minerology, and soil microbial community composition. I found that the

root exudates-soil minerology interaction was dominant in regulating soil C cycling.

More specifically, the positive effect of root exudates on soil respiration decreased with

increasing soil clay activity. This chapter suggests that plant-soil interactions play a great

role in soil C formation and loss.

vii

ACKNOWLEDGMENTS

I would like to thank my PhD supervisors, Dr. Bonnie Waring and Dr. John Stark,

for their years of guidance and support. This dissertation would not have been possible

without their help. I would also like to thank my committee members, Dr.

Jeanette Norton, Dr. Andrew Kulmatiski, and Dr. Will Pearse, for their help and service.

I thank the help from my colleagues during my PhD program: Jessica Murray,

Karen Foley, Kirsten Butcher, and Savannah Adkins. Thanks to the undergraduate

technicians of Waring lab: Preston Christensen, Camilla Moses, and Jalynn Jones for

their help. In addition, I acknowledge the scholarships and grants from The Department

of Biology at Utah State University and Department of Energy of the US.

Thanks to my parents, who are always there for me. Most importantly, I would

like to thank my wife, Pengyan Sun, for all her support, encouragement, and patience and

especially for bringing up our daughter Sophie Liang.

Guopeng Liang

viii

CONTENTS

Page

ABSTRACT ....................................................................................................................... iii

PUBLIC ABSTRACT .........................................................................................................v

ACKNOWLEDGMENTS ................................................................................................ vii

LIST OF TABLES ...............................................................................................................x

LIST OF FIGURES .......................................................................................................... xii

CHAPTER

1. INTRODUCTION ..............................................................................................1

Introduction ..........................................................................................................1

Impacts of N deposition on plant productivity .....................................................2

Effects of multiple global change factors on soil respiration in drylands ............4

Roles of plant-microbe-soil interactions in soil organic C formation and loss ....6

Dissertation Outline ..............................................................................................9

Reference ............................................................................................................10

2. NITROGEN EFFECTS ON PLANT PRODUCTIVITY CHANGE AT

DECADAL TIMESCALES ..............................................................................22

Abstract ..............................................................................................................22

Introduction ........................................................................................................23

Methods ..............................................................................................................26

Results ................................................................................................................32

Discussion ..........................................................................................................35

References ..........................................................................................................40

Figures ................................................................................................................50

3. EFFECTS OF MULTIPLE GLOBAL CHANGE FACTORS ON SOIL

RESPIRATION IN A DRYLAND ECOSYSTEM ..........................................54

Abstract ..............................................................................................................54

Introduction ........................................................................................................55

Methods ..............................................................................................................59

Results ................................................................................................................67

Discussion ..........................................................................................................69

Conclusion ..........................................................................................................77

References ..........................................................................................................78

Tables .................................................................................................................87

Figures ................................................................................................................89

ix

4. ROLES OF PLANT-MICROBE-SOIL INTERACTIONS IN SOIL

CARBON CYCLING .......................................................................................95

Abstract ..............................................................................................................95

Introduction ........................................................................................................96

Methods ............................................................................................................101

Results ..............................................................................................................111

Discussion ........................................................................................................116

Conclusion ........................................................................................................122

References ........................................................................................................123

Tables ...............................................................................................................133

Figures ..............................................................................................................135

5. CONCLUSIONS.............................................................................................143

APPENDICES .................................................................................................................146

Appendix A – Supplementary Information for Chapter 2 ................................147

Appendix B – Supplementary Information for Chapter 3 ................................154

Appendix C – Supplementary Information for Chapter 4 ................................164

CURRICULUM VITAE ..................................................................................................193

x

LIST OF TABLES

Table Page

3.1 Change (%) in soil respiration due to effects of the five treatment factors.......87

4.1 Effects of all treatments on soil respiration in synthetic soil system during

Phase 2 ............................................................................................................133

4.2 Effects of all treatments on soil variables in synthetic soil system during

Phase 2 ............................................................................................................134

B1 Change (%) in mean weighted diameter due to the effects of the five

treatment factors ..............................................................................................154

B2 Change (%) in mean soil enzyme activity due to the effects of five


B3 Change (%) in soil total carbon concentration due to the effects of five


B4 Change (%) in soil total nitrogen concentration due to the effects of five


B5 Change (%) in soil microbial biomass carbon due to the effects of five


B6 Change (%) in soil priming effect due to the effects of three treatment

factors ..............................................................................................................160

C1 Nutrient content of the Master Blend fertilizer ...............................................164

C2 Effects of all treatments on soil respiration in synthetic soil system during

Phase 1 ............................................................................................................165

C3 Effects of all treatments on soil variables in synthetic soil system during

Phase 1 ............................................................................................................166

C4 Change (%) in soil respiration due to the effects of the treatment factors

during Phase 2 .................................................................................................167

C5 Change (%) in soil microbial biomass carbon due to the effects of the

treatment factors during Phase 2 .....................................................................169

C6 Change (%) in mean soil enzyme activity due to the effects of the treatment

factors during Phase 2 .....................................................................................170

xi

C7 Change (%) in heavy fraction carbon content due to the effects of the

treatment factors during Phase 2 .....................................................................171

C8 Change (%) in net change in soil carbon due to the effects of the treatment

factors during Phase 2 .....................................................................................172

xii

LIST OF FIGURES

Figure Page

2.1 Weighted response ratios of plant productivity (WRR) under N addition in

studies grouped by biome, climate condition, N fertilization rate, initial soil

pH, N fertilizer type, and forest age ..................................................................50

2.2 Relative influence of predictor variables on (a) 𝑅𝑅𝑓𝑖𝑟𝑠𝑡, 𝑅𝑅𝑚𝑒𝑎𝑛, and 𝑅𝑅𝑙𝑎𝑠𝑡

or (b) the coefficient of variation (CV) of the response ratio of the effect of

N addition on plant productivity (RR) and the slope of the linear regression

between RR and time ........................................................................................51

2.3 The slope of the linear regression between response ratio (RR) and time (a)

and the coefficient of variation (CV) of RR with time (b) in studies grouped

by biome, climate condition, N fertilization rate, initial soil pH, N fertilizer

type, and forest age ...........................................................................................52

2.4 Long-term N addition impacts on plant productivity and their temporal

trends over time .................................................................................................53

3.1 A diagram representing the experimental setup including the treatments and

sampling timelines ............................................................................................89

3.2 Interactive effects of global change factors on mean soil respiration rates

during both rewetting and recovery periods......................................................90

3.3 Interactive effects of global change factors on mean weighted diameter

(MWD, an index of aggregate stability) during both rewetting and recovery

periods ...............................................................................................................91

3.4 Interactive effects of global change factors on the soil priming effect during

both rewetting and recovery periods .................................................................92

3.5 NMDS of soil fungal communities (a) and the relative abundance of fungal

phyla (b) under carbon input treatment .............................................................93

3.6 Correlations among mean soil respiration rates (CO2), mean weighted

diameter (MWD), mean enzyme activity (Enzyme), soil microbial biomass

carbon (MBC), soil total carbon concentration (TC), and mean priming

effect (PE) .........................................................................................................94

4.1 Soil variables in both synthetic and real soil systems by the end of Phase 1 ..135

4.2 Effects of carbon input quality on soil variables in synthetic soil system

during Phase 2 .................................................................................................136

4.3 Net change in carbon under all treatments in synthetic soil system during

xiii

the course of experiment .................................................................................137

4.4 Effects of root exudates on soil variables in synthetic soil system during

Phase 2 ............................................................................................................138

4.5 Effects of soil microbial community composition on soil variables in

synthetic soil system during Phase 2 ...............................................................139

4.6 Effects of soil clay activity on soil variables in synthetic soil system during

Phase 2 ............................................................................................................140

4.7 Effects of root exudates-soil clay activity interactions on soil variables in


4.8 Effects of root exudates-soil microbial community composition-soil clay

activity interactions on heavy fraction carbon content, net change in carbon,

and mean enzyme activity in synthetic soil system during Phase 2 ................142

A1 Soil variables in both synthetic and real soil systems by the end of Phase 1 ..147

A2 Effects of carbon input quality on soil variables in synthetic soil system

during Phase 2 .................................................................................................148

A3 Net change in carbon under all treatments in synthetic soil system during

the course of experiment .................................................................................149

A4 Effects of root exudates on soil variables in synthetic soil system during

Phase 2 ............................................................................................................150

A5 Effects of soil microbial community composition on soil variables in


A6 Effects of soil clay activity on soil variables in synthetic soil system during

Phase 2 ............................................................................................................152

A7 Effects of root exudates-soil clay activity interactions on soil variables in


B1 Correlations among soil enzyme activities .....................................................161

B2 Soil respiration rates under control (a) and carbon input treatments (b) over

the course of the incubation experiment .........................................................162

B3 NMDS of soil fungal communities (a-d) and the relative abundance of

fungal phyla (e-h) under different treatments .................................................163

C1 The pictures of artificial soil (a) and root system (b) ......................................173

C2 Effects of root exudates on soil variables in synthetic soil system during

xiv

Phase 1 ............................................................................................................174

C3 Effects of soil microbial community composition on soil variables in


C4 Effects of soil clay activity on soil variables in synthetic soil system during

Phase 1 ............................................................................................................176

C5 The temporal pattern of soil respiration under all treatments in synthetic

soil system during Phase 1 ..............................................................................177

C6 The interactive effects of root exudates and soil mineralogy on soil

microbial biomass carbon in synthetic soil system during Phase 1 ................178

C7 The interactive effects of root exudates and soil microbial community

composition on soil microbial carbon use efficiency in synthetic soil system

during Phase 1 .................................................................................................179

C8 The interactive effects of root exudates and soil clay activity on mean

enzyme activity in synthetic soil system during Phase 1 ................................180

C9 The interactive effects of C input quality, soil microbial community

composition, and soil clay activity on soil respiration in synthetic soil

system during Phase 2 .....................................................................................181

C10 The interactive effects of C input quality, root exudates, and soil clay

activity on soil microbial biomass carbon in synthetic soil system during

Phase 2 ............................................................................................................182

C11 The interactive effects of C input quality, root exudates, and soil microbial

community composition on soil microbial biomass carbon in synthetic soil

system during Phase 2 .....................................................................................183

C12 The interactive effects of C input quality and soil clay activity on mean

enzyme activity in synthetic soil system during Phase 2 ................................184

C13 The interactive effects of C input quality, root exudates, and soil microbial

community composition on mean enzyme activity in synthetic soil system

during Phase 2 .................................................................................................185

C14 The interactive effects of root exudates, soil microbial community

composition, and soil clay activity on mean enzyme activity in synthetic


C15 The interactive effects of root exudates, soil microbial community

composition, and soil clay activity on heavy fraction carbon content in


xv

C16 The interactive effects of carbon input quality, root exudates, and soil clay

activity on heavy fraction carbon content in synthetic soil system during

Phase 2 ............................................................................................................188

C17 The interactive effects of root exudates, soil clay activity, and harvest on

heavy fraction carbon content in synthetic soil system during Phase 2 ..........189

C18 The interactive effects of carbon input quality, root exudates, soil microbial

community composition, and harvest on heavy fraction carbon content in


C19 The interactive effects of C input quality, soil microbial community

composition, soil clay activity, and harvest on heavy fraction carbon

content in synthetic soil system during Phase 2 ..............................................191

C20 The interactive effects of treatments on net change in carbon in synthetic


CHAPTER 1

INTRODUCTION

Introduction

Terrestrial ecosystems are critical to the global carbon (C) cycle. The atmosphere

currently contains about 750 GtC (1 GtC = 1012 kg C), and terrestrial ecosystems contain

about 2190 GtC, of which about 610 GtC is living vegetation and about 1580 GtC is in

the top 1 m of soil (Schimel, 1995). By definition, the C balance of terrestrial ecosystems

at any point in time is the difference between its C gains and losses. Terrestrial

ecosystems gain C through photosynthesis and lose it primarily as CO2 through plant

(autotrophic) respiration and soil (heterotrophic) respiration (Cao & Woodward, 1998).

Given that terrestrial ecosystems store approximately 3 times more C than the

atmosphere, small changes in terrestrial C caused by climate change may have a serious

impact on CO2 concentration in the atmosphere, which in turn exacerbates climate

change. Terrestrial ecosystems may switch from C sinks to C source under future climate

change (Heimann & Reichstein, 2008). Although many studies have been done to

determine the response of terrestrial ecosystems to global climate changes, unknowns

remain regarding the underlying mechanisms. Due to the lack of this information, there

are large uncertainties in the predictions of future climate change effects on terrestrial C

dynamics (Ahlström et al., 2012; Huntzinger et al., 2017). To improve our understanding

and the accuracy of earth system models (ESMs), more studies are needed to unravel the

mechanisms of terrestrial ecosystems responses to climate change.

In this chapter, I do literature review on “Impacts of nitrogen (N) deposition on

plant productivity”, “effects of multiple global change factors on soil respiration in

2

drylands”, and “roles of plant-microbe-soil interactions in soil organic C formation and

loss” first. I then outline the studies that I have conducted to address relevant knowledge

gaps in C cycling in terrestrial ecosystems.

Impacts of N deposition on plant productivity

N deposition is one of the most important global change factors affecting

terrestrial ecosystem (Janssens & Luyssaert, 2009; Reay et al., 2008). Due to the great

role of N deposition in terrestrial ecosystems, many experiments have been conducted to

study effects of N deposition on plant productivity in the past decades. For example,

(Reich et al., 2020) conducted a long-term N deposition experiment in a grassland, and

found that N deposition increased net primary productivity (NPP) by 16.2%. The similar

result was also found by another 10-year N addition field study in grassland ecosystems

(Ren et al., 2017). They found that N deposition could significantly increase NPP in the

long term. The large amount of N addition studies at the local scale provides an

opportunity to summarize the general pattern of N impacts on plant productivity.

Therefore, some meta-analysis studies have been conducted, and they found that N

deposition usually stimulates plant productivity in terrestrial ecosystems (Du et al.,

2020a; Lebauer & Treseder, 2008; Song et al., 2019). Overall, although insignificant N

impacts on plant productivity have been observed in some studies (Lu et al., 2018),

nitrogen limitation of plant productivity in terrestrial ecosystems is globally distributed.

Since long-term experiments require enormous resource investment, the results of

positive N impacts on plant productivity are mainly from short-term studies. This may

lead to inaccurate estimation of N effects on terrestrial because there may be a temporal

trend of long-term N impacts on plant productivity over time (Leuzinger et al., 2011).

3

There are some potential reasons leading to the temporal change in long-term N impacts.

On one hand, short term N addition can stimulate plant growth by increasing soil N

availability (Ren et al., 2017). On the other hand, terrestrial ecosystems maybe N-

saturated after long-term N addition (Tian et al., 2016). In addition, N addition can cause

soil acidification (Falkengren-Grerup & Tyler, 1993; Tian & Niu, 2015); increase N

immobilization (Zheng et al., 2017); and lead to N losses due to leaching and

denitrification (Lu et al., 2011). All these can weaken positive effects of N on plant

productivity, and these negative impacts on plant productivity may strengthen or weaken

over time, depending on the resistance and adaptation capacities of ecosystems.

The predictive abilities of earth system models have been significantly improved

by incorporating C-N interactions (Tang & Riley, 2018; Thomas et al., 2015; Wieder,

Cleveland, Lawrence, et al., 2015). However, the potentially temporal trends in N

impacts on terrestrial ecosystems are not considered in most models. To further advance

model development, we must determine whether long-term N impacts on plant

productivity change over time and what controls the temporal trend. Although the value

of long‐term studies for advancing knowledge of global change effects on ecosystems

and C cycling is widely recognized among ecologists (Kuebbing et al., 2018), no study

has been done to summarize results of temporal trend in plant productivity responses to N

addition based on the long-term experiments. Addressing this knowledge gap will

improve our understanding of N impacts on terrestrial and also improve models’

predictive abilities.

4

Effects of multiple global change factors on soil respiration in drylands

Drylands cover approximately 45% of the planet’s surface and store

approximately 32% of soil organic C (SOC) in terrestrial ecosystems (Plaza et al., 2018;

Prăvălie, 2016). However, drylands are considered as one of the most sensitive areas to

climate change (Huang et al., 2016), which can usually switch between C sinks and

sources (Biederman et al., 2017). Due to the large amount of soil C in drylands, they have

a large potential for C sequestration and climate change mitigation (Lal, 2004).

Therefore, it is essential to study effects of multiple global change factors on soil

respiration in drylands, which can provide more useful information for future climate

change mitigation.

Relatively low and highly variable precipitation in drylands lead to the scarcity of

water (Plaza et al., 2018). Drought is projected to be more severe globally (Dai, 2011),

and drylands may expand to cover 56% of the world’s land area by the end of this

century (Huang et al., 2016). Because of this, thousands of studies have been conducted

in drylands to determine the responses of soil respiration to drought. For example,

Escolar et al. (2015) conducted a rainfall exclusion experiment in a semi-arid grassland,

and found that drought significantly decreased soil respiration. In addition, Talmon et al.

(2011) determined precipitation effects on soil respiration in a desert ecosystem. This

experiment consisted of three treatments: wet (30% increase in precipitation amount),

control (natural precipitation amount), and drought (30% decrease in precipitation

amount). They found that annual rate of soil respiration were 564 g C m-2 yr-1, 472 g C m-

2 yr-1, and 177 g C m-2 yr-1 under wet, control, and drought treatments, respectively. In

addition to change in precipitation amount, variable precipitation (e.g. drying-rewetting)

5

is very common in drylands (Plaza et al., 2018). However, the effects of rewetting on soil

respiration were less studied when compared to effects of precipitation amount. (Li et al.,

2018a) collected soil samples from a semiarid ecosystem with different plantations (e.g.

polar and Mongolian pine) to conduct an incubation experiment to examine rewetting

effects on soil respiration. They found that drying-rewetting cycles increased the respired

CO2 by 68 g C m−2 in the poplar soils and 19 g C m−2 in the Mongolian pine soils, when

compared to constant moisture treatment. Overall, drying-rewetting cycles usually

stimulate soil respiration by disrupting soil aggregates and releasing osmolytes from the

microbial biomass and/or cell lysis (Hu et al., 2018; Lado-Monserrat et al., 2014;

Schimel, 2018).

Warming is happening at the global scale and will be more serious in the future. It

is considered as another important factor that can significantly affect soil respiration in

drylands. (Dacal et al., 2020) assessed short- (0-2 years) and long-term (8-10 years)

warming effects on soil respiration in a biocrust-dominated dryland ecosystem. They

found that warming could stimulate soil respiration in the short term by increasing soil

temperature. Insignificant difference in soil respiration was found between warming and

control in the long-term, which could be explained by thermal acclimation and warming-

induced reductions in biocrust cover. However, opposite results were found in other

studies (e.g. Guan et al., 2019). They simulated warming (+ 1.5 °C) in the Tengger

Desert, northern China, and found that warming decreased soil respiration because of the

reduction in soil moisture caused by warming. The inconsistent results about warming

effects on soil respiration in drylands which were reported by different studies require

more future studies to be done to unravel the underlying mechanisms of how warming

6

affects dryland soil respiration. Comparing to precipitation and warming, the effects of

substrate availability (e.g. C input) on soil respiration have been less studied although soil

microbes in drylands are thought to be C starved (Schimel, 2018). The limited

information inhibits our understanding of soil C cycling in drylands; therefore, it is

essential to investigate how dryland soil respiration responds to C input.

The individual effects of the global change factors on soil respiration in drylands

are well known. However, little is known about how they interactively affect dryland soil

C cycling because few relevant studies have been conducted. For example, Escolar et al.

(2015) found a significant interactive effects of warming and drought in a semiarid

grassland from central Spain during a dry year (mean annual precipitation: 214 mm). This

study indicates that interactions between global change factors are common in drylands.

Nonetheless, nearly no studies have been conducted to assess how soil respiration

responds to more than two global change factors in drylands, although they happen

simultaneously in the real world. The unknowns regarding whether multiple global

change factors can interactively affect soil respiration may inhibit our prediction about

dryland soil C cycling in the future.

Roles of plant-microbe-soil interactions in soil organic C formation and loss

Soils store at least two times more C than the atmosphere (Batjes, 1996;

Scharlemann et al., 2014). Therefore, small changes in soil C pool can result in large

variations in the atmospheric CO2 concentration, which in turn aggravate or slow global

warming (Minasny et al., 2017; Smith et al., 2020). Although many relevant studies have

been conducted, our mechanistic understanding of SOC formation and loss is still limited

(Cotrufo et al., 2013; Lehmann et al., 2020; Sokol et al., 2018). Partly due to this, models

7

exhibit large divergences and great uncertainty among predictions of changes in soil C

pool (Shi et al., 2018). Therefore, it is urgent to examine the relative importance of each

of the controls on SOC dynamics, and to unravel the mechanisms by which they affect

the formation and loss of SOC.

There are four major controls (e.g. C input quality, root exudates, soil microbial

community traits, and soil minerology) over soil C cycling along the plant-microbe-soil

continuum (Merino et al., 2015; Sokol et al., 2019; Sokol & Bradford, 2019).

Traditionally, recalcitrant C input was thought to be the dominant mechanism of SOC

formation because it is inherently resistant to microbial decay (Melillo et al., 2008).

However, a suite of emerging paradigms hold that labile C input may constitute an

important pathway of SOC formation (Cotrufo et al., 2013, 2015; Sokol et al., 2018). Soil

microbial C use efficiency (CUE), the fraction of substrate that is assimilated into

microbial biomass vs. respired (Sinsabaugh et al., 2013), has been used to elucidate the

mechanisms of effects of C input quality on soil C cycling. For example, the

decomposition of labile C requires less activation energy (Manzoni et al., 2012), CUE

should be higher under labile than recalcitrant C input (Frey et al., 2013a; Qiao et al.,

2019). Given that soil microbial biomass significantly contributes to persistent SOC

formation (Liang et al., 2017), labile C input can lead to much more persistent SOC

accumulation than recalcitrant C input (Cotrufo et al., 2013). However, after continuous

labile C input, copiotrophic can be the dominated soil microorganism, which has an

inherently lower CUE (Geyer et al., 2016; Roller & Schmidt, 2015). Therefore, the

effects of C input quality on SOC formation are likely to hinge upon plant-microbe

interactions which develop over time.

8

There is growing recognition that root exudates have a significant impact on SOC

formation and loss (Sokol et al., 2018, 2019). Briefly speaking, on one hand, root

exudates mainly provide labile C which has higher CUE, which in turn contribute to SOC

accumulation (Sokol et al., 2019; Sokol & Bradford, 2019). On the other hand, root

exudates can result in large SOC loss thorough positive rhizosphere priming effect

(Cheng et al., 2014; Kuzyakov et al., 2000). Dijkstra et al. (2021) hold that the net effect

of root exudates on SOC stabilization is dependent on soil minerology. For example, root

exudates may increase persistent SOC accumulation in clayey soils which provide a large

potential to stabilize SOC into mineral-associated organic C (MAOC). However, root

exudates may result in SOC loss in sandy soils which do not have enough minerals to

bond with SOC to form MAOC. Although this conceptual framework seems reasonable,

no relevant studies have been done to test it.

Although consensus is emerging that microbial residues greatly contribute to SOC

stabilization, no direct evidence has been provided by experimental studies. Therefore,

Kallenbach et al. (2016) utilized synthetic soil systems to determine the roles of soil

microbial traits in SOC formation and loss. They found greatest microbial-derived SOC

accumulation in soils with higher fungal abundances, which indicated that soil microbial

community was the most important factor affecting SOC stabilization. Given that soil

minerology (e.g. clay content and activity) mainly controls the formation of MAOC, it

should significantly affect SOC cycling. However, some recent experimental studies did

not find significant effects of soil minerology on SOC formation (Kallenbach et al., 2016;

Rasmussen et al., 2018). These surprising results could emerge from the interactions

between soil minerology and plant inputs and/or soil microbes that we mentioned above.

9

Soil C cycling models have been developed for decades, but they adopt different

model structures and/or mechanisms partly because of the limited information of roles of

plant-soil-microbe interactions in SOC dynamics that experimental studies can provide

(Shi et al., 2018). Therefore, it is essential to reconcile contradictory findings about

effects of C input quality, root exudates, soil microbial communities, and soil minerology

on SOC formation. The opposite results which were reported by previous studies are

partly because most of them have considered some, but not all, of these four critical

drivers, which also strongly co-vary in real soils. Therefore, conducting a study including

all potential factors affecting SOC formation and loss independently can help us unravel

their interactions and isolate direction of causality.

Dissertation Outline

Chapter 2 - Nitrogen effects on plant productivity change at decadal timescales

Many experimental and meta-analysis studies have found N limitation of plant

productivity in terrestrial ecosystems (Du et al., 2020a; Lebauer & Treseder, 2008; Reich

et al., 2020; Song et al., 2019). However, unknown remains regarding whether the

positive effect of N deposition on plant productivity can change over time in the long

term. By overlooking the potentially temporal trend of N impacts, we may under- or

overestimate N effects on terrestrial ecosystems. To address this knowledge gap, I

conducted a meta-analysis study to collect data from 63 long-term N addition studies

across the world.

Chapter 3 - Effects of multiple global change factors on soil respiration in drylands

Due to the significant role of drylands in climate mitigation (Lal, 2004; Plaza et

al., 2018), thousands of studies have been conducted to examine climate change effects

10

on dryland soil respiration (Dacal et al., 2020; Escolar et al., 2015; Guan et al., 2019,

2021; Li et al., 2018a). Most studies only focus on the individual effects of global change

factors on soil C cycling although the interactive effects have been found (Escolar et al.,

2015). I conducted an incubation experiment to assess how multiple global change

factors (e.g. warming, drought, drying-rewetting cycles, and C input) interactively affect

soil respiration in drylands.

