the molecular composition of soil organic matter … · 2013-11-07 · plant inputs under elevated...

THE MOLECULAR COMPOSITION OF SOIL ORGANIC

MATTER (SOM) AND POTENTIAL RESPONSES TO GLOBAL

WARMING AND ELEVATED CO2

by

Xiaojuan Feng

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Geography

University of Toronto

© Copyright by Xiaojuan Feng (2009)

THE MOLECULAR COMPOSITION OF SOIL ORGANIC MATTER (SOM)

AND POTENTIAL RESPONSES TO GLOBAL WARMING AND ELEVATED CO2

Xiaojuan Feng Doctor of Philosophy Graduate Department of Geography University of Toronto 2009

ABSTRACT

Soil organic matter (SOM) contains about twice the amount of carbon in the

atmosphere. With global changes, the potential shifts in SOM quantity and quality are a

major concern. Due to its heterogeneity, SOM remains largely unknown in terms of its

molecular composition and responses to climatic events. Traditional bulk soil analysis

cannot depict the structural changes in SOM. This thesis applies two complementary

molecular-level methods, i.e., SOM biomarker gas chromatography/mass spectrometry

(GC/MS) and nuclear magnetic resonance (NMR) spectroscopy, to examine the origin

and degradation of various SOM components in grassland and temperate forest soils, and

to investigate the shifts in microbial community and SOM composition with both

laboratory- and field-simulated global changes, such as increasing soil temperatures,

frequent freeze-thaw cycles, elevated atmospheric CO2 levels, and nitrogen (N)

deposition.

This thesis has several major findings. First, as the most active component in soil,

microbial communities were sensitive to substrate availability changes resulting from

prolonged soil incubation, freeze-thaw-induced cell lyses, N fertilization and increased

ii

plant inputs under elevated CO2 or soil warming. Microbial community shifts have direct

impacts on SOM decomposition patterns. For instance, an increased fungal community

was believed to contribute to the enhanced lignin oxidation in an in situ soil warming

experiment as the primary degrader of lignin in terrestrial environments. Second, contrast

to the conventional belief that aromatic structure was recalcitrant and stable in SOM,

ester-bond aliphatic lipids primarily originating from plant cutin and suberin were

preferentially preserved in the Canadian Prairie grassland soil profiles as compared with

lignin-derived phenols. Cutin- and suberin-derived compounds also demonstrated higher

stability during soil incubation. With an increased litter production under elevated CO2 or

global warming, an enrichment of alkyl structures that had strong contributions from leaf

cuticles was observed in the Duke Forest Free Air CO2 Enrichment (FACE) and soil

warming experiments, suggesting an accumulation of plant-derived recalcitrant carbon in

the soil. These results have significant implications for carbon sequestration and

terrestrial biogeochemistry. Overall, this thesis represents the first of its kind to employ

comprehensive molecular-level techniques in the investigation of SOM structural

alterations under global changes.

iii

Acknowledgements

I sincerely thank my supervisor, Dr. Myrna Simpson, who has always been highly

organized, supportive, and encouraging to me during my research for this thesis. Her

vision and enthusiasm for research have inspired me, and I have greatly benefited from

her professional advice, intellect and wisdom. Much of my work within and beyond this

thesis would be impossible without the guidance and opportunities she offered me.

I am obliged to Dr. André Simpson for his support to my research through a joint

research grant and kind help with NMR experiments and data interpretation. I deeply

treasured the opportunity to work within both M. and A. Simpson’s laboratories, which

turned out to be pleasant as well as fruitful. Many thanks are due to my doctoral

committee members, Drs. Brian Branfireun, Sharon Cowling, Nathan Basiliko, and Tony

Price, who offered great insights and advice during my thesis preparation and throughout

my Ph.D. degree. I’d also like to thank Drs. George Arhonditsis and Joseph Yavitt for

their willingness to serve on my defence committee.

All members in the M. and A. Simpson groups are acknowledged for their help and

support in the laboratory. I am indebted to Dr. Yunping Xu, who gave me tips and

suggestions on my studies and beyond, and Dr. Jennifer McKelvie, who kindly advised

me on thesis writing, postdoctoral application, and my CSIA proposal. Dr. Angelika Otto

and Chuba Shunthirasingham are thanked for training me in my first year of Ph.D. Dr.

Andrew Baer is acknowledged for help with NMR experiments. I would also like to

thank Janice Austin, Leah Nielsen, Pui Sai Lau, Jennifer Heidenheim, Katherine Hills,

and Magda Celejewski for help with sample extraction and preparation and for not

iv

running away from me after all the hard work.

I am thankful to my collaborators, Drs. Kevin Wilson and D. Dudley Williams, for

their hard work in designing and conducting the warming experiment. Catherine Febria is

thanked for analyzing part of my samples for carbon content. I thank Dick Puurveen at

the University of Alberta for providing soil samples from the Ellerslie Research Station

and Dr. Henry Janzen for assistance with sampling at the Agriculture and Agri-Food

Canada Research Station near Lethbridge, Alberta. I am grateful to Drs. William

Schlesinger and Ram Oren for facilitating our collaboration with the Duke Forest FACE

experiment and Jeff Pippen is greatly thanked for help with sampling.

Funding from the Canadian Foundation for Climate and Atmospheric Sciences

(CFCAS) supported the research within this thesis. I am thankful for support from the

Department of Geography through UofT Fellowships, Griffith Taylor Scholarship,

Donald F. Putman/George Tatham Ontario Graduate Scholarship in Geography, and the

Neptis Foundation/Ontario Graduate Scholarship. Many thanks go to the Graduate

Counsellor, Marianne Ishibashi, for her kind help. I want to acknowledge the University

of Toronto Centre for Global Change Science for a Graduate Student Award. Ontario

Graduate Scholarship and Kwok Sau Po Scholarship are also greatly appreciated.

Finally, I want to give my special thanks to my parents and friends who mentally

supported me and cheered me up in the past five years. Ying Zheng, Qifan Zhang, Jess

Zhang, Lydia Chen, Yi Pan, Lu Tang, Yuyang Jiang, Frankie, and Cowye, thank you all

for making my life in Toronto so much memorable.

v

Table of Contents

Abstract ii Acknowledgements iv List of Tables x List of Figures xi Abbreviations xiv Chapter 1: Introduction 1

1.1 Literature Review 2 1.1.1 Introduction to Soil Organic Matter (SOM) 2 1.1.2 Major SOM Components and Biomarkers 3 1.1.2.1 Soil Lipids 4 1.1.2.2 Lignin-Derived Phenols 6 1.1.2.3 Microbial Phospholipid Fatty Acids (PLFAs) 8 1.1.2.4 Other SOM Components 8 1.1.3 Molecular-Level Techniques Used to Analyze SOM 9 1.1.4 Environmental Controls on SOM 12 1.1.5 Soil Respiration and Temperature Sensitivity 14 1.1.6 Microbial Decomposition of SOM 15 1.1.7 Grassland and Forest Soils 17

1.2 Objectives and Hypotheses 18 1.3 Thesis Summary 21 1.4 Statement of Authorship and Publication Status 25

Chapter 2: The Distribution and Degradation of Biomarkers in Alberta Grassland

Soil Profiles 28 2.1 Abstract 29 2.2 Introduction 29 2.3 Methods 32

2.3.1 Soil Samples 32 2.3.2 Particle Size Distribution and Carbon and Nitrogen Analyses 33 2.3.3 Sequential Extraction 33 2.3.4 Derivatization and GC/MS Analysis 34

2.4 Results and Discussion 36 2.4.1 Particle Size Distribution, Carbon and Nitrogen Contents, and

Extract Yields 36 2.4.2 Composition and Source of Total Solvent Extracts 38 2.4.3 Composition and Degradation of Bound Lipids 42 2.4.4 Distribution and Degradation of Lignin Compounds 47 2.4.5 Contribution of Above-Ground versus Below-Ground Residues 50 2.4.6 Changes in SOM Composition with Soil Depth 51

vi

2.5 Conclusions 53 2.6 Acknowledgements 53

Chapter 3: Temperature Responses of Individual Soil Organic Matter Components 54

3.1 Abstract 55 3.2 Introduction 56 3.3 Materials and Methods 60

3.3.1 Soil Incubation 60 3.3.2 Microbial Respiration 61 3.3.3 Chemical Analyses 62 3.3.4 Data Analyses 65

3.4 Results 66 3.4.1 Microbial Respiration and Soil Carbon and Nitrogen Contents 66 3.4.2 Decomposition of Solvent Extractable Compounds 68 3.4.3 Decomposition of Suberin- and Cutin-Derived Compounds 71 3.4.4 Decomposition of Lignin-Derived Compounds 77 3.4.5 Response of SOM Decomposition and Microbial Respiration to

Temperature Changes 80 3.5 Discussion 82 3.5.1 Decompositional Patterns of SOM Components 82 3.5.2 Recalcitrance of SOM Components 84 3.5.3 Temperature Sensitivity of SOM Components 87 3.6 Acknowledgements 90

Chapter 4: Temperature and Substrate Controls on Microbial Phospholipid Fatty

Acid Composition During Incubation of Grassland Soils Constrasting in Organic Matter Quality 91


4.3.1 Soil Incubation 96 4.3.2 Microbial Respiration 98 4.3.3 Measurements of Soil Carbon and Nitrogen Contents 98 4.3.4 PLFA Analyses 99 4.3.5 Statistical Analysis 100

4.4 Results 101 4.4.1 Microbial Respiration and Soil Carbon Contents 101 4.4.2 Microbial PLFA Distribution During Soil Incubation at Elevated

Temperatures 102 4.4.3 PLFA Indicators of Microbial Community Structure and Stress 104 4.4.4 Metabolic Quotient 108

4.5 Discussion 109 4.5.1 Soil Microbial Biomass and Activity During Soil Incubation 109

vii

4.5.2 Effects of Soil Disturbance and Substrate Constraints on Microbial Community Composition 112

4.5.3 Temperature and Substrate Effects on PLFA Stress Indicators 114 4.5.4 Implications for Global Warming 116 4.6 Acknowledgements 117

Chapter 5: Increased Cuticular Carbon Sequestration and Lignin Oxidation

in Response to Soil Warming 119 5.1 Abstract 120 5.2 Introduction 120 5.3 Results and Discussion 121 5.4 Methods 129

5.4.1 Soil Warming Experiment 129 5.4.2 Chemical Analyses 130 5.4.3 NMR Experiments 131 5.4.4 Statistical Analyses 132

5.5 Supplementary Information 132 5.5.1 Supplementary Calculation for Cuticular Carbon Sequestration 132 5.5.2 Supplementary Information for Methods – PLFA Nomenclature 133 5.6 Acknowledgements 133

Chapter 6: Responses of Soil Organic Matter and Microorganisms to

Freeze-Thaw Cycles 134 6.1 Abstract 135 6.2 Introduction 136 6.3 Materials and Methods 138

6.3.1 FTC Treatment of Soil Samples 138 6.3.2 Measurement of Microbial Respiration, Carbon and Nitrogen

Content 140 6.3.3 PLFA Analyses 140 6.3.4 Sequential Extractions of SOM 141 6.3.5 Derivatization and GC/MS Analysis 142

6.4 Results 144 6.4.1 Microbial Respiration 144 6.4.2 Carbon and Nitrogen Content 146 6.4.3 PLFAs 146 6.4.4 Free Lipids 150 6.4.5 Bound Lipids 150 6.4.6 Lignin-Derived Phenols 154

6.5 Discussion 157 6.5.1 Controls on Microbial Respiration 157 6.5.2 Source of the CO2 Flush 158 6.5.3 Responses of Microbial Biomass to Substrate Availability

and FTCs 159

viii

6.5.4 Stability of SOM Fractions 161 6.6 Conclusions 162 6.7 Acknowledgements 162

Chapter 7: Altered Microbial Community Structure and Organic Matter

Composition under Elevated CO2 and N Fertilization in the Duke Forest 164


7.3.1 Site Description and Sample Collection 169 7.3.2 Chemical Extractions and GC/MS Analysis 170 7.3.3 Compound Groupings and Parameters 173 7.3.4 NMR Analysis 175 7.3.5 Statistical Analysis 176

7.4 Results 176 7.4.1 Chemical Composition of the Forest Floor OM 176 7.4.2 Microbial and SOM Composition in the Surface Soil 180

7.5 Discussion 184 7.5.1 Microbial Responses to Elevated CO2 and N Fertilization 184 7.5.2 Molecular Indicators of Increased OM Inputs at Elevated CO2

Levels 187 7.5.3 Enrichment of Refractory Alkyl Carbon in SOM at

Elevated CO2 Levels 188 7.5.4 Fertilization-Induced Changes in OM Composition and

Degradation 190 7.6 Conclusions 191 7.7 Acknowledgements 192

Chapter 8: Conclusions 194 8.1 Summary 195 8.2 Recommended Future Research 199 Appendix 1: Preliminary GC/MS Analysis of Mineral-Protected Soil Lipids

From the Duke Forest FACE Experiment 202 References 207

ix

List of Tables

Chapter 2 Table 2.1: Particle size distribution, carbon and nitrogen contents, and extract yields of Alberta grassland soils………………………………...……………………………….. 37 Table 2.2: Compounds identified in the total solvent extracts………………………… 39 Table 2.3: Compounds identified in base hydrolysis products of grassland soils........... 43 Table 2.4: Compounds identified in CuO oxidation products………………………..... 48

Chapter 3 Table 3.1: Model fitting parameters of SOM components in grassland soils………….. 72

Chapter 5 Table 5.S1: Concentrations and ratios of soil organic matter components before and after soil warming………….……………………………………………………..………… 123

Chapter 7 Table 7.1: Chemical composition and organic matter degradation parameters of the Duke forest floor under elevated CO2 and N fertilization……………………...…………… 177 Table 7.2: Microbial and SOM composition in the Duke Forest surface soil under elevated CO2 and N fertilization……………………………………………………… 181

Appendix 1 Table A1.1: The composition and abundance of soil lipids in the Duke Forest soil.… 206

x

List of Figures

Chapter 1 Figure 1.1: Structures of lignin-derived phenols (monomers) isolated by CuO oxidation ………………………………………………………………………..………. 7

Chapter 2 Figure 2.1: GC-MS chromatograms (TIC) of the major SOM components extracted from the Brown Chernozem soils from Alberta, Canada. (a) Silylated solvent extracts from the Bm horizon; (b) Methylated and silylated base hydrolysis products from the Cca horizon; (c) Silylated CuO oxidation products from the Ah horizon…………………….…… … 40 Figure 2.2: Degradation parameters of the bound lipids for Alberta grassland soils. (a) suberin/cutin ratio = (∑S+∑S∨C)/(∑C+∑S∨C). (b) ω-C16/∑C16 ratio. (c) ω-C18/∑C18 ratio. (d) ∑Mid/∑SC ratio…………………………………………………………....... 46 Figure 2.3: Degradation parameters of lignin compounds in Alberta grassland soils. (a) VSC. (b) (Ad/Al)v. (c) (Ad/Al)s. (d) Ratio of S/V. (e) Ratio of C/V…………………... 49 Figure 2.4: The relative contribution of different soil fractions to the identified Alberta grassland SOM……………………………………………………………….……….. 52

Chapter 3 Figure 3.1: Illustration of the sequential chemical extractions and compositional information of SOM components obtained from the extraction procedure….………... 59 Figure 3.2: Microbial respiration rate (r) during soil incubation. …………................. 67 Figure 3.3: Exponential decomposition of solvent extractable compounds…….……. 70 Figure 3.4: The decomposition of suberin- and cutin-derived compounds with time…74 Figure 3.5: Degradation parameters of suberin- and cutin-derived compounds. (a, b) ω-C16/∑C16 ratio. (c, d) ω-C18/∑C18 ratio. (e, f) Suberin/cutin ratio = (∑S+∑S∨C) /(∑C+∑S∨C). (g, h) ∑Mid/∑SC ratio………………………………………………… 75

xi

Figure 3.6: Exponential decomposition of lignin monomers……………….……......... 78 Figure 3.7: Degradation parameters of lignin monomers. (a, b) (Ad/Al)v. (c, d) (Ad/Al)s............................................................................................................................ 79 Figure 3.8: Arrhenius relationship between respiration or decomposition rates and temperature…………………………………………………………………………..… 81

Chapter 4 Figure 4.1: Changes in microbial PLFAs in grassland soils during incubation….…... 103 Figure 4.2: Correlation between incubation temperatures and the average ratios of microbial PLFAs across different sampling dates (except Day 0)……………………. 105 Figure 4.3: Correlation between incubation temperatures and the average PLFA stress indicators across different sampling dates (except Day 0)………………….………… 107 Figure 4.4: Metabolic quotient (qCO2) of both grassland soils on Day 1…….……… 108 Figure 4.5: Correlation between incubation temperatures and the average metabolic quotient (qCO2) in both grassland soils across different sampling dates…….….……. 109

Chapter 5 Figure 5.1: Relative abundance of major soil organic matter components in the control and treatment plots……………………………………………….………………….... 122 Figure 5.2: Differences in lignin degradation parameters from both the control and treatment plots before and after soil warming………………………………………… 126 Figure 5.3: 13C NMR projections from 2-D 1H-13C spectra of humic extracts from the warmed and control soil………………………………………….………………….... 127

Chapter 6 Figure 6.1: Microbial respiration before and after FTCs (measured at 17°C). (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L)…………………………………………………….…………………... 145 Figure 6.2: Microbial responses to FTC. (a) Soil samples (S). (b) Soil samples amended

xii

with dry grass (G). (c) Soil samples amended with lignin (L)………………………... 147 Figure 6.3: Ratios of fungal marker to bacterial markers (F/B). (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L)…………………………………………………………………………………….. 148 Figure 6.4: Microbial recovery from the 8th FTC in samples amended with grass (Gf)................................................................................................................................ 149 Figure 6.5: Changes in free lipid components with FTC. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L)…..... 151 Figure 6.6: Changes in suberin and cutin markers. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L)……………... 153 Figure 6.7: Lignin degradation parameters. (a) Concentrations of VSC. (b) S/V ratio. (c) C/V ratio. (d) (Ad/Al)v. (e) (Ad/Al)s………………………………………….….…... 155 Figure 6.8: Changes in lignin degradation parameters of sample Gc with time…...... 156

Chapter 7 Figure 7.1: 1H NMR spectra of soil humic substances from the Duke Forest soil...... 184

Appendix 1 Figure A1.1: Extraction scheme to assess the ‘mineral-protected’ soil lipids……..... 203 Figure A1.2: GC/MS chromatograms (TIC) of the major soil lipids extracted from the Duke Forest soil under ambient CO2, N-fertilized treatment……….………….……. 205

xiii

Abbreviations

Ad/Al Ratio of acid to aldehyde of lignin-derived phenols (Ad/Al)s Ratio of syringic acid to syringaldehyde (Ad/Al)v Ratio of vanillic acid to vanillin AF Ambient CO2 level with N fertilization treatment AU Ambient CO2 level without N fertilization treatment BL Black (Chernozem soil) BR Brown (Chernozem soil) BSTFA N,O-bis-(trimethylsilyl)trifluoroacetamide C Cinnamyls Clabile Concentration of the labile SOM pool Cmic Concentration of microbial biomass Cstable Concentration of the stable SOM pool C/Na Atomic ratio of carbon to nitrogen CP/MAS Cross polarization/magic angle spinning CSIA Compound-specific isotopic analysis C/V Ratio of cinnamyls to vanillyls CuO Copper (II) oxide cy17:0/16:1ω7c Ratio of cyclopropane PLFA (cy17:0) to its

monoenoic precursor (16:1ω7c) cy19:0/18:1ω7c Ratio of cyclopropane PLFA (cy19:0) to its

monoenoic precursor (18:1ω7c) ∑C16 ω-hydroxy C16 acid + α,ω-dioic C16 acid + ∑C16

mid-chain-substituted acids ∑C18 ω-hydroxy C18 acid + α,ω-dioic C18 acid + ∑C18

mid-chain-substituted acids ∑C Summary of cutin biomarkers DB Dark brown (Chernozem soil) DOM Dissolved organic matter Ea Activation energy EBL Eluviated black (Chernozem soil) EF Elevated CO2 level with N fertilization treatment EI Electron impact mode ESR Electron spin resonance EU Elevated CO2 level without N fertilization treatment FA Fatty acid FACE Free Air CO2 Enrichment FAMEs Fatty acid methyl esters F/B Ratio of fungal PLFA to bacterial PLFAs FTCs Freeze-thaw cycles GC/MS Gas chromatography/Mass spectrometry GC/FID Gas chromatography/Flame ionization detector

xiv

Gram-negative/Gram-positive Ratio of Gram-negative to Gram-positive bacterial PLFAs

HF Hydrofluoric acid HMQC Heteronuclear Multiple Quantum Coherence HR-MAS High-Resolution Magic Angle Spinning IR Infra-red k Reaction/decomposition rate LFA Long-chain fatty acid MAT Mean annual temperature mono/sat Ratio of monoenoic to saturated PLFAs ∑Mid Mid-chain-substituted acids N Nitrogen NIST National Institute of Standards and Technology NMR Nuclear magnetic resonance OC Organic carbon OM Organic matter P Phosphorus PLFA Phospholipid fatty acid Py-GC/MS Pyrolysis-GC/MS Q10 A parameter to describe the temperature sensitivity of

respiration qCO2 Metabolic quotient R Gas constant r Respiration rate S Syringyls sample G Soil sample amended with grass in Chapter 6 sample L Soil sample amended with lignin in Chapter 6 sample S Soil sample in Chapter 6 SFA Short-chain fatty acid SOC Soil organic carbon SOM Soil organic matter Soil E Soil from Ellerslie Research Station Soil L Soil from the Research Station near Lethbridge S/V Ratio of syringyls to vanillyls ∑S Summary of suberin biomarkers ∑S∨C ω-hydroxy acids C16, C18 + C18 di- and trihydroxy

acids + 9,10-ep-ω-OH C18 + α,ω-diacids C16, C18 ∑SC ∑S + ∑C + ∑S∨C T Absolute temperature (°K) TIC Total ion current TMAH Tetramethylammonium hydroxide TMS Trimethylsilyl V Vanillyls VSC Lignin monomers (V, S and C)

xv

CHAPTER 1

INTRODUCTION

1

1.1 Literature Review

1.1.1 Introduction to Soil Organic Matter (SOM)

Soil organic matter (SOM) is an important component of the terrestrial ecosystem

and global carbon cycle. The SOM carbon reservoir is about twice the amount of

atmospheric CO2 (Batjes, 1996). SOM quality (in the sense of how easily carbon in SOM

can be mineralized) and substrate availability are directly related to soil microbial

activity (Fierer et al., 2005; Davidson et al., 2006) and plant productivity (Ågren et al.,

1996). The quantity and quality of SOM are dependent on plant inputs and microbial

decomposition processes, both of which are regulated by climate (Trumbore, 1997). With

global changes in air temperature and atmospheric CO2 levels, the potential

transfomation of SOM and the potential CO2 emissions from soil are major concerns and

a source of uncertainty in climate change models (Melillo et al., 2002; Knorr et al.,

2005b).

Despite its importance, SOM remains poorly characterized in terms of its chemical

composition. Biogeochemical models usually divide SOM into active (labile), slow

(intermediate), and passive (resistant or recalcitrant) carbon pools with distinct intrinsic

turnover time or residence time1 ranging from 1 year to 6×103 years (Parton et al., 1987;

Knorr et al., 2005b; Davidson and Janssens, 2006). The chemical composition is believed

to vary among different pools, with the active SOM pool composed of easily degradable

compounds such as proteins and sugars, and with the passive SOM pool made up of

macromolecular lipids and aromatic ring structures that are much more resistant to 1 Turnover time or mean residence time is the inverse of the first-order rate constant for the

decomposition process.

2

microbial attack (Melillo et al., 2002). Mineral-associated SOM is also known to have

longer turnover time in soil than ‘free’ or light SOM fractions that are not protected

against microbial decomposition by mineral matrix (Sollins et al., 1996; Baldock and

Skjemstad, 2000; Mikutta et al., 2006). Similarly, highly transformed or cross-linked

SOM is believed to be more resistant to microbial attack than fresh SOM

(Kögel-Knabner et al., 1992; Berg, 2000; Quénéa et al., 2005). However, current studies

mostly rely on bulk analysis of SOM (i.e., the total weight of soil fractions or elements),

and no study has fully characterized and differentiated the molecular composition of

these SOM pools (Davidson and Janssens, 2006). It remains unclear which chemical

structures will be preferentially consumed by the microbes in the field or at higher

temperatures. Therefore, a fundamental study that examines the molecular composition

of SOM and its potential response to global warming will be important for understanding

soil carbon dynamics and optimizing existing biogeochemical models.

1.1.2 Major SOM Components and Biomarkers

SOM consists of a heterogeneous mixture of organic matter (OM) originating from

plant (the major source), microbial (a minor source), and animal (a minor source)

residues, exhibiting different stages of biological oxidation (Baldock and Skjemstad,

2000). A substantial fraction of SOM may be associated with mineral surfaces and

protected against microbial enzyme attack (Sollins et al., 1996; Baldock and Skjemstad,

2000; Mikutta et al., 2006), which complicates the study of SOM composition and

dynamics by hindering extractibility and measureability. Therefore, SOM remains largely

3

uncharacterized at the molecular-level. However, structural information can be obtained

on various SOM components that are amenable to advanced analytical techniques

(Kögel-Knabner, 2000). Many structurally unique biochemicals (‘biomarkers’) carry the

information of their origins and/or environmental settings, and hence can be used to

analyze the source and/or degradation stage of SOM (Hedges et al., 2000; Amelung et al.,

2008). Some common SOM biomarkers include plant wax lipids, cutin- or

suberin-derived lipids, lignin-derived phenols, and microbial phospholipid fatty acids

(PLFAs; Otto et al., 2005; Frostegård and Bååth, 1996; Amelung et al., 2008).

1.1.2.1 Soil Lipids

Soil lipids, operationally defined as a heterogeneous group of organic substances that

are insoluble in water but extractable with non-polar solvents, are common constituents

of SOM (Dinel et al., 1990; Kögel-Knabner, 2002). They range from simple structures

such as alkanes, alkanols, alkanoic acids, and steroids to complex unknown lipids. Soil

lipids bear important information about SOM inputs (Otto et al., 2005) and influence the

surface properties of aggregates and contaminant sorption (Feng et al., 2006). Soil lipids

are primarily plant-derived and include plant waxes and biopolymers (such as suberin in

the peridermis of barks and roots and cutin in leaf cuticles). Microorganisms are minor

contributers to soil lipids, such as branched short-chain (<C20) alkanoic acids, hopanoids,

and ergosterol (from fungi; Kögel-Knabner, 2002; Otto et al., 2005). Based on their

structure and transformation processes in the soil, soil lipids can be readily extracted by

organic solvents (solvent-extractable lipids), be bound to SOM by ester-linkages (bound

4

lipids), or be non-extractable even through mild chemolytic methods such as catalyzed

hydrolysis (Guignard et al., 2005).

Solvent-extractable (free) lipids usually comprise less than 10% of SOM (Dinel et al.,

1990) and they contain characteristic biomarkers that can give information about the

source and degradation stage of residues in SOM. For instance, even-numbered

long-chain (>C20) alkanoic acids are common constituents of plant wax lipids whereas

the degradation stage of plant-derived steroids may be assessed by comparing the ratio of

plant steroids (β-sitosterol, stigmasterol, and stigmastanol) to their degradation products

(stimasta-3,5-dien-7-one and sitosterone) in the soil (Otto and Simpson, 2005).

By comparison, ester-bound lipids are not extractable with organic solvents, but they

can be cleaved from SOM using chemolytic methods such as alkaline hydrolysis

(Kögel-Knabner, 2000). The predominant long-chain ω-hydroxyalkanoic and

α,ω-alkanedioic acids are typical biomarkers for suberin, primarily indicating root or

bark inputs into the soil, while C16 and C18 ω-hydroxyalkanoic acids with mid-chain

hydroxy or epoxy groups are biomarkers for cutin or leaf inputs (Holloway and Deas,

1973; Goñi and Hedges, 1990; Otto et al., 2005). Suberin and cutin biopolymers are the

major sources of bound (hydrolysable) aliphatic lipids in SOM (Otto and Simpson,

2006a), but not much is known about the degradation of suberin and cutin in soils (Otto

et al., 2005). Bound lipids are considered to be less prone to microbial attack through

chemical bonding and thus more stable than solvent-extractable lipids (Hedges et al.,

2000). Recent studies have shown that recalcitrant aliphatic SOM components have the

potential to promote carbon sequestration in soils (Lorenz et al., 2007). It is hence

5

important to examine the transformation and preservation of bound lipids in the soil

under a changing climate.