Chapter 4 - Roles of plant-microbe-soil interactions in soil organic C formation and loss

There is growing recognition that plant-microbe-soil interactions significantly

affect SOC formation and loss (Cotrufo et al., 2013; Lehmann et al., 2020; Sokol et al.,

2018). However, opposite results regarding effects of plant input, soil microbial traits, or

soil minerology on SOC stabilization (Cotrufo et al., 2013; Dijkstra et al., 2021;

Kallenbach et al., 2016; Lavallee et al., 2020; Pierson et al., 2021; Rasmussen et al.,

2018; Schnecker et al., 2019). These surprising results could emerge from the interactions

among plant input, soil microbes, and soil minerology. I utilized synthetic root/soil

systems to disentangle the pathways by which plants, microbes, and soils interactively

affect SOC cycling and assess the relative importance of each driver.

Chapter 5 - Conclusions

I summarize the major results from each chapter and point out some suggests for

future studies.

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4

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CHAPTER 2

NITROGEN EFFECTS ON PLANT PRODUCTIVITY CHANGE AT DECADAL

TIMESCALES1

Abstract

Although some long-term studies have been conducted to quantify nitrogen (N)

impacts on plant productivity, uncertainties remain regarding whether these impacts

change over time and the underlying mechanisms. By overlooking this, we may over- or

under-estimate N impacts on terrestrial ecosystems. Our goal was to determine whether N

impacts on plant productivity increase, decrease, or do not change over time in the long

term, and what controls these dynamics. We synthesized 63 N addition studies with

duration 8 years in natural terrestrial ecosystems. Our results showed temporally

dynamic N impacts on plant productivity in terrestrial ecosystems: the interannual

coefficient of variation (CV) of N impacts ranged from 19% to 768% across 63 studies,

with higher variability in acidic soils. Moreover, a substantial proportion (44%) of studies

showed evidence of a consistent directional change in the strength of N effects over time.

The direction of change varied with biome type (forests: decrease; grasslands and

shrublands: increase). The temporal trend of N impacts was mostly responsive to mean

annual precipitation (MAP), mean annual temperature (MAT), and initial soil pH, which

accounted for 24%, 19%, and 19% of the variation, respectively. Our findings indicate

that effects of long-term N addition on plant productivity tend to shift (e.g. increase or

decrease), and N impacts have large fluctuations between years in terrestrial ecosystems.

1 Co-authors: Yiqi Luo, Zhenghu Zhou, Bonnie G. Waring

23

Therefore, not only the magnitude of N impacts on plant productivity, but also their

temporal trend and variability, should be considered in future experimental and model

research.

Introduction

Nitrogen (N) addition can enhance the plant uptake of atmospheric carbon dioxide

by increasing plant productivity in terrestrial ecosystems (Janssens & Luyssaert, 2009;

Reay et al., 2008). While N is considered as a significant factor affecting plant

productivity in terrestrial ecosystems (Du et al., 2020a; Dukes et al., 2005; Lebauer &

Treseder, 2008; Lu et al., 2018; Vitousek & Howarth, 1991), most Earth System

Models implement C-N interactions inconsistently because the underlying mechanisms of

linking terrestrial C and N cycling remain far from clear (Thomas et al., 2015). This leads

to large uncertainties in predicting climate change feedbacks in terrestrial ecosystems

(Ciais et al., 2019; Green et al., 2019; Wieder et al., 2015). Therefore, in order to provide

valuable information for future climate change mitigation and food security policies, a

deep understanding of plant productivity responses to N addition is essential.

Given the large impact of N availability on terrestrial ecosystems, many N

fertilization experiments have been conducted in the past decades (Kunzová & Hejcman,

2010; Oishi et al., 2014; Reich & Hobbie, 2013). However, most of them focus on short-

term N effects because long-term experiments require enormous resource investment.

Moreover, very few meta-analysis studies have been done to collect measurements from

long-term N addition experiments. Suding et al. (2005) conducted a meta-analysis study

and found that N addition led to declines in plant diversity over time, but studies of this

type are rare. As a result, there is much less information regarding long-term N addition

24

effects on plant productivity when compared to its short-term impacts. Meta-analyses of

shorter-term responses have revealed that N impacts on plant productivity vary across

ecosystem types, and in relation to mean annual temperature (MAT), precipitation

(MAP), and N fertilizer type and application rate (Chen et al., 2015; Lebauer & Treseder,

2008; Yan et al., 2019; Yue et al., 2016). Yet few of them quantified how soil properties

(e.g. soil texture, nutrient availability, and pH) mediate N impacts on plant productivity

(Fay et al., 2015).

Due to the limited information data synthesis can provide, few earth system

models include the roles of soil properties in regulating N impacts on terrestrial

ecosystems, which may reduce their predictive ability. These models exhibit large

divergences among predictions of terrestrial C sink pattern at decadal and centennial

timescales. This may occur, in part, because the relative contributions of factors affecting

N impacts on plant productivity change over longer timescales. Therefore, it is urgent to

assess the factors controlling N effects on terrestrial ecosystems in the both short- and

long-term.

Although temporal trend in N effects on plant productivity have been reported by

some individual studies (Brooks & Coulombe, 2009), the underlying mechanisms remain

poorly explored. First, long-term N addition can stimulate plant productivity by relieving

nitrogen limitation. However, the magnitude of this stimulation can change over time,

due to the degree of co-limitation by other resources (e.g. phosphorus (P), potassium (K),

microelements, light, and water) (Du et al., 2020b; Fay et al., 2015). Second, N addition

can result in soil acidification (Falkengren-Grerup & Tyler, 1993; Tian & Niu, 2015);

increase N immobilization and sequestration into organic pools (Zheng et al., 2017); lead

25

to N losses due to leaching and denitrification (Lu et al., 2011); decrease plant

biodiversity (which is positively related to plant productivity) (Chalcraft et al., 2008;

Chen et al., 2018; Liang et al., 2015; Midolo et al., 2019; Suding et al., 2005); and reduce

relative abundances of mycorrhizal fungi and oligotrophic bacteria, which are crucial for

nutrient cycling in N limited ecosystems (Collins et al., 2008; Leff et al., 2015). These

negative impacts on plant productivity may strengthen or weaken over time, depending

on the resistance and adaptation capacities of ecosystems. Thus, the sign and temporal

trend of plant responses to N addition will therefore depend both on the absolute

magnitude of positive vs. negative effects, and the relative rates at which these effects

change over time.

Temporal trends in plant responses to fertilization can be mediated by a variety of

site-specific factors, e.g. initial soil pH, C:N, P availability, MAT, and MAP. For

example, in extremely N limited (high soil C:N) sites, plant productivity may increase

over time as added N accumulates in the ecosystem. Consequently, positive N impacts on

plant productivity may predominate and strengthen through time. Conversely, in sites

where the soil is extremely acidic, plant productivity responses may diminish over time

as soil acidification progresses. Climatic variables such as MAT and MAP indirectly

affect these patterns through their influences on soil pH, soil nutrient availability, soil

microbial community structure, and plant diversity (Crowther et al., 2019; Hou et al.,

2018; Jing et al., 2015). Interannual variability in these climatic drivers might also lead to

temporal fluctuations in plant community responses to added N: for example, N

fertilization may not stimulate plant growth in a drought year, when water becomes

limiting. Moreover, N use efficiency and uptake rate vary across N fertilizer types

26

(Abbasi et al., 2013; Kaštovská & Šantrůčková, 2011), the magnitude of the decrease in

soil pH, and the time needed for ecosystems to approach N saturation are dependent on N

application rate (Tian et al., 2016; Tian & Niu, 2015). Consequently, fertilizer type and

application rate may also play a critical role in regulating temporal trend of N effects on

plant productivity.

The performance of earth system models has been improved by incorporating C-

N interactions (Tang & Riley, 2018; Thomas et al., 2015; Wieder et al., 2015), however,

most models do not consider the temporal trends in N impacts. To advance model

development, we must determine when and where N impacts on plant productivity vary

through time. Although the value of long‐term studies for advancing knowledge of global

change effects on ecosystems and C cycling is widely recognized among ecologists

(Kuebbing et al., 2018), no study has been done to summarize results of temporal trend

and/or interannual variability in plant productivity responses to N addition based on the

long-term experiments. Here, we synthesized 63 long-term ( 8 years) N addition studies

from around the world to address three research objectives: 1) to determine the short- and

long- term and overall effects of N addition on plant productivity in terrestrial

ecosystems; 2) to quantify temporal trend and variability in these plant responses; and 3)

to identify underlying controls on the magnitude, temporal trend and variability of plant

responses to N.

Methods

Data sources

Publications reporting the response of plant productivity to experimental N addition in

terrestrial ecosystems were collected by searching Web of Science. The keywords used

27

for the literature search consisted of “long-term nitrogen addition” OR “long-term

nitrogen fertilizer” OR “long-term nitrogen deposition” AND “biomass” OR

“productivity” OR “tree growth". The dataset provided by a recent meta-analysis about

the effect of N addition on forest carbon (Schulte-Uebbing & de Vries, 2018) was also

used in the present study. We screened all resulting publications using the following

criteria: 1) The studies were conducted at a field site for greater than or equal to 8 years.

The distribution of experimental duration of 63 case studies selected in this study can be

found from Fig. A1 in the appendices. 2) Plant productivity under both control (0 N kg

ha-1 yr-1) and N addition treatment (no other nutrients were added) was continuously

measured every year from the beginning of the experiment. 3) Since aboveground and

belowground plant productivity may show divergent responses to N addition (Chen et al.,

2018; Cusack et al., 2011; Li et al., 2011), we only collected data from the studies that

reported aboveground (56 out of 63 studies) or whole plant productivity (7 out of 63

studies) if aboveground plant productivity was not provided. 4) The data from different

plant productivity types or N application rate in the same study were regarded as

independent observations. 5) Plant productivity was assessed either by measuring basal

area increment (for forests), biomass (for grasslands), or canopy height (for shrublands).

Note that for some experiments, plant productivity was not measured every year;

however, if the number of measurements was equal to or greater than five, and the

frequency of measurements could capture both short- (e.g. < 3 years), medium-, and

long-term (e.g. > 8 years) trends well, they were also included in our study. It also should

be noted that, in 16 out of 63 studies, N was only applied at the beginning of the

experiment; however, N was applied continuously each year in the remaining 47 studies.

28

The geographical locations (latitude and longitude), climate factors (MAT and MAP),

initial soil variables (organic C, total N and P, extractable N, P, and K, pH, clay content,

bulk density), N fertilizer variables (N type and application rate), and ecosystem related

variables (ecosystem type and forest age) were also obtained from the papers. If papers

did not include MAT and MAP, we extracted them from the database at

http://www.worldclim.org/ using latitude and longitude with “raster” package (Hijmans

& van Etten, 2012). In the event that soil pH, bulk density, and clay content were not

provided in the papers, they were extracted from the database at https://www.isric.org/

using “GSIF” package with the help of the latitude and longitude (Hengl, 2020). The data

presented in figure form were extracted by using Engauge Digitizer software (Free

Software Foundation, Inc., Boston, MA, USA). All data were grouped by biome, climate

condition, N fertilization rate, initial soil pH, N fertilizer type, and forest age. Median

values (6.3 °C, 875 mm, 50 kg N hr-1 yr-1, and 30 years) were used to separate MAT,

MAP, N fertilization rate, and forest age into “high” and “low” categories, respectively.

Following this preliminary screening, our meta-analysis included 20 publications

providing 63 N fertilizer case studies (44 for forests, 25 for grasslands, and 4 for

shrublands) from 20 field sites (Fig. A2 in the appendices).

Data analysis

Determining N impacts on plant productivity

The means of plant productivity under control (Xc) and N addition treatment (Xt) in every

year in each case study were used to compute a response ratio (RR) as follows:

RR = ln (Xt/Xc) = ln (Xt) – ln (Xc) (1)

https://www.isric.org/

29

Next, we generated three summary response ratios (RR) for each study: 1)

RRfirst: RR in the first year for each study; 2) RRmean: average RR across all years for

each study; and 3) RRlast: RR in the last year for each study. Most studies did not provide

standard deviation of plant productivity (60 out of 63 studies). Therefore, RRmean and

RRlast were weighted by sample size and experimental duration by following (Terrer et

al., 2016):

W = (nc × nt)/( nc + nt) + (yr × yr)/(yr + yr) (2)

RRfirst was weighted by sample size:

Wfirst = (nc × nt)/( nc + nt) (3)

where W is the weighting factor; nc and nt is sample size under control and N addition

treatment, respectively; and yr is the length of the study in years. If sample sizes were not

provided (3 out of 63 studies), they were assigned as the median of sample sizes of other

60 studies. For WRRfirst, WRRmean, and WRRlast, the 95% confidence interval (95% CI)

was calculated, and N effects on plant productivity were considered significant if the 95%

CI did not overlap with zero. Since some field sites included multiple case studies, we

calculated the average of RRmean (RRaverage) for each field site if appropriate.

The quality check of this meta-analysis and statistical choices were done

following previous papers (Gurevitch & Hedges, 1999; Hedges et al., 1999; Viechtbauer,

2010). The meta-analyses were performed using “metafor” package in the R studio

Version v.1.2.5033 program (Viechtbauer, 2010). We created funnel plots to detect

publication bias using the “funnel” function, and no significant publication bias was

30

found. The “leave1out” function was used to perform sensitivity test, we found that

excluding any one of the studies did not affect the overall results.

Quantifying temporal trend and variability in long-term N impacts on plant productivity

We used simple linear regression to determine the relationship between

experimental duration (year) and RR for each study (Fig. A3 in the appendices). The

slope of this linear regression for each study and the corresponding standard error were

recorded. To permit comparison across studies, the weighted mean of slopes (S) was

calculated as follows (Kim, 2011):

S = ∑Si vi⁄

∑1 vi⁄ (4)

where Si is the slope for each study; and vi is the squared standard error for each study.

The corresponding standard error of the weighted mean (SE) was given by:

SE = √1

∑ 1vi

(5)

We recognize that a simple linear relationship might not be the best-fit model to

describe the temporal dynamics of N impacts in each study; it would be theoretically

possible to observe exponential relationships, saturating relationships, or the absence of a

relationship between RR and time. In the context of a meta-analysis, however, we had to

derive a summary statistic that could be directly compared across studies, so we

parsimoniously selected a linear model to minimize the number of parameters estimated.

However, we also evaluated the suitability of these linear models by performing

Spearman Rank-Order Correlation tests. 35 out of 63 studies had an absolute value of the

Spearman correlation coefficient ≥ 0.40, above which value we considered the monotonic

relationship to be moderately strong (Fig. A4 in the appendices). Since some field sites

31

included multiple case studies, we described the directionality of temporal trend of N

impacts on plant productivity at one field site as “increased” if the slopes of the linear

regression between RR and time were higher than zero in all case studies; as “decreased”

if the slopes of the linear regression between RR and time were lower than zero in all

case studies; and as “mixed” if the field site included case studies showing both

“increased” and “decreased” trends of N impacts on plant productivity.

Given that there was weak evidence of directional relationships between RR and

time in about half of studies examined, we also determined the temporal variability of

long-term N impacts on plant productivity. The coefficient of variation (CV) for each

study was calculated as follows:

CV = σ

μ (6)

where σ is the standard deviation of RR for each study; and μ is the absolute value of

mean RR for each study.

Predictors of N impacts on plant productivity, and the temporal trend and variability of

these impacts

The relative influences of predictor variables were quantified by using boosted

regression tree (BRT) analysis, including predictor variables that were reported in more

than 50% of studies. Multicollinearity was tested to eliminate the variables that were

redundant before running the BRT analysis. We included MAT, MAP, N application rate,

RRfirst, initial soil pH, initial soil clay content, and initial soil bulk density as predictor

variables (except when the response variable was RRfirst; in this case it was obviously

not included as a predictor). The reason why we included RRfirst as a predictor variable

32

was to determine whether the overall or long-term response of plant productivity to N

addition is predictable based on the short-term response. The BRT analysis was run in R

with “gbm” package (Greenwell, 2019) using the appropriate weighting factor. The three

main parameters needing optimization in the BRT analysis were the learning rate

(shrinkage), the depth of each regression tree (interaction.depth), and the number of

iteration (ntree). In the present study, the three optimized parameters for shrinkage,

interaction.depth, and ntree were 0.01, 2, and 12000, respectively. Overall, the BRT

analysis explained 97% of the variation in RRfirst, 97% in RRmean, 96% in RRlast, 89%

in S, and 87% in CV. All statistical analyses and graphs were performed using the R

studio Version v.1.2.5033 program.

Results

Plant productivity responses to N addition

Short-term (WRRfirst) effects of N addition on plant productivity in terrestrial

ecosystems were insignificant, with detectable effects only under conditions of high (> 7)

initial soil pH. By contrast, most long-term (WRRlast) and overall (WRRmean) N impacts

were significantly positive (Fig. 2.1). Positive overall N impacts on plant productivity

were found at most field sites (19 out of 20), and they were distributed evenly at the

global scale without showing a strong latitudinal pattern (Fig. A5 in the appendices). By

contrast, negative effects of N addition on plant productivity were found in 6 out of 63

studies (data not shown). Overall and long-term N impacts showed the same trends

regarding their significance and magnitude (Fig. 2.1). N impacts on overall productivity

were positive in forests and grasslands, but non-significant in shrublands. N also boosted

productivity under conditions of low MAT (< 6.3 °C) and low MAP (< 875 mm),

33

whereas fertilization did not impact productivity under warmer and wetter conditions.

Effects were also more positive for younger forests (< 30 years at the start of the

experiment) rather than older stands. Generally speaking, N impacts on plant productivity

were non-significant in the short term but became significant and positive in the long

term under most situations.

MAP was the most influential factor determining the magnitude of overall

(RRmean) plant productivity responses (Fig. 2.2a). By contrast, the influence of N

application rate on plant productivity responses to N addition was relatively small, and its

influence did not change when measured in the first year, last year, or as the mean

response across all years. Additionally, except for initial soil pH, the overall contribution

of soil properties (e.g. initial soil clay content and bulk density) to N impacts on plant

productivity was also relatively minor. The most influential factor determining the

magnitude of plant productivity responses varied according to when the productivity

response was measured (short-term: initial soil pH, long-term: MAP).

Temporal variability and trend of long-term N impacts

Impacts of N on plant productivity varied greatly from year to year. The CV of

RR ranged from 19% to 768% across studies, and the mean value was 109% ± 148%

(Fig. 2.3b and Fig. A6 in the appendices). CV of RR in alkaline soils (24%) was much

lower than that in acidic soils (121%), but there were no other consistent differences in

CV across experimental groups. According to BRT analysis, soil variables (53%) mostly

explained cross-study patterns in CV, followed by fertilizer variables (18%), RRfirst

(17%), and climate variables (12%).

34

Spearman’s rank correlation coefficient between RR and experimental duration

was greater than 0.5 in 18 out of 63 studies and was less than 0.5 in 10 out of 63 studies

(Fig. A4 in the appendices). In other words, 44% of studies showed evidence of a

consistent directional change in the strength of N effects over time. Despite the fact that

only half of the studies showed a monotonic relationship between RR and time, our

overall weighted mean slope estimate was negative but relatively small (-0.003). In other

words, the response ratio of the effect of N on plant productivity in terrestrial ecosystems

decreased 0.3% annually (Fig. 2.3a).

The temporal trend of N impacts was randomly distributed at the global scale and

did not show specific spatial patterns (Fig. A5 in the appendices). Moreover, temporal

variations in the sign and magnitude of plant responses were not only found across field

sites, but were also found from different case studies within one field site. The direction

of temporal trend of N impacts varied among biomes: N impacts decreased over time in

forests but increased in grasslands and shrublands. N effects declined over time in sites

characterized by warm or wet climates or acidic soils. By contrast, N effects strengthened

over time in sites characterized by cold, dry climates or alkaline soils (Fig. 2.3a).

However, the temporal trend of N fertilization was not dependent on N application rate,

N fertilizer type, or forest age. MAP had the greatest influence on the temporal response

of plant productivity to N addition, accounting for 24% of the variation among studies.

Additional variance was explained by MAT (19%), initial soil pH (19%), RRfirst (15%),

N application rate (9%), initial soil bulk density (8%), and initial soil clay content (6%)

(Fig. 2.2b).

Relationship between N effect sizes and their temporal trends

35

The magnitude of N effects on productivity (i.e., the degree to which N increased

growth at the first, last, or time-integrated measurement intervals) was not consistently

related to the temporal trend of these responses (Fig. A7 in the appendices). For studies

that showed decreases in N impacts through time, the effect of N addition on plant

productivity was positive in the first year (WRRfirst) but became insignificant by the last

year (WRRlast). Studies that showed increases in N impacts through time showed

positive effects in both the first and last years.

Discussion

Overall N impacts on plant productivity and the controls

Our study found significant positive overall N impacts on plant productivity in

terrestrial ecosystems (Fig. 2.1), which is consistent with previous studies (Lebauer &

Treseder, 2008; Vitousek & Howarth, 1991). However, negative effects of N addition on

plant productivity were found in six studies. These studies had higher MAT and MAP,

resulting in more severe nutrient loss and soil acidification under long-term N addition.

Most earth system models do not incorporate the mechanisms by which N addition might

slow plant productivity (e.g. soil acidification and micronutrients losses associated with

N leaching) (Fig. 2.4), this may result in the large uncertainties in their predictions.

The most influential factor determining the magnitude of N impacts on plant

productivity changed according to the time frame over which productivity responses were

measured (short-term: initial soil pH, long-term: MAP) (Fig. 2.2a). The positive impact

of N on plant growth was generally relatively minor at the first year of N application,

which could be due to initial soil pH and its significant negative effect on plant growth.

(Tian & Niu, 2015) found that soil pH decreased by 0.25, 0.34, 0.24, and 0.12 for studies

36

with durations of < 5 year, 5-10 year, 10-20 year, and > 20 year, respectively. This

suggests that the impacts of fertilizer-related soil acidification could weaken over time.

Meanwhile, the importance of MAP for predicting long-term growth responses may

relate to the availability of non-N nutrients: P limitation becomes more severe with

increasing MAP. Overall, our study indicates that the controls of N impacts on plant

productivity change over time. This finding may be used to calibrate earth system models

to improve their long-term predictive ability.

Temporal variability of long-term impacts on plant productivity and the controls

Our study found that for most long-term studies, N impacts on plant productivity

had enormous variation between years (Fig. 2.3b). This finding could reflect that the

response of plant productivity to N addition in a particular year would be highly

dependent on the local precipitation and temperature in that year. We did not find any

evidence that mean climatic conditions (i.e., MAT or MAP) mediated the degree of

interannual variability in plant responses. The CV was lower on alkaline soils; however,

this may simply be an artefact of the relatively small number of studies conducted in

high-pH sites. Most studies determining N impacts are only conducted over short

timeframes. If any one of these years is particularly hot, dry, etc., the study may not

capture representative responses of plant production to N inputs. Therefore, more long-

term studies should be encouraged, and climatic covariates should be measured and

reported in each year to better understand ecosystem responses to N deposition.

Temporal trend of long-term impacts on plant productivity and the controls

Ecologists have argued that global change experiments may overestimate impacts

on terrestrial ecosystems because the effects of global change drivers (e.g. elevated CO2,

37

warming, and N deposition) can diminish over time at the local scale (Leuzinger et al.,

2011). However, since few studies have synthesized the results from long-term N

fertilization studies at the global scale, unknowns remain regarding whether N impacts on

plant productivity also shift over time. We found that N impacts on plant productivity in

the present study decreased very weakly through time (Fig. 2.3a), which indicates that the

effect of N on plant productivity can be temporally dynamic in terrestrial ecosystems.

It should be noted that large variations in the directionality of response (i.e.,

increasing vs. decreasing slopes of RR vs time) were not only found across field sites but

were also found from different case studies within one field site (Fig. A5 in the

appendices). This might because that a field site with multiple case studies usually

included different plant species or N application rates. Therefore, in order to improve the

accuracy of estimating N impacts on terrestrial ecosystems, it is necessary to consider the

drivers of variation at both small and large scales.

Changing plant responses to N may be explained by concomitant shifts in soil pH,

nutrient availability, microbial community, and plant biodiversity. However, most long-

term experiments do not continuously measure these variables together with plant

productivity, which complicates efforts to unravel the underlying mechanisms. Yet site-

specific variables (e.g. MAT, MAP, initial soil pH, N application rate, and fertilizer type)

may provide clues about the drivers of temporal trend in N effects on plant productivity.

The direction of temporal trends of long-term N impacts on plant productivity was

different between sites with low and high MAT, between sites with low and high MAP,

and between sites with low and high initial soil pH, and between ecosystem types (Fig.

2.3a). This finding was supported by the BRT analysis, which showed that the temporal

38

trend of N impacts was mostly regulated by MAP, MAT, and initial soil pH (Fig. 2.2b).

N effects tended to decrease in sites characterized by warm and wet climates; or acidic

soils but tended to increase in sites characterized by cold and dry climates, or alkaline

soils (Fig. 2.3a). These patterns likely reflect non-target effects of N on soil properties. N

leaching rate is higher under high MAP and MAT, and it becomes more severe with

continuous N application. Soil acidification, base cations (Ca2+, K+, Mg2+, and Na+)

losses, and soil erosion may occur with N leaching and become more serious over time.

Additionally, soil acidification caused by N addition and Al3+ accumulation associated

with soil acidification tend to be more severe in acidic soils (Tian & Niu, 2015).