Non-hydrolysable soil lipids are the lipid fraction that is not extractable by mild

chemolytic methods (solvent extraction and hydrolysis) and are the least chemically

understood. Non-hydrolysable SOM is considered to be a major part of the stable soil

carbon pool (Quénéa et al., 2004), and mineral protection or chemical transformation

(such as cross-linking) may contribute to its recalcitrance (Kögel-Knabner et al., 1992;

Quénéa et al., 2005). Alternatively, non-hydrolysable soil lipids may include inputs from

cutan (the non-hydrolysable and non-extractable biopolymer in leaf cuticles) and suberan

(the non-hydrolysable and non-extractable biopolymer in plant roots and tree barks;

Augris et al., 1998; Nierop, 1998).

1.1.2.2 Lignin-Derived Phenols

Lignin is the second most abundant biopolymer (after cellulose and hemicellulose)

in nature and a large contributor to SOM (Kögel-Knabner, 2002). So far, there is no

method for analyzing or quantifying the lignin macromolecules directly in soil. However,

lignin-derived phenols or monomers (vanillyls, syringyls, and cinnamlyls; Figure 1.1)

can be released from the macromolecular matrix of the biopolymer or soils by

chemolytic methods such as CuO oxidation (Kögel-Knabner, 2000). The composition of

lignin monomers is characteristic for major plant groups (angiosperms, gymnosperms)

and is commonly used to describe the major plant sources (Hedges and Mann, 1979; Otto

et al., 2005). Ratios of lignin-derived phenolic acids to their corresponding aldehydes

6

(Ad/Al) are useful tools for determining the stage of lignin degradation in soils and

sediments (Hedges et al., 1988; Goñi et al., 1993; Otto et al., 2005). Lignin in SOM is

usually considered to be refractory due to its aromaticity and slow decomposition

Vanillin (Vl)

Vanillic acid (Vd)

OCH3

OH

CH3O

COH

Syringaldehyde(Sl)

OCH3

OH

CH3O

COHO

Syringic acid(Sd)

OCH3

OH

CH3O

HC O

CH3

Acetosyringone(Sn)

OCH3

OH

HC O

CH3

Acetovanillone(Vn)

OCH3

OH

C

OHO

OCH3

OH

C

OH

OCH3

OH

CH

CH

COHO

Ferulic acid(Fd)

OH

CH

CH

COHO

p-Coumaric acid(p-Cd)

Vanillyl (V) Syringyl (S) Cinnamyl (C)

Vanillin (Vl)

Vanillic acid (Vd)

OCH3

OH

CH3O

COH

Syringaldehyde(Sl)

OCH3

OH

CH3O

COHO

Syringic acid(Sd)

OCH3

OH

CH3O

HC O

CH3

Acetosyringone(Sn)

OCH3

OH

HC O

CH3

Acetovanillone(Vn)

OCH3

OH

C

OHO

OCH3

OH

C

OH

OCH3

OH

CH

CH

COHO

Ferulic acid(Fd)

OH

CH

CH

COHO



Vanillin (Vl)

Vanillic acid (Vd)

OCH3

OH

CH3O

COH

Syringaldehyde(Sl)

OCH3

OH

CH3O

COHO

Syringic acid(Sd)

OCH3

OH

CH3O

HC O

CH3

Acetosyringone(Sn)

OCH3

OH

HC O

CH3

Acetovanillone(Vn)

OCH3

OH

C

OHO

OCH3

OH

C

OH

OCH3

OH

CH

CH

COHO

Ferulic acid(Fd)

OH

CH

CH

COHO



Figure 1.1: Structures of lignin-derived phenols (monomers) isolated by CuO oxidation.

rates in litters (Derenne and Largeau, 2001; Gleixner et al., 2001; Melillo et al., 2002).

Only certain groups of fungi (white-rot and brown-rot fungi) are able to efficiently

7

biodegrade lignin in terrestrial environments (Carlile et al., 2001). It is therefore

important to monitor lignin degradation with environmental changes and with potential

shifts in the microbial community structure brought on by environmental changes.

1.1.2.3 Microbial Phospholipid Fatty Acids (PLFAs)

Compared with plant inputs, microbial biomass represents a minor (1~5%) yet

arguably the most active fraction of SOM (Anderson and Domsch, 1989; Simpson et al.,

2007a). Characteristic biomarkers of microbial biomass include ergosterol (from fungi),

amino sugars (such as glucosamine and muramic acid), branched short-chain alkanoic

acids, 3-hydroxyalkanoic acids, hopanoids, and PLFAs (Frostegård and Bååth, 1996;

Guggenberger et al., 1999; Shunthirasingham and Simpson, 2006). Among them, PLFAs

are only found in viable cells and hence are characteristic biomarkers for living

microorganisms (Frostegård and Bååth, 1996; Evershed et al., 2006; Webster et al., 2006).

Based on their chemical structures, such as branching within the molecule or the

occurrence of double bonds, various PLFAs can be used to establish the notional

proportions of fungi, Gram-positive bacteria (including actinomycetes) or Gram-negative

bacteria (Frostegård and Bååth, 1996), and hence to characterize microbial community

structure in the soil.

1.1.2.4 Other SOM Components

It is worth noting that the SOM biomarkers mentioned previously are of significant

importance in investigating SOM structural alterations under global changes such as

8

global warming and elevated CO2. Soil lipids and lignin-derived phenols provide insights

into the inputs and degradation of plant-derived aliphatic molecules and lignin in the soil

respectively, which may contribute to the sequestration of soil carbon in the long term

(Derenne and Largeau, 2001; Gleixner et al., 2001; Melillo et al., 2002; Lorenz et al.,

2007), whereas PLFAs yield information on the living microbial biomass and community

composition. By comparison, cellulose and hemicellulose, the most abundant

biopolymers on earth, are efficiently degraded by fungi and bacteria in the aerobic litter

layer (Melillo et al., 1989; Berg, 2000) and hence found in only low amounts in mineral

soils (Kögel-Knabner, 2002). Similarly, non-cellulosic carbohydrates and proteins that

occur in plants as well as microorganisms are either not source-specific or biochemically

labile, such that they are not very useful indicators of SOM degradation or do not have

large carbon sequestration potentials in the long term (Gleixner et al., 2001; Melillo et al.,

2002). Finally, the identified biomarkers only represent a small fraction of SOM

(Amelung et al., 2008). The majority of SOM remains uncharacterized at the

molecular-level (Hedges et al., 2000) because they have undergone extensive

transformations during humification processes (Gregorich et al., 1996) or are closely

associated with mineral matrix and/or present in macromolecules (Baldock and

Skjemstad, 2000; Mead and Goñi, 2008).

1.1.3 Molecular-Level Techniques Used to Analyze SOM

Molecular-level investigations into the chemical composition of SOM usually

involve two complementary analytical techniques, i.e., compound-specific analysis by

9

chromatographic means or non-destructive spectroscopic methods that examine the bulk

chemical structures in SOM or SOM fractions (Kögel-Knabner, 2000). The first

technique includes:

- Gas Chromatography/Mass Spectrometry (GC/MS) or Gas Chromatography

/Flame Ionization Detector (GC/FID) analysis of various SOM extracts or biomarkers

that are extracted from bulk soil by different soil fractionation or extraction methods

(Otto et al., 2005). GC/MS is more advantageous to GC/FID in terms of compound

identification and structural elucidation of heterogeneous SOM components;

- analytical pyrolysis (Py)-GC/MS, which separates SOM pyrolysis products into

single components detected and quantified by MS (Saiz-Jimenez, 1994); and

- thermochemolysis, which transforms SOM components with chemical reagents and

quantifies individual compounds by MS. In particular, thermochemolysis with

tetramethylammonium hydroxide (TMAH) that methylates SOM components has quite a

few applications in soil studies in the past decade (Challinor, 1995; Challinor, 2001).

Many pyrolysis products have multiple origins from the chemically diverse SOM

components and thermal secondary reactions may modify the original SOM structures

and hence the interpretation of pyrolysis data can be challenging (Saiz-Jimenez, 1994;

Kögel-Knabner, 2000). Alternatively, biomarker GC/MS techniques can provide valuable

information on the structure, quantity, and degradation stage of various SOM

components of specific origins. For instance, solvent-extractable soil lipids contain

characteristic biomarkers that are indicators of the SOM source (grass/higher plant roots,

waxes, microbial biomass, etc.), while the base hydrolysis and CuO oxidation products

10

contain information on the degradation stage of certain groups of SOM (cutin, suberin,

and lignin; Otto et al., 2005). Microbial PLFAs can also be analyzed with GC/MS to

characterize soil microbial communities (McKinley et al., 2005).

By comparison, spectroscopic methods such as nuclear magnetic resonance (NMR)

spectroscopy, infra-red (IR) spectroscopy and electron spin resonance (ESR)

spectroscopy examine the gross chemical composition of SOM that is hardly identified

by specific compounds. NMR spectroscopy, in particular, is a promising and increasingly

popular tool to analyze OM composition in modern geochemical studies (Preston, 1996;

Kögel-Knabner, 2000; Simpson, 2001; Simpson et al., 2007b). Advanced NMR methods,

such as multidimensional solution-state NMR and 1H High-Resolution Magic Angle

Spinning (HR-MAS) NMR, have greatly improved the analytical resolution and proven

to be powerful in the structural elucidation of complex mixtures such as SOM (Simpson,

2001; Simpson et al., 2002; Kelleher et al., 2006). For instance, aromatic (partially lignin)

and aliphatic components (waxes, cuticles) of plant litter were shown to persist and to

become highly functionalized over time during decomposition by HR-MAS NMR

(Kelleher et al., 2006). Moreover, using multidimensional solution-state NMR techniques,

Simpson et al. (2001) revealed strong contributions of peptides, aliphatic structures,

carbohydrates, peptidoglycan, and lignin to soil humin (the SOM fraction that is

insoluble in alkaline solutions), the most recalcitrant and least understood fraction of

SOM.

NMR techniques coupled with biomarker GC/MS methods can add innovative and

complementary information to the traditional soil quality analysis that only measures the

11

amount of soil elements and fractions, such as water-soluble organic carbon (OC) content,

fulvic acids, humic acids, and humin fractions, etc. Detailed investigations into targeted

SOM molecules and structures may shed light onto the transformation and dynamics of

soil carbon under a changing climate that are difficult to detect by conventional methods

due to the large spatial variability and heterogeneous composition of SOM.

1.1.4 Environmental Controls on SOM

The environment controls the amount and composition of SOM by regulating both

its inputs from vegetation and microorganisms and its rate of losses through microbial

decomposition, fire, and dissolved organic matter (DOM) export. The major

environmental controls on SOM include soil temperature, moisture, litter quality (such as

lignin content), decomposer (microbial) community composition, fire frequency, and

several other physical factors (such as soil clay content, and polyvalent cations; Ågren et

al., 1996). Current climate models have predicted increases in global mean surface air

temperature and more frequent extreme weather conditions such as freeze-thaw cycles

(FTCs) over the 21st century (Schlesinger, 1991; Cox et al., 2000; Meehl et al., 2007).

An increase in the mean annual temperature of ~3-5 °C is predicted for the mid- and

high-latitude regions in the northern hemisphere for the 21st century, with the greatest

temperature increases in high-latitude and polar regions (Christensen et al., 2007). As the

modification of SOM quantity and quality under increasing temperatures and extreme

weather conditions is one of the most important consequences of climate change (Ågren

et al., 1996; Matzner and Borken, 2008), it is important to understand how SOM is likely

12

to be affected. Temperature increases over most normal ranges can increase both

vegetation and microbial inputs to SOM and the decomposition rate and pattern of SOM.

Post et al. (1982) demonstrated that increasing temperatures increased the rate of soil

carbon output more than the input, suggesting that the temperature response function for

decomposition must be steeper than for production. It is therefore vital to find out the

compositional changes in SOM through increased decomposition at higher temperatures.

Meanwhile, global warming is likely to change vegetation distribution (Filley et al., 2008)

and microbial community composition (Biasi et al., 2005; Frey et al., 2008), which also

affect SOM composition. The effect of changing vegetation inputs should hence be

investigated together with the enhanced SOM decomposition.

Increasing atmospheric CO2 levels and nitrogen (N) deposition are two other major

global changes that affect terrestrial biogeochemical cycles. Rising atmospheric CO2

levels are reported to increase plant primary productivity (DeLucia et al., 1999), enhance

plant root biomass allocation (Matamala and Schlesinger, 2000; Norby et al., 2004), and

promote microbial decomposition of native SOM (known as ‘priming effect’; Drissner et

al., 2007), while N fertilization is known to promote plant growth (Oren et al., 2001)

and/or microbial decomposition in N-limited environments (Neff et al., 2002; Knorr et al.,

2005a). However, questions remain as to whether the chemical composition of SOM is

influenced by elevated CO2 or N fertilization or both.

Furthermore, precipitation is predicted to increase in a warmer climate in monsoon

regimes and at high latitudes whereas drying of the mid-continental areas tends to

increase risks of droughts in the summer (Meehl et al., 2007). Alterations in the

13

hydrological cycle are likely to change moisture availability and distribution in the soil

and hence affect microbial decomposition of SOM and DOM export from soil. Similarly,

increased fire frequency in a drier climate may play a key role in decreasing carbon

storage in dry forests and peatlands (Christensen et al., 2007; Meehl et al., 2007). These

research aspects are however beyond the scope of this thesis.

1.1.5 Soil Respiration and Temperature Sensitivity

The flux of carbon from SOM to the atmosphere occurs primarily in the form of CO2

as a result of ‘soil respiration’. Soil respiration represents the combined respiration of

roots (autotrophic) and soil micro- and macro-organisms (heterotrophic). Critical factors

reported to influence rates of soil respiration include: temperature, soil moisture,

vegetation and substrate quality, net ecosystem productivity, the relative allocation of net

primary productivity above- and belowground, population and community dynamics of

the aboveground vegetation and belowground flora and fauna, and land-use and/or

disturbance regimes, including fire (Rustad et al., 2000; and references therein). Among

them, temperature is the primary rate determinant of microbial processes (i.e., microbial

decomposition of SOM; Lal, 2004).

Soil respiration is most commonly expressed by an exponential model (Davidson et

al., 2006):

r = a×ebT (1.1)

where r is respiration rate, a and b are fitted parameters, respectively. The temperature

sensitivity of respiration is typically expressed by Q10 (the average increase in respiration

14

rates for a 10 °C increase in temperature), and can be derived as:

Q10 = e10b (1.2)

The exponential model assumes a constant Q10 (or b) value for all the temperatures.

However, such is not the case in reality where temperature does not only affect the

activities of enzymes but also changes the microbial community composition, moisture

and substrate availabilities (e.g., diffusion rates, etc.; Davidson et al., 2006). Nevertheless,

exponential models are most commonly used for soil respiration studies due to its

simplicity, and the parameter a in equation (1.1) provide an index of the overall quality

(the availability and the lability) of the carbon substrates that are being used by

decomposer organisms at a given point in time (Fierer et al., 2005). Combined with SOM

degradation studies at the molecular-level, soil respiration measurement will shed light

on the relationship between SOM composition and soil respiration.

1.1.6 Microbial Decomposition of SOM

Soil microorganisms (mainly fungi and bacteria) are primarily responsible for the

biological decomposition of SOM, during which SOM is structurally transformed and/or

mineralized to CO2. Microbial decomposition of soil carbon is closely related with the

substrate availability and SOM quality (Fierer et al., 2005; Davidson et al., 2006).

According to the fundamental principles of enzyme kinetics, the temperature sensitivity

of microbial respiration should be inversely related to litter/SOM carbon quality (Bosatta

and Ågren, 1999). By adding ground plant shoot and root material to soils incubated

under controlled conditions, Fierer et al. (2005) showed that substrate carbon quality had

15

a significant and predictable influence on the temperature sensitivity of microbial

respiration, i.e., as the overall quality of the litter OC declined, litter decomposition

became more sensitive to temperature. However, it remains unclear which chemical

structures are indicators of the SOM quality and will enhance microbial respiration in the

field.

Moreover, soil microbes demonstrate changes in substrate use with changes in their

metabolism, community composition, and during temperature changes, such as during

FTCs (Schimel and Mikan, 2005; Matzner and Borken, 2008). Monson et al. (2006)

found that a unique soil microbial community that exhibited exponential growth and high

rates of substrate utilization at the cold temperatures was responsible for the high

sensitivity of winter forest soil respiration. Alternatively, only certain groups of fungi are

able to efficiently biodegrade lignin in terrestrial environments (Carlile et al., 2001).

While Gram-positive bacteria are well adapted to soils with low substrate availability and

in subsoils with lower OC content (Griffiths et al., 1999; Fierer et al., 2003),

Gram-negative bacteria are more dependent on the input of fresh organic material to

create ‘hot spots’ of decomposition in soils (Griffiths et al., 1999; Kramer and Gleixner,

2006; Potthoff et al., 2006). Shifts between these microbial groups have been reported

with soil warming (Biasi et al., 2005; Frey et al., 2008) and with SOM substrate changes

under elevated CO2 or N fertilization (Rillig et al., 1999; Lipson et al., 2005; Carney et

al., 2007; Treseder, 2008). Hence, it is of ecological importance to investigate the

temperature and substrate controls on the fungal and bacterial communities during SOM

degradation and to monitor microbial community shifts and SOM composition with

16

global changes in the field. Microbial community composition can be analyzed using

PLFA analysis.

1.1.7 Grassland and Forest Soils

Grasslands are one of the most widespread vegetation types world-wide, covering

nearly one-fifth of the Earth’s land surface (24×106 km2), and containing >10% of global

soil carbon stocks (Anderson, 1991). The total amount of soil carbon in grasslands is

lower than that in peatlands (up to 25% of world’s soil carbon; Gorham, 1991). However,

as compared to the biologically labile OM stored in water-logged peatlands (Freeman et

al., 2004), grassland SOM is considered to be relatively stable partly due to its

association with mineral surfaces (Cambardella and Elliott, 1993) and the complex grass

root mats that hold the soil in place (Jackson et al., 1996). Therefore, grassland SOM is

an important component of the stabilized soil carbon pool. Over the centuries, grassland

ecosystems have been modified extensively through agricultural conversion, resulting in

large release of CO2 into the atmosphere (Houghton, 1994). The Prairie Ecozone of

Western Canada accounts for 80% of the arable land in Canada and contains large

reserves of stabilized SOM (Janzen et al., 1998). With predicted global changes, there is

a great potential for enhanced carbon transformation in this area.

Forests, on the other hand, are estimated to contain up to 80% of all aboveground

carbon and ~40% of all below-ground (soils, litter, and roots) terrestrial carbon in the

world (Dixon et al., 1994). As compared with grassland ecosystems, forests stores

considerable amounts of carbon in the aboveground plant biomass and litter layer (Dixon

17

et al., 1994) and has a lower root-to-shoot ratio (Jackson et al., 1996). Forest OM has

significant inputs from woody plant tissues and fungi play a more important role in the

decomposition of forest litter (Wardle, 1992). Changes in forest land use are also known

to release a significant amount of carbon into the atmosphere, mostly from low-latitude

forests (Dixon et al., 1994). Alternatively, growing forests in the northern temperate

regions under elevated CO2 levels and N deposition may represent a ‘missing carbon

sink’ for the global atmospheric CO2 (Schindler, 1999; Gaudinski et al., 2000) and are

considered to be more strongly influenced by environmental changes than the

low-latitude forests (Dixon et al., 1994). It is hence important to examine the potential

changes in SOM storage and composition in temperate forests with predicted global

changes.

1.2 Objectives and Hypotheses

The overall purpose of this thesis is to examine the origin, degradation, and stability

of various SOM components in grassland and temperate forest soils, and to investigate

the shifts in microbial community and SOM composition with environmental changes. In

particular, global changes such as increasing air/soil temperatures, frequent FTCs,

elevated atmospheric CO2 levels, and N deposition are simulated in both laboratory and

field experiments. Typical Canadian Prairie grassland soils are studied for soil incubation

at elevated temperatures and simulated FTCs in the laboratory due to the concern of

increased soil carbon degradation and transformation with global warming in the Prairie

Ecozone of Western Canada. Alternatively, an in situ soil warming experiment is

18

conducted in a temperate mixed forest in southern Ontario to investigate microbial and

SOM compositional changes with elevated soil temperatures in the field. This study site

is chosen due to its ideal moisture condition as well as its convenient location to facilitate

experimental setup and monitoring. Finally, forest floor litter and surface soils are

collected from the Duke Forest Free Air CO2 Enrichment (FACE) experiment to examine

microbial community structure and OM composition after over ten years of FACE

treatment. The Duke Forest FACE experiment is one of the few and earliest forest FACE

experiments in the world, located in a young temperate forest dominated by pine trees.

The soils studied in this thesis represent the typical temperate grassland and forest soils

in North America. The various climatic, edaphic, and biotic conditions of their source

site also allow the investigation of environmental influences on the SOM composition

and responses to climatic changes. Two complementary molecular-level methods, i.e.,

biomarker GC/MS techniques and NMR spectroscopy, are used to delineate microbial

community structure and SOM composition in this thesis. The targeted SOM components

include solvent-extractable (free) lipids and carbohydrates, bound lipids (mainly cutin-

and suberin-derived compounds), and lignin-derived phenols. Microbial biomass and

community composition are assessed using PLFAs.

The specific objectives of the thesis research are:

Objective 1: To examine the origin and chemical composition of SOM in grassland and

temperate forest soils;

Objective 2: To assess the temperature sensitivity of decomposition for various SOM

19

components;

Objective 3: To characterize shifts in microbial community composition with changes in

temperature and substrate availability;

Objective 4: To identify the chemical structures in SOM that are likely to be preserved or

degraded under increasing air/soil temperatures, frequent FTCs, elevated CO2, or N

fertilization;

Objective 5: To measure and predict changes in SOM composition with global warming

or rising atmospheric CO2.

The scientific hypotheses being tested in this thesis include three major aspects:

(1) Both global warming and elevated CO2 are known to increase plant productivity

(DeLucia et al., 1999; Hyvönen et al., 2006) and hence alter substrate availability to soil

microorganisms. The altered soil substrate availability are very likely to induce shifts in

microbial community composition due to the varied microbial demands on nutrients or

substrates (Schimel and Mikan, 2005; Matzner and Borken, 2008). Because various

microorganisms are responsible for the decomposition of different SOM structures,

global warming or elevated CO2 is hypothesized to enhance or retard the decomposition

of certain SOM components through changing microbial community structure.

(2) As discussed in Section 1.1.2, solvent-extractable soil lipids are considered to be

more labile and accessible to microbial attack than bound soil lipids and lignin-derived

phenols. With an increased microbial activity at higher temperatures and/or with higher

substrate availability, solvent-extractable lipids are hypothesized to be more easily

20

degraded and influenced in a changing climate as compared with bound soil lipids and

lignin-derived phenols. Meanwhile, because fungi are the primary decomposer of lignin

in the soil, the decomposition of lignin is hypothesized to increase if there is an increase

in the fungal community or activity with climate changes.

(3) Based on Hypotheses 1 and 2, SOM composition is very likely to change with

global warming and elevated CO2. Moreover, recalcitrant SOM components are

hypothesized to accumulate due to higher plant inputs and a faster utilization of labile

SOM constituents by soil microorganisms under elevated temperatures or elevated CO2.

1.3 Thesis Summary

Chapter 1: Introduction

Chapter 2: The distribution and degradation of biomarkers in Alberta grassland soil

profiles

This chapter has been published in Organic Geochemistry and addresses Objective 1.

Biomarker GC/MS methods were employed in this chapter to investigate the distribution

of solvent-extractable lipids, carbohydrates, cutin-, suberin-, and lignin-derived

compounds in four typical Canadian Prairie grassland soils. A series of geochemical

parameters were compared down the soil profile and validated to indicate SOM

degradation for subsequent chapters. Aliphatic lipids from suberin and cutin were shown

to be preferentially preserved in the deeper horizons in comparison to lignin-derived

phenols, indicating their stability in the environment. The analysis of SOM at the

21

molecular-level helped to build up a ‘budget’ or ‘archive’ of the present soil carbon

stocks in the Canadian grasslands.

Chapter 3: Temperature responses of individual soil organic matter components

This chapter has been published in the Journal of Geophysical Research -

Biogeosciences and addresses Objectives 2 and 5. The decomposition of various SOM

components from two grassland soils was investigated in a one-year laboratory

incubation at six different temperatures. The stability of SOM components was assessed

using geochemical parameters and kinetic parameters derived from an exponential decay

model. The decomposition of lignin-derived phenols exhibited higher temperature

sensitivity than that of solvent-extractable compounds. The temperature responses

determined in this laboratory-simulated warming experiment were not affected by plant

inputs from the field. The results shed light onto the intrinsic recalcitrance of various

SOM components and the influence of plant inputs on SOM composition, in combination

with subsequent field studies (Chapters 5 and 7).

Chapter 4: Temperature and substrate controls on microbial phospholipid fatty acid

composition during incubation of grassland soils contrasting in organic matter quality

This chapter has been published in Soil Biology & Biochemistry and addresses

Objective 3. As an integrated part of the incubation experiment (Chapter 3), this chapter

analyzed fungal and bacterial PLFAs in two grassland soils. While the overall microbial

activity and biomass were constant during the incubation, fungi and Gram-negative

22

bacteria declined relative to Gram-positive bacteria at higher temperatures, presumably

due to their vulnerability to disturbance and substrate constraints. Varied microbial decay

patterns were also observed in these two grassland soils contrasting in SOM quality,

suggesting a substrate control over microbial responses to global warming. These

findings have important implications for field studies (Chapters 5 and 7) and terrestrial

carbon cycling under global warming.

Chapter 5: Increased cuticular carbon sequestration and lignin oxidation in response to

soil warming

This chapter has been published in Nature Geoscience and addresses Objectives 1, 4

and 5. A 14-month soil warming experiment was conducted in a temperate forest and

SOM components were examined by biomarker GC/MS and NMR methods. Leaf

cuticle-derived (cuticular) component (such as cutin-derived compounds and alkyl

carbon) was shown to accumulate with elevated plant inputs into the soil while lignin

was preferentially degraded by an increased fungal community. This chapter reports

warming-induced compositional changes to SOM at the molecular-level and

demonstrates the potential for enhanced lignin oxidation and cuticular carbon

sequestration with future warming. The results are complementary to the laboratory

incubation study (Chapters 3 and 4) and highlighted the importance of altered plant

inputs and microbial decomposition patterns in regulating SOM composition in the field.

Chapter 6: Responses of soil organic matter and microorganisms to freeze-thaw cycles

23

This chapter has been published in Soil Biology & Biochemistry and addresses

Objectives 3 and 4. Global warming is known to increase extreme weather conditions

such as FTCs other than increase air temperatures. In this chapter, SOM components and

microbial PLFAs were analyzed in soil samples that were subject to 10

laboratory-simulated FTCs. Solvent-extractable (free) lipids underwent a considerable

size of decrease after repeated FTCs, while bound lipids and lignin-derived compounds

remained stable. Bacterial biomass were unaffected by repeated FTCs while fungi were

greatly reduced, likely due to freezing stress and competition for freeze-thaw-induced

substrate release. These findings suggested that labile SOM (free lipids) may decrease

with increasing FTCs and that extreme weather conditions may have a large impact on

microbial community structure.

Chapter 7: Altered microbial community structure and organic matter composition under

elevated CO2 and N fertilization in the Duke Forest

This chapter has been submitted to Global Change Biology and addresses Objectives

1, 3, 4, and 5. Rising atmospheric CO2 and N deposition are two major global changes

that affect terrestrial biogeochemical cycles. Soil microbial PLFAs and various OM

components were examined in the forest floor and surface soil under elevated CO2 and N

fertilization in the Duke Forest FACE experiment. N fertilization altered microbial

community composition and enhanced lignin degradation. More importantly, the 1H

NMR spectra of soil humic substances revealed an enrichment of leaf-derived alkyl

structures with experimental treatments, suggesting an accumulation of plant-derived

24

recalcitrant structures (such as alkyl carbon) in the soil with increased plant inputs under

elevated CO2 or N fertilization.