Increases in the magnitude of these negative impacts may explain why the benefits of N

addition declined with time at high MAP and low pH sites, but continued to increase with

time at low MAP and high pH sites.

Limitations of the study

First, the goal of this study was to determine whether N impacts on plant

productivity change over time; we used the slope of the relationship between RR and

time to assess this. This simple linear model might not be the best-fit model for any

particular study, especially in the case of a non-monotonic relationship (e.g., a hump-

shaped relationship between RR and time). When inspecting the raw data, we saw that

most studies displayed: 1) a monotonic relationship between RR and time, which could

be well-described by a linear model, or 2) no relationship, in which case the slope

estimate was close to zero, and the SE was large. Our meta-analysis calculations

accurately captured both of these scenarios, as they incorporated slope estimates and their

associated standard errors. It should be noted that the linear slopes for RR vs time ranged

39

from -0.03 to 0.02 y-1 (i.e. the magnitude of plant responses to N declined up to 3%

annually or increased up to 2% annually). In other words, directional changes in plant

responses to N, when present, are relatively mild. Second, we were not able to assess

with certainty the mechanisms that drove temporal trend of ecosystem N response. This

mainly reflects a lack of time-series data related to changes in soil pH, nutrient

availability, microbial communities, and plant biodiversity. More replicated manipulative

studies should be conducted in the future to measure these variables together with plant

productivity continuously. Third, we used the data in the first year and in the last year to

represent short-term and long-term responses, respectively. It should be noted that the

response of plant productivity to N addition in a particular year would be highly

dependent on the local climatic conditions in that year – a conclusion which is

highlighted by the extremely high interannual variability in N responses observed within

and among studies. Last, most long-term N studies were conducted in the temperate and

continental regions, which means that our study cannot shed light on long-term N trend in

tropical zones. More studies in tropical ecosystems should be done in the future because

of their great role in climate change mitigation (Bonan, 2008).

Implications

Our study found that long-term N impacts on plant productivity in terrestrial

ecosystems are positive – higher N availability generally stimulates plant growth.

However, the degree of stimulation shows large temporal variability from year to year,

with a substantial number of ecosystems displaying consistent increases or declines in the

magnitude of response (Fig. 2.1, 2.3, and 2.4). This finding indicates that we may over-

or underestimate N effects on terrestrial ecosystems at a specific field site by making

40

inferences based on short-term measurements. Unfortunately, the magnitude of N impacts

in the short term (RRfirst) was not a very strong predictor of the magnitude of long-term

N impacts, their temporal trend, or their temporal variability (Fig. 2.2). This result

demonstrates the value of long‐term experiments which can be used to explore the

mechanisms that drive decadal responses to N addition. Most field experiments last less

than 3 years, which means that patterns extrapolated from short-term measurements may

not reflect long-term ecosystem dynamics. Our study highlights the necessity of long-

term experiments: it is essential for funding agencies to invest more in experiments at

multi-decadal timescales.

Terrestrial ecosystems play a great role in mitigating climate warming by

absorbing atmospheric carbon dioxide. For Earth System Models that include C-N

interactions, most consider the positive effect of N on plant growth rate, which has been

widely observed from many experimental studies. However, they do not incorporate the

mechanisms of potentially decreased N impacts on plant productivity over time via soil

acidification, N saturation, and so on. As a result, Earth System Models may overestimate

terrestrial ecosystems’ future climate mitigation potential. In order to predict C cycling in

terrestrial ecosystems under future climate change, it is urgent to pay more attention to

the underlying mechanisms that drive temporal trend of plant responses to N. Moreover,

when quantifying N impacts on terrestrial ecosystems in the long term, future meta-

analysis studies should generate time-integrated response metrics to avoid inaccurate

estimation.

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4

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Figures

Figure 2.1. Weighted response ratios of plant productivity (WRR) under N addition in

studies grouped by biome, climate condition, N fertilization rate, initial soil pH, N

fertilizer type, and forest age. These weighted response ratios were calculated using data

from only the first year (WRRfirst, a), all timepoints (WRRmean, b), and only the most

recent timepoint (WRRlast, c). Values represent effect sizes ± 95% confidence intervals.

The size of each point is proportional to the sample size (legend in upper left). Median

values (6.3 °C, 875 mm, 50 kg N hr-1 yr-1, and 30 years) were used to separate MAT,

MAP, N fertilization rate, and forest age into “high” and “low” categories, respectively.

The cut-off value of pH was 7. MAT: mean annual temperature, MAP: mean annual

precipitation, pH: initial soil pH.

High stand age

Low stand age

Urea

NH4NO3

High pH

Low pH

High rate

Low rate

High MAP

Low MAP

High MAT

Low MAT

Shrubland

Grassland

Forest

All

−0.5 0.0 0.5

WRRfirst

20

40

60

−0.5 0.0 0.5

WRRmean

−0.5 0.0 0.5

WRRlast

(a) (b) (c)

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Figure 2.2. Relative influence of predictor variables on (a) RRfirst, RRmean, and RRlast

or (b) the coefficient of variation (CV) of the response ratio of the effect of N addition on

plant productivity (RR) and the slope of the linear regression between RR and time.

Relative influences of variables were determined using a boosted regression tree analysis

(BRT). MAT: mean annual temperature, MAP: mean annual precipitation, RRfirst: RR in

the first year, RRlast: RR in the last year, RRmean: average RR across all years, N rate:

nitrogen application rate, pH: initial soil pH, Clay: initial soil clay content, BD: initial

soil bulk density.

BD

Clay

pH

N rate

RRfirst

MAP

MAT

0 10 20 30 40

Climate

Fertilizer

RRfirst

Soil

First

Last

Mean

BD

Clay

pH

N rate

RRfirst

MAP

MAT

0 10 20 30 40Relative influence (%)

Climate

Fertilizer

RRfirst

Soil

CV

Slope

(a)

(b)

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Figure 2.3. The slope of the linear regression between response ratio (RR) and time (a)

and the coefficient of variation (CV) of RR with time (b) in studies grouped by biome,

climate condition, N fertilization rate, initial soil pH, N fertilizer type, and forest age.

Values represent effect sizes ± 95% confidence intervals. The size of each point is

proportional to the sample size. Cut-off values of MAT, MAP, N fertilization rate, and

forest age were their medians (6.3 °C, 875 mm, 50 kg N hr-1 yr-1, and 30 years). The cut-

off value of pH was 7.

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Figure 2.4. Long-term N addition impacts on plant productivity and their temporal trends

over time. Red arrows represent negative N impacts/decreasing plant productivity

responses, whereas green arrows represent positive N impacts/increasing plant

productivity responses. The purple bowties represent the factors influencing overall N

impacts or its temporal trend, the relative contribution is shown next to the corresponding

factor.

-

+

N additionClimate: 49%Soil: 32%

RRfirst: 12%Fertilizer: 7%

↑ Soil acidification;↓ Fungi/Bacteria;

↓ Plant biodiversity;↑ Al3+ accumulation;

etc.

↓ Soil C/N;↑ Soil enzyme activity;

etc.

Climate: 43%Soil: 33%

RRfirst: 15%Fertilizer: 9%

Magnitude

Temporal pattern

Un

ders

tan

din

g

Eff

ort

Time

Neff

ects

Eff

ort

Grasslands ShrublandsForests

54

CHAPTER 3

EFFECTS OF MULTIPLE GLOBAL CHANGE FACTORS ON SOIL RESPIRATION

IN A DRYLAND ECOSYSTEM

Abstract

Due to their large stocks of carbon (C) and strong sensitivity to climate, drylands have a

large potential for C sequestration and climate change mitigation. However, uncertainties

remain regarding how multiple global change factors affect soil C cycling in drylands

individually and interactively. To address this knowledge gap, we conducted a dryland

soil incubation experiment including four global change factors (prior warming, C

availability, soil moisture content, and soil moisture variability [rewetting]) combined in

a factorial design. The experiment was divided into two periods: 1) a rewetting period

consisting of six 14-d drying-rewetting cycles; and 2) a recovery period lasting 28 days.

The individual effects of all four global change factors on soil respiration were

significant. In contrast to many in situ studies, rates of respiration under variable soil

moisture (drying-rewetting cycles) were lower than stable soil moisture regimes. In many

cases, past treatments (e.g. prior warming and drying-rewetting cycles) continued to

influence soil biogeochemical patterns. Importantly, interactive effects of global change

drivers on soil C cycling were found. For example, effects of soil moisture and prior

warming on soil respiration were insignificant in soils without C input; however, higher

soil respiration was found under high soil moisture and prior warming in soils with C

input. Our study strongly suggests not only the individual factor but also the interactive

effects of multiple global change factors are critical for improving our understanding and

forecasting of soil C cycling in drylands.

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Introduction

Drylands, which cover ~45% of the world’s land area and store 32% of all

terrestrial soil organic carbon (SOC) (Plaza et al., 2018; Prăvălie, 2016), have a

significant effect on global carbon (C) cycling. Drylands store a significant amount of C

and are highly sensitive to climate change (Huang et al., 2016). For example, drylands

often switch between functioning as C sinks and sources, which mainly depend on

precipitation (Biederman et al., 2017). Therefore, they are considered to have a large

potential for C sequestration and climate change mitigation (Lal, 2004). To better

understand how soil C cycling will change under future climate change, it is essential to

determine the individual and interactive effects of multiple global change factors on

dryland C cycling.

Water is thought to be the most important factor regulating soil biogeochemical

processes in drylands, and the magnitude and timing of precipitation events have strong

impacts on both above- and belowground processes (Cable et al., 2008; Sala et al., 2012).

In addition to natural seasonal variation in precipitation, drought events are common in

drylands (Schimel, 2018), and are likely to become more severe in the future. Drought

includes two types: 1) the amount of precipitation is constantly low for a long time, and

precipitation events are relatively evenly distributed; and 2) infrequent large precipitation

events are followed by a long period without any precipitation (Seneviratne et al., 2012).

It is important to determine how these two types of droughts affect soil respiration

because they can impact soil C cycling differently. Water affects microbial dynamics in

three fundamental ways: as resource, as solvent, and as transport medium (Schimel,

2018). Low soil moisture negatively impacts all three of these functions, decreasing

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microbial activity and soil respiration. Drying-rewetting cycles can further affect soil

respiration by disrupting soil aggregates and releasing osmolytes from the microbial

biomass and/or cell lysis (Hu et al., 2018; Lado-Monserrat et al., 2014; Schimel, 2018).

This is hypothesized to be the mechanism underlying the pulse of CO2 emitted from dry

soils upon re-wetting, the so-called ‘Birch effect.’ It is possible for the temporal

distribution of precipitation to change without a change in the total amount of water

inputs during a specific period. However, few studies have disentangled the effects of

mean soil moisture content from its variability (Fierer & Schimel, 2002). Conducting

related studies to answer this question is crucial for better understanding of soil

respiration responses to drought in drylands.

Although precipitation is often thought to be the dominant control on soil C

cycling in arid environments, there is strong evidence that C limitation also plays an

important role (Austin, 2011). Due to the scarcity of water and nutrients, litterfall inputs

and root exudates (the main C source for soil microbes) are relatively low in drylands

(Berhongaray et al., 2019; Cui et al., 2019; Dijkstra et al., 2021). As a result, soil

microbes in drylands are thought to be C starved (Schimel, 2018). Therefore, C addition

can stimulate soil microbial activity and soil respiration. However, the vegetation in

drylands usually forms a mosaic structure in which plant and litterfall patches are

separated by areas of exposed soil (Meloni et al., 2017). Once precipitation happens, a

large amount of soluble organic matter from litter can be released into soils (Austin,

2011). Therefore, it is essential to study the response of soil respiration to high substrate

availability and the underlying mechanisms.

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Although variation in soil abiotic conditions (temperature, water, and substrate

availability) may occur simultaneously, most studies only determine the effect of one

global change factor on soil functions (Rillig et al., 2019). Some of these studies provide

the clues to infer the interactive effects of two global change factors on soil C cycling.

For example, substrate availability and soil moisture should interactively affect soil

respiration: C addition may not have significant effects on soil respiration when soil

moisture is extremely low because water is too limited to dissolve, diffuse, and transport

substrate. When soil moisture increases, however, C may be rapidly metabolized by the

microbial biomass and a significant stimulation of soil respiration is observed (Schimel,

2018). In addition, warming can both increase metabolic rates (Davidson & Janssens,

2006) but also decrease soil moisture (Luo et al., 2010); therefore, there should be an

interactive effect of warming and soil moisture on soil respiration. Once the number of

global change factors is greater than two, it can be hard to infer the interactive effects

because the underlying processes can be complex. Global climate events, even if not

extreme in a statistical sense, can still lead to extreme impacts on ecosystem function,

either by crossing a critical threshold in a social, ecological, or physical system, or by

occurring simultaneously with other events (Seneviratne et al., 2012). Few studies have

been conducted to assess how soil responds to more than two environmental factors at a

time, although this has been called for (Rillig et al., 2019). The unknowns regarding

whether multiple global change factors can interactively affect soil respiration may

inhibit our prediction about soil C cycling in the future.

Finally, another major uncertainty concerns the temporal dimension of soil

responses to global change drivers. The so-called ‘legacy effects’ of drying-rewetting

58

cycles on soil respiration have been relatively well recognized (Li et al., 2018b). For

example, Fierer and Schimel (2002) found that the effect of drying-rewetting cycles on

soil respiration lasted for more than a month once drying-rewetting cycles stopped.

Drylands are exquisitely sensitive to perturbations and are generally slow to recover post

disturbance because of their limited resource availability (Poulter et al., 2014; Steven et

al., 2021). However, few studies have directly assessed patterns in soil biogeochemistry

following the cessation of climatic perturbations, like warming or drying-rewetting

cycles. Therefore, it is necessary to determine the potential legacy impacts of drying-

rewetting cycles in drylands. Otherwise, we may over- or underestimate climate effects

on soil respiration.

Our objective was to determine the individual and interactive effects of four

global change factors (prior warming, soil moisture content, soil moisture variability

[drying-rewetting cycles], and substrate availability) on soil respiration in drylands, and

to identify the underlying mechanisms. We hypothesized that 1) because these dryland

soils are extremely C-poor, C availability is the most influential global change driver that

affects soil respiration in drylands. 2) After controlling for differences in mean soil

moisture, variable soil moisture regimes (e.g., drying-rewetting cycles) produce greater

cumulative soil respiration than stable soil moisture regimes, due to the “Birch effect”. 3)

Since drylands are highly sensitive to climate change, the effect of past climate

manipulations (e.g., warming and drying-rewetting cycles) on soil biogeochemistry can

last for a relatively long time once they stop. 4) Interactive effects of global change

factors on soil respiration are common in drylands. In the present study, we conducted an

incubation experiment of 16 weeks combining six 2-week drying-rewetting cycles and 4

59

weeks of post-wetting recovery during which dryland soils were maintained at a stable

moisture content. To help us interpret patterns in the respiration data, we also quantified

the size of the microbial biomass, the activity of extracellular enzymes involved in

organic matter decay, and the protection of organic matter in different aggregate size

classes. Finally, given that fungi plays a critical role in dryland C cycling under climate

change (Collins et al., 2008), we assessed the composition of the soil fungal community

via ITS sequencing to determine if changes in fungal community structure could explain

treatment effects.

Methods

Site description

We sampled soils from a study site located in a semiarid ecosystem on the

Colorado Plateau near Castle Valley, Utah, USA (38◦ 38’ 4’’ N, 109◦ 24’ 38’’ W; 1310 m

above sea level). Mean annual temperature and precipitation are 13 °C and 269 mm,

respectively. The soils are classified as Rizno fine sandy loam (Loamy, mixed,

superactive, calcareous, mesic Lithic Ustic Torriorthents), which are shallow, well

drained soils formed in residuum, colluvium, and eolian material derived from sandstone,

siltstone and limestone. The vegetation is a mix of grasses (Achnatherum hymenoides)

and shrubs (e.g. Atriplex confertifolia and Gutierrezia sarothrae). A field experiment was

conducted at this site starting in August 2017, and it included a fully factorial

combination of two treatments: 1) warming (4°C above ambient conditions vs. control);

and 2) C input (Atriplex confertifolia leaf litter vs. biocrust). At the beginning of the

experiment, approximately 400 g Atriplex confertifolia leaf litter was applied to 3 cm soil

depth in the appropriate chamber. Sections of live biocrust from adjacent soils were

60

transferred to the appropriate chamber until covering 0.11 m2 soil surface area. There

were 20 plots in total, and each plot contained an automated measurement chamber (38

cm diameter) in which CO2 fluxes were measured hourly. At the start of this experiment,

soils (0.040 ± 0.013 g organic C kg-1 soil) from all 20 autochambers were excavated,

pooled together, and sieved, then were washed with concentrated sodium hypochlorite

solution to remove soil organic matter by using methods modified from (Mikutta et al.,

2005). Treated soils had a remaining organic C concentration of 0.029 ± 0.001 g organic

C kg-1 soil, representing a 28% decrease from initial values. This aimed to keep initial

SOC at low and comparable levels for both C input treatments. The autochambers were

then back-filled with treated soils to a consistent depth and allowed to incubate in situ for

15 months. Because no vascular plants grew within the chambers, the only organic C

input to the soils during this period was from the Atriplex litter layer or biocrusts. We

collected soils from all 20 autochambers in November 2018; however, for this study, we

only used soils collected from the 10 plots treated with Atriplex litter. We composited

soil samples from the five chambers under control climate and composited the samples

from the five warming chambers. The former and latter composite soil samples were used

to represent control and prior warming treatments in this incubation experiment,

respectively. The goal was to examine the initial and legacy effects of the field warming

treatment on soil physico-chemical and biological parameters.

Experimental design

Laboratory treatments were applied to 50 g of air-dried soils incubated at room

temperature in 250 mL Mason jars. The experiment was a fully factorial combination of

the following treatments: 1) prior warming: prior warmed soils versus unwarmed soils; 2)

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soil moisture: 0.04 g H2O g-1 dry soil (20% water-holding capacity) versus 0.08 g g-1

(40% water-holding capacity); 3) soil moisture variability: stable versus variable (see

below); and 4) C addition: control versus C addition (2.0 mg glucose-C g−1 dry soil for

each drying-rewetting cycle). Prior to the start of the experiment, soils were sieved to 4

mm, homogenized, and conditioned for one week at the appropriate soil water content

(0.04 g g-1 or 0.08 g g-1). For the ‘variable’ soil moisture treatment, soils were subject to

two-week drying/rewetting cycles. During each cycle, soils in the ‘variable’ treatment

were rewetted to 0.16 g g-1 (80% water-holding capacity) gravimetric soil moisture, then

allowed to dry sufficiently to achieve a time-weighted mean moisture equal to one of the

mean soil moistures described in 2). With 16 unique treatment combinations and 5

replicates per treatment, there were 80 microcosms in total.

This experiment had two periods: 1) a rewetting period including six two-week

drying-rewetting cycles; and 2) a recovery period that lasted four weeks (Fig. 1). To

maintain both stable and variable soil moisture treatments at the same average water

content (0.04 g g-1 or 0.08 g g-1) during the rewetting period, preliminary studies were

conducted to determine soil drying curves during the ‘drying period’. For example, for

variable soil moisture treatment with 0.04 g g-1 average soil moisture, soils were rewetted

to 0.16 g g-1 soil moisture at the beginning of each drying-rewetting cycle using sterile

deionized water. The soils were allowed to dry to 0.003 g g-1 soil moisture, which we

accomplished by removing the jar lid for 168 hours. Then the jar lid was closed loosely

until next drying-rewetting cycle (Fig. 3.1a). This protocol ensured that mean soil

moisture over the course of the drying-rewetting cycle was equivalent to 0.04 g g-1.

Similarly, for the variable soil moisture treatment with 0.08 g g-1 average soil moisture,

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soils were rewetted to 0.16 g g-1 soil moisture at the beginning of each drying-rewetting

cycle. The soils were dried to 0.062 g g-1 soil moisture by removing the jar lid for 92.9

hours, and the jar lid was closed loosely until next drying-rewetting cycle (Fig. 3.1b). For

stable soil moisture treatments, jar lid was kept closed loosely to avoid soil evaporation

during rewetting period. Appropriate, small amounts of sterile deionized water was added

to soils to keep soil moisture at 0.04 g g-1 or 0.08 g g-1 at the same time when we rewetted

soils for variable soil moisture treatments. During the rewetting period, 0.625 ml glucose

solution (which is equal to 2.0 mg glucose-C g−1 dry soil) was added into soils under the

C input treatment; this volume was accounted for in our calculations of soil moisture so

that we did not over-wet the soils when adding glucose. We did not add glucose solution

to C input treatment during recovery period.

At the beginning of the last drying-rewetting cycle, a 13C glucose solution (2.0 mg

glucose-C g−1 dry soil at 5 atom % 13C) was added instead of the 12C glucose solution to

the C input treatment to quantify priming effects. After six two-week drying-rewetting

cycles, variable soil moisture treatments were adjusted to the corresponding stable soil

moisture treatments for the four-week recovery period.

Measurements of soil respiration and soil parameters

During the course of the incubation experiment, headspace samples (10 ml) were

collected by using syringe once a week after sealing jars tightly for 24 hours (Fig. 3.1).

Headspace CO2 concentration was analyzed using gas chromatography (GC-2016

Greenhouse Gas Analyzer, Shimadzu, Kyoto, Japan). Headspace 13CO2 concentration and

isotopic abundance was measured during the last drying-rewetting cycle of rewetting

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period using a G2131-i Isotope and Gas Concentration Analyzer (Picarro Inc., Santa

Clara, CA, USA).

We destructively harvested subsets of the microcosms in each treatment at the end

of rewetting and recovery periods, respectively (Fig. 3.1). Soils from 3 of the 5 replicate

jars under each treatment were collected during the first harvest, at the end of the

rewetting period, and soils from the remaining 2 jars were collected during the second

harvest, at the end of the recovery period. Soil samples collected during harvests were

immediately used to measure soil microbial biomass C. Soil subsamples were also air-

dried at room temperature for measuring soil total organic C and nitrogen (N)

concentrations. Additional subsamples were stored at either 4 °C for measuring soil

aggregate stability or at -80 °C for measuring soil enzyme activities and soil fungal

community composition.

Soil microbial biomass C was determined via a modified fumigation-extraction

technique (Brookes et al., 1985). Briefly, two aliquots of fresh soil samples

(approximately 5.0 g each aliquot) were collected. One aliquot was fumigated by adding

2 ml of ethanol-free chloroform to the soil sample and incubating for 24 hours, and the

other aliquot was kept untreated as control. The fumigated and unfumigated soil samples

were then extracted with 25 ml of 0.5 M K2SO4. After shaking one hour, soil solutions

were filtered through Whatman filters, and the filtrates were stored at -20 °C. The

extracts from unfumigated and fumigated soils were analyzed for dissolved organic C

concentration (TOC-L, Shimadzu, Kyoto, Japan). 0.45 was used as the conversion factor

to calculate soil microbial biomass C. If organic C content for the fumigated samples was

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lower than that under the corresponding unfumigated samples, we set soil microbial

biomass C as zero.

Before measuring soil total organic C and N via an elemental analyzer (ECS 4010

Elemental Analyzer, Costech Analytical Technologies, Valencia, CA, USA), inorganic C

was first removed from the soil using 5 mL of 6% sulfurous acid (H2SO3) at 80 °C for 24

h. For soil samples from the first harvest, atom % 13C of the soil organic C was

determined when measuring soil total organic C and total N through the elemental

analyzer. Soil aggregates were fractionated into four different size classes (>2, 2-0.25,

0.25-0.053, and <0.053 mm) via wet sieving following (Denef & Six, 2005), and the

mass of soil in each size class was quantified.

Activities of the extracellular enzymes acid phosphatase, β-glucosidase,

cellobiohydrolase, leucine aminopeptidase, and β-N-acetylglucosaminidase were assayed

for each of subsamples using p-nitrophenol conjugated substrates following (German et

al., 2011). Approximately 1.0 g soil sample was homogenized by using 30 ml of sodium

acetate trihydrate buffer. 40 µL of soil slurry was transferred to 96-well microplate, and

180 µL of sodium acetate trihydrate buffer and 20 L of appropriate substrate solution

were added to each well. The microplate was incubated at room temperature for one hour.

90 L of supernatant from each well was transferred to a fresh microplate, and then 10

L of 1M NaOH was added to each well. Absorbance of each microplate was read at 410

nm on a microplate spectrophotometer (Spectramax M2 microplate spectrophotometer,

Molecular Devices, San Jose, CA, USA). The enzyme activities were expressed in units

of nmol h−1 g−1. Since significant and positive correlations were found among the

65

activities of all five soil enzymes (Fig. B1 in the appendices), we reported the mean

activity of all enzymes combined.

A DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) was used to extract DNA

for all soil subsamples from both first and second harvests. Since the concentration of

DNA extract from each soil sample was low, we combined all DNA extracts within a

treatment and a harvest, respectively. As a result, we had a total of 32 samples (16

samples each for the first and second harvests). We concentrated the DNA extracts using

a kit (ReliaPrep™ DNA Clean-Up and Concentration System). DNA extracts were then

pooled in equimolar concentrations and sequenced on the Illumina MiSeq platform.

Primers ITS1f-ITS2 were used to amplify fungal marker genes following Earth

Microbiome Project protocols, and we used the QIIME 2 (version 2020.11.0) to analyze

the sequencing data (Bolyen et al., 2019). The DADA2 algorithm was used to denoise

sequences and to produce putatively error-free amplicon sequence variants (ASVs) that

lacked chimeras. A Naïve Bayesian classifier was trained on the UNITE (version 8.2)

training dataset to assign fungal taxonomy (Abarenkov et al., 2020). In order to remove

non-fungal ASVs from the dataset, a taxonomy-based filtering procedure was applied.

Before determining α-diversity for each treatment by using package “vegan” in R, fungal

sequences were rarified to 12,000.

Data calculation and statistical analyses

The aggregate mean weighted diameter (MWD, mm) which is an index of

aggregate stability was calculated as follows:

MWD = ∑ Xi Wi 4i=1 (1)

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Where Xi is the mean diameter of the ith size fraction (mm), and Wi is the proportion of

the ith size fraction in the whole soil (%).

The priming effect (μg CO2-C g-1 soil) was determined following Eq. (2) (Chen et

al., 2019):

Priming effect = Ctreat × (1 – (at%treat − at%control) / (at%glucose − at%SOM)) –

Ccontrol (2)

where Ctreat and Ccontrol are the total CO2-C (μg CO2-C g−1 soil) from the C input and

non-C input treatments, respectively. at%treat and at%control are the 13C isotope

abundances (in atom% 13C) of CO2 from the C input soil and non-C input soil,

respectively. at%glucose and at%SOM are the 13C isotope abundances of added glucose

and soil organic matter, respectively. It should be noted that at%control and at%SOM had

the same value, which was equal to the 13C isotope abundances (in atom% 13C) of CO2

from the non-C input soil. Ctreat and Ccontrol were measured 1 and 13 days after adding

13C-glucose. at%treat, at%control, and at%SOM were measured 13 days after adding 13C-

glucose. Therefore, priming effect was measured two times during the last drying-

rewetting cycle. Since the priming effect did not change significantly between two times

that we measured it, we reported the mean priming effect for these two times.

Soil fungal community data were visualized with non-metric multidimensional

scaling (NMDS) plots. Repeated-measures five-way ANOVA analysis was conducted to

quantify the individual and interactive effects of the four global change factors and two

incubation periods on soil respiration, and five-way ANOVA analysis was conducted for

MWD, soil enzyme activity, soil microbial biomass C, priming effect, and soil total C

67

and N concentrations. For ANOVA analysis, the random effect was microcosm, and R

package “lmertest” was used to estimate P values in mixed effects models. Pearson’s

correlations were used to determine relationships among measured soil variables. All

statistical analyses were conducted in R.

Results

Soil respiration

The four experimental treatments (prior warming, C addition, soil moisture

content, soil moisture variation) had strong individual and interactive effects on soil

respiration (Table 3.1). C input enhanced soil respiration responses to soil moisture -

higher soil moisture increased soil respiration by 1.0% in soils without C input versus

79.4% in soils with C input (Fig. 3.2a). C input also increased respiration responses to

prior warming, and prior warmed soils had 14.4% higher respiration in soils without C

input, but 35.3% in soils with C input (Fig. 3.2b). Moreover, higher soil moisture

increased soil respiration by 38.3% in prior unwarmed soils versus 66.0% in prior

warmed soils (Fig. 3.2c). We did not find significant differences in soil respiration rates

between the rewetting and recovery periods (Table 3.1 and Fig. B2 in the appendices).

Soil physiochemical and biological properties

Soil aggregate mean weighted diameter responded to a three-way interaction

among C input, soil moisture content, and prior warming (Fig. 3.3a and b and Table B1 in

the appendices) and the interaction among C input, soil moisture content, and soil

moisture variability (Fig. 3.3c and d). In general, MWD was greatest in soils with high

soil moisture, added C input, and a stable, non-fluctuating soil moisture regime (Fig.

3.3d).

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Only soil moisture and prior warming showed significant individual effects on

soil enzyme activity (Table B2 in the appendices), increasing soil enzyme activity by

22.6% and 12.8%, respectively. Soil enzyme activity was significantly higher during the

recovery than the rewetting period. We also found higher soil total C concentration under

C input, rewetting, and high soil moisture (Table B3 in the appendices). Total C

concentration responded to the four-way interaction among C input, soil moisture

content, soil moisture variability, and prior warming, with a similar pattern for total N,

although C input had by far the largest absolute effect. By contrast, only C input showed

significant and positive individual effect on soil microbial biomass C (Table B5 in the

appendices).

Soil moisture content and prior warming significantly and individually affected

the soil priming effect (Table B6 in the appendices). More specifically, high soil moisture

and prior warming significantly increased the soil priming effect (the enhanced

degradation of SOC due to addition of exogenous C) by 128.9% and 66.8%, respectively.

Soil moisture content significantly mediated the effects of prior warming or drying-

rewetting cycles on the soil priming effect. More specifically, when compared to stable

soil moisture treatment, soil priming effect under drying-rewetting cycles was lower

under low soil moisture content, but became higher under high soil moisture content (Fig.

3.4). In addition, the difference in soil priming effect between control and prior warming

was minor under low soil moisture content; however, higher soil priming effect was

found under prior warming when soil moisture content was high.

Significant interactions of experiment period (rewetting vs. recovery) with C

input, prior warming, and soil moisture content treatments were found; however, the

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interactive effects of soil moisture variability and period on soil respiration and

physiochemical properties were not significant (Table 3.1 and Table B1-B5 in the

appendices). In other words, once rewetting stopped, its effect on soil functions persisted.

Soil fungal community

According to the results of the NMDS analysis, soil fungal communities were

significantly affected by C input and prior warming (Fig. 3.5a and Fig. B3a in the

appendices). In particular, the percentage of Ascomycota was increased by C input but

was decreased by prior warming (Fig. 3.5b and Fig. B3e in the appendices). The relative

abundance of fungal phyla was not significantly affected by soil moisture content, soil

moisture variability, or experimental period (Fig. B3 in the appendices).

Correlations between soil respiration and soil parameters

According to Pearson’s correlation analysis, mean soil respiration was positively

correlated with soil total C concentration (p < 0.001), soil priming effect (p < 0.01), soil

microbial biomass C (p < 0.001), and MWD (p < 0.01) (Fig. 3.6). No significant

relationship between mean soil respiration and mean enzyme activity was found.

Discussion

Our dryland soil incubation experiment provided mixed support for our four

hypotheses. First, among all four global factors (prior warming, sugar addition, soil

moisture content, and soil moisture variability), sugar addition was the most influential

factor affecting soil respiration in drylands, which is consistent with our hypothesis.

Second, after controlling for differences in mean soil moisture, variable soil moisture

regimes produced lower cumulative soil respiration than stable soil moisture regimes,

which is inconsistent with our expectation. Third, as we expected, legacy effects of

70

warming and drying-rewetting cycles on soil biogeochemistry were observed. Last, many

global change drivers showed interactive effects on soil C cycling, which is consistent

with our initial prediction. Below, we discuss the biogeochemical mechanisms underlying

these patterns.

Individual effects of global change factors on soil C dynamics

The magnitude of C input effects on soil respiration was much greater when

compared to soil moisture content, rewetting, and prior warming, which is consistent with

our first hypothesis: substrate availability is the most influential global change factor that

affects soil respiration in these severely C-depleted soils, which are typical of many

drylands (Schimel, 2018). We note that soil moisture was present in all treatments and

that the effects of C input would be unlikely to be observed in dry soils, but for the

conditions of this experiment, C input had the largest effect. In particular, substrate

availability contributed to soil C cycling by affecting processes that lead to C loss and

accumulation. On one hand, C input stimulated soil C loss by enhancing total respiration,

including increased mineralization of both added C and native soil organic matter (i.e., a

positive priming effect) (Kuzyakov, 2010). On the other hand, these same C input had a

positive influence on the size of soil aggregates and the soil microbial biomass, both of

which are associated with the formation of stable and mineral-protected soil C (Cotrufo et

al., 2013; C. Liang et al., 2017). We also found that C input increased the relative

abundance of Ascomycota. This is because Ascomycota has higher capacity to

decompose labile C (e.g. glucose that we added as C input in the present study) (Treseder

& Lennon, 2015). Overall, in our study, soils gained more C from fresh inputs than they

lost via respiration; therefore, higher soil C concentration was found under the C input

71

treatment relative to the control. However, in this experimental context, net accumulation

of C may reflect N limitation of soil microorganisms, as we added glucose only and N

limitation could develop over time. Nonetheless, our results emphasize that relieving

microbes from C limitation can simultaneously enhance the soil C losses as well as the

potential stabilization of fresh C input.

We expected soil respiration rates would increase with soil moisture content and

soil moisture variability. Previous studies have found that drying-rewetting cycles are

associated with pulses of soil respiration, presumably driven by enhanced microbial

consumption of osmolytes or organic matter released from disrupted aggregates (Barnard

et al., 2020; Fierer & Schimel, 2002; Schimel, 2018). Consistent with this expectation,

we found that repeated rewetting cycles were associated with slower rates of aggregate

formation on average, as the physical disruption associated with rewetting can prevent

aggregate formation (Denef et al., 2001). We would expect that a decrease in the physical

protection of organic matter would increase soil respiration. However, we found that the

moisture variability treatment decreased soil respiration when compared to the stable soil

moisture treatment, which is contrary to our second hypothesis. This might reflect sharp

transitions in soil moisture status over the course of the drying rewetting cycle.

Immediately following rewetting, soil moisture was extremely high (80% WHC),

potentially reducing oxygen for soil microbes. To maintain the same mean moisture

content as observed in the ‘stable’ moisture treatment, the soils were then dried to below

the target gravimetric soil moisture (0.04 g g-1 or 0.08 g g-1, respectively) at the end of the

rewetting cycle, presumably inducing moisture limitation. The relatively brief window of

time during which moisture conditions were optimal could have contributed to the slow

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soil C turnover rate. According to the equation calculating water potential by using soil

texture and gravimetric soil (Saxton et al., 1986), we roughly calculated time-weighted

mean soil water potentials under stable and variable soil moisture treatments. We found

that time-weighted mean soil water potentials were substantially lower in the variable

than the stable soil moisture treatment. For example, when the average soil moisture

content was 0.04 g g-1, soil water potential under variable and stable soil moisture

treatment was less than -15.0 MPa and -3.1 MPa, respectively. When the average soil

moisture content was 0.08 g g-1, soil water potential under variable and stable soil

moisture treatment was -0.4 MPa and -0.2 MPa, respectively. As a result, the microbes in

the variable soil moisture treatment experienced greater metabolic stress because they

were forced to deal with more time under water stressed conditions. This finding is

important because it indicates that effects of drying-rewetting cycles on soil respiration

can vary from positive to negative, dependent on the intensity and duration of drying-

rewetting cycles. In contrast to the surprising effects of drying/rewetting on soil

respiration, we did find support for our prediction that soil moisture availability would be

positively correlated with microbial activity. For example, our higher soil moisture

treatment was beneficial for microbial activity, resulting in higher soil enzyme activities

and CO2 flux. Moreover, an increase in the magnitude of the priming effect under higher

soil moisture suggests that water availability stimulates decomposition of both fresh and

background organic matter.

We expected to see biogeochemical legacies of past treatments (e.g. warming and

drying-rewetting cycles) in these dryland soils. Higher soil respiration was found in the

prior warmed soils than in control soils, which demonstrated a potential legacy effect of

73

warming on soil C cycling in drylands. This result might be explained by three potential

mechanisms. First, warming might lead to lower soil microbial C use efficiency by

altering soil fungal community (Frey et al., 2013b), which has been found in the present

study (Fig. B3a in the appendices). Second, warming usually stimulates soil enzyme

activity (Sardans et al., 2008; Zuccarini et al., 2020), and we found a positive effect of

prior warming on soil enzyme activity in the present study. Higher soil enzyme activity

under prior warming treatment may increase soil C decomposition rate and thus enhance

soil respiration. Third, warming can significantly decrease soil labile C content (Melillo

et al., 2017; Qi et al., 2016), which can influence how subsequent C input is utilized. For

example, soil priming effects are usually stronger in soils with low than high quantity and

quality of soil C (Kuzyakov, 2002), which was supported by the findings of our study.

There was not a significant interaction between the rewetting treatment and

experimental period on soil respiration, which means that rewetting effects can persist for

a relatively long time once rewetting stops. This could be explained by insignificant

interactive effects of rewetting and experimental period on soil aggregate, soil microbial

biomass C and soil fungal community. Overall, this finding indicates that soil

physicochemical and biological parameters can be significantly affected by rewetting,

and it takes a long time for them to recover, resulting in legacy effects of rewetting on

soil C cycling.

Interactive effects of global change factors on soil C dynamics

To improve the predictive ability of soil C cycling models, experiments

determining the relative impacts of and interactions among different global change

drivers have been called for repeatedly (Reich et al., 2020). However, a lack of general

74

understanding of interactive effects of global change factors on soil C cycling remains.

Our study showed two-way, three-way, or even four-way interactions among global

change drivers. We calculated the expected (additive effects of each individual global

change factor) and observed interactive effects of multiple global change factors on soil

parameters in the present study (Table 3.1 and Table B1-B6 in the appendices). Large

differences between observed and expected interactive effects (e.g. -51.6 % to 78.9% for

soil respiration) were found. It means that we cannot simply add up individual effect of

each global change factor to estimate their interactive effects. Therefore, more complex

experiments including multiple global change factors should be conducted in the future

for a better understanding of the mechanisms of the responses of soil functions to

multiple global change factors.

Significant interactive effects of global change factors on soil respiration and

other soil variables were found in the present study, which is consistent with our fourth

hypothesis. The effects of soil moisture content and prior warming were amplified in

soils with C input. This reflects the facts that soil microorganisms in these dryland soils

may be C starved, and C substrate availability probably is a limiting resource for

microbial growth. Therefore, it is not surprising that C input was involved in almost all

interactive effects on soil respiration. Since climate change (e.g. warming, drought, and N

deposition) can significantly affect above- and belowground biomass which is the main C

source of soils (Song et al., 2019), it is essential to consider and determine the roles of

climate change in mediating C input effects on soil C cycling in drylands.

Since soil aggregates can protect organic matter from decomposition, thereby

regulating C cycling rates (Wang et al., 2019), studying interactive effects of global

75

factors on soil aggregates is crucial for understanding soil C cycling in drylands. Soil

aggregate development was sensitive to interactions among a number of factors: substrate

availability, prior warming, soil moisture content, and drying-rewetting cycles. For

example, the difference in MWD between control and prior warmed soils was small when

soil moisture was low, but was amplified when soil moisture was high, with smaller

aggregates in warmed soils. Associated patterns in soil enzyme activity might help

explain these results. The higher soil enzyme activity characteristic of the prior warmed

soils might result in a higher decomposition rate of soil organic matter, resulting in lower

rates of soil aggregate formation (Amézketa, 1999). However, our results suggest that

this decomposition and enzyme activity were restricted by low soil moisture, such that

prior warming treatment had little impact on MWD when soil moisture was limiting.

Similarly, soil moisture content and variability did not show profound impacts on MWD

in soils without C input, but aggregate formation was increased in soils with high, stable

soil moisture and C addition. The impacts of soil moisture regime on aggregate dynamics

are fairly straightforward to explain: high soil water availability can more easily and

quickly dissolve, diffuse, and transport compounds which are essential for soil aggregate

formation (e.g. polyvalent cations, polysaccharides, and organic acids) (Amézketa, 1999;

Schimel, 2018; Wang et al., 2019). Moreover, repeated drying-rewetting cycles can break

down soil aggregates and decrease its formation rate (Hu et al., 2018; Lado-Monserrat et

al., 2014); thus, it makes sense that the stable soil moisture regime enhanced soil

aggregate formation when compared to variable soil moisture. However, the combined

effects of high soil moisture content and low soil moisture variation were only observed

under the C addition treatment. Soil microorganisms can secrete enzymes responsible for

76

mineralizing high molecular weight compounds, and also release extracellular

polysaccharides which bind soil particles, stabilizing soil aggregates (Amézketa, 1999). C

input may have contributed to soil aggregate formation by stimulating soil microbial

growth. Overall, these results suggest that the processes controlling the physical

protection of soil organic matter are complex and regulated both by climate and C input.

Priming of old or ‘background’ soil organic matter by fresh C input can

significantly influence the magnitude of C losses from soil (Kuzyakov, 2002), which is

supported by our results. We found a significant and positive correlation between the soil

priming effect and soil respiration rates, confirming that added glucose stimulated release

of C (0.009 µg CO2-C g-1 soil in the present study) from background soil organic matter,

which was approximately 0.6% of soil respiration under non-C input treatment (0.062 µg

CO2-C g-1 soil). Therefore, in order to deeply understand soil respiration in drylands, it is

essential to disentangle the mechanisms of interactive effects of multiple global factors

on any soil priming effects. In the present study, we found that prior warming and soil

moisture content interactively affected the magnitude of the soil priming effect. This

pattern may be partially explained by observed patterns of soil enzyme activities, which

play a large role in priming, since the decomposition of old SOC is enzymatically

catalyzed (Kuzyakov, 2010; Kuzyakov et al., 2000). Therefore, higher soil enzyme

activities are often associated with higher soil priming effects (R. Chen et al., 2014). In

our study, we found soil enzyme activities were higher overall under prior warmed soils

versus control soils, potentially reflecting greater capacity for enzyme stimulation by

added C. In addition, Sardans & Peñuelas (2005) found that low soil moisture

significantly inhibited soil enzyme activity, which is also supported by our results. Soil

77

water limitation constrained the soil priming effect across all treatments, likely reflecting

constrains on microbial activity. We also found that rewetting treatment decreased the

soil priming effect when soil moisture was low but increased priming when soil moisture

was high. Our study did not provide enough evidence to explain this result, although it is

consistent with patterns in aggregate formation, which suggest soil organic matter was

less protected from decomposition in soils with high and variable soil moisture content.

Conclusion

There is uncertainty about how multiple global change factors interactively affect soil C

cycling in drylands, as well as the underlying mechanisms that drive these interactions.

Our incomplete understanding of dryland C cycling controls and responses to change lead

to inaccurate estimation and prediction of the potential of drylands for C sequestration

and mitigate climate change. Here we showed that multiple global change factors

individually and strongly interactively affected soil respiration and a host of associated

soil variables. Among four global change factors that were included in the present

experiment (prior warming, substrate availability, soil moisture content, and soil moisture

variability), substrate availability was the most influential factor affecting soil C cycling,

and it was involved in almost all interactive effects. In addition, legacy effects of prior

warming and drying-rewetting cycles on soil biogeochemistry were found. Taken

together, these data point to the need for future studies to include multiple global change

factors to determine how they individually and interactively affect soil C cycling in

drylands. Only in this way can we better estimate and predict how global drylands will

respond to the climate changes they face in the future.

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Tables

Table 3.1. Change (%) in soil respiration due to the effects of the five treatment factors

Treatment Observed main effect

Carbon input vs. control + 177.7 ***

Repeated rewetting vs. stable moisture - 14.5 *

High vs. low soil moisture + 53.2 ***

Prior warmed soil vs. control + 29.4 ***

Recovery vs. Rewetting period - 10.1

Time points ***

Joint effects

Interactions Expected Observed Difference

C × R + 163.2 + 133.7 - 29.5

C × M + 230.9 + 258.3 *** + 27.4

C × W + 207.1 + 242.3 *** + 35.2

C × P + 167.6 + 142.0 * - 25.6

R × M + 38.7 + 29.9 - 8.8

R × W + 14.9 + 10.5 - 4.4

R × P - 24.6 - 20.2 + 4.4

M × W + 82.6 + 94.4 * + 11.8

M × P + 43.1 + 28.9 * - 14.2

W × P + 19.3 + 15.7 - 3.6

C × R × M + 216.4 + 221.7 + 5.3

C × R × W + 192.6 + 186.4 - 6.2

C × R × P + 153.1 + 101.5 - 51.6

C × M × W + 260.3 + 339.2 + 78.9

C × M × P + 220.8 + 184.9 * - 35.9

C × W × P + 197.0 + 202.7 + 5.7

R × M × W + 68.1 + 63.0 - 5.1

R × M × P + 28.6 + 17.1 - 11.5

R × W × P + 4.8 + 2.7 - 2.1

M × W × P + 72.5 + 58.0 - 14.5

C × R × M × W + 245.8 + 284.2 + 38.4

C × R × M × P + 206.3 + 165.8 - 40.5

C × R × W × P + 182.5 + 147.2 - 35.3

C × M × W × P + 250.2 + 246.6 - 3.6

R × M × W × P + 58.0 + 49.3 - 8.7

C × R × M × W × P + 235.7 + 226.7 - 9.0

Note: Expected effects were the additive effects sizes of each treatment when imposed

individually. Observed effects were equal to (soil respiration between treatment

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combination – soil respiration under the corresponding control) / soil respiration under

the corresponding control. Difference was equal to observed effect – expected effect. For

Carbon × Rewetting × Moisture × Warming × Period, the expected effect was equal to

177.7 + (-14.5) + 53.2 + 29.4 + (-10.1); the observed effect was equal to (mean soil

respiration under carbon input, repeated rewetting cycles, high soil moisture, prior

warming during recovery period - mean soil respiration under no carbon input, stable soil

moisture, low soil moisture, no prior warming during rewetting period) / mean soil

respiration under no carbon input, stable soil moisture, low soil moisture, no prior

warming during rewetting period. Time points: different time points that we measured

soil respiration. C: carbon input; R: rewetting; M: soil moisture; W: prior warming; P:

period. * P<0.05; ** P<0.01; *** P<0.001.

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Figures

Figure 3.1. A diagram representing the experimental setup including the treatments and

sampling timelines.

So

il m

ois

ture

(%

)

0

4

8

12

16

1 2 3 4 5 6 10 11 12 13 147 8 9 15 16 17

0.3

18

168 hours

Rewetting period Recovery period

Variable soil moisture

Stable soil moisture

Both

4% soil moisture

So

il m

ois

ture

(%

)

Weeks

0

4

8

12

16

1 2 3 4 5 6 10 11 12 13 147 8 9 15 16 17

0.3

18

92.9 hours

Rewetting period Recovery period

Variable soil moisture

Stable soil moisture

Both

8% soil moisture

6.2

Collecting headspace samples

Collecting soil samples

Collecting headspace samples

Collecting soil samples

(a)

(b)

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Figure 3.2. Interactive effects of global change factors on mean soil respiration rates

during both rewetting and recovery periods. Control and carbon input represent soils

without and with glucose addition during the experiment, respectively. Control and

warmed represent soils that were under ambient air temperature and had been prior

warmed in situ (for 15 months), respectively. Low and high moisture treatments represent

soils that received 0.04 g g-1 and 0.08 g g-1 soil moisture, respectively. Error bars

represent ±1 standard error of the mean. See Table 3.1 for statistical results.

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Figure 3.3. Interactive effects of global change factors on mean weighted diameter

(MWD, an index of aggregate stability) during both rewetting and recovery periods.

Control and carbon input represent soils without and with glucose addition during the

experiment, respectively. Control and warmed represent soils that were under ambient air

temperature and had been prior warmed in situ (for 15 months), respectively. Low and

high moisture treatments represent soils that received 0.04 g g-1 and 0.08 g g-1 soil

moisture, respectively. Stable and variable represent soils with stable soil moisture during

the course of the experiment and soils that experienced rewetting cycle each two weeks,

respectively. Error bars represent ±1 standard error of the mean. See Table B1 in the

appendices for statistical results.

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Figure 3.4. Interactive effects of global change factors on the soil priming effect during

both rewetting and recovery periods. Control and warmed represent soils that were under

ambient air temperature and had been prior warmed in situ (for 15 months), respectively.

Low and high moisture treatments represent soils that received 0.04 g g-1 and 0.08 g g-1

soil moisture, respectively. Stable and variable represent soils with stable soil moisture

during the course of the experiment and soils that experienced rewetting cycle each two

weeks, respectively. Error bars represent ±1 standard error of the mean. See Table B6 in

the appendices for statistical results.

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Figure 3.5. NMDS of soil fungal communities (a) and the relative abundance of fungal

phyla (b) under carbon input treatment. Control and carbon input represent soils without

and with glucose addition during the experiment, respectively.

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Figure 3.6. Correlations among mean soil respiration rates (CO2), mean weighted

diameter (MWD), mean enzyme activity (Enzyme), soil microbial biomass carbon

(MBC), soil total carbon concentration (TC), and mean priming effect (PE). The size and

the darkness of the color of the circle represent the correlation coefficients based on

Pearson’s correlation analysis. * P<0.05; ** P<0.01; *** P<0.001.

*

*

**

**

**

**

***

***

***

***

***

***

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

CO

2

MW

D

TC

Enzym

e

MB

C

PE

CO2

MWD

TC

Enzyme

MBC

PE

95

CHAPTER 4

ROLES OF PLANT-MICROBE-SOIL INTERACTIONS IN SOIL CARBON

CYCLING

Abstract

It has been accepted that soil organic carbon (SOC) is strongly regulated by at least four

factors along the plant-microbe-soil continuum: C input quality, root exudates, soil

microbial community structure, and soil mineralogy. However, conflicting effects on

SOC cycling have been reported. This is partly because most previous studies only

examined a subset of these drivers, and the results may have been influenced by variation

the factors that were not considered. To disentangle the pathways by which these four

factors interactively influence SOC cycling, we conducted a fully factorial manipulation

using a synthetic root and soil system. We found that all four factors markedly affected

SOC cycling: 1) soil respiration increased with increasing C input quality, and

interactions were found between C input quality, soil microbial community structure, and

soil mineralogy; 2) root exudates generally stimulated soil respiration and decreased

mineral-associated organic C (MAOC), and their effect was mediated by the other three

drivers; 3) the bacteria + fungi treatment generally had higher MAOC than the bacteria-

only treatment, and this result was also modified by the other three drivers; and 4) higher

soil respiration and MAOC was found in high-activity clay, which had higher surface

area, charge density, and cation + anion exchange capacity, than in low- and medium-

activity clay. Interactions between soil clay activity and other drivers were also common,

especially a strong and consistent interaction between root exudates and soil clay activity.