Chapter 8: Conclusions

1.4 Statement of Authorship and Publication Status

Chapter 2: The distribution and degradation of biomarkers in Alberta grassland soil

profiles

Authors: Xiaojuan Feng, Myrna J. Simpson

Contributions: XF framed the research questions. XF and Janice Austin performed

chemical extractions and analyses. Leah Nielson conducted the analysis of clay

content. All data interpretation and writing was carried out by XF with input from

the co-author.

Status: Published in Organic Geochemistry, 38, 1558-1570 (2007).

Chapter 3: Temperature responses of individual soil organic matter components


Contributions: XF conceived the research idea and framed the research questions. XF

and MJS designed the experiment. XF and Leah Nielson performed the

experiment and chemical extractions. All data interpretation and writing was

carried out by XF with input from the co-author.

Status: Published in the Journal of Geophysical Research-Biogeosciences, 113, G03036,

25

doi:10.1029/2008JG000743 (2008).

Chapter 4: Temperature and substrate controls on microbial phospholipid fatty acid

composition during incubation of grassland soils contrasting in organic matter quality



and MJS designed the experiment. XF and Leah Nielson performed the

experiment and chemical extractions. All data interpretation and writing was

carried out by XF with input from the co-author.

Status: Published in Soil Biology & Biochemistry, 41, 804-812 (2009).

Chapter 5: Increased cuticular carbon sequestration and lignin oxidation in response to

soil warming

Authors: Xiaojuan Feng, André J. Simpson, Kevin P. Wilson, D. Dudley Williams,

Myrna J. Simpson

Contributions: All authors commented on the manuscript and performed research. KPW

and DDW designed and performed field experiment. XF, MJS, and AJS designed

and performed sample analysis, analyzed the data and wrote the paper.

Status: Published in Nature Geoscience, 1, 836-839 (2008).

Chapter 6: Responses of soil organic matter and microorganisms to freeze-thaw cycles

Authors: Xiaojuan Feng, Leah L. Nielsen, Myrna J. Simpson

26


and MJS designed the experiment. XF and LLN performed the experiment and

chemical extractions. All data interpretation and writing was carried out by XF

with input from the co-authors.

Status: Published in Soil Biology & Biochemistry, 39, 2027-2037 (2007).

Chapter 7: Altered microbial community structure and organic matter composition under

elevated CO2 and N fertilization in the Duke Forest

Authors: Xiaojuan Feng, André J. Simpson, William H. Schlesinger, Myrna J. Simpson

Contributions: XF framed the research questions. WHS and Ram Oren designed the

FACE experiment. XF extracted plant and soil samples for biomarkers and humic

substances. Pui Sai Lau and Jennifer Heidenheim extracted the forest floor

samples. XF, MJS, and AJS analyzed the data. XF wrote the paper with input

from the co-authors.

Status: Submitted to Global Change Biology.

27

CHAPTER 2

THE DISTRIBUTION AND DEGRADATION OF BIOMARKERS

IN ALBERTA GRASSLAND SOIL PROFILES*

* Reprinted from Organic Geochemistry, 38: 1558-1570. Authors: Feng, X., Simpson, M.J.,

Copyright (2007), with permissions from Elsevier.

28

2.1 Abstract

Recent studies have shown that subsoil (B and C horizons) can store significant

amounts of soil organic matter (SOM). Yet the quantity, source, turnover, and chemical

composition of subsoil SOM are largely unknown. Biomarker methods were employed in

this study to investigate the vertical distribution and degradation of SOM in Alberta

grassland soils. Specifically, the composition of solvent extracts, bound lipids, and lignin

compounds in the Ah, Bm, and Cca horizons of four Chernozemic soils was analyzed using

chemolysis and gas chromatography/mass spectrometry (GC/MS) techniques. Suberin,

cutin, and lignin compounds were observed to degrade with soil depth. Aliphatic

molecules (such as hydroxyalkanoic acids) from suberin and cutin were preferentially

preserved in the deeper horizons in comparison to lignin compounds. Trehalose, a

carbohydrate found in high abundance in fungal tissues, was detected in significant

abundance in the Bm and Cca horizons of three grassland soils, suggesting that non-plant

biomass may strongly contribute to the deposition of carbon into the subsoil. It was also

demonstrated that soil-forming processes (such as eluviation) played a role on the

composition of organic carbon in the lower soil horizons.

2.2 Introduction


and global carbon cycle: SOM quality is directly related to soil microbial activities (Fierer

et al., 2005; Davidson et al., 2006), plant productivity (Ågren et al., 1996), and the

terrestrial carbon pool in the SOM is about twice the amount of atmospheric carbon pool

29

in the world (Batjes, 1996; Janzen, 2004). Recent carbon inventories have shown that

subsoil (B and C horizons) can store significant amounts of SOM (Swift, 2001; Lorenz

and Lal, 2005). Yet the quantity, source, turnover, and chemical composition of subsoil

SOM are largely unknown (Kögel-Knabner, 2002). SOM composition and deposition/

degradation processes may be substantially different between surface and subsoils. Plant

litter deposited on the surface soil contributes the majority of the organic matter to the

surface soil horizons and degrades progressively with soil depth. In comparison, root litter,

rhizodeposition, and translocation of dissolved organic matter (DOM) may play a

significant role in the composition of subsoil carbon (Lorenz and Lal, 2005). However,

information is lacking about the contribution of above-ground versus below-ground

residues to SOM sequestered in the subsoil.

Several studies have examined the distribution of SOM with soil depth in forest and

arable soils (Rumpel et al., 2002; Ussiri and Johnson, 2003; Rumpel et al., 2004). It was

hypothesized that carbohydrates were preferentially degraded at depth, whereas

recalcitrant alkyl carbon (from cutin, suberin, and waxes) was preserved at depth (Rumpel

et al., 2002; Ussiri and Johnson, 2003). Lignin units identified with chemolysis methods

were observed to decrease with soil depth (Kögel-Knabner et al., 1991; Chefetz et al.,

2000; Rumpel et al., 2002; Rumpel et al., 2004), but aromatic carbon measured with

solid-state 13C nuclear magnetic resonance (NMR) was reported to increase in some cases

(Fox et al., 1994). Soil processes (such as drainage and aeration, Rumpel et al., 2002) and

vegetation distribution (Jobbagy and Jackson, 2000) may have a strong influence on the

vertical distribution and degradation of SOM, and thus result in varying conclusions of

30

these studies. For instance, eluviation (vertical translocation) of DOM (mainly

ligno-cellulosic materials with a high degree of oxidation) during podzolisation is

considered to lead to the presence of mainly alkyl carbon in the B horizons of the Dystric

Cambisol (Rumpel et al., 2002).

To date, only a few studies have examined the SOM composition of grassland

subsoils, which have distinct plant inputs, root distribution (Jackson et al., 1996),

microbial communities (Balser and Firestone, 2005; Bottomley et al., 2006), and therefore,

possibly different vertical soil carbon distribution, compared with forest soils. Moreover,

grasslands are one of the most widespread vegetation types world-wide (Anderson, 1991).

The Prairie Ecozone of Western Canada accounts for 80% of the arable land in Canada

and contains large reserves of SOM (Janzen et al., 1998). Consequently, it is important to

investigate the vertical distribution and composition of grassland SOM in the Canadian

Prairies.

Biomarker methods have been applied to examine the composition of Alberta Prairie

grassland soils in a previous paper (Otto et al., 2005), which provides valuable

information on the composition, source, and degradation stage of SOM. For instance, in

the solvent extracts of the grassland soils, even-numbered n-alkanoic acids and n-alkanols

in the range of C16-C32 indicate an SOM input from higher plant waxes and roots. Steroids

like β-sitosterol, stigmasterol, and campesterol originate from plants while ergosterol is a

characteristic biomarker from fungi (Otto et al., 2005; and references therein). The

chemolysis products of base hydrolysis and CuO oxidation are considered more refractory

and contain information on the degradation stage of certain groups of SOM components,

31

such as ester-bound hydroxyalkanoic acids derived from suberin (a biopolyester abundant

in bark and roots of vascular plants) and/or cutin (a biopolymer in the epidermis of

leaves), and phenols derived from lignin (an important cell wall component of vascular

plants, ferns, and mosses; Otto and Simpson, 2006a and 2006b). The objective of this

study is to investigate the vertical distribution of selected biomarker compounds from

SOM inputs in Alberta grassland soils (such as ergosterol, suberin and cutin biomarkers,

and lignin monomers) and to examine the degradation of SOM with soil depth and the

relative contribution of root-derived carbon to SOM in the subsoil. Furthermore, we aim

to determine the nature of SOM that is preferentially preserved versus that which is

degraded in lower soil horizons.

2.3 Methods

2.3.1 Soil Samples

Soil samples were collected from well-drained, pristine grassland soils in western

Alberta. Soils in this area develop on calcareous glacial till or glacio-lacustrine parent

materials of late Pleistocene age (Dudas and Pawluk, 1969), and under native grassland

vegetation. The sites examined in this study included four soil zones: Brown (BR), Dark

Brown (DB), Black (BL), and Eluviated Black (EBL) Chernozems. Soil samples from Ah,

Bm, and Cca horizons were collected for each soil zone (see Table 2.1 for the depth of soil

horizons).

The mean annual soil temperature varies from 1.7°C in the BL Chernozemic soil

zone to 5°C in the BR Chernozemic soil zone, while the annual precipitation is reported

32

to be 452 mm in the BL Chernozemic soil zone and 413 mm in the BR Chernozemic soil

zone (Janzen et al., 1998). The BR and DB Chernozems were sampled near and west of

Lethbridge, Alberta, respectively. The BL Chernozem was sampled just west of

Edmonton, Alberta, and the EBL Chernozem, which had a translocated Ae horizon below

the organic rich Ah horizon, was sampled from the University of Alberta Ellerslie

Research Station, located south of Edmonton. After sampling, the soil samples were

air-dried and then passed through a 2-mm sieve. Soil samples were stored at room

temperature in the dark prior to analysis.

2.3.2 Particle Size Distribution and Carbon and Nitrogen Analyses

Clay content was determined by the hydrometer method (Sheldrick and Wang, 1993)

and reported in percentages. Organic carbon (OC, i.e. total carbon subtracted by inorganic

carbon) and total nitrogen contents of soil samples were determined in triplicate with a

Shimadzu TOC 5000 total organic carbon analyzer coupled with a solid sample module

(Shimadzu Scientific Instruments, Columbia, MD, USA).

2.3.3 Sequential Extraction

Sequential chemical extractions (solvent extraction, base hydrolysis, and CuO

oxidation) were conducted on soil samples to produce total solvent extracts, bound lipids,

and lignin-derived phenols, respectively (Otto et al., 2005). Briefly, soil samples were

first sonicated with double deionized water to remove polar compounds. The

water-extracted soils (~20 g) were then freeze-dried and extracted with 50 ml of

33

dichloromethane, dichloromethane:methanol (1:1; v/v) and methanol, respectively. The

combined solvent extracts were filtered through glass fiber filters (Whatman GF/A and

GF/F), concentrated by rotary evaporation, and then dried under nitrogen gas in 2-ml

glass vials. The air-dried soil residues from solvent extraction were then subject to base

hydrolysis to yield ester-linked lipids (Otto and Simpson, 2006a). Briefly, the residues

(2-10 g, depending on OC content) were heated at 100°C for 3 hours in Teflon-lined

bombs with 20 ml of 1 M methanolic KOH. The extracts were acidified to pH 1 with 6 M

HCl, and the solvents were removed by rotary evaporation. Lipids were recovered from

the water phase by liquid–liquid extraction with diethyl ether, concentrated by rotary

evaporation, and dried under nitrogen gas in 2ml glass vials. The base hydrolysis residues

were air-dried and further oxidized with CuO to release lignin-derived phenols. Soil

residues (2-10 g, depending on OC content) were extracted with 1 g CuO, 100 mg

ammonium iron (II) sulfate hexahydrate [Fe(NH4)2(SO4)2·6H2O] and 15ml of 2 M NaOH

in teflon-lined bombs at 170°C for 2.5 hours. The extracts were acidified to pH 1 with 6

M HCl, and kept for 1 hour at room temperature in the dark to prevent reactions of

cinnamic acids. After centrifugation (at 2500 rpm for 30 min), the supernatants were

liquid–liquid extracted with diethyl ether. The ether extracts were concentrated by rotary

evaporation, transferred to 2-ml glass vials and dried under nitrogen gas.

2.3.4 Derivatization and GC/MS Analysis

Yields of the sequential chemical extractions were determined by weight. The extracts

were re-dissolved, and aliquots (containing ~1 mg extracts) were derivatized for GC/MS

34

analysis. Solvent extracts and CuO oxidation products were converted to trimethylsilyl

(TMS) derivatives by reaction with 90 μl N,O-bis-(trimethylsilyl)trifluoroacetamide

(BSTFA) and 10 μl pyridine for 3 hours at 70°C. After cooling, 100 μl hexane was added

to dilute the extracts. The base hydrolysis products were first methylated by reacting with

600 μl of diazomethane in ether at 37°C for 1 hour, evaporated to dryness under nitrogen,

and then silylated with BSTFA and pyridine as described above. Oleic acid (C18:1 alkanoic

acid) and ergosterol were derivatized and used as external standards for solvent extracts

(ergosterol-TMS as standard for steroids and terpenoids). Oleic acid methyl ester was

used as external standard for base hydrolysis products, while vanillic acid-TMS was used

for CuO oxidation products. GC/MS analysis was performed on an Agilent model 6890N

GC coupled to a Hewlett-Packard model 5973 quadrupole mass selective detector.

Separation was achieved on a HP5-MS fused silica capillary column (30 m × 0.25 mm

i.d., 0.25 μm film thickness). The GC operating conditions were as follows: temperature

held at 65 °C for 2 min, increased from 65 to 300 °C at a rate of 6 °C min-1 with final

isothermal hold at 300 °C for 20 min. Helium was used as the carrier gas. The sample was

injected with a 2:1 split ratio and the injector temperature was set at 280 °C. The samples

(1 μl) were injected with an Agilent 7683 autosampler. The mass spectrometer was

operated in the electron impact mode (EI) at 70 eV ionization energy and scanned from

50 to 650 daltons. Data were acquired and processed with the Chemstation G1701DA

software. Individual compounds were identified by comparison of mass spectra with

literature, NIST and Wiley MS library data, authentic standards, and interpretation of

mass spectrometric fragmentation patterns. External quantification standards were used

35

36

and the response factor was assumed to be 1 for all compound classes. Concentration of

individual compound was calculated by comparison of the peak area of the compound to

that of the standard in the total ion current (TIC) and was then normalized to the sample

OC content. Based on our preliminary work and research by others in our laboratory (Otto

and Simpson, 2007), the biomarker GC/MS methods employed in this study are highly

reproducible and the standard deviation between the same samples is typically <5%.

2.4 Results and Discussion

2.4.1 Particle Size Distribution, Carbon and Nitrogen Contents, and Extract Yields

The clay content increased with soil depth in the four Alberta grassland soils (Table

2.1), with EBL Chernozem showing the highest accumulation of clay in the subsoils due

to the eluviation process. The OC content generally decreased with soil depth in three

Chernozems while the EBL-Cca soil had higher OC content than the EBL-Bm soil due to

the illuviation of organic matter presumably leached from the soil surface (Table 2.1).

Meanwhile, total nitrogen content decreased from 0.2-0.4% in the Ah horizon to 0.1% in

the Cca horizon in all Chernozems (Table 2.1). The atomic C/N ratio (C/Na) accordingly

decreased from the Ah horizon to the Bm horizon, indicating the degradation of SOM with

soil depth. However, the C/Na ratio in the Cca horizon was higher than that in the

corresponding Bm horizon in three soils (BR, BL, and EBL), which is related to the SOM

composition presumably influenced by the fresh input of organic matter derived from root

exudates or/and root-associated fungi, vertical translocation of dissolved organic matter,

and/or preservation of SOM in the subsoils.

37

Table 2.1: Particle size distribution, carbon and nitrogen contents, and extract yields of Alberta grassland soils

Orthic Brown

Chernozem (BR) Orthic Dark Brown

Chernozem (DB) Orthic Black

Chernozem (BL) Eluviated Black

Chernozem (EBL) A Bm h Cca A B Ah m Cca h Bm Cca Ah Bm Cca

Depth (cm) 0-16 16-54 54+ 0-18 18-61 61+ 0-30 30-70 70+ 0-28 38-74 74+ Sand (%) 47.3 46.6 35.8 46.9 42.0 41.8 44.1 43.9 43.9 35.0 34.5 27.5 Silt (%) 32.6 26.3 28.6 29.9 28.5 23.6 29.4 23.0 22.0 36.4 19.0 24.0 Clay (%) 20.1 27.1 35.6 23.2 29.5 34.6 26.5 33.1 34.1 28.6 46.5 48.5 Total carbon (%) 2.08 1.15 3.25 2.77 1.86 3.00 4.41 0.69 1.23 5.26 0.52 0.88 Inorganic carbon (%) 0 0.16 2.32 0 0.04 2.23 0 0 0.57 0 0 0 Organic carbon (OC) (%) 2.08 0.99 0.93 2.77 1.82 0.77 4.41 0.69 0.66 5.26 0.52 0.88 Total nitrogen (%) 0.2 0.2 0.1 0.3 0.2 0.1 0.4 0.1 0.1 0.4 0.1 0.1 Atomic C/N ratio 12 7 9 11 10 7 13 8 9 15 7 13 Free lipids (mg/g OC) 38.5 9.6 30.0 28.9 20.9 18.2 29.5 21.3 10.8 7.6 11.1 11.8 Bound lipids (mg/g OC) 28.8 54.0 67.0 50.5 49.4 60.8 40.8 27.7 32.7 17.1 17.5 8.9 CuO oxidation products (mg/g OC) 52.9 157.4 111.3 57.8 73.3 103.5 36.2 79.9 96.4 25.6 79.8 14.1

Note: Extract yields of Ah horizons from Otto et al. (2005).

38

A linear trend of extract yields was not observed with soil depth (Table 2.1). The

highest solvent extract yield was observed in BR-Ah (38.5 mg/g C), the lowest in EBL-Ah

(7.6 mg/g C). The base hydrolysis yield was highest in BR-Cca (67.0 mg/g C) and lowest

in EBL-Cca (8.9 mg/g C), while the CuO oxidation yield was highest in BR-Bm (157.4

mg/g C) and lowest in EBL-Cca (14.1 mg/g C). The large variance in extract yields

indicates great variability of SOM composition with soil type and soil depth.

2.4.2 Composition and Source of Total Solvent Extracts

The major components of the total solvent extracts included steroids, terpenoids,

carbohydrates, aliphatic lipids (n-alkanoic acids, n-alkanols, n-alkanes, and

ω-hydroxyalkanoic acids), monoacylglycerides, and one phenol (ferulic acid; Table 2.2).

The GC-MS chromatogram (TIC) of the major components in the silylated solvent

extracts from the BR-Bm horizon is shown in Figure 2.1a. The sources of these

compounds have been explained in detail elsewhere (Otto et al., 2005; and references

therein). Generally, the composition of the aliphatic lipids (with a higher abundance of

even-numbered n-alkanoic acids in the range of C9-C30 and even-numbered n-alkanols in

the range of C16-C32) and steroids (with β-sitosterol, stigmasterol, and campesterol being

the dominant) is in accordance with that of the solvent extracts from the overlaying

vegetation (Western Wheatgrass) in these grasslands (Otto and Simpson, 2005), and

revealed a major input from plants into SOM (Otto et al., 2005; and references therein).

Ergosterol, a viable fungal biomarker (Grant and West, 1986), was only detected in the Ah

horizons of these grasslands, indicating the relatively higher fungal activity in the upper

39

Table 2.2: Compounds identified in the total solvent extracts (mg/g OC)

BR DB BL EBL Compound Name Ah Bm Cca Ah Bm Cca Ah Bm Cca Ah Bm Cca

n-Alkanols (C16-C32) 0.14 0.18 0.45 0.19 0.79 0.56 0.11 0.40 0.14 0.11 0.23 0.11n-Alkanoic acids (C9-C30) 0.16 0.15 0.18 0.10 0.20 0.12 0.08 0.22 0.04 0.14 1.06 0.19n-Alkanes (C24-C33) 0.02 0.07 0.29 0.04 0.18 0.22 0.03 0.11 0.09 0.02 0.04 0.03　-Hydroxyalkanoic acids (C22,C24) 0.02 0.06 0.06 0.02 0.12 nd 0.01 nd nd 0.03 nd nd Monoacylglycerides (C16-C26) 0.02 0.02 0.11 0.01 0.14 0.18 nd 0.10 0.003 0.004 0.04 0.02Carbohydrates 0.07 1.52 4.24 0.05 4.59 3.09 0.05 1.92 0.50 0.04 nd 0.01

Glucose 0.002 0.02 0.04 0.002 0.03 0.02 0.001 0.02 0.004 0.001 nd nd Mannose 0.001 0.02 0.04 0.001 0.04 0.03 0.001 0.02 0.01 0.001 nd nd

Sucrose 0.005 0.10 0.28 0.002 0.22 0.08 nd 0.06 0.06 nd nd 0.002Trehalose 0.06 1.38 3.88 0.04 4.30 2.96 0.05 1.82 0.43 0.04 nd 0.003

Aliphatics total 0.43 2.00 5.33 0.41 6.02 4.17 0.28 2.75 0.78 0.35 1.37 0.36Steroids and Terpenoids 0.21 0.18 0.85 0.21 0.90 0.75 0.12 0.29 0.20 0.07 0.51 0.09

Cholesterol 0.01 0.02 nd 0.01 0.05 0.03 0.003 0.01 0.01 0.00 0.02 0.01Ergosterol 0.02 nd nd 0.03 nd nd 0.01 nd nd 0.01 nd nd

Campesterol 0.04 nd 0.28 0.04 0.25 0.23 0.01 0.06 0.05 0.01 nd nd Stigmasterol 0.02 0.04 0.09 0.02 0.12 0.08 0.01 0.03 0.02 0.01 0.09 0.02　-Sitosterol 0.08 0.13 0.42 0.08 0.43 0.38 0.07 0.19 0.13 0.03 0.40 0.07

Stigmasta-3,5-dien-7-one 0.004 nd nd 0.02 nd nd 0.01 nd nd nd nd nd Sitosterone 0.02 nd nd 0.01 nd nd 0.005 nd nd 0.004 nd nd

Stigmastanol nd nd 0.05 nd 0.06 0.04 nd nd nd nd nd nd 　-Amyrin 0.02 nd nd 0.02 nd nd 0.01 nd nd 0.01 nd nd

Phenol (ferulic acid) 0.001 nd nd 0.002 nd nd nd nd nd 0.001 nd nd TOTAL SOLVENT EXTRACTS 0.65 2.19 6.17 0.64 6.93 4.93 0.41 3.04 0.99 0.43 1.88 0.46

nd: not detected. Note: Ah horizon data from Otto et al. (2005).

A. Solvent extracts from the Bm horizon

14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46

C. CuO oxidation products from the Ah horizon

syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30

10 15 20 25 30 35 40 45

B. Base hydrolysis products from the Cca horizon

16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω

Retention time (min)

Rel

ativ

e ab

unda

nce


14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46


14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46


syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30


syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30

10 15 20 25 30 35 40 45


16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω

10 15 20 25 30 35 40 4510 15 20 25 30 35 40 45


16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω


Rel

ativ

e ab

unda

nce

a.

b.

c.


14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46


syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30

10 15 20 25 30 35 40 45


16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω


Rel

ativ

e ab

unda

nce


14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46


14+

18+

18

18:1+

24 24+

30

16+

2629°

20+

22+

31°

28

16:1+ 18:2

+ 25° 27

°gl#

*

ma#

st1

st4

st2

st3

24ω

C16

:1M

AG

C16

MA

G su#

12+

15+

tr#

22 24 26 28 30 32 34 36 38 40 42 44 46


syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30


syrin

gald

ehyd

e

4-O

H-b

enzo

ic a

c

vani

llicac

syrin

gic

acp-

coum

aric

ac

benz

oic

ac

p-O

H-b

enza

ldeh

yde

*

vani

llin3-

OH

-ben

zoic

ac

acet

ovan

illone

feru

licac

sina

pic

ac

1,2,

4-be

nzen

etric

arbo

xylic

ac

pyrr

ol-2

-car

boxy

lic a

c

3,5-

OH

-ben

zoic

ac

14 16 18 20 22 24 26 28 30

10 15 20 25 30 35 40 45


16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω

10 15 20 25 30 35 40 4510 15 20 25 30 35 40 45


16ω

18ω

20ω

24

22ω

24α

24ω

26ω

28ω26

28

20

18:1ωva

nilli

cac

p-co

umar

icac

feru

licac

30ω

st4

16:1*

16

18:1

1822

*

3015

i1616

2917 20α

16α 18

α

10,1

6-O

H C

16ac

23 22α

9,10

,18-

OH

C18

ac

23α25 25

α

23ω 26

α25ω 28

α

27ω31

3229ω


Rel

ativ

e ab

unda

nce

a.

b.

c.

Figure 2.1: GC-MS chromatograms (TIC) of the major SOM components extracted from the Brown Chernozem soils from Alberta, Canada. (a) Silylated solvent extracts from the Bm horizon; (b) Methylated and silylated base hydrolysis products from the Cca horizon; (c) Silylated CuO oxidation products from the Ah horizon. + = n-alkanoic acids, # = carbohydrates (gl = glucose, ma = mannose, su = sucrose, tr = trehalose), = ▽ n-alkanols, o = n-alkanes, ω = ω-hydroxyalkanoic acids, MAG = monoacylglycerides, st1 = cholesterol, st2 = campesterol, st3 = stigmasterol, st4 = β-sitosterol, i = iso-alkanoic acid, ♦ = n-alkanoic acids (as methyl esters), = n-alkanedioic acids, α = α-hydroxyalkanoic acids, u = unknowns, * = contamination, ac = acid. Numbers refer to total carbon numbers in aliphatic lipid series.

40

soil horizons.

Four carbohydrates (glucose, mannose, sucrose, and trehalose) were identified in the

solvent extracts of the soil samples (Table 2.2). Sucrose, the primary carbohydrate found

in the solvent extracts of Western Wheatgrass (Otto and Simpson, 2005), was only

detected in small amounts in the total solvent extracts of the grassland soils. By contrast,

the disaccharide trehalose was found in significant amounts in the Bm and Cca horizons of

BR, BL, and DB Chernozems (Table 2.2), and contributed up to 64% of the identified

total solvent extracts. As a reserve carbohydrate and stress protectant, trehalose occurs in

a wide range of organisms, such as fungi, bacteria, and insects, but is only rarely found in

plants (Müller et al., 1995; Wingler, 2002). Its detection in the roots and green leaves of

Western Wheatgrass (around 0.6 mg/g OC; Otto and Simpson, 2005) is most probably

due to fungi contamination (Müller et al., 1995). Therefore, trehalose found in the solvent

extracts of these grassland soils is most likely to derive from a non-plant source, such as

bacteria, soil-dwelling insects, or mycorrhizal fungi associated with grass roots in the

subsoil. However, because the fungal biomarker, ergosterol, was not detected in the

grassland subsoils, fungi is presumably not prevailing in the lower horizons, and

consequently, the direct deposit of trehalose by fungi into the Bm and Cca horizons is

unlikely. Alternatively, this relatively polar and stable disaccharide may be leached into

subsoils from the upper horizon and consequently preserved in the regolith due to

decreased microbial activity and/or the increased content of clay minerals in the deeper

horizons (Baldock and Skjemstad, 2000). Trehalose was not identified in EBL-Bm soil

and occurred only in a small amount in EBL-Cca soil, which was probably related to

41

42

lateral translocation of the dissolved organic matter in this Chernozem with a high degree

of eluviation (Rumpel et al., 2002).