Specifically, root exudates stimulated soil respiration in all soil clay activities, but the

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magnitude of the positive effect tended to decrease with increasing soil clay activity.

Furthermore, MAOC was decreased by root exudates under low and medium clay

activity; however, root exudates resulted in higher MAOC under high clay activity.

Finally, the interactions between these drivers varied with time. Our study provides

evidence for the critical role of root exudates-soil mineralogy interaction in SOC cycling,

which should be studied more in the future.

Introduction

Soils store at least twice as much carbon (C) as is in the atmosphere (Batjes, 1996;

Scharlemann et al., 2014), such that modest changes in soil C cycling can significantly

impact atmospheric CO2 concentration (Minasny et al., 2017; Smith et al., 2020). Yet our

mechanistic understanding of soil organic C (SOC) formation and loss is still limited

(Cotrufo et al., 2013; Lehmann et al., 2020; Sokol et al., 2018), and soil feedbacks on

climate change represent one of the largest sources of uncertainty in Earth system models

(Luo et al., 2016; Shi et al., 2018). Four fundamental factors shape soil C cycling along

the plant-microbe-soil continuum: the quantity and chemical composition of plant inputs,

their point of entry (above- vs. belowground), the functional capacity of the soil

microbial community, and soil mineralogy (Merino et al., 2015; Schnecker et al., 2019;

Sokol et al., 2019; Sokol & Bradford, 2019). Yet the relative importance of each of these

controls, and the mechanisms by which they impact the formation and loss of SOC, are

all topics of debate.

For decades, one of the guiding paradigms of soil biogeochemistry was the idea of

selective preservation of recalcitrant (i.e. chemically complex) plant inputs, which were

thought to resist microbial decomposition and therefore contribute to the formation of

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‘persistent’ SOC (Melillo et al., 2008). However, this hypothesis has been largely

rejected by newer hypotheses which hold that microbial access to organic matter, rather

than its chemical composition, controls the fate of soil C inputs. Studies have consistently

shown that soil microbial biomass residues are themselves the primary constituent of

mineral-associated organic C (MAOC), which is thought to be the most persistent form of

SOC (Cotrufo et al., 2013; Lavallee et al., 2020; C. Liang et al., 2017). This new

understanding has upended long-held theories about the relationship between plant tissue

chemistry and soil biogeochemistry; in fact, it is labile C inputs which are now thought to

contribute more to SOC stabilization (Cotrufo et al., 2013, 2015; Sokol et al., 2018).

Because C use efficiency (CUE), the fraction of substrate that is assimilated into

microbial biomass vs. respired (Sinsabaugh et al., 2013), is higher when microbes are

consuming labile substrates (Frey et al., 2013a; Qiao et al., 2019), these inputs should

favor retention of plant-derived C belowground. Since this new framework was proposed

(Cotrufo et al., 2013), some studies have confirmed the importance of microbial-mineral

interactions for SOC cycling (Frey et al., 2016; Neurath et al., 2021), but significant gaps

in our understanding remain.

The new paradigm holds that labile litterfall inputs lead to more persistent SOC

stabilization, relative to recalcitrant litterfall inputs, because they are utilized more

efficiently by microbes. Although this hypothesis has been increasingly accepted,

conflicting results have been reported by some studies. Results from a 20-year field

manipulation, for example, showed that labile litterfall input (e.g. needle and leaf litter)

did not significantly increase MAOC when compared to recalcitrant litterfall input

(e.g. wood debris) (Pierson et al., 2021). By contrast, particulate organic matter (POM)

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was significantly higher under labile than recalcitrant litterfall input. This may be because

the effect of C input chemistry on SOC stabilization is markedly mediated by soil

microbial community composition. For instance, labile C input typically results in higher

CUE but may also shift soil microbial community to a copiotrophic-dominated one over

time, which has an inherently lower CUE (Geyer et al., 2016; Roller & Schmidt, 2015).

Therefore, the role of C input chemistry in SOC stabilization is likely to hinge upon

plant-microbe interactions which develop through time. In the real world, new C inputs

are constantly arriving, so the mixture of labile/recalcitrant C is constantly changing. To

isolate these community shifts, it is essential to conduct controlled experiments.

In addition to the chemical composition of plant inputs, it is increasingly

acknowledged that the ‘point of entry’ (above- vs. belowground) also plays a significant

role in SOC cycling (Sokol et al., 2018). Rhizosphere processes significantly regulate

SOC stabilization and loss (Finzi et al., 2015); therefore, a growing body of studies have

been done to determine the effects of root exudates on SOC cycling, but opposite results

have been reported. On one hand, some studies suggest that root exudates can stimulate

SOC stabilization and reduce SOC loss because root exudates mainly consist of labile C,

which have higher CUE, thereby promoting MAOC formation (Sokol et al., 2019; Sokol

& Bradford, 2019). On the other hand, other studies find that root exudates accelerate

SOC decomposition and destabilize SOC thorough the rhizosphere priming effect (Cheng

et al., 2014; Kuzyakov et al., 2000). A framework named “Rhizo-Engine” was recently

proposed, which acknowledges that the effect of root exudates on SOC cycling is a

‘double-edged sword’, and the net effect of exudates on the SOC stock will be mediated

by other factors (e.g. soil mineralogy and nutrient status) (Dijkstra et al., 2021). For

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example, root exudates can lead to SOC accumulation if there is a large potential to

stabilize SOC into MAOC, such as in clayey soils. On the other hand, root exudates can

lead to SOC loss if there is little potential to stabilize SOC into MOAM, such as in sandy

soils or when soils are already saturated with MAOC. Although these hypotheses seem

plausible, they have not been tested explicitly (Pierson et al., 2021).

Finally, variation in microbial community structure may be important in driving

SOC cycling, such that soil C losses and gains depend not only on climate, soil

mineralogy, and plant litter inputs, but also community composition (Glassman et al.,

2018). For example, the “home-field advantage” phenomenon describes a scenario where

soil microbial communities adapted to recalcitrant C input may degrade and assimilate

recalcitrant C more easily and efficiently than labile C (Keiser et al., 2014). Although

such variation in the catabolic potential of the soil microbial community has long been

acknowledged as a major control on SOC cycling, newer frameworks emphasize not only

the role of catabolism but also microbial anabolism, as 50-80% of persistent SOC is

microbially derived (Liang & Balser, 2011; Simpson et al., 2008). Just as not all soil

microbial communities perceive litter ‘recalcitrance’ the same way, different microbial

communities might convert identical plant biomolecules into a different suite of

compounds, which are stabilized to different degrees (Grandy et al., 2009). Nonetheless,

the role of soil microbial community composition in SOC stabilization has not been

systematically evaluated along gradients of plant input chemistry and soil mineralogy.

A primary tenet of our modern understanding of SOC stabilization is the

importance of soil mineralogy: clay minerals are what generate chemical bonds with

organic C to form MAOC (Cotrufo et al., 2019; Kaiser et al., 2016; Kleber et al., 2007;

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Kleber, 2010; Kleber et al., 2015; Lavallee et al., 2020). High clay content and/or high

clay activity can fundamentally stimulate SOC stabilization because of their high surface

area, charge density, and cation + anion exchange capacity (Cotrufo et al., 2013; Lavallee

et al., 2020). However, some recent experimental studies showed that SOC stabilization

does not significantly respond to soil mineralogy (either clay content (Rasmussen et al.,

2018) or clay activity (Kallenbach et al., 2016)). These surprising results could emerge

from the interactions between soil mineralogy and plant inputs and/or soil microbes. For

instance, recalcitrant plant inputs or soil microbial communities with low CUE cannot

result in much persistent SOC accumulation regardless of soil mineralogy (Kallenbach et

al., 2016). However, few studies have been conducted to test this hypothesis, and this

inhibits our understanding of how SOC cycling responds to interactions between clay

mineralogy and plants or microbes.

As our understanding of SOC dynamics rapidly evolves, there is a need to

reconcile contradictory findings about effects of substrate quality and point-of-entry,

microbial communities, and organo-mineral interactions on SOC stabilization. Some of

these uncertainties may arise because previous studies have considered some, but not all,

of these four critical drivers, which also strongly co-vary in real soils. Manipulating all

potential factors affecting SOC cycling independently can help us unravel their

interactions and isolate direction of causality. Therefore, in the present study, we used

synthetic root/soil systems to quantify effects of litter input quality, root exudates, soil

microbial community composition, and soil clay mineralogy on SOC stabilization and

loss. In addition, real soils were used to determine the efficacy of our synthetic soil

system. Our overall goal was to disentangle the pathways by which plants, microbes, and

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soils interactively affect SOC cycling and assess the relative importance of each driver.

We hypothesized that: 1) compared with recalcitrant C input, labile C inputs will elevate

microbial CUE, leading to more MAOC stabilization and less soil respiration losses; 2)

root exudates will lead to more soil respiration but less MAOC via priming effects; 3)

due to the greater CUE and slower turnover rates of fungi, communities consisting of

both bacteria and fungi will result in less soil respiration and more MAOC, when

compared to communities of only bacteria; and 4) soils with higher clay activity will

stimulate more C stabilization via organo-mineral bonds.

Methods

Experimental design

We developed synthetic root/soil systems to conduct a 13-month incubation

experiment including a fully factorial manipulation of C input quality, root exudation

quantity, soil microbial community composition (only bacteria vs. both bacteria and

fungi), and soil clay mineralogy (clay content was the same among three clay activities,

but the surface area, charge density, and cation + anion exchange capacity increased with

increasing clay activity). The experiment consisted of two phases, which lasted 3 and 10

months, respectively, with CO2 fluxes being measured at two-week intervals throughout.

Prior to the beginning of Phase 1, we constructed synthetic root/soil systems (see below).

Treatments applied to these systems included root exudates, soil microbial community

composition, and soil clay mineralogy, and each treatment combination had 24 replicates

(N = 288 in total). In order to test the efficacy of synthetic soil system, real soils were

also incubated under the same treatments with the synthetic soil system (N = 108 in

total). At the end of Phase 1, six replicates of each treatment combination were harvested

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to assess treatment effects on CUE, soil enzyme activity, and soil microbial biomass C

(MBC). Next, at the beginning of Phase 2, within each root x microbe x mineralogy

treatment combination, six microcosms were randomly assigned to each C input quality

treatment (glucose, cellobiose, and xylan). A subset of these microcosms (three replicates

for each harvest in the synthetic soil system and two replicates for each harvest in the real

soil system) was destructively harvested at 4 and 10 months into Phase 2 to quantify soil

enzyme activity, MBC, and heavy fraction C content.

Creating synthetic soils

We used organic-matter-free sand, pure minerals (kaolinite, montmorillonite, and

goethite), and ground corn leaves (sterilized via autoclaving) to construct sterile artificial

soils with equivalent clay contents (0.1 kg clay kg-1 soil) and carbon concentrations

(0.017 kg C kg-1 soil), but different levels of clay activity. All soils contained 0.86 kg

quartz Granusil sand kg-1 soil and 0.04 kg leaves kg-1 soil, ground to pass through a 1 mm

mesh (Fig. C1a in the appendices). Soils in the ‘low’ clay activity treatment contained 0.1

kg kaolinite kg-1 soil (surface area of kaolinite: 17.5 m2 g-1, cation exchange capacity of

kaolinite: 9 meq/100g); soils in the ‘medium’ clay activity treatment included 0.1 kg

montmorillonite kg-1 soil (surface area of montmorillonite: 60 m2 g-1, cation exchange

capacity of montmorillonite: 100 meq/100g); and those in the ‘high’ clay activity

treatment contained 0.09 kg montmorillonite kg-1 soil and 0.01 kg goethite kg-1 soil

(surface area of goethite: 200 m2 g-1, cation exchange capacity of goethite: 52 meq/100g)

(Macht et al., 2011). All three clays were bought from Fisher Scientific International, Inc.

Synthetic soils in each of the three mineralogy treatments were mixed well in sterilized

bins, and then 20.0 g of synthetic soils were added to 60-mL borosilicate glass

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microcosms before the incubation experiment. Finally, we made a nutrient solution by

blending NH4Cl and fertilizer (MasterBlend 4-18-38, Masterblend International, Inc.)

with sterile DI water. More information about the fertilizer is described in Table C1 in the

appendices. An appropriate amount of this solution was added to each microcosm to

bring initial soil C/nitrogen (N) and C/phosphorus to their global averages of (25:1) and

(195:1), respectively (Cleveland & Liptzin, 2007), after accounting for nutrient content

of the ground corn tissues. To evaluate the capacity of synthetic soils to capture

biogeochemical dynamics of real soils, we constructed 108 microcosms, each with 20 g

soil collected from a sagebush shrubland in Rich County, UT, USA. This was a coarse-

silty Xeric Haplocalcid with a soil C concentration of 17.3 g kg-1 soil and clay content of

0.121 kg kg-1 soil. The soil was sterilized via double-autoclaving prior to microcosm

construction. These microcosms were also subject to a fully factorial manipulation of root

exudates and soil microbial community composition in Phase 1. Each treatment

combination had 18 replicates.

Creating artificial roots

In order to realistically mimic root exudation, we modified the methods described

by (Van Bruggen et al., 2000) to create a gravity-fed artificial root system. Specifically,

each artificial root system consisted of one 5-mL syringe (with the plunger removed) and

ten, 10-cm long polysulfone hollow fiber dialysis membranes (HFMs) (Fresenius

Medical Care North America, Ogden, UT). These HFMs were characterized by a lumen

diameter of <200 m and wall thickness of <50 m, allowing the exchange of

biomolecules <60 kDa across either side of the membrane, and are therefore ideal for the

construction of ‘root mimetics’ (Bonebrake et al., 2018). To create artificial roots, we

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used parafilm to tightly wrap one end of the 10-HFM bundle to the end of a metal wire,

and then guided the HFM bundle through the hole at the bottom of the syringe (Fig. C1b

in the appendices). Approximately 2 cm of HFM was left inside the syringe, and then

UV-cured Dymax glue (Dymax, Torrington, CT) was added to the inside of the syringe

opening so that liquid could only exit the syringe tip by moving through the HFMs. At

the beginning of the experiment, we installed one artificial root system, consisting of the

syringe and its ten HFMs, in each microcosm. The artificial root was positioned such that

the tip of the syringe, from which the HFMs emerged, was flush with the soil surface.

Each root system received 5 mL of either root exudate solution (see below) or sterile

water (depending upon treatment) every two weeks. For the real-soil microcosms, 72

microcosms received artificial root systems, and the remaining 36 were planted with five

sterile Lolium perenne seedlings. This helped us evaluate whether real and simulated root

exudations affect soils in similar ways.

Soil microbial community composition treatment

To create the inoculum (both fungi and bacteria vs. only bacteria), 5 g of compost

(EcoScraps, Marysville, OH) was blended in 1 L sterile water. Half of the blend was set

aside as inoculum for the ‘fungi + bacteria’ treatment. To create bacterial inoculum, the

antifungal captan was added to the remaining inoculum (0.05 mg mL-1), and then the

mixture was filtered through a 2 m filter, using a fresh filter for every 5 mL of fluid.

Appropriate amounts of these two inoculums were filtered through a 0.45 m

nitrocellulose filter, stained with acid fuchsin, and mounted on a microscope slide to

verify the presence or absence of fungi. A 0.6 ml aliquot of the inoculum was added to

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the appropriate microcosms (both synthetic and ‘real’ soils) before the incubation

experiment.

Phase 1

During Phase 1, we quantified changes in soil C cycling as microbial

communities became established. During this phase of the experiment, microcosms in the

‘artificial root’ treatment received a low level of exudate C input. We used glucose,

fructose, sucrose, and tryptic soy broth to create root exudates at a concentration of 200

mg C L-1 by following the recipe from van Bruggen et al. (2000). At the beginning of

two-week ‘cycles’, the syringes in each microcosm under the artificial soil treatment

were loaded with 5 mL of either sterile water or the root exudate solution, which

provided 0.025 mg C g-1 soil per week. This quantity of C is similar to root exudation

rates measured in a variety of ecosystems (Finzi et al., 2015; Phillips et al., 2011; Yin et

al., 2014). During Phase 1, soil respiration was measured at the beginning of the two-

week ‘cycles’ which began when artificial root systems were reloaded with either exudate

solution or sterile water. One day before the beginning of each cycle, an appropriate

amount of DI water was added to each microcosm to adjust soil moisture to 45% water

holding capacity (WHC), following which microcosms were sealed tightly. After 24

hours, headspace samples (10 ml) were collected using a gastight syringe. At the end of

Phase 1, six microcosms per treatment combination for synthetic soils and two for real

soils were used to measure CUE. A syringe was used to inject 0.5 mL of a solution to add

0.116 mg glucose-C g-1 soil with an enrichment of 26 atom % 13C to ~1 cm depth of the

soil evenly around the artificial roots 3 times. The lids of microcosms were closed for 24

hours, and then headspace samples were collected to measure 12C and 13C-CO2

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concentrations (see below). After collecting headspace samples, subsamples of the soil

were immediately extracted with 0.5 M K2SO4 for MBC measurement (Brookes et al.,

1985). The remaining soil samples were stored at -80 °C for determining soil enzyme

activity.

Phase 2

During Phase 2, in addition to continuing the three treatments in Phase 1, we

implemented an aboveground C input treatment. Within each root x microbe x

mineralogy treatment combination, six microcosms were randomly assigned to a C input

treatment representing a gradient of chemical complexity: glucose (monosaccharide),

cellobiose (disaccharide), and xylan (polysaccharide). Overall, each microcosm under C

input treatment received a solution supplying 0.385 mg C g-1 soil every two weeks. In

addition, an appropriate amount of NH4Cl was also added along with the C to keep C/N

of the solution as 25:1. The solutions containing the appropriate C-compound were added

to each microcosm by dripping them onto the soil surface as evenly as possible. The same

C-input treatments were also applied to the real-soil microcosms. We also increased the

concentration of the root exudation solution to increase the supply rate from 0.05 mg C g-

1 soil during Phase 1 to 0.5 mg C g-1 soil each two-week cycle during Phase 2. Soil

respiration was determined every two weeks at the beginning of each two-week cycle

described for Phase 1. Destructive harvests (N = 3 per unique treatment combination for

synthetic soils, and N = 2 for real soils) were conducted at 4 and 10 months into Phase 2.

In addition to soil variables that we measured during Phase 1 (except CUE, which was

not quantified), light and heavy fractions of SOC were obtained using density

fractionation (see below).

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Measurements of soil variables

Soil respiration

CO2 concentration of headspace samples that we collected every two weeks was

determined by gas chromatography using a flame ionization detector and hydrogen as the

carrier gas (GC-2016 Greenhouse Gas Analyzer, Shimadzu, Kyoto, Japan). We analyzed

headspace 12C- and 13C-CO2 concentrations during the last cycle of Phase 1 using a

G2131-i Isotope and Gas Concentration Analyzer (Picaro Inc., Santa Clara, CA, USA).

Net change in soil C

We calculated the net change in soil C as Cinput - Coutput, where Cinput is the amount

of C input through substrate addition and/or root exudates, and Coutput is the amount of C

released during soil respiration.

MBC

A modified fumigation-extraction technique was used to measure MBC (Brookes

et al., 1985), where MBC = (the difference in extractable soil C between chloroform

fumigated and nonfumigated samples)/Kec. Briefly, for nonfumigated samples,

approximately 5.0 g of fresh soil sample from each microcosm was weighed and added

into a 50-mL Falcon tube with 25 ml of 0.5 M K2SO4. After shaking one hour, soil

solutions were filtered through Whatman filters, and the filtrates were stored in

scintillation vials before analyzing filtrates for DOC. In addition, the residual

unfumigated soil samples (soil remaining following extraction) were oven dried, and the

δ13C and C concentration were measured via an elemental analyzer (ECS 4010 Elemental

Analyzer, Costech Analytical Technologies, Valencia, CA, USA). The 13C retained in

these residual soil samples (mg 13C excess kg-1 soil) was used to estimate glucose-C

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assimilation by microbes during calculation of microbial C-use efficiency (CUE) (see

below). For fumigated samples, we weighed approximately 5.0 g of fresh soil sample

from each microcosm into a Falcon tube with 2 ml of ethanol-free chloroform. All tubes

were capped and stored in the dark for 24 hours, and then we uncapped all tubes and

allowed them to vent in a fume hood. After approximately 6 hours, 25 ml of 0.5 M K2SO4

was added into each tube to extract the soil as described for unfumigated samples. All

extracts were stored at -20 C before measuring C and N concentrations using a TOC-L

(Shimadzu, Kyoto, Japan). By using isotope data from both fumigated and unfumigated

soil residues at the end of Phase 1, we calculated Kec as (the amount of 13C from

unfumigated soil residue - the amount of 13C from fumigated soil residue) / the amount of

13C from unfumigated soil residue. Briefly, Kec only significantly varied with clay

activity (e.g. high: 0.82; medium: 0.72; low: 0.61). Therefore, MBC was calculated using

different values of Kec for samples with the three different clay activities.

Soil enzyme activities

Following the method of (German et al., 2011), we measured activities of the

extracellular enzymes including β-xylosidase, acid phosphatase, β-glucosidase,

cellobiohydrolase, leucine aminopeptidase, and β-N-acetylglucosaminidase. p-

nitrophenol conjugated substrates were used to assay all these six enzymes. Briefly, we

used 30 ml of sodium acetate trihydrate buffer to homogenize approximately 1.0 g soil

sample. After transferring 40 µL of soil slurry to each well in a 96-well microplate, we

added 180 µL of sodium acetate trihydrate buffer and 20 L of the appropriate substrate

solution to each well. After one hour of incubation at room temperature, we transferred

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90 L of supernatant from each well to a fresh microplate. Finally, 10 L of 1M NaOH

was added to each well. Absorbance was read at 410 nm on a microplate

spectrophotometer (Spectramax M2 microplate spectrophotometer, Molecular Devices,

San Jose, CA, USA). The enzyme activities were expressed in units of nmol g−1 soil h−1.

Since the activities of all enzymes were significantly related to each other, we only report

the mean activity of all enzymes.

Soil microbial C use efficiency

We calculated soil microbial C use efficiency as dBC / (dBC + ∑ CO2 − C), where

dBC is the amount of glucose-C incorporated into microbial biomass (determined from

the 13C content of unfumigated soil residues), and ∑ CO2 − C is the cumulative

respiration of glucose C (based on 13CO2 measurements) over 24-h. Following the

methods of Herron et al. (2009) and Butcher et al. (2020), we estimated dBC by extracting

and then oven-drying (24 h at 65 °C) unfumigated soil residues following incubation with

13C-glucose, and then measuring the mass of 13C in excess of natural abundance that was

remaining in the sample. This method assumes that there is no abiotic sorption of glucose

and that all of the non-extractable 13C resulted from microbial assimilation (Herron et al.

2009; Butcher et al. 2020).

Light and heavy fractions of SOC

The method described by (Six et al., 1998) was modified to separate light and

heavy SOC fractions. Approximately 2 g of air-dried soil was placed into a 50 mL vial

with 10 mL sodium polytungstate (SPT, 1.6 g/cm3). The vial was shaken for 1h at 160

rpm to mix the sample with SPT well. After shaking, SPT was used to rinse residual soil

and debris adhering to the vial’s cap and sides. The vial was then centrifuged at 4000 rpm

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for 30 minutes. The supernatant containing the floating light fraction was filtered through

a pre-combusted quartz filter placed onto a Nalgene vacuum filter holder. SPT solution

was pulled through with a vacuum pump. The aforementioned steps were repeated twice

more. After that, 25 mL of DI water was added to the vial with the heavy fraction pellet.

The vial was shaken by hand to fully resuspend the pellet and then was centrifuged at

4000 rpm for 15 minutes. The supernatant was vacuumed through the same filters that we

used before. These steps were repeated two more times. The pellet remaining in the vial

at this point was considered to be heavy SOC fraction. The light SOC fraction material on

the filter was rinsed with a total 175 mL of DI water. Both light and heavy SOC fractions

were dried in the oven at 65°C for 48 h and then weighed. In addition, the dried heavy

SOC fraction was ground to a fine powder to measure C and N concentration with an

elemental analyzer (ECS 4010 Elemental Analyzer, Costech Analytical Technologies,

Valencia, CA, USA). Light and heavy SOC fractions represented particulate (POC) and

mineral associated organic C (MAOC), respectively.

Statistical analyses

Phase 1 and 2 of the experiment were analyzed separately, as distinct

experimental manipulations were performed in each. Three-way ANOVAs were

performed to determine effects of synthetic root exudates, soil microbial community

composition, and soil mineralogy on soil variables (e.g. CUE, soil enzyme activity, MBC,

and net change in C) during Phase 1. Repeated-measures four-way ANOVAs were

conducted to quantify effects of aboveground C input type, root exudates, soil microbial

community composition, and clay mineralogy on soil variables measured during Phase 2.

Repeated-measures three-way ANOVAs and mixed models (using R package lme4; fixed

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effects included four treatments and the two-week cycle, and random effect was

microcosm) were conducted to quantify effects of treatments on soil respiration during

both Phase 1 and 2, respectively. R package “lmertest” was used to estimate P values in

mixed effects models. All analyses were conducted in R.