2.4.3 Composition and Degradation of Bound Lipids

The observed bound soil lipids consisted of ω-hydroxyalkanoic acids,

α-hydroxyalkanoic acids, n-alkanoic acids, n-alkane-α,ω-dioic acids, n-alkanols,

mid-chain substituted acids, benzyls, phenols, and small amounts of steroids and

terpenoids (Table 2.3). The GC-MS chromatogram (TIC) of the major components in the

methylated and silylated base hydrolysis products from the BR-Cca horizon is shown in

Figure 2.1b. The composition of the bound soil lipids (aliphatic lipids and phenols) is

similar to that of the overlaying vegetation (Otto and Simpson, 2006a) and indicates

major inputs from suberin, cutin, and plant waxes (Otto et al., 2005; Otto and Simpson,

2006a). Microbial biomarkers, i.e., short-chain and branched aliphatic lipids, and widely

distributed α-hydroxyalkanoic acids and benzyls from various sources (Otto and Simpson,

2006a) were detected in the bound lipid fraction only in minor amounts (Table 2.3).

Based on their structural units and degradation patterns, suberin and cutin biomarkers

were summarized and calculated based on parameters developed by Otto and Simpson

(2006a; Table 2.3). In general, suberin biomarkers (∑S = ω-hydroxyalkanoic acids

C20-C32 + n-alkane-α,ω-dioic C20-C32 + 9,10-epoxy-18-hydroxy C18 acid; after Otto and

Simpson, 2006a) increased with soil depth in BR, DB, and BL Chernozems, while cutin

biomarkers (∑C = mid-chain hydroxy C14, C15, C17 acids + C16 mono- and dihydroxy

acids and diacids; after Otto and Simpson, 2006a) decreased. In ecosystems dominated by

Table 2.3: Compounds identified in base hydrolysis products of grassland soils (mg/g OC)


n-Alkanols (C16-C30) 0.64 1.95 1.56 0.61 2.70 3.71 0.44 2.03 2.34 0.15 0.62 0.47n-Alkanoic acids (C14-C32) 2.60 10.38 6.82 1.67 9.86 7.26 0.94 5.39 4.51 0.48 1.29 0.29Branched alkanoic acids (iso-C16, C18) 0.07 0.20 0.13 0.04 0.13 0.38 0.05 0.17 0.12 0.02 0.04 0.04n-Alkanedioic acids (C8-C28) 0.60 nd nd 0.43 0.41 nd 0.44 nd nd 0.11 nd nd Mid-chain hydroxy and epoxy acids 0.55 0.43 0.25 0.94 0.59 0.37 0.64 1.08 0.76 0.18 0.06 0.01

7- or 8-Hydroxyhexadecane- 1,16-dioic acid 0.11 nd nd 0.19 nd nd 0.06 nd nd 0.03 nd nd 10,16-Dihydroxyhexadecanoic acid 0.17 0.28 0.14 0.28 0.40 0.23 0.15 nd nd 0.07 0.04 0.01

9,10,18-Trihydroxyoctadecanoic acid 0.21 0.15 0.12 0.39 0.19 0.14 0.22 1.08 0.76 0.07 0.01 nd 9,10-Dihydroxyoctadecane-1,18-dioic acid 0.05 nd nd 0.05 nd nd 0.08 nd nd 0.01 nd nd

9,10-Epoxy-18-hydroxy C18 acid1 nd nd nd 0.03 nd nd 0.12 nd nd 0.01 nd nd 　-Hydroxyalkanoic acids (C9-C30) 4.29 19.99 17.65 2.77 15.34 24.26 1.30 2.95 4.66 0.51 0.53 0.05a-Hydroxyalkanoic acids (C15-C28) 0.60 2.63 2.14 0.69 3.77 2.83 0.37 0.50 0.57 0.12 0.33 0.01

ALIPHATIC LIPIDS TOTAL 9.35 35.58 28.57 7.15 32.81 38.81 4.17 12.12 12.96 1.56 2.86 0.87Benzyls and phenols 1.62 0.99 0.61 1.55 2.22 3.63 0.65 0.80 0.83 0.50 nd nd Steroids and Terpenoids 0.08 0.29 0.51 0.12 0.33 0.55 0.10 0.77 0.88 0.03 0.02 0.01

TOTAL BOUND LIPIDS 11.06 36.86 29.69 8.82 35.36 42.99 4.92 13.69 14.66 2.09 2.89 0.88Suberin and cutin monomers Suberin ∑S2 4.57 15.31 10.82 4.20 13.75 11.22 2.66 8.68 8.41 0.96 1.60 0.40Cutin ∑C3 0.07 0.20 0.13 0.04 0.13 0.38 0.05 0.17 0.12 0.02 0.04 0.04Suberin or cutin ∑S∨C4 0.66 0.68 0.66 0.30 1.25 0.48 0.27 0.35 0.50 0.12 0.17 0.03Sum Suberin and cutin ∑SC5 5.30 16.20 11.61 4.55 15.13 12.08 2.98 9.21 9.02 1.09 1.82 0.46∑C166 0.55 0.74 0.62 0.19 1.21 0.77 0.18 0.53 0.61 0.11 0.18 0.04∑C187 0.17 0.15 0.17 0.15 0.17 0.08 0.18 nd nd 0.03 0.04 0.03

43

44


1 Detected as methoxy, chlorohydrine and 9,10-dihydroxy derivatives. 2 ∑S = ω-hydroxyalkanoic acids C20-C32 + α,ω-diacids C20-C32 + 9,10-ep C18 dioic acid. 3 ∑C = mid-chain hydroxy C14, C15, C17 acids + C16 mono- and dihydroxy acids and diacids. 4 ∑S∨C = ω-hydroxy acids C16, C18 + C18 di- and trihydroxy acids + 9,10-ep-ω-OH C18 + α,ω-diacids C16, C18 (Otto and Simpson, 2006a). 5 ∑SC = ∑S + ∑C + ∑S∨C. 6 ∑C16 = ω-hydroxy C16 acid + α,ω-dioic C16 acid + ∑C16 mid-chain-substituted acids (Goñi and Hedges, 1990). 7 ∑C18 = ω-hydroxy C18 acid + α,ω-dioic C18 acid + ∑C18 mid-chain-substituted acids (Goñi and Hedges, 1990).

non-woody species (such as grass), suberin can be a good tracer of root biomass, while

cutin is a shoot-specific compound (Rasse et al., 2005). The distribution of suberin and

cutin markers in the subsoil indicated the elevated contribution of grass roots to SOM

with soil depth and the increasing degradation of plant litter. Suberin biomarkers were

detected in relatively lower abundances in the subsoils of EBL Chernozem (0.04-0.48

mg/g OC), probably due to the eluviation process, which translocated surface SOM (such

as cutin-derived acids) into the subsoils and consequently diluted the relative abundance

of suberin biomarkers.

To assess the degradation of bound lipids in the subsoil, degradation parameters were

calculated (Otto and Simpson, 2006a; Figure 2.2). Similar to a trend observed in forest

soils (Kögel-Knabner et al., 1989; Nierop, 1998), an increasing ratio of suberin/cutin with

soil depth were observed in both BR and DB soils (Figure 2.2a), which may result from

fresh root input in deeper horizons and/or preferential degradation of cutin (Riederer et al.,

1993). Such a trend was not obvious in BL Chernozem and completely reversed in EBL

subsoils presumably due to the relative enrichment of cutin biomarkers illuviated from the

soil surface. Increased ratios of ω-C16/∑C16 and ω-C18/∑C18 have been reported with

progressing cutin degradation in marine sediments (Goñi and Hedges, 1990) and with soil

depth (Otto and Simpson, 2006a), because cutin acids containing double bonds or more

than one hydroxyl group are preferentially degraded compared to ω-hydroxyalkanoic

acids. A similar trend was observed in this study (Figures 2.2 b-c), suggesting elevated

cutin degradation with soil depth. Lastly, the decrease of mid-chain-substituted acids

(∑Mid) relative to total suberin and cutin acids (∑SC) from Ah to Bm horizon (Figure

45

Ah Bm Cca

2

3

4

5

6Su

berin

/Cut

in(a)

Ah Bm Cca0.2

0.4

0.6

0.8

1.0

1.2 BR DB BL EBL

ω-C

16/Σ

C16

(b)

Ah Bm Cca0.0

0.5

1.0

1.5

ω-C

18/Σ

C18

(c)

Ah Bm Cca0.0

0.1

0.2

0.3

ΣMid

/ΣS

C

(d)

Figure 2.2: Degradation parameters of the bound lipids for Alberta grassland soils. (a) suberin/cutin ratio = (∑S+∑S∨C)/(∑C+∑S∨C), where ∑S = ω-hydroxyalkanoic acids C20-C32 + α,ω-diacids C20-C32 + 9,10-ep C18 dioic acid, ∑C = mid-chain hydroxy C14, C15, C17 acids + C16 mono- and dihydroxy acids and diacids, ∑S∨C = ω-hydroxy acids C16, C18 + C18 di- and trihydroxy acids + 9,10-ep-ω-OH C18 + α,ω-diacids C16, C18 (based on Otto and Simpson, 2006a). (b) ω-C16/∑C16 ratio; ∑C16 = ω-hydroxy C16 acid + α,ω-dioic C16 acid + ∑C16 mid-chain-substituted acids (Goñi and Hedges, 1990). (c) ω-C18/∑C18 ratio; ∑C18 = ω-hydroxy C18 acid + α,ω-dioic C18 acid + ∑C18 mid-chain-substituted acids (Goñi and Hedges, 1990). (D) ∑Mid/∑SC ratio; ∑Mid = mid-chain hydroxy and epoxy acids, ∑SC = ∑S + ∑C + ∑S∨C.

2.2d) suggests that the degradation of suberin and cutin acids may be due to greater

abundances of mid-chain hydroxy and epoxy acids in fresh vegetation. Again, the

degradation parameters could be biased by the fresh input of root-derived organic matter

in the subsoil, which is high in ω-C16/∑C16 and ω-C18/∑C18 ratios and low in ∑Mid/∑SC

46

ratio (Otto and Simpson, 2006a), and the parameter of EBL subsoils is influenced by the

illuviated organic matter from the upper horizon.

2.4.4 Distribution and Degradation of Lignin Compounds

CuO oxidation released benzyls and lignin-derived phenols from SOM (Table 2.4).

The GC-MS chromatogram (TIC) of the major components in the silylated CuO oxidation

products from the BR-Ah horizon is shown in Figure 2.1c. Among the identified benzyls,

benzoic acid, p-hydroxybenzaldehyde, m-hydroxybenzoic acid, p-hydroxybenzoic acid,

and 2-carboxypyrrole are considered to be the oxidation products of proteinaceous

material (Goñi et al., 2000), while the source of 3,5-dihydroxybenzoic acid and

benzenepolycarboxylic acids remains undetermined (Otto and Simpson, 2006b). The

identified lignin monomers included vanillyls (V; vanillin, acetovanillone, vanillic acid,

and vanillylglyoxalic acid), syringyls (S; syringaldehyde, acetosyringone, syringic acid,

syringylglyoxalic acid, and sinapic acid), and cinnamyls (C; p-coumaric acid, ferulic acid,

and hydrocinnamic acid), and were in accordance with those identified in the overlaying

grasses (Otto and Simpson, 2006b).

The concentrations of lignin monomers (Figure 2.3a) were generally highest in Ah

horizon and decreased with soil depth, reflecting the progressive degradation of lignin

with soil depth (Hedges et al., 1988; Kögel-Knabner et al., 1991; Opsahl and Benner,

1995; Chefetz et al., 2000; Rumpel et al., 2004). In the DB Chernozem, however, lignin

monomers were more abundant in the Cca horizon. Enhanced degradation of lignin is

indicated by elevated acid/aldehyde (Ad/Al) ratios of vanillyl and syringyl units (Hedges

47

et al., 1988; Opsahl and Benner, 1995; Otto et al., 2005). The ratios of vanillic

acid/vanillin and syringic acid/syringaldehyde both increased with soil depth in BR, DB,

and BL Chernozems (Figure 2.3 b-c). Acid/aldehyde ratios were not calculated for EBL

Chernozem, because vanillin and syringaldehyde were not detected in EBL-Cca horizon

(Table 2.4).

Table 2.4: Compounds identified in CuO oxidation products (mg/g OC)


Benzyls Benzoic acid 0.21 0.12 0.12 0.15 0.09 0.21 0.09 0.06 0.04 0.13 nd 0.002p-Hydroxybenzaldehyde 0.27 0.15 0.12 0.17 0.06 0.11 0.08 nd nd 0.10 nd nd m-Hydroxybenzoic acid 0.08 0.77 0.64 0.07 0.35 0.59 0.09 0.38 0.27 0.02 0.10 0.03p-Hydroxybenzoic acid 0.67 0.92 0.98 0.44 0.79 1.10 0.27 0.47 0.29 0.43 0.16 0.052-Carboxypyrrole 0.21 0.53 0.62 0.27 0.48 0.63 0.30 0.52 0.31 0.32 nd 0.043,5-Dihydroxybenzoic acid 0.15 0.30 0.15 0.13 0.14 0.19 0.31 0.28 0.27 0.35 0.27 0.041,2,4-Benzenetricarboxylic acid 0.06 0.27 0.17 0.07 0.22 0.34 0.23 0.24 0.24 0.17 0.24 0.061,3,5-Benzenetricarboxylic acid 0.02 0.27 0.17 0.02 0.11 0.17 0.17 0.14 0.13 0.15 0.16 0.03Lignin monomers Vanillyls 1.83 3.12 2.29 1.72 1.73 3.15 1.06 1.23 1.31 1.29 0.17 0.03

Vanillin 0.57 0.72 0.56 0.48 0.36 0.74 0.24 0.23 0.11 0.29 0.07 nd Acetovanillone 0.24 0.19 0.10 0.24 0.14 0.17 0.16 0.15 0.10 0.19 nd nd

Vanillic acid 0.70 1.64 1.27 0.70 0.93 1.63 0.43 0.64 0.53 0.55 0.10 0.03Vanillylglyoxalic acid 0.32 0.58 0.36 0.31 0.31 0.61 0.23 0.22 0.58 0.25 nd nd

Syringyls 1.43 1.80 1.45 1.43 1.01 2.14 1.10 0.69 0.74 1.04 0.10 0.02Syringaldehyde 0.55 0.46 0.41 0.47 0.24 0.53 0.29 0.16 0.05 0.24 nd nd Acetosyringone 0.22 0.41 0.21 0.26 0.12 0.26 0.19 0.07 0.09 0.15 nd nd

Syringic acid 0.45 0.55 0.55 0.50 0.45 0.88 0.41 0.31 0.28 0.40 0.10 0.02Syringylglyoxalic acid 0.15 0.38 0.28 0.14 0.20 0.46 0.16 0.16 0.32 0.18 nd nd