Results

The efficacy of synthetic soil system and treatments during Phase 1

By the end of Phase 1, mean enzyme activities in synthetic and real soils were

comparable (synthetic soil: 2.79 ± 0.22 [SE] µmol g-1 soil h-1; real soil: 4.15 ± 0.15 µmol

g-1 soil h-1). However, soil respiration (synthetic soil: 1.90 ± 0.05 µg CO2-C g-1 soil h-1;

real soil: 0.12 ± 0.00 µg CO2-C g-1 soil h-1), CUE (synthetic soil: 0.71 ± 0.01; real soil:

0.50 ± 0.01), and MBC (synthetic soil: 1822 ± 110 µg g-1 soil; real soil: 868 ± 50 µg g-1

soil) were lower in real soils (Fig. 4.1).

The presence of root exudates, soil microbial community composition, and soil

mineralogy all created some differences in ‘baseline’ functions across soils at the end of

Phase 1 (Fig. C2-C5 and Table C2 and C3 in the appendices). Soil respiration was

affected by the four-way interaction between root exudates, inoculum type, soil

mineralogy, and time (Table C2 in the appendices). More specifically, soil respiration

was initially highest under bacteria-only treatment in soils with medium and high clay

activities when root exudates were not present (Fig. C5 in the appendices). By contrast,

when root exudates were present, higher soil respiration was found under bacteria-only

treatment in soils with low clay activity, at least early on during Phase 1. By the end of

Phase 1, however, there were no appreciable differences among treatments.

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The microbial biomass C pool responded to the interaction between root exudates

and soil mineralogy (Table C3 in the appendices). Root exudates did not markedly affect

biomass C under medium and high clay activities, but decreased MBC under low clay

activity (Fig. C6 in the appendices). Soil mineralogy also significantly affected CUE

(Table C3 in the appendices), with lower CUE under low clay activity (Fig. C4 in the

appendices). In addition, we found interactive effects of root exudates and soil microbial

community composition on CUE (Table C3 in the appendices). Specifically, soil

microbial community composition did not markedly affect CUE under root exudates, but

higher CUE was found under bacteria only (0.75 ± 0.01) than the bacteria + fungi

treatment (0.68 ± 0.02) when root exudates were not present (Fig. C7 in the appendices).

Soil mineralogy showed a strong effect on mean enzyme activity (Table C3 and

Fig. C4 in the appendices). Mean enzyme activity was greatest in high-activity clays but

lowest in medium-activity clays. As was true for the microbial biomass C, mean enzyme

activity responded to the interaction between root exudates and soil mineralogy, with the

presence of root exudates reducing enzyme activity only under high clay activity (Table

C3 and Fig. C8 in the appendices).

By the end of Phase 1, SOC remaining in each microcosm (i.e., calculated net

change in SOC) varied in response to soil microbial community composition and root

exudates (Table C3 in the appendices). More SOC was lost under bacteria only (-2.58 ±

0.12 mg g-1 soil) than the bacteria + fungi treatment (-1.97 ± 0.08 mg g-1 soil). Moreover,

root exudates stimulated less SOC loss (-2.12 ± 0.10 mg g-1 soil) than the control (-2.43 ±

0.12 mg g-1 soil). It should be noted that real soil experiment was mainly conducted to

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test the efficacy of synthetic soil system, and it did not include clay activity treatment.

Therefore, we only described results from the artificial soil system during Phase 2 below.

Roles of plant-microbe-soil interactions in soil carbon cycling during Phase 2

In the second phase of the experiment, all four treatments generally had strong

impacts on the soil variables we measured, both alone and through two-, three-, and even

four-way interactions. However, the strongest and most consistent interaction was

between root exudates and soil mineralogy (Table 4.1 and 4.2).

Soil respiration was significantly affected by root exudates (Table 4.1), with soils

from root exudates respiring approximately 34% more on average than those from the

control (Fig. 4.4 and Table C4 in the appendices). Respiration also increased with clay

activity, with CO2 fluxes 22% higher, on average, in high- vs. low-activity clays (Fig. 4.6

and Table C4 in the appendices). In addition, soil respiration also responded to a much

smaller extent on the four-way interaction of soil mineralogy, inoculum type, C input

quality, and time (Table 4.1). Overall, the highest rates of soil respiration were found

under high-activity clays with addition of glucose or cellobiose. The lowest soil

respiration was found under low-activity clays with xylan as the C input (Fig. C9 in the

appendices). C input patterns in the medium-activity clays were more dependent upon

inoculum type. It should be noted that the interaction of soil mineralogy, inoculum type,

and C input quality varied between the times that we measured soil respiration.

Root exudates tended to enhance the size of the biomass, but this was dependent

upon clay type, external C input, and microbial inoculum (Fig. 4.4, Table 4.2, and Table

C5 in the appendices). In low- and medium-activity clays, effects of root exudates on the

biomass were dependent upon whether glucose, cellobiose, or xylan was added. In high

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activity clays, root exudates always increased the biomass, regardless of C input quality

(Fig. C10 in the appendices). Moreover, the positive effect of root exudates on soil

microbial biomass C increased with C input quality under the bacteria only treatment, but

the opposite trend was observed in the bacteria + fungi treatment (Fig. C11 in the

appendices). We also found that the interactive effects of root exudates and inoculum and

the interactive effects of soil mineralogy and C input quality varied between harvests

(Table 4.2).

Harvest and soil mineralogy or inoculum interactively affected mean enzyme

activity (Table 4.2 and Table C6 in the appendices). In addition, mean enzyme activity

depended on the two-way interactions of soil mineralogy and C input quality, the three-

way interactions of root exudates, soil microbial community composition, and C input

quality, and the four-way interactions of root exudates, soil mineralogy, soil microbial

community composition, and harvest. Specifically, mean enzyme activity declined with

increasing C input quality in high activity clay but did not markedly respond to C input

quality in soils with low and medium clay activities (Fig. C12 in the appendices). Mean

enzyme activity in the presence of fungi was lower than in the bacteria-only treatment

regardless of C input quality, except in the root exudate treatment when the ‘background’

C input was xylan (Fig. C13 in the appendices). Root exudates slightly increased mean

enzyme activity when C input quality was glucose, regardless of inoculum type.

However, when the non-root C input was cellobiose or xylan, root exudates slightly

decreased mean enzyme activity under bacteria only treatment but increased it under the

bacteria + fungi treatment. Finally, within the bacteria-only treatment, mean enzyme

activity did not markedly respond to root exudates in soils with low clay activity, but was

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increased and decreased by root exudates in soils with medium and high clay activity,

respectively (Fig. C14 in the appendices). For the bacteria + fungi treatment, there was no

large difference in mean enzyme activity between root exudates and control in medium

and high activity clay; however, higher mean enzyme activity was found under root

exudates in low activity clay.

MAOC was strongly influenced by the interaction between soil mineralogy and

root exudates (Table 4.2 and Table C7 in the appendices). However, these effects were

modified to some extent by non-root carbon inputs, the composition of the microbial

community, and harvest. For example, root exudates increased MAOC in high-activity

clay in the presence of bacteria but not fungi (Fig. C15 in the appendices), and when C

was added as cellobiose or xylan but not glucose (Fig. C16 in the appendices). By

contrast, root exudates decreased MAOC in low-activity clay, regardless of inoculum or

non-root C input, with intermediate and more complex patterns in medium-activity clays.

To a lesser extent we also observed three-way interactions among root exudates, soil

mineralogy, and harvest (Fig. C17 in the appendices), four-way interactions among C

input quality, root exudates, soil microbial community composition, and harvest (Fig.

C18 in the appendices), and four-way interactions among C input quality, soil microbial

community composition, soil mineralogy, and harvest (Fig. C19 in the appendices).

Overall, net change in SOC was always negative, which means that microcosms

always lost C during the course of the experiment. The effects of all four treatments on

net change in SOC varied with harvest (Table 4.2 and Table C8 in the appendices). For

example, more SOC was lost with increasing C input quality and soil clay activity during

both second and third harvests, but larger differences among C inputs and soil clay

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activities were found during third harvest. In addition, the bacteria-only treatment had

less SOC loss than the bacteria + fungi treatment during second harvest, but the opposite

result was found during third harvest. Root exudates led to less SOC loss than control

during both harvests; however, larger difference was found during third harvest. Mineral

type had a strong effect on SOC change that was modified by root exudates, inoculum

type, and C input quality (Fig. C20 in the appendices). In general, net carbon loss

increased with clay activity (Fig. 4.3). Root exudates had positive effect of on net change

in SOC (i.e., root exudates tended to counteract SOC loss), and this effect also increased

with soil clay activity. Moreover, while less SOC was lost under xylan addition vs.

glucose and cellobiose, the difference between C inputs was more pronounced under

high-activity clay.

Discussion

We utilized synthetic root and soil systems to determine roles of plant-microbe-

soil interactions (C input quality, root exudates, soil microbial community, and soil

mineralogy) in SOC cycling. Before the experiment, we hypothesized that: 1) with

increasing C input quality, CUE increases, soil respiration decreases, and MAOC

increases; 2) root exudates stimulate soil respiration through rhizosphere priming effect;

3) the bacteria + fungi treatment would have less soil respiration and more MAOC than

the bacteria only treatment because fungi are believed to have greater CUE and slower

turnover rates; and 4) SOC stabilization would increase with increasing soil clay activity.

We found that only some of our hypotheses were supported by the findings of this study.

We discuss the overall effects of four drivers on SOC cycling and the common

interactions between root exudates and soil mineralogy below.

117

Effects of C input quality on SOC cycling

Overall, we found that among the four drivers, C input quality had the strongest

effects on soil respiration. Soil respiration was significantly lower when C input was

xylan than glucose or cellobiose. However, MAOC did not significantly respond to C

input quality. These findings were inconsistent with our hypotheses and prevalent

conceptual framework (Cotrufo et al., 2013), but were consistent with the results from a

recent long-term field study (Pierson et al., 2021) which found insignificant effects of C

input quality on MAOC. According to the MEMS framework, labile C has higher CUE

than recalcitrant C, resulting in more microbial-derived C, which in turn stimilates SOC

accumulation (Cotrufo et al., 2013; C. Liang et al., 2017). However, our results did not

fully surpport this idea. For example, slightly higher soil microbial biomass C was found

under cellobiose than glucose and xylan (Table 4.2 and Fig. 4.2). This finding was

consistent with a previous study which reported that, when compared to cellobiose, xylan

produced greater bacterial and fungal PLFA and ergosterol content, which are good

indicators for soil microbial biomass (Ali et al., 2018). In addition, on one hand, different

substrates might trigger shifts in microbial community composition, such that xylan could

lead to a more growth-efficient community (Kallenbach et al., 2016). On the other hand,

labile C input (e.g. glucose) can result in higher priming effect (Kuzyakov, 2010;

Kuzyakov et al., 2000), thus stimulating soil respiration. Finally, since xylan mainly

consisted of recalcitrant C, some of the xylan was still laying around unmetabolized,

whereas most of the glucose and cellobiose was consumed by soil microbes. Because of

potential higher priming effect and decomposition rate but slightly lower soil microbial

biomass C, we found higher soil respiration when C input was glucose.

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Effects of root exudates on SOC cycling

We found that root exudates strongly influenced soil respiration and were the

most important factor affecting MAOC (Table 4.1 and 4.2), which was consistent with

our hypothesis. According to some previous studies, root exudates can promote SOC

accumulation because they mainly consist of labile C which has a higher CUE (Schmidt

et al., 2011; Slessarev et al., 2020; Sokol et al., 2019; Sokol & Bradford, 2019). This

opinion was partly supported by our results. For example, we found that root exudates

tended to increase soil microbial biomass C (Fig. 4.4). By contrast, other studies found

that root exudates could result in rhizosphere priming effect, thus destabilizing SOC

(Cheng et al., 2014). Since the decomposition of persistent SOC is enzymatically

catalyzed (Kuzyakov, 2010; Kuzyakov et al., 2000), higher soil enzyme activities usually

represent higher soil priming effect (Chen et al., 2014). Despite the four-way interactions

of root exudates, soil mineralogy, soil microbial community composition, and harvest, we

found that root exudates generally tended to slightly increase mean enzyme activity,

which also suggests that root exudates may have led to a positive rhizosphere priming

effect. Overall, dual effects of root exudates on SOC cycling were found in the present

study. Root exudates contributed to SOC loss via rhizosphere priming effects, but also

promoted SOC accumulation by increasing soil microbial biomass C. Overall, the dual

effects of root exudates on SOC cycling were mediated by other factors like soil

mineralogy, which we will discuss more below.

Effects of soil microbial community composition on SOC cycling

We hypothesized that more SOC will be lost when soil microbial community

composition is bacteria only rather than bacteria + fungi. This hypothesis was supported

119

by our findings, which showed higher MAOC under the bacteria + fungi treatment (Table

4.2 and Fig. 4.5), although this effect also depended upon the other three drivers, and

harvest. Many previous studies have found that fungi are better for SOC stabilization than

bacteria because fungi have longer turnover time (Ali et al., 2020). In addition,

significantly higher enzyme activities under bacteria only treatment were found in our

study. This may reflect that more enzymes were involved in destabilizing persistent SOC

under bacteria only treatment.

Effects of soil mineralogy on SOC cycling

It has been widely accepted that high clay activity and/or content can stimulate

persistent SOC accumulation because these clays have high surface area and/or great

affinity to chemically bond with organic C to form MAOC (Cotrufo et al., 2013; Lavallee

et al., 2020). Consistent with this, we also found the highest value of MAOC in high-

activity clay (Fig. 4.6). However, our results showed that soil respiration increased with

the increasing clay activity (Fig. 4.6), which is opposite to what we expected. Similarly,

higher enzyme activity was also found in soils with high clay activity (Fig. 4.6). This

finding suggests that high-activity clay also can stabilize more enzymes, thus increasing

the decomposition rate of SOC and soil respiration. Compared to MAOC, soil respiration

responded to clay activity more greatly (Table 4.1 and 4.2). As a result, more SOC loss

was found in soils with high clay activity (Fig. 4.3). Overall, our results indicate that the

effect of soil clay activity on SOC cycling is a ‘double-edged sword’.

Responses of soil C cycling to plant-microbe-soil interactions

Although most variables responded to complex treatment interactions, the root

exudates x mineral interaction was usually the biggest in the present study. For example,

120

soil respiration ranged from 2.24 g CO2-C g-1 soil h-1 under low clay activity in soils

without root exudates to 3.78 g CO2-C g-1 soil h-1 under high clay activity in soils with

root exudates (Fig. 4.7). In addition, MAOC ranged from 4.37 g kg-1 under low clay

activity in soils with root exudates to 6.30 g kg-1 under low clay activity in soils without

root exudates. There has been a greater recognition that root exudates play a great role in

SOC stabilization and loss (Sokol et al., 2019; Sokol & Bradford, 2019). However, either

positive or negative effects of root exudates on SOC stabilization have been found by

previous studies (Cheng et al., 2014; Slessarev et al., 2020). Dijkstra et al. (2021) raised a

framework named “Rhizo-Engine”, which held that whether root exudates lead to more

SOC stabilization or loss depends on other factors such as soil mineralogy and nutrient

condition. In other words, the opposite results that were reported before might be because

the experiments studying root effects on SOC were conducted in different soil conditions

(e.g. different soil microbial community composition and clay activities). Nonetheless, no

study has been done to test this hypothesis. Our study provides the first direct evidence

that root effects on SOC cycling are significantly mediated by soil mineralogy (e.g. the

strongest and most consistent interaction between root exudates and soil clay activity)

(Fig. 4.7). For example, root exudates decreased MAOC in soils with low and medium

clay activities but increased MAOC in soils with high clay activity. This might be

because of relatively low potential of low- and medium-activity clay to generate chemical

bonds with C from root exudates to form MAOC. However, relatively high area surface

and affinity of high-activity clay can absorb more organic C, resulting in MAOC

accumulation. Although high clay activity could reduce respiration caused by root

exudates, C still tended to release from soils regardless of clay activity (Fig. 4.7). This

121

indicates that SOC loss caused by root exudates via rhizosphere priming effect was

greater than SOC stabilization via MAOC accumulation. However, unknowns remain

whether the results can vary with the amount of root exudates, which should be further

studied in the future.

In addition to the interactions between root exudates and soil mineralogy, we also

found some interesting interactions between root exudates and soil microbial community

composition and between soil microbial community composition and soil mineralogy

(Fig. 4.8). Without root exudates, higher MAOC was found under bacteria + fungi than

under the bacteria only treatment. In the presence of root exudates, however, no marked

difference in MAOC was found between two soil microbial community compositions.

Fungi usually have longer turn-over time than bacteria (Ali et al., 2020). In addition, soil

enzyme activity was low under bacteria + fungi relative to bacteria only (Fig. 4.8). As a

result, higher MAOC was found under the bacteria + fungi treatment when root exudates

were not present. Once root exudates were loaded, however, soil enzyme activity

increased significantly under the bacteria + fungi treatment (Fig. 4.8). This increase in

enzymatic activity may have resulted in a greater rhizosphere priming effect under the

bacteria + fungi treatment. Overall, our study indicates that the effects of root exudates on

SOC cycling were not only mediated by soil mineralogy but also soil microbial

community composition. In addition, the net change in SOC responded to the interactions

of soil microbial community composition and soil mineralogy (Fig. 4.8). It is interesting

that when compared to bacteria only, SOC loss was slightly lower under the bacteria +

fungi in low-and high-activity clay but became higher in soils with medium clay activity.

However, our measurements did not provide enough hints to explain this, and thus, more

122

studies will be required to disentangle the underlying mechanisms. Finally, it should be

noted that the individual effects of four drivers and their interactions varied over time

(Table 4.1 and 4.2). However, most laboratory studies are conducted in very short time

(e.g. < 3 months), and variables related to SOC cycling are only measured one time

(Schnecker et al., 2019; Sokol & Bradford, 2019). In order to unravel the mechanisms of

SOC cycling more accurately, our results strongly suggest that long-term experiment

should be conducted in the future and that relevant variables should be measured at

multiple times.

Conclusion

Soil C cycling plays a great role in global climate mitigation. However, unknowns

still remain regarding how SOC cycling responds to plant-microbe-soil interactions. A

synthetic root and soil system was utilized to assess how C input quality, root exudates,

soil microbial community, and soil mineralogy interactively affect SOC stabilization and

loss, and to quantify the relative importance of each of these controls. C input quality was

the most important factor affecting soil respiration, and soil respiration was significantly

lower under xylan than glucose or cellobiose. In addition, the variation of MAOC was

most explained by root exudates and soil microbial community composition. Specifically,

root exudates decreased MAOC, and higher MAOC was found under the fungi + bacteria

than bacteria only treatment. Along the plant-microbe-soil continuum, the strongest and

most consistent interactive effects on SOC cycling was found between root exudates and

soil mineralogy. The increase in soil respiration stimulated by root exudates tended to

decrease with the increasing of soil clay activity, and root exudates decreased MAOC

under low and medium clay activities but increased MAOC under high soil clay activity.

123

We strongly suggest more studies should be conducted to disentangle the mechanisms of

how root-soil interactions affect SOC cycling.

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Tables

Table 4.1. Effects of all treatments on soil respiration in synthetic soil system during

Phase 2

Root: root exudates; Mineral: soil clay activity; Inoculum: soil microbial community

composition; Carbon: carbon input quality; Time: time points that soil respiration was

measured. * P<0.05; ** P<0.01; *** P<0.001.

Effects F P

Root exudates 41.8 ***

Mineral activity 13.0 ***

Inoculum 13.7 ***

Carbon quality 46.3 ***

Time 105.4 ***

Root*Mineral 1.9

Root*Inoculum 0.0

Mineral*Inoculum 0.8

Root*Carbon 1.8

Mineral*Carbon 2.9 *

Inoculum*Carbon 2.2

Root*Time 7.7 **

Mineral*Time 2.4

Inoculum*Time 10.4 **

Carbon*Time 1.9

Root*Mineral*Inoculum 0.7

Root*Mineral*Carbon 1.1

Root*Inoculum*Carbon 0.6

Mineral*Inoculum*Carbon 3.2 *

Root*Mineral*Time 1.2

Root*Inoculum*Time 0.1

Mineral*Inoculum*Time 0.8

Root*Carbon*Time 1.1

Mineral*Carbon*Time 1.1

Inoculum*Carbon*Time 3.3 *

Root*Mineral*Inoculum*Carbon 0.7

Root*Mineral*Inoculum*Time 0.1

Root*Mineral*Carbon*Time 1.2

Root*Inoculum*Carbon*Time 0.6

Mineral*Inoculum*Carbon*Time 2.8 *

Root*Mineral*Inoculum*Carbon*Time 1.7

134

Table 4.2. Effects of all treatments on soil variables in synthetic soil system during Phase

2

EEA: mean soil enzyme activity; MBC: soil microbial biomass carbon; HF: heavy

fraction carbon concentration; Net C: net change in carbon. R: root exudates; M: soil clay

activity; I: soil microbial community composition; C: carbon input quality; H: two

harvests during Phase 2. * P<0.05; ** P<0.01; *** P<0.001.

Effects EEA MBC HF Net C

F P F P F P F P

Root exudates 2.9 11.3 *** 20.2 *** 45.5 ***

Mineral activity 138.7 *** 29.7 *** 10.0 *** 19.8 ***

Inoculum 34.1 *** 10.5 ** 19.8 *** 0.0

Carbon quality 5.8 ** 4.1 * 1.0 86.5 ***

Harvest 34.0 *** 260.4 *** 78.7 *** 176.0 ***

R*M 3.8 * 4.0 ** 19.5 *** 7.2 **

R*I 10.1 ** 0.1 10.1 ** 0.4

M*I 4.0 * 2.0 0.0 5.1 **

R*C 3.1 * 0.0 5.5 ** 1.7

M*C 8.4 *** 1.9 0.8 2.5 *

I*C 3.2 * 0.1 2.9 0.1

R*H 1.7 2.3 6.9 ** 5.7 *

M*H 5.3 ** 10.6 *** 4.9 ** 4.4 *

I*H 5.4 * 0.0 2.7 5.8 *

C*H 0.1 1.9 1.2 10.6 ***

R*M*I 6.6 ** 1.4 4.6 * 0.7

R*M*C 1.2 2.8 * 6.3 *** 0.9

R*I*C 3.8 * 3.4 * 2.4 1.0

M*I*C 1.9 0.7 1.8 1.8

R*M*H 4.4 * 0.7 12.9 *** 1.2

R*I*H 0.0 4.8 * 0.0 4.1 *

M*I*H 3.5 * 0.2 1.1 1.3

R*C*H 1.5 1.1 0.5 0.2

M*C*H 1.7 3.4 * 1.4 1.2

I*C*H 1.2 2.5 4.2 * 2.4

R*M*I*C 1.0 2.2 1.3 0.9

R*M*I*H 4.0 * 1.2 2.2 0.2

R*M*C*H 1.9 1.0 0.4 1.0

R*I*C*H 2.8 2.6 5.1 ** 2.0

M*I*C*H 1.1 1.8 3.0 * 0.3

R*M*I*C*H 1.3 1.7 1.5 0.2

135

Figures

Figure 4.1. Soil variables in both synthetic and real soil systems by the end of Phase 1.

Significant differences between synthetic and real soil systems at P < 0.05 are indicated

by different letters. Low, Medium, and High represent low, medium, and high clay

activity in synthetic soil system, respectively. The black lines within the boxes indicate

the medians, the boxes represent the 25th and 75th percentiles, the whiskers indicate the

lowest and highest values excluding outliers, and the circles represent outliers.

136

Figure 4.2. Effects of carbon input quality on soil variables in synthetic soil system

during Phase 2. Significant differences among treatments at P < 0.05 are indicated by

different letters.

a a a

0

1000

2000

3000

4000

Glucose Cellobiose XylanSo

il m

icro

bia

l b

iom

ass c

arb

on

(µ

g g

-1)

a a a

0.0

2.5

5.0

7.5

Glucose Cellobiose Xylan

Me

an

en

zym

e a

ctivity (

mm

ol g

-1 h

-1)

a a a

0.0

2.5

5.0

7.5

10.0

12.5

Glucose Cellobiose XylanHe

avy f

ractio

n c

arb

on

co

nte

nt

(g k

g-

1)

a a b

0.0

2.5

5.0

7.5

10.0

Glucose Cellobiose XylanSo

il re

sp

ira

tio

n (

mg

CO

2−

C g

-1so

il h

-1)

137

Figure 4.3. Net change in carbon under all treatments in synthetic soil system during the

course of experiment. Net change in carbon was equal to the amount of carbon input

through substrate addition and/or root exudates - the amount of carbon release as soil

respiration. Significant differences among treatments at P < 0.05 are indicated by

different letters. B and FB represent bacteria only and both fungi and bacteria in synthetic

soil system, respectively. High, Medium, and Low represent high, medium, and low clay

activity in synthetic soil system, respectively.

138

Figure 4.4. Effects of root exudates on soil variables in synthetic soil system during

Phase 2. Significant differences among treatments at P < 0.05 are indicated by different

letters.

139

Figure 4.5. Effects of soil microbial community composition on soil variables in

synthetic soil system during Phase 2. Significant differences among treatments at P <

0.05 are indicated by different letters. B: bacteria only; FB: both fungi and bacteria.

140

Figure 4.6. Effects of soil clay activity on soil variables in synthetic soil system during

Phase 2. Low, Medium, and High represent low, medium, and high clay activity in

synthetic soil system, respectively. Significant differences among treatments at P < 0.05

are indicated by different letters.

141

Figure 4.7. Effects of root exudates-soil clay activity interactions on soil variables in

synthetic soil system during Phase 2. Low, Medium, and High represent low, medium,

and high clay activity in synthetic soil system, respectively. Error bars represent ± 1

standard error of the mean. See Table 4.1 and 4.2 for statistical results.

142

Figure 4.8. Effects of root exudates-soil microbial community composition-soil clay

activity interactions on heavy fraction carbon content, net change in carbon, and mean

enzyme activity in synthetic soil system during Phase 2. Error bars represent ±1 standard

error of the mean. See Table 4.2 for statistical results.

143

CHAPTER 5

CONCLUSIONS

Due to the great role of terrestrial ecosystems in climate mitigation, it is essential

to study climate change effects on C cycling in terrestrial ecosystem. In my PhD

dissertation, I utilized several approaches (e.g. meta-analysis, experiments, and soil

microbial community analysis) to address some knowledge gaps in the responses of

terrestrial C cycling to global change factors.

In chapter 2, I conducted a meta-analysis to determine whether long-term N

impacts on plant productivity change over time. I found that 44% of long-term N addition

studies showed significant temporal trend of N effects on plant productivity in terrestrial

ecosystem. The direction of change varied with biome type (forests: decrease; grasslands

and shrublands: increase). The temporal trend of N impacts was mostly explained by

mean annual precipitation, mean annual temperature, and initial soil pH, which accounted

for 24%, 19%, and 19% of the variation, respectively.

In chapter 3, I investigated how multiple global change factors (e.g. prior

warming, C input, soil water content, and soil moisture variability [rewetting])

individually and interactively affect soil respiration in drylands. I found that prior

warming, high soil water content, and C input stimulated soil respiration; however,

multiple rewetting cycles decreased soil respiration when compared to stable soil

moisture. Importantly, substantial interactive effects on soil respiration were found

among global change drivers. For example, effects of soil water content and prior

warming on soil respiration were not significant when C was not added. However, high

soil water content and prior warming stimulated soil respiration when C was added.

144

In chapter 4, I utilized synthetic root/soil systems to disentangle the pathways by

which plants, microbes, and soils interactively affect SOC cycling and assess the relative

importance of each driver. I found that high C input quality, high soil clay activity, and

root exudates stimulated soil respiration. Root exudates-soil minerology interactions

played a great role in SOC formation and loss. Specifically, the positive effect of root

exudates on soil respiration decreased with the increasing of soil clay activity. In

addition, root exudates decreased MAOC content in soils with low and medium clay

activity; however, the opposite result was found when soil clay activity was high.

Overall, my PhD dissertation advanced our understanding of global change

effects on above- and belowground C cycling in terrestrial ecosystems by addressing

three knowledge gaps. More specifically, first, chapter 1 found that long term N impacts

on aboveground activity in terrestrial ecosystems decreased over time. Second, chapter 2

showed that interactive effects of global change drivers on soil respiration in drylands

were ubiquitous. Third, chapter 3 indicated that root exudates-soil minerology interaction

played a critical role in SOC stabilization. In order to determine climate change effects on

terrestrial C cycling more accurately, three chapters in my PhD dissertation have three

suggestions for future research: 1) not only the magnitude of N impacts on plant

productivity, but also their temporal trend, should be considered in future experimental

and model research. Otherwise, we may under- or overestimate N impacts on terrestrial

ecosystem. 2) in order to estimate and predict soil C cycling in drylands more accurately,

future studies should not only determine the individual factor but also examine the

interactive effects of multiple global change factors. 3) due to the great role of

interactions between root exudates and soil minerology in SOC cycling, more research on

145

studying this interaction should be done in the future to improve our understanding of

mechanisms of how plant-microbe-soil interactions affect SOC dynamics.

146

APPENDICES

147

Appendix A – Supplementary Information for Chapter 2

Figure A1. The distribution of experimental duration of 63 case studies selected in this

meta-analysis study.

148

Figure A2. Global distribution of N addition experiments selected in this meta-analysis.

150°W 90°W 30°W 30°E 90°E 150°E

60°S

30°S

0°

30°N

60°N

60°S

30°S

0°

30°N

60°N

150°W 90°W 30°W 30°E 90°E 150°E

10

20

30

40

Duration (yr)

Ecosystem

forest

grassland

shrubland

149

Figure A3. Relationship between response ratio (RR) and time (year) across 63 studies.

57 58 59 60 61 62 63

49 50 51 52 53 54 55 56

41 42 43 44 45 46 47 48

33 34 35 36 37 38 39 40

25 26 27 28 29 30 31 32

17 18 19 20 21 22 23 24

9 10 11 12 13 14 15 16

1 2 3 4 5 6 7 8

0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40

0 10 20 30 40

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

−0.5

0.0

0.5

1.0

Year

RR

150

Figure A4. The distribution of Spearman’s rank correlation coefficient between response

ratio (RR) and time (year).

151

Figure A5. Global distribution of overall N impacts on plant productivity (RRaverage)

and their temporal patterns at each field site. Note: There were multiple case studies at

some field sites. RRaverage was the average of RRmean at field sites with multiple case

studies and was equal to RRmean at field sites with one case study. Overall N impacts on

plant productivity were positive if RRaverage is greater than 0 but were negative if

RRaverage is less than 0. If the field sites showed different temporal patterns of N impacts

on plant productivity across multiple case studies, they were defined as “Mixed”.

150°W 90°W 30°W 30°E 90°E 150°E

60°S

30°S

0°

30°N

60°N

60°S

30°S

0°

30°N

60°N

150°W 90°W 30°W 30°E 90°E 150°E

0.0

0.2

0.4

0.6

RRaverage

Temporal pattern

Decreased

Increased

Mixed

152

Figure A6. Global distribution of coefficient of variation (CV) of long-term N impacts

on plant productivity.

150°W 90°W 30°W 30°E 90°E 150°E

60°S

30°S

0°

30°N

60°N

60°S

30°S

0°

30°N

60°N

150°W 90°W 30°W 30°E 90°E 150°E

100

200

300

400

500

CV (%)

Ecosystem

forest

grassland

shrubland

153

Figure A7. Weighted response ratios from different temporal patterns (decreased and

increased) of N impacts on plant productivity calculated using data from only the first

year (WRRfirst), all timepoints (WRRmean), and the most recent timepoint (WRRlast).

154

Appendix B – Supplementary Information for Chapter 3

Table B1. Change (%) in mean weighted diameter due to the effects of the five treatment

factors.


Carbon input vs. control + 18.4 **

Rewetting cycles vs. stable moisture - 12.8 **


Prior warmed soil vs. control - 13.9 **

Recovery vs. Rewetting period - 11.5 *

Joint effects


C × R + 5.6 + 3.2 - 2.4

C × M + 42.6 + 43.0 *** + 0.4

C × W + 4.5 + 1.9 - 2.6

C × P + 6.9 + 4.2 - 2.7

R × M + 11.4 + 8.8 ** - 2.6

R × W - 26.7 - 24.8 + 1.9

R × P - 24.3 - 22.3 + 2.0

M × W + 10.3 + 7.7 *** - 2.6

M × P + 12.7 + 10.9 - 1.8

W × P - 25.4 - 20.9 * + 4.5

C × R × M + 29.8 + 1.9 *** - 27.9

C × R × W - 8.3 - 8.0 + 0.3

C × R × P - 5.9 - 10.9 - 5.0

C × M × W + 28.7 + 39.9 * + 11.2

C × M × P + 31.1 + 50.1 ** + 19.0

C × W × P - 7 - 11.4 - 4.4

R × M × W - 2.5 - 4.1 - 1.6

R × M × P - 0.1 + 3.1 + 3.2

R × W × P - 38.2 - 27.6 + 10.6

M × W × P - 1.2 - 2.3 - 1.1

C × R × M × W + 15.9 + 4.2 - 11.7

C × R × M × P + 18.3 + 13.0 - 5.3

C × R × W × P - 19.8 - 17.6 + 2.2

C × M × W × P + 17.2 + 41.4 + 24.2

R × M × W × P - 14 - 4.1 + 9.9

C × R × M × W × P + 4.4 + 10.3 + 5.9

Expected effects were the additive effects sizes of each treatment when imposed

individually. C: carbon input; R: rewetting; M: soil moisture; W: prior warming; P:

155

period. * P<0.05; ** P<0.01; *** P<0.001. See Table 3.1 for more details about

calculating expected and observed effects and the difference between them.

156

Table B2. Change (%) in mean soil enzyme activity due to the effects of five treatment

factors


Carbon input vs. control - 5.4

Rewetting cycles vs. stable moisture - 5.0


Prior warmed soil vs. control + 12.8 **

Recovery vs. Rewetting period + 23.7 ***

Joint effects


C × R - 10.2 - 10.4 - 0.2

C × M + 16.5 + 17.2 + 0.7

C × W + 6.6 + 7.4 + 0.8

C × P + 16.8 + 18.3 + 1.5

R × M + 16.0 + 17.6 + 1.6

R × W + 7.3 + 7.8 + 0.5

R × P + 17.1 + 18.7 + 1.6

M × W + 37.3 + 35.4 - 1.9

M × P + 51.7 + 46.3 - 5.4

W × P + 39.9 + 36.5 - 3.4

C × R × M + 15.0 + 12.2 - 2.8

C × R × W + 8.4 + 2.4 - 6.0

C × R × P + 7.9 + 13.3 + 5.4

C × M × W + 33.1 + 30.0 - 3.1

C × M × P + 33.6 + 40.9 * + 7.3

C × W × P + 19.0 + 31.1 ** + 12.1

R × M × W + 22.5 + 30.4 + 7.9

R × M × P + 26.8 + 41.3 ** + 14.5

R × W × P + 44.7 + 31.5 * - 13.2

M × W × P + 48.5 + 59.1 ** + 10.6

C × R × M × W + 33.1 + 25.0 - 8.1

C × R × M × P + 11.9 + 35.9 + 24.0

C × R × W × P + 27.6 + 26.1 - 1.5

C × M × W × P + 19.5 + 53.7 + 34.2

R × M × W × P + 28.1 + 54.1 + 26.0

C × R × M × W × P + 10.3 + 48.7 + 38.4





157

Table B3. Change (%) in soil total carbon concentration due to the effects of five

treatment factors



Rewetting cycles vs. stable moisture + 5.7 *


Prior warmed soil vs. control + 0.6

Recovery vs. Rewetting period - 0.6

Joint effects


C × R + 713.6 + 687.0 - 26.6

C × M + 760.5 + 697.7 *** - 62.8

C × W + 714.0 + 681.9 - 32.1

C × P + 695.2 + 680.7 - 14.5

R × M + 23.3 + 22.1 - 1.2

R × W + 6.3 + 6.3 0.0

R × P + 4.8 + 5.1 + 0.3

M × W + 16.7 + 17.0 + 0.3

M × P + 15.9 + 15.8 - 0.1

W × P + 0.9 + 0.0 - 0.9

C × R × M + 746.6 + 703.4 - 43.2

C × R × W + 751.4 + 687.6 - 63.8

C × R × P + 667.8 + 686.4 + 18.6

C × M × W + 714.6 + 698.3 - 16.3

C × M × P + 739.9 + 697.1 - 42.8

C × W × P + 741.2 + 681.3 - 59.9

R × M × W + 30.1 + 22.7 - 7.4

R × M × P + 23.8 + 21.5 - 2.3

R × W × P + 6.1 + 5.7 - 0.4

M × W × P + 16.9 + 16.4 - 0.5

C × R × M × W + 711.7 + 704.0 * - 7.7

C × R × M × P + 685.0 + 702.8 + 17.8

C × R × W × P + 754.5 + 687.0 - 67.5

C × M × W × P + 709.3 + 697.7 - 11.6

R × M × W × P + 30.6 + 22.1 - 8.5

C × R × M × W × P + 671.6 + 703.4 + 31.8





158

Table B4. Change (%) in soil total nitrogen concentration due to the effects of five

treatment factors


Carbon input vs. control + 0.94


High vs. low soil moisture + 2.8


Recovery vs. Rewetting period + 1.7

Joint effects


C × R - 0.5 - 0.5 0.0

C × M + 3.7 + 3.7 *** 0.0

C × W + 1.8 + 1.8 ** 0.0

C × P + 2.6 + 3.7 * + 1.1

R × M + 1.4 + 1.4 0.0

R × W - 0.5 - 0.5 0.0

R × P + 0.3 + 0.4 + 0.1

M × W + 3.7 + 3.8 + 0.1

M × P + 4.5 + 4.9 + 0.4

W × P + 2.6 + 2.9 + 0.3

C × R × M + 2.3 + 5.6 + 3.3

C × R × W + 0.4 0.0 - 0.4

C × R × P + 1.2 + 3.5 + 2.3

C × M × W + 4.6 + 6.3 + 1.7

C × M × P + 5.4 + 0.7 ** - 4.7

C × W × P + 3.5 + 14.0 *** + 10.5

R × M × W + 2.3 + 1.9 - 0.4

R × M × P + 3.1 + 1.2 - 1.9

R × W × P + 1.2 + 2.8 + 1.6

M × W × P + 5.4 + 6.6 + 1.2

C × R × M × W + 3.2 + 7.3 + 4.1

C × R × M × P + 4.0 + 0.7 - 3.3

C × R × W × P + 2.1 + 14.3 + 12.2

C × M × W × P + 6.3 + 12.5 + 6.2

R × M × W × P + 4.0 + 3.1 - 0.9

C × R × M × W × P + 4.9 + 17.2 * + 12.3





159

Table B5. Change (%) in soil microbial biomass carbon due to the effects of five

treatment factors



Rewetting cycles vs. stable moisture + 32.2

High vs. low soil moisture - 65.1


Recovery vs. Rewetting period + 33.9

Joint effects


C × R + 449.6 + 842.0 + 392.4

C × M + 352.3 + 115.3 - 237.0

C × W + 591.2 + 849.7 + 258.5

C × P + 451.3 + 458.9 + 7.6

R × M - 32.9 - 51.6 - 18.7

R × W + 206.0 + 196.0 - 10.0

R × P + 66.1 + 83.3 + 17.2

M × W + 108.7 - 3.9 - 112.6

M × P - 31.2 - 67.8 - 36.6

W × P + 207.7 + 316.3 + 108.6

C × R × M + 384.5 + 357.5 - 27.0

C × R × W + 623.4 + 2320.0 + 1696.6

C × R × P + 483.5 + 1043.7 + 560.2

C × M × W + 526.1 + 331.4 - 194.7

C × M × P + 386.2 + 9.0 - 377.2

C × W × P + 625.1 + 791.0 + 165.9

R × M × W + 140.9 - 1.5 - 142.4

R × M × P + 1.0 - 11.5 - 12.5

R × W × P + 239.9 + 198.0 - 41.9

M × W × P + 142.6 - 2.3 - 144.9

C × R × M × W + 558.3 + 856.3 + 298.0

C × R × M × P + 418.4 + 423.9 + 5.5

C × R × W × P + 657.3 + 1989.1 + 1331.8

C × M × W × P + 560.0 + 103.6 - 456.4

R × M × W × P + 174.8 + 13.2 - 161.6

C × R × M × W × P + 592.2 + 943.9 + 351.7





160

Table B6. Change (%) in soil priming effect due to the effects of three treatment factors




Prior warmed soil vs. control + 66.8 ***

Joint effects


R × M + 116.2 + 76.6 * - 39.6

R × W + 54.1 + 40.4 - 13.7

M × W + 195.7 + 191.6 *** - 4.1

R × M × W + 183.0 + 163.6 - 19.4


individually. See Table 3.1 for more details about calculating expected and observed

effects and the difference between them. Priming effects were calculated as the difference

in respiration of SOM-C between C-input and control (no C) treatments. Since no

significant difference in priming effect between two times that we measured it, values of

priming effects from the two times were averaged prior to determining the effects of the

three other factors. R: rewetting; M: soil moisture; W: prior warming. * P<0.05; **

P<0.01; *** P<0.001.

161

Figure B1. Correlations among soil enzyme activities. BG: β-glucosidase; CB:

cellobiohydrolase; NAG: β-N-acetylglucosaminidase; LAP: leucine aminopeptidase; AP:

acid phosphatase. The size and the darkness of the color of the circle represent the

correlation coefficients based on Pearson’s correlation analysis. * P<0.05; ** P<0.01;

*** P<0.001.

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

***

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

BG

CB

NA

G

LA

P

AP

BG

CB

NAG

LAP

AP

162

Figure B2. Soil respiration rates under control (a) and carbon input treatments (b) over

the course of the incubation experiment. Note the difference in y-axis scale between

panels (a) and (b). The grey area represents the weeks when rewetting treatment were

conducted. The grey solid vertical lines between weeks 12 and 13 represents the start of

recovery period.

163

Figure B3. NMDS of soil fungal communities (a-d) and the relative abundance of fungal

phyla (e-h) under different treatments. Control and warmed represent soils that were

under ambient air temperature and had been prior warmed in situ (for 15 months),

respectively. Low and high moisture treatments represent soils that received 0.04 g g-1

and 0.08 g g-1 soil moisture, respectively. Stable and variable represent soils with stable

soil moisture during the course of the experiment and soils that experienced rewetting

cycle each two weeks, respectively. Recover and rewetting represent the period that

rewetting cycles were applied and the period that rewetting cycles stopped, respectively.

164

Appendix C – Supplementary Information for Chapter 4

Table C1. Nutrient content of the Master Blend fertilizer

Nutrients Content (%)

Total Nitrogen 4

Available Phosphate 18

Soluble Potash 38

Total Magnesium 0.5

Boron 0.2

Copper 0.05

Iron 0.4

Manganese 0.2

Molybdenum 0.01

Zinc 0.05

165

Table C2. Effects of all treatments on soil respiration in synthetic soil system during

Phase 1


composition; Time: time points that soil respiration was measured. * P<0.05; ** P<0.01;

*** P<0.001.

Effects F P

Root exudates 4.0 *

Mineral activity 12.9 ***

Inoculum 8.1 **

Time 1170.9 ***

Root*Mineral 0.8

Root*Inoculum 0.0

Mineral*Inoculum 3.4 *

Root*Time 0.0

Mineral*Time 2.8

Inoculum*Time 32.3 ***

Root*Mineral*Inoculum 2.6

Root*Mineral*Time 4.8 **

Root*Inoculum*Time 2.0

Mineral*Inoculum*Time 2.9

Root*Mineral*Inoculum*Time 7.2 ***

166

Table C3. Effects of all treatments on soil variables in synthetic soil system during Phase

1


composition. * P<0.05; ** P<0.01; *** P<0.001.

Effects CUE EEA MBC Net C

F P F P F P F P

Root exudates 0.5 3.4 16.6 *** 4.7 *

Mineral activity 4.9 * 62.2 *** 1.2 1.8

Inoculum 4.7 * 0.2 0.0 19.4 ***

Root*Mineral 0.5 10.0 *** 10.9 *** 0.1

Root*Inoculum 4.9 * 1.2 1.4 2.8

Mineral*Inoculum 0.7 1.9 1.0 0.7

Root*Mineral*Inoculum 2.4 0.5 1.8 0.3

167

Table C4. Change (%) in soil respiration due to the effects of the treatment factors during

Phase 2

Note: Expected effects were the additive effects sizes of each treatment when imposed

individually. Observed effects were equal to (soil respiration between treatment

combination – soil respiration under the corresponding control) / soil respiration under

the corresponding control. Difference was equal to observed effect – expected effect. For


Glucose vs. Xylan 52.4

Cellobiose vs. Xylan 50.1

Root exudates vs. Control 34.4

Fungi + Bacteria vs. Bacteria 3.5

High vs. Low clay activity 21.5

Medium vs. Low clay activity 10.7

Joint effects


Glucose × Root 86.8 104.5 17.7

Glucose × FB 55.9 58.2 2.2

Glucose × High 73.9 90.4 16.5

Glucose × Medium 63.1 56.1 -6.9

Cellobiose × Root 84.5 109.2 24.8

Cellobiose × FB 53.6 56.9 3.3

Cellobiose × High 71.6 91.9 20.3

Cellobiose × Medium 60.7 57.4 -3.4

Root × FB 37.9 38.8 0.9

Root × High 55.9 68.6 12.7

Root × Medium 45.1 65.8 20.7

FB × High 25.0 21.0 -4.0

FB × Medium 14.2 15.8 1.6

Glucose × Root × FB 90.3 118.8 28.5

Glucose × Root × High 108.3 157.2 49.0

Glucose × Root × Medium 97.5 144.3 46.8

Glucose × FB × High 77.4 90.8 13.4

Glucose × FB × Medium 66.6 72.4 5.8

Cellobiose × Root × FB 88.0 127.5 39.6

Cellobiose × Root × High 106.0 175.4 69.4

Cellobiose × Root × Medium 95.1 160.2 65.1

Cellobiose × FB × High 75.1 102.9 27.8

Cellobiose × FB × Medium 64.3 77.3 13.1

Root × FB × High 59.4 71.3 11.9

Root × FB × Medium 48.6 71.2 22.6

Glucose × Root × FB × High 111.8 184.1 72.3

Glucose × Root × FB × Medium 101.0 162.3 61.3

Cellobiose × Root × FB × High 109.5 210.7 101.2

Cellobiose × Root × FB × Medium 98.7 184.0 85.3

168

Glucose × Root × FB × High, the expected effect was equal to 52.2 + 34.4 + 3.5 + 21.5;

the observed effect was equal to (mean soil respiration under glucose, root exudates,

fungi + bacteria, and high clay activity - mean soil respiration under xylan, no root

exudates, bacteria only, and low clay activity) / mean soil respiration under xylan, no root

exudates, bacteria only, and low clay activity.

169

Table C5. Change (%) in soil microbial biomass carbon due to the effects of the

treatment factors during Phase 2

Note: See Table C4 for more details about calculating expected and observed effects and

the difference between them.


Glucose vs. Xylan -8.0



Fungi + Bacteria vs. Bacteria -9.2

High vs. Low clay activity -24.0

Medium vs. Low clay activity -6.9

Joint effects


Glucose × Root 2.4 1.5 -1.0

Glucose × FB -17.1 -16.5 0.6

Glucose × High -32.0 -25.2 6.8

Glucose × Medium -14.8 -27.2 -12.4

Cellobiose × Root 11.5 11.1 -0.4

Cellobiose × FB -8.1 -8.5 -0.5

Cellobiose × High -23.0 -19.9 3.0

Cellobiose × Medium -5.8 -22.1 -16.3

Root × FB 1.2 0.3 -0.9

Root × High -13.6 -13.7 0.0

Root × Medium 3.5 -3.2 -6.8

FB × High -33.2 -28.3 4.9

FB × Medium -16.0 -16.9 -0.9

Glucose × Root × FB -6.7 -15.6 -8.9

Glucose × Root × High -21.6 -17.0 4.5

Glucose × Root × Medium -4.4 -14.1 -9.7

Glucose × FB × High -41.2 -27.5 13.6

Glucose × FB × Medium -24.0 -30.4 -6.4

Cellobiose × Root × FB 2.3 -0.8 -3.2

Cellobiose × Root × High -12.6 -6.1 6.5

Cellobiose × Root × Medium 4.6 2.8 -1.8

Cellobiose × FB × High -32.1 -28.0 4.1

Cellobiose × FB × Medium -14.9 -18.1 -3.1

Root × FB × High -22.8 -19.6 3.2

Root × FB × Medium -5.6 -17.6 -11.9

Glucose × Root × FB × High -30.8 -30.0 0.8

Glucose × Root × FB × Medium -13.6 -34.3 -20.7

Cellobiose × Root × FB × High -21.7 -19.7 2.0

Cellobiose × Root × FB × Medium -4.5 -16.9 -12.3

170

Table C6. Change (%) in mean soil enzyme activity due to the effects of the treatment

factors during Phase 2




Glucose vs. Xylan -18.1

Cellobiose vs. Xylan -7.5





Joint effects


Glucose × Root -8.9 -9.4 -0.5

Glucose × FB -43.7 -35.6 8.1

Glucose × High 10.2 2.4 -7.8

Glucose × Medium -80.8 151.2 232.0

Cellobiose × Root 1.7 -6.6 -8.3

Cellobiose × FB -33.1 -32.0 1.0


Cellobiose × Medium -70.1 234.3 304.5

Root × FB -16.4 -17.5 -1.1

Root × High 37.5 34.7 -2.8

Root × Medium -53.5 -50.4 3.0

FB × High 2.7 -5.3 -8.0

FB × Medium -88.2 -73.1 15.2

Glucose × Root × FB -34.5 -32.9 1.6

Glucose × Root × High 19.4 18.9 -0.6

Glucose × Root × Medium -71.6 -33.2 38.4

Glucose × FB × High -15.3 -6.1 9.2

Glucose × FB × Medium -106.3 -65.1 41.3

Cellobiose × Root × FB -23.9 -22.9 1.0


Cellobiose × Root × Medium -60.9 -55.0 5.9

Cellobiose × FB × High -4.7 8.1 12.8

Cellobiose × FB × Medium -95.7 -75.7 20.0

Root × FB × High 11.9 -5.1 -17.0

Root × FB × Medium -79.0 -71.3 7.7

Glucose × Root × FB × High -6.1 -0.2 5.9

Glucose × Root × FB × Medium -97.1 -65.6 31.5


Cellobiose × Root × FB × Medium -86.5 -71.9 14.7

171

Table C7. Change (%) in heavy fraction carbon content due to the effects of the

treatment factors during Phase 2






Root exudates vs. Control -13.2

Fungi + Bacteria vs. Bacteria 15.1



Joint effects


Glucose × Root -13.0 -14.2 -1.2

Glucose × FB 15.3 9.5 -5.8

Glucose × High 12.0 18.0 6.0

Glucose × Medium -5.2 16.3 21.5

Cellobiose × Root -8.4 -3.0 5.4

Cellobiose × FB 19.9 22.6 2.7


Cellobiose × Medium -0.6 18.2 18.9

Root × FB 1.9 -0.1 -2.0

Root × High -1.4 -0.2 1.2

Root × Medium -18.7 -28.1 -9.5

FB × High 26.8 27.9 1.1

FB × Medium 9.6 10.0 0.4

Glucose × Root × FB 2.1 2.3 0.2

Glucose × Root × High -1.2 -12.3 -11.1

Glucose × Root × Medium -18.4 -30.8 -12.4

Glucose × FB × High 27.1 38.9 11.8


Cellobiose × Root × FB 6.7 24.4 17.8


Cellobiose × Root × Medium -13.8 -21.3 -7.5



Root × FB × High 13.6 4.8 -8.8

Root × FB × Medium -3.6 -14.7 -11.0

Glucose × Root × FB × High 13.9 -6.1 -20.0

Glucose × Root × FB × Medium -3.4 -13.8 -10.4


Cellobiose × Root × FB × Medium 1.2 11.9 10.6

172

Table C8. Change (%) in net change in soil carbon due to the effects of the treatment

factors during Phase 2






Root exudates vs. Control -27.7



Medium vs. Low clay activity 20.8

Joint effects


Glucose × Root 94.3 39.1 -55.2

Glucose × FB 121.2 116.4 -4.8

Glucose × High 166.8 252.4 85.6

Glucose × Medium 142.9 125.9 -17.0

Cellobiose × Root 89.1 45.7 -43.5

Cellobiose × FB 116.0 114.1 -1.9


Cellobiose × Medium 137.7 129.5 -8.2

Root × FB -28.6 -28.0 0.6

Root × High 17.0 3.6 -13.4

Root × Medium -6.9 -3.8 3.1

FB × High 43.9 28.3 -15.6

FB × Medium 20.0 21.1 1.2

Glucose × Root × FB 93.5 42.1 -51.4

Glucose × Root × High 139.1 100.1 -39.0

Glucose × Root × Medium 115.2 76.7 -38.5

Glucose × FB × High 165.9 181.4 15.5


Cellobiose × Root × FB 88.2 57.0 -31.2

Cellobiose × Root × High 133.9 128.5 -5.4

Cellobiose × Root × Medium 110.0 98.6 -11.4



Root × FB × High 16.2 -1.5 -17.7

Root × FB × Medium -7.8 0.5 8.2

Glucose × Root × FB × High 138.2 96.9 -41.3

Glucose × Root × FB × Medium 114.3 76.8 -37.5


Cellobiose × Root × FB × Medium 109.1 105.9 -3.2

173

Figure C1. The pictures of artificial soil (a) and root system (b).

a b

174

Figure C2. Effects of root exudates on soil variables in synthetic soil system during


letters.

175

Figure C3. Effects of soil microbial community composition on soil variables in

synthetic soil system during Phase 1. Significant differences among treatments at P <

0.05 are indicated by different letters. B: bacteria only; FB: both fungi and bacteria.

176

Figure C4. Effects of soil clay activity on soil variables in synthetic soil system during


letters.

177

Figure C5. The temporal pattern of soil respiration under all treatments in synthetic soil

system during Phase 1.

178

Figure C6. The interactive effects of root exudates and soil mineralogy on soil microbial

biomass carbon in synthetic soil system during Phase 1. Error bars represent ±1 standard

error of the mean. See Table C2 in the appendices for statistical results.

0

1000

2000

3000

4000

Low Medium HighSo

il m

icro

bia

l b

iom

ass c

arb

on

(m

g g

-1)

root

control

root exudates

179

Figure C7. The interactive effects of root exudates and soil microbial community

composition on soil microbial carbon use efficiency in synthetic soil system during Phase

1. Error bars represent ±1 standard error of the mean. See Table C2 in the appendices for

statistical results.

0.60

0.65

0.70

0.75

0.80

Root exudates Control

Soil

mic

rob

ial ca

rbo

n u

se

eff

icie

ncy

inoculum

b

fb

180

Figure C8. The interactive effects of root exudates and soil clay activity on mean


error of the mean. See Table C2 in the appendices for statistical results.

0.60

0.65

0.70

0.75

0.80

Root exudates Control

Soil

mic

rob

ial ca

rbo

n u

se

eff

icie

ncy

inoculum

b

fb

181

Figure C9. The interactive effects of C input quality, soil microbial community

composition, and soil clay activity on soil respiration in synthetic soil system during

Phase 2. Error bars represent ±1 standard error of the mean. See Table 4.1 for statistical

results.

182

Figure C10. The interactive effects of C input quality, root exudates, and soil clay

activity on soil microbial biomass carbon in synthetic soil system during Phase 2. Error

bars represent ±1 standard error of the mean. See Table 4.2 for statistical results.

183

Figure C11. The interactive effects of C input quality, root exudates, and soil microbial

community composition on soil microbial biomass carbon in synthetic soil system during

Phase 2. Error bars represent ±1 standard error of the mean. See Table 4.2 for statistical

results.

184

Figure C12. The interactive effects of C input quality and soil clay activity on mean


error of the mean. See Table 4.2 for statistical results.

0

2

4

6

Low Medium High

Mea

n e

nzym

e a

ctivity (

mm

ol g

-1 h

-1)

carbon

cellobiose

glucose

xylan

185

Figure C13. The interactive effects of C input quality, root exudates, and soil microbial

community composition on mean enzyme activity in synthetic soil system during Phase

2. Error bars represent ±1 standard error of the mean. See Table 4.2 for statistical results.

186

Figure C14. The interactive effects of root exudates, soil microbial community

composition, and soil clay activity on mean enzyme activity in synthetic soil system

during Phase 2. Error bars represent ±1 standard error of the mean. See Table 4.2 for

statistical results.

187

Figure C15. The interactive effects of root exudates, soil microbial community

composition, and soil clay activity on heavy fraction carbon content in synthetic soil

system during Phase 2. Error bars represent ±1 standard error of the mean. See Table 4.2

for statistical results.

188

Figure C16. The interactive effects of carbon input quality, root exudates, and soil clay

activity on heavy fraction carbon content in synthetic soil system during Phase 2. Error

bars represent ±1 standard error of the mean. See Table 4.2 for statistical results.

189

Figure C17. The interactive effects of root exudates, soil clay activity, and harvest on

heavy fraction carbon content in synthetic soil system during Phase 2. Error bars

represent ±1 standard error of the mean. See Table 4.2 for statistical results.

190

Figure C18. The interactive effects of carbon input quality, root exudates, soil microbial

community composition, and harvest on heavy fraction carbon content in synthetic soil

system during Phase 2. Error bars represent ±1 standard error of the mean. See Table 4.2

for statistical results.

191

Figure C19. The interactive effects of C input quality, soil microbial community

composition, soil clay activity, and harvest on heavy fraction carbon content in synthetic

soil system during Phase 2. Error bars represent ±1 standard error of the mean. See Table

4.2 for statistical results.

192

Figure C20. The interactive effects of treatments on net change in carbon in synthetic

soil system during Phase 2. Error bars represent ±1 standard error of the mean. See Table

4.2 for statistical results.

193

CURRICULUM VITAE

Guopeng Liang, Ph.D.

Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108

[email protected] | https://guopengliang.wixsite.com/ecosystem-ecology

EDUCATION

Ph.D. in Ecology 2016 - 2021

Utah State University, Logan, UT.

M.S. in Soil Science 2013 - 2016

Chinese Academy of Agricultural Sciences, Beijing, China.

B.S. in Agricultural Resources and Environment/Laws 2009 - 2013

Shandong Agricultural University, Tai’an, China

PROFESSIONAL EXPERIENCE

Postdoctoral associate, University of Minnesota 2022 -

RESEARCH INTERESTS

Global Change Ecology, Ecosystem Carbon Cycling, Plant Ecology, Soil

Biogeochemistry, Plant-Soil-Microbe Interactions

PEER-REVIEWED PUBLICATIONS (Google Scholar: H index = 12, 560+ citations)

In review, revision, or preparation:

1. Liang, G., Sasha, R., Stark, J., Waring, B (2022) Effects of multiple global change

factors on soil respiration in a dryland ecosystem. Submitted to Soil Biology and

Biochemistry.

2. Liang, G., Waring, B (2022) Nitrogen agronomic efficiency decreases at decadal

timescales: evidence from 164 global long-term nitrogen application studies. Submitted

to PNAS.

3. Liang, G (2022) Long-term nitrogen fertilization not only increases crop yield but also

promotes its stability and sustainability: a global synthesis. Field Crops Research. In

review.

4. Li, S., Lu, J., Liang, G., Wu, X., Zhang, M., Plougonven, E., Wang, Y., Gao, L.,

Abdelrhman, A., Song, X., Liu, X., Degré, A (2022) Does soil water repellency reduce

corn yield by changing soil water availability under long-term tillage management?

SOIL. In revision.

5. Wen, J., Brahney, J., Sun, N., Zheng, J., Ji, H., Kang, H., Du, B., Liang, G., Umair, M.,

Lin, Y., Ma, Z., Liu, C (2022) Plant functional type is the primary driver of allometric

scaling relationships between leaf nitrogen and phosphorus across a gradient in P

availability in a subtropical forest ecosystem. Forestry Research. In review.

6. Song, X., Li, J., Liu, X., Liang, G., Li, S., Zhang, M., Zheng, F., Wang, B., Wu, X.,

Wu, H (2022) Altered microbial resource limitation regulates soil organic carbon

sequestration based on ecoenzyme stoichiometry under long-term tillage systems.

Geoderma. In review.

194

7. Yin, S., Liang, G., Wang, C., Zhou, Z (2022) Temporal niche differentiation between

plants and soil microorganisms is common in nature. Submitted to Ecology.

8. Liu, X., Li, Q., Tan, S., Wu, X., Song, X., Gao, H., Han, Z., Jia, A., Liang, G., Li, S

(2022) Lower carbon mineralization but higher temperature sensitivity is linked with soil

macro-aggregates at various soil moistures in 21-year conservation tillage systems.

Submitted to Soil and Tillage Research.

9. Waring, B., Gee, A., Liang, G., Adkins, S (2022) How much do the microbes matter?

A quantitative analysis of microbial community structure-function relationships in litter

decay. Submitted to iScience.

Published:

1. Liang, G., Luo, Y., Zhou, Z., Waring, B (2021) Nitrogen effects on plant productivity

change at decadal timescales. Global Ecology and Biogeography.

2. Lu, J., Li, S., Liang, G., Wu, X., Zhang, Q., Gao, C., Li, J., Jin, D., Zheng, F., Zhang,

M., Abdelrhman, A., Degré, A (2021) The contribution of microorganisms to soil organic

carbon stock under fertilization varies among aggregate size classes. Agronomy.

3. Liu, X., Wu, X., Liang, G., Zheng, F., Li, S (2021) A global meta-analysis of the

impacts of no-tillage on soil aggregation and aggregate-associated organic carbon. Land

Degradation and Development.

4. Lu, J., Li, S., Wu, X., Liang, G., Gao, C., Li, J., Jin, D., Wang, B., Zhang, M., Zheng,

F., Degré, A (2021) The dominant microorganisms vary with aggregates sizes in

promoting soil C accumulation under straw application. Archives of Agronomy and Soil

Science.

5. Xu, H., Cai, A., Wu, D., Liang, G., Xiao, J., Gao, Q., Li, Y., Zhang, W., Xu, M (2021)

Effects of biochar application on crop productivity, soil carbon sequestration, and global

warming potential controlled by biochar C:N and soil pH: a global meta-analysis. Soil

and Tillage Research. 213: 105125

6. Liang, G., Wu, X., Cai, A., Dai, H., Zhou, L., Cai, D., Houssou, A.A., Gao, L., Wang,

B., Li, S., Song, X., Wu, H (2021) Correlations among soil biochemical parameters, crop

yield, and soil respiration vary with growth stage and soil depth under fertilization.

Agronomy Journal. 113: 2450–2462

7. Li, S., Tan, D., Wu, X., Degré, A., Long, H., Zhang, S., Lu, J., Gao, L., Zheng, F., Liu,

X., Liang, G (2021) Negative pressure irrigation increases vegetable water productivity

and nitrogen use efficiency by improving soil water and NO3-N distributions.

Agricultural Water Management 251: 106853

8. Li, S., Lu, J., Liang, G., Wu, X., Zhang, M., Plougonven, E., Wang, Y., Gao, L.,

Abdelrhman, A., Song, X., Liu, X., Degré, A (2021) Factors governing soil water

repellency under tillage management: the role of pore structure and hydrophobic

substances. Land Degradation and Development 32: 1046–1059

9. Wilcox, K., Komatsu, K., Avolio, M., C2E consortium (2020) Improving

collaborations between empiricists and modelers to advance grassland community

dynamics in ecosystem models. New Phytologist 228: 1467–1471

10. Cai, A., Liang, G., Yang, W., Zhu, J., Han, T., Zhang, W., Xu, M (2020) Patterns and

driving factors of litter decomposition across Chinese terrestrial ecosystems. Journal of

Cleaner Production 278: 23964.

195

11. Li, Y., Li, Z., Cui S., Liang, G., Zhang, Q (2020) Microbial-derived carbon

components are critical for enhancing soil organic carbon in no-tillage croplands: A

global perspective. Soil and Tillage Research 105: 104758

12. Li, S., Wu, X., Liang, G., Gao, L., Wang, B., Lu, J., Abdelrhman, A., Song, X.,

Zhang, M., Zheng, F., Degré, A (2020) Is least limiting water range a useful indicator of

the impact of tillage management on maize yield? Soil and Tillage Research 199:

104602.

13. Cai, A., Chang, N., Zhang, W., Liang, G., Zhang, X., Hou, E., Jiang, L., Chen, X.,

Xu, M., Luo, Y (2020) The spatial patterns of litter turnover time in Chinese terrestrial

ecosystems. European Journal of Soil Science 71: 856-867.

14. Gao, L., Wang, B., Li, S., Han, Y., Zhang, X., Gong, D., Ma, M., Liang, G., Wu, H.,

Wu, X., Cai, D., Degré, A (2019) Effects of different long-term tillage systems on the

composition of organic matter by 13C CP/TOSS NMR in physical fractions in the Loess

Plateau of China. Soil and Tillage Research 194: 104321.

15. Wang, B., Yu, W., Wu, X., Gao, L., Li, J., Li, S., Liu, C., Li, Q., Liang, G., Cai, D.,

Zhang, J (2019) Effect of straw addition on the formation of aggregates and accumulation

of organic carbon in dryland soil. Scientia Agricultura Sinica 9: 1553-1563. (in Chinese

with English abstract)

16. Wang, B., Gao, L., Yu, W., Wei, X., Li, S., Li, J., Liang, G., Song, X., Cai, D., Wu, X

(2019) Soil aggregates and organic carbon distribution in deep soil under dryland long-

term tillage and residue retention. Journal of Arid Land 11: 241-254.

17. Wang, J., Yang, Q., Zhai, D., Jiang, L., Liang, G., Qiao, Y., Sun, X., Wei, N., Wang,

X., Xia, J (2019) Relative contributions of biotic and abiotic factors to the spatial

variation of litter stock in an old-growth subtropical forest. Journal of Plant Ecology 12:

769–780

18. Qiao, Y., Wang, J., Liang, G., Du, Z., Zhou, J., Zhu, C., Huang, K., Zhou, X., Luo,

Y., Yan, L., Xia, J (2019) Global variation of soil microbial carbon-use efficiency in

relation to growth temperature and substrate supply. Scientific Reports 9: 5621.

19. Cai, A., Xu, M., Wang, B., Zhang, W., Liang, G., Hou, E., Luo, Y (2019) Manure

acts as a better fertilizer for increasing crop yields than synthetic fertilizer does by

improving soil fertility. Soil and Tillage Research 189: 168-175.

20. Liang, G., Cai, A., Wu, H., Wu, X., Houssou, A.A., Ren, C., Wang, Z., Gao, L.,

Wang, B., Li, S., Song, X., Cai, D (2019) Soil biochemical parameters in the rhizosphere

contribute more to changes in soil respiration and its components than those in the bulk

soil under nitrogen application in croplands. Plant and soil 435: 111-125.

21. Gao, L., Wang, B., Li, S., Wu, H., Wu, X., Liang, G., Gong, D., Zhang, X., Cai, D.,

Degré, A (2019) Soil wet aggregate distribution and pore size distribution under different

tillage systems after 16 years in the Loess Plateau of China. Catena 173: 38-47.

22. Song, X., Wu, H., Wu, X., Li, Q., Wang, B., Li, S., Liang, G., Li, J., Liu, C., Zhang,

M (2018) Long-term conservation tillage improves surface soil carbon and nitrogen

content and rhizosphere soil enzyme activities. Journal of Plant Nutrition and Fertilizer

24: 1588-1597. (in Chinese with English abstract)

23. Cai, A., Liang, G., Zhang, X., Zhang, W., Li, L., Rui, Y., Xu, M., Luo, Y (2018)

Long-term straw decomposition in agro-ecosystems described by a unified three-

196

exponentiation equation with thermal time. Science of the Total Environment 636: 699-

708.

24. Liang, G., Wu, H., Houssou, A.A., Cai, D., Wu, X., Gao, L., Wang, B., Li, S (2018)

Soil respiration, glomalin content, and enzymatic activity response to straw application in

a wheat-maize rotation system. Journal of Soils and Sediments 18: 697-707.

25. Gao, L., Becker, E., Liang, G., Houssou, A.A., Wu, H., Wu, X., Cai, D., Degré, A

(2017) Effect of different tillage systems on aggregate structure and inner distribution of

organic carbon. Geoderma 288: 97-104.

26. Li, S., Wu, X., Dang J., Pei, X., Gao, L., Li, J., Wang, B., Liang, G., Long, H (2017)

Effects of negative pressure irrigation on yield, quality and water and nitrogen use

efficiency of cucumber. Soil and Fertilizer Sciences in China 2: 55-62. (in Chinese with

English abstract)

27. Li, S., Wu, X., Long, H., Zhang, S., Wang, X., Liang, G., Gao, L., Li, J., Wang, B.,

Hao, X., Li, J., Zhang, S (2017) Water and nitrogen use efficiencies of cucumber under

negatively pressurized fertigation. Journal of Plant Nutrition and Fertilizer 23: 416-426.

(in Chinese with English abstract)

28. Houssou, A.A., Liang, G., Gao, L., Li, J., Wu, X., Wu, H., Wang, X., Cai, D (2016)

Effect of conservation tillage on soil respiration rate and water content under

wheat/maize system in North China Plain. Journal of Soil Science and Environmental

Management 7: 10-22.

29. Liang, G., Houssou, A.A., Wu, H., Wu, X., Cai, D., Gao, L., Li, J., Wang, B., Li, S

(2016) Soil nitrogen content and enzyme activities in the rhizosphere and non-

rhizosphere of summer maize under different application rates. Chinese Journal of

Applied Ecology 27: 1917-1924. (in Chinese with English abstract)

30. Liang, G., Houssou, A.A., Wu, H., Cai, D., Wu, X., Gao, L., Li, J., Wang, B., Li, S

(2015) Seasonal patterns of soil respiration and related soil biochemical properties under

nitrogen addition in winter wheat field. PLoS ONE 10(12): e0144115.

31. Houssou, A.A., Liang, G., Gao, L., Wu, X., Wu, H., Wang, X., Cai, D (2015) Effect

of conservation tillage on soil respiration, organic carbon, moisture and yield of wheat

/maize system on North China Plain. International Journal of Science and Research 4:

2460-2467.

32. Wang, B., Wu, X., Yu, W., Yang, Y., Wang, X., Li, J., Liang, G., Cai, D (2015)

Different carbon and nitrogen managements on soil respiration of spring maize farmland.

Soil and Fertilizer Sciences in China 4: 1-7. (in Chinese with English abstract)

33. Wang, B., Cai, D., Li, J., Liang, G., Yu, W., Wang, X., Yang, Y., Wang, X (2015)

Effects of long-term conservation tillage on soil organic carbon, corn yield and water

utilization. Journal of Plant Nutrition and Fertilizer 21: 1455-1464. (in Chinese with

English abstract)

34. Li, J., Wu, H., Wu, X., Cai, D., Wang, B., Liang, G., Yao, Y., Lv, J (2015) Effects of

15-year conservation tillage on soil and aggregate organic carbon sequestration in the

Loess Hilly Region of China. Scientia Agricultura Sinica 48: 4690-4697. (in Chinese

with English abstract)

35. Dou, Q., Wang, J., Yin, B., Liang, G., Cui, X (2015) Alleviating effects of exogenous

EBR on tomato seedlings during copper stress. Plant Physiology Journal 3: 287-294. (in

Chinese with English abstract)

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36. Yin, B., Liang, G., Jia, W., Cui, X (2014) Exogenous EBR mediated the plant growth

and absorption and accumulation of Cu, Fe and Zn in tomato seedlings under Cu stress.

Chinese Journal of Eco-Agriculture 22: 578-584. (in Chinese with English abstract)

37. Zhang, M., Liang, G., Jiang, C., Cui, X (2014) Exogenous nitric oxide involved in the

accumulation and subcellular distribution of Fe, Zn and Mn in tomato seedlings under

copper stress. Journal of Plant Nutrition and Fertilizer 3: 670-680. (in Chinese with

English abstract)

TEACHING EXPERIENCE

• Teaching Assistant, Biology lab. Utah State University, Fall 2019.

• Teaching Assistant, Microbiology lab. Utah State University, Spring 2019.

• Teaching Assistant, Agricultural Water Resources and Utilization, Chinese Academy of

Agricultural Sciences, Spring 2014.

• Teaching Assistant, Modern Soil Tillage, Chinese Academy of Agricultural Sciences,

Fall 2013.

MENTORING EXPERIENCE

• Yundi Bai (2020 - 2021), master student at Imperial College London.

• Camilla Moses (2020 - 2021), undergraduate technician at Utah State University.

• Jalynn Jones (2019 - 2021), undergraduate technician at Utah State University.

• Preston Christensen (2018 - 2019), undergraduate technician at Utah State University.

ACADEMIC SERVICE

• Guest editor for Agronomy

• Journal peer review (> 100 reviews, verified by Publons): Agricultural Water

Management, Agricultural and Forest Meteorology, Agronomy, Applied Soil Ecology,

Archives of Agronomy and Soil Science, CATENA, Environmental Microbiology,

Environmental Research, Field Crops Research, Geoderma, Global Change Biology,

Global Ecology and Biogeography, Heliyon, Journal of Agronomy and Crop Science,

Journal of Environmental Management, Journal of Cleaner Production, Land Degradation

& Development, Plant & Soil, PLOS ONE, Soil & Tillage Research

GRANTS

• Guopeng Liang. Modeled carbon cycle responses to altered precipitation and

interannual variation in desert grasslands. Sevilleta LTER Graduate Fellowship funded

by Sevilleta LTER. 2018. $4,000.

• Guopeng Liang. The impacts of manure on vegetable growth. Undergraduate

Fellowship funded by Tai'an Environmental Protection Agency, China. 2011. $1,000.

AWARDS

• Joseph E. Greaves Endowed Scholarship ($3,000), 2021

Sponsored by Utah State University

• Travel Award for attending SSSA meeting ($500), 2020

Sponsored by Utah State University

• IPNI Scholarship ($2,000), 2016

Sponsored by International Plant Nutrition Institute (IPNI)

• Monsanto Scholarship ($1,000), 2016

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Sponsored by Monsanto Company

• Best Oral Presentation, 2016

Sponsored by Symposium for Chinese Young Soil Scientists

• Academic Scholarship, 2013-2014

Sponsored by Chinese Academy of Agricultural Sciences

• Outstanding Student in Social Work, 2011-2012

Sponsored by Shandong Agricultural University

• Academic Scholarship, 2011-2012

Sponsored by Shandong Agricultural University

• Second prize of Environmental design contest, 2011

Sponsored by Shandong Society of Environmental Sciences

OUTREACH ACTIVITIES

• Proposal reviewer for 2019 Biology Graduate Student Association (BGSA) award

sponsored by Department of Biology at Utah State University.

• Member of both the Ecology Center Seminar Committee in 2019 and the Biology

Department Seminar Committee in 2021 at Utah State University to invite and host

seminar speakers.

METHODS EXPERTISE

Design, installation, and maintenance of manipulative experiments in the field

(treatments included: different nitrogen application levels, straw, inorganic and organic

fertilizer application, warming and altered precipitation amount).

Design and maintenance of incubation experiments in the laboratory (treatments

included: rewetting cycles and artificial root-soil systems [e.g. different inoculums,

different substrate types, and different soil clay contents]).

Soil respiration: Li-COR 8100 soil CO2 flux chamber in the field; gas chromatography

and PICARRO in the lab.

Leaf photosynthesis: Li-COR 6400.

Biological soil analyses: microbial biomass (using chloroform fumigation extraction);

enzyme activities (using fluorimetric assays); glomalin content (using Bradford method);

soil DNA extraction (using DNA extraction kit); PCR.

Physical and chemical soil analyses: pH (using probe in laboratory); soil bulk density;

total carbon and nitrogen (Elementar Analysen-systeme GmbH); available nitrogen

(using a flow injection autoanalyzer); aggregate size distribution (wet sieving); density-

based fractionation of soil organic carbon (using SPT).

Isotope technique: using 13C-glucose to measure soil microbial carbon use efficiency.

Statistical analysis: QIIME; using R to run ANOVA and regression, structural equation

modeling, PCA and RDA analysis, and boosted regression trees.

Scientific graphing software: R; Sigmaplot; Origin.

Synthesis of ecological data using meta-analysis: using engauge digitizer and package

“metafor” in R.

Earth system models: Terrestrial ECOsystem (TECO) model (using Fortran).

Documents

Global Change Effects on Carbon Cycling in Terrestrial