Sinapic acid 0.06 nd nd 0.05 nd nd 0.06 nd nd 0.08 nd nd Cinnamyls 2.57 0.26 0.23 1.55 0.25 0.43 1.54 0.20 0.15 0.85 0.16 0.04

p-Coumaric acid 0.71 0.14 0.14 0.63 0.08 0.14 0.95 0.09 0.07 0.39 0.10 0.04Ferulic acid 1.33 0.12 0.09 0.90 0.16 0.29 0.60 0.11 0.08 0.46 0.06 0.01

Hydrocinnamic acid 0.52 nd nd 0.02 nd nd nd nd nd nd nd nd TOTAL LIGNIN MONOMERS 5.83 5.18 3.96 4.70 2.99 5.71 3.71 2.12 2.21 3.18 0.43 0.09


48

Grassland soils from the Ah horizon demonstrated a V:S:C ratio of 1:1:1, consistent

with the non-woody angiosperm source (Otto et al., 2005). By comparison, cinnamyls

sharply decreased in Bm and Cca horizons relative to syringyls and vanillyls, resulting in a

decreasing C/V ratio with soil depth (Figure 2.3e). Cinnamyls occur in non-woody

vascular plant tissues, linking carbohydrates and lignin in the ligno-cellulose complex

(Lam et al., 2001), and are therefore more accessible to decomposition than vanillyls

Ah Bm Cca0

1000

2000

3000

4000

5000

Ah Bm Cca0

2

4

6

VSC

(μg/

g C

)

(a)

Ah Bm Cca0.4

0.6

0.8

1.0

1.2 BR DB BL EBL

S/V

Ah Bm Cca1

2

3

4

5

(Ad/

Al) v

(b)

(C)

Ah Bm Cca0.0

0.5

1.0

1.5

2.0

C/V

(Ad/

Al) s

(d)

(e)

Figure 2.3: Degradation parameters of lignin compounds in Alberta grassland soils. (a) VSC: V = Vanillyls (vanillin, acetovanillone, and vanillic acid); S = Syringyls (syringaldehyde, acetosyringone, and syringic acid); C = Cinnamyls (p-coumaric acid, and ferulic acid). (b) (Ad/Al)v: ratio of vanillic acid/vanillin. (c) (Ad/Al)s: ratio of syringic acid/ syringaldehyde. (d) Ratio of S/V. (e) Ratio of C/V.

49

(Bahri et al., 2006). The sharp decrease of cinnamyls in Bm horizons is most likely due to

the reduced input of leaf litter in the subsoil and/or the preferential degradation of

cinnamyls. An elevated ratio of C/V was observed in EBL subsoils, indicating a strong

influence of the surface litter. Again, this may be caused by the eluviation process, which

translocated grass-litter-derived compounds from the soil surface to the lower horizons.

Syringyls were also reported to degrade faster than vanillyls in the environment (Hedges

et al., 1988; Opsahl and Benner, 1995; Otto et al., 2005). The ratio of S/V was

accordingly observed to decrease sharply from Ah to Bm horizon (Figure 2.3d). The slight

increase in S/V ratio from Bm to Cca horizon in the Chernozemic soils was probably

caused by a fresh input of root-derived organic matter.

2.4.5 Contribution of Above-Ground versus Below-Ground Residues

Based on datasets from a variety of in situ and incubation experiments, Rasse et al.

(2005) estimated that root-derived carbon played a dominant role in soils relative to shoot

tissues. In a study of the total lipid extracts of grass and soil from the Rothamsted

grassland experiments, root material was also shown to be a predominant source of

aliphatic organic acids in the soil (Bull et al., 2000). Such is the case for the subsoils in

this study, where suberin (a key component of root) biomarkers (Table 2.3) made up a

significant part of identified SOM (17-47%), and the high suberin/cutin ratio (ranging

from 1.77 to 5.98, Figure 2.2a) indicated the dominant contribution of root-derived

organic matter to the subsoil. The changes in the lignin monomer ratio (V:S:C, Table 2.4)

also reflected the dominance of root biomass in SOM with increasing soil depth (plant

50

litter and topsoil are more enriched in cinnamyls while root and subsoil are depleted in

cinnamyls), although this observation may be biased by the selective degradation of

cinnamyl units. At the same time, high concentrations (up to 4.30 mg/g OC) of trehalose

were detected in the subsoils of three grassland Chernozems. Although it is difficult to tell

the exact source of this non-specific carbohydrate, it seems more likely that the detected

trehalose came from a non-plant origin. Its high abundance in the SOM suggested that

non-plant biomass could strongly contribute to the deposition of carbon into the subsoil.

These findings collectively emphasized the importance of below-ground biomass in the

distribution of SOM down the soil profile.

2.4.6 Changes in SOM Composition with Soil Depth

The relative contribution of each soil fraction (total solvent extracts, bound lipids,

and lignin compounds) to the identified SOM is displayed in Figure 2.4. This figure does

not represent the ‘real’ contribution of different SOM components in a quantitative

sense because CuO oxidation is not a quantitative method to measure the lignin

macromolecules in the soil. Instead, this is a comparison of the relative importance of

lignin monomers and aliphatic lipids in the composition of SOM down the soil profile.

For instance, the bound lipids made up an increasing percentage of SOM with increasing

soil depth, suggesting that bound soil lipids are a stable fraction of SOM. As suggested by

Nierop et al. (2003), ester-linked macromolecules such as cutin and suberin may be very

important with regards to carbon storage in subsoil environments. By comparison, lignin

compounds decreased with soil depth both in actual concentrations (Table 2.4) and in

51

4%

63%

33%

5%

83%

12%

16%

74%

10%

4%

63%

33%

15%

78%

7%

9%

80%

11%

4%

55%

41%

16%

73%

11%

5%

83%

12%

7%

37%56%

36%

56%

8%

Solvent extractsBound lipidsLignin monomers

32%

61%

7%

BR DB BL EBL

Ah

Bm

Cca

4%

63%

33%

5%

83%

12%

16%

74%

10%

4%

63%

33%

15%

78%

7%

9%

80%

11%

4%

55%

41%

16%

73%

11%

5%

83%

12%

7%

37%56%

36%

56%

8%


32%

61%

7%

4%

63%

33%

5%

83%

12%

16%

74%

10%

4%

63%

33%

15%

78%

7%

9%

80%

11%

4%

55%

41%

16%

73%

11%

5%

83%

12%

7%

37%56%

36%

56%

8%


32%

61%

7%

BR DB BL EBL

Ah

Bm

Cca

Figure 2.4: The relative contribution of different soil fractions to the identified Alberta grassland SOM.

relative percentages (Figure 2.4). This finding is somehow counter-intuitive because

lignin is considered to be highly recalcitrant and more abundant in roots than in shoots

(Rasse et al., 2005), and since root biomass contributes strongly to subsoil SOM, one

would expect increasing lignin content in the subsoil. However, according to the results

from this study, aliphatic molecules (such as n-alkanoic acids, and hydroxyalkanoic acids)

not lignin compounds are the major component of identified grassland subsoil SOM,

probably resulting from the preferential preservation of aliphatics through selective

sorption by clay minerals (Feng et al., 2005). Our findings are consistent with the

distribution of phenols and hydroxyalkanoic acids in a Dystric Cambisol, where

suberin/cutin compounds were preferentially preserved at depth compared to lignin

52

carbon (Rumpel et al., 2004). Our data suggests that lignin is not preserved in the subsoil

horizons in grassland ecosystems.

2.5 Conclusions

Grassland soil biomarkers are excellent indicators of the source and degradation of

SOM in the soil profile. While degradation of suberin, cutin, and lignin compounds was

enhanced with soil depth, aliphatic molecules (such as hydroxyalkanoic acids) from

suberin and cutin were preferentially preserved at depth in comparison to lignin

compounds. Trehalose, found in high abundance in fungal tissues, was detected at a high

concentration in grassland B and C horizons, suggesting that fungal biomass may strongly

contribute to the deposition of organic carbon in subsoils. Furthermore, there is a strong

influence of soil-forming processes (such as eluviation) on the composition of organic

carbon in subsoils. Isotopic analysis may be utilized in future studies to further investigate

SOM decomposition pattern and microbial degradation mechanisms in deeper horizons.

2.6 Acknowledgements

Leah Nielson and Janice Austin are thanked for conducting the analysis of clay

content and part of the chemical extractions, respectively. Funding from the Canadian

Foundation for Climate and Atmospheric Sciences (GR-520) is gratefully acknowledged.

MJS thanks the National Science and Engineering Research Council (NSERC) of Canada

fro support via a University Faculty Award (UFA). NSERC is also thanked for providing

an Undergraduate Student Research Award (USRA) to both L. Nielson and J. Austin.

53

CHAPTER 3

TEMPERATURE RESPONSES OF

INDIVIDUAL SOIL ORGANIC MATTER COMPONENTS*

* Reprinted from Journal of Geophysical Research-Biogeosciences, 113, G03036,

doi:10.1029/2008JG000743. Authors: Feng, X., Simpson, M.J., Copyright (2008), with

permissions from the American Geophysical Union.

54

3.1 Abstract

Temperature responses of soil organic matter (SOM) remain unclear partly due to its

chemical and compositional heterogeneity. In this study, the decomposition of SOM from

two grassland soils was investigated in a one-year laboratory incubation at six different

temperatures. SOM was separated into solvent extractable compounds, suberin- and

cutin-derived compounds, and lignin-derived monomers by solvent extraction, base

hydrolysis, and CuO oxidation, respectively. These SOM components have distinct

chemical structures and stabilities and their decomposition patterns over the course of the

experiment were fitted with a two-pool exponential decay model. The stability of SOM

components was also assessed using geochemical parameters and kinetic parameters

derived from model fitting. Compared with the solvent extractable compounds, a low

percentage of lignin monomers partitioned into the labile SOM pool. Suberin- and

cutin-derived compounds were poorly fitted by the decay model, and their recalcitrance

was shown by the geochemical degradation parameter (ω-C16/∑C16), which was observed

to stabilize during the incubation. The temperature sensitivity of decomposition,

expressed as Q10, was derived from the relationship between temperature and SOM decay

rates. SOM components exhibited varying temperature responses and the decomposition

of lignin monomers exhibited higher Q10 values than the decomposition of solvent

extractable compounds. Our study shows that Q10 values derived from soil respiration

measurements may not be reliable indicators of temperature responses of individual SOM

components.

55

3.2 Introduction


and global carbon cycle (Batjes, 1996; Schlesinger and Andrews, 2000). The acceleration

of SOM decomposition with global warming has become one of the major concerns in

predicting future climate change. However, SOM decomposition remains unclear in terms

of its temperature sensitivity and the decay patterns of heterogeneous SOM components

(Melillo et al., 2002; Knorr et al., 2005b; Davidson and Janssens, 2006). Investigations

into SOM decomposition have suggested varying and even contrasting responses of SOM

components to temperature increases (Fang et al., 2005; Knorr et al., 2005b). According

to the Arrhenius theory, the reaction rate (k) of SOM mineralization is a function of the

activation energy of SOM components (Ea, J mol-1) within the enzyme-active temperature

ranges (~5-40ºC; Winkler et al., 1996):

k = a×exp(-Ea/RT), (3.1)

where a is the theoretical rate at Ea = 0, R is the gas constant (8.314 J mol-1 K-1), and T is

the absolute temperature (°K). In other words, the temperature sensitivity of SOM

mineralization, Q10, defined as the factor by which the reaction rate differs for a

temperature interval of 10ºC, should increase with increasing Ea or chemical recalcitrance,

and decrease with increasing temperature:

Q10 = kT+10/kT = exp [10×Ea/RT(T+10)] (3.2)

However, k and Q10 values derived from the modeling of soil respiration data do not

always follow the Arrhenius theory (Giardina and Ryan, 2000; Fang et al., 2005). A

similar mean residence time (the inverse of reaction rate) and a similar temperature

56

sensitivity have been reported for soils with different recalcitrance (Giardina and Ryan,

2000; Fang et al., 2005). Alternatively, a higher Q10 value has been calculated for the

decomposition of recalcitrant SOM when the soil respiration data were fitted with a

multi-pool model (Knorr et al., 2005b). It has been suggested that the single-pool model

of soil respiration ignores the heterogeneity of SOM, and hence the k and Q10 values

derived from such models are not reliable indicators of the intrinsic kinetic properties of

individual SOM components (Davidson and Janssens, 2006). Even when a multi-pool soil

carbon model is used, SOM is divided into stable and labile pools based on curve fitting

of the respiration data rather than the chemical structure of components within SOM.

Therefore, each SOM pool consists of a continuum of soil carbon substrates of varying

chemical complexity and such an approach may also conceal the kinetic characteristics of

individual SOM structures (Davidson and Janssens, 2006). To better understand the

temperature sensitivity of individual SOM components, it is necessary to examine the

decomposition of various SOM components with similar chemical properties.

The stability of SOM components is associated with their intrinsic chemical

recalcitrance and their interaction with the soil matrix (Baldock and Skjemstad, 2000).

Macromolecular lipids and aromatic structures are usually considered to be recalcitrant

because they are much more resistant to microbial attack in comparison to easily

degradable compounds such as proteins and carbohydrates (Melillo et al., 1982; Gleixner

et al., 2001; Melillo et al., 2002). Similarly, chemically-bound or mineral-associated SOM

is more stable than SOM in a ‘free’ form (Baldock and Skjemstad, 2000). Based on the

chemical form of SOM, components can be separated into solvent extractable compounds

57

58

(including n-alkanes, n-alkanols, n-alkanoic acids, carbohydrates, and steroids),

ester-bound lipids mainly derived from plant cutin (a biopolymer in the epidermis of

leaves), suberin (a biopolymer abundant in bark and roots of vascular plants), and waxes,

and phenolic monomers that are ether-linked in lignin macromolecules (Figure 3.1; Otto

et al., 2005). These SOM components have various stabilities in the natural environment

(Chapter 2) and respond differently to environmental changes (Chapter 6). Generally,

lignin monomers are considered to be more resistant to biodegradation due to their

aromaticity (Melillo et al., 1982; Gleixner et al., 2001) and suberin- and cutin-derived

compounds are more stable than solvent extractable compounds because they are

predominantly linked to soil macromolecules by ester bonds (Riederer et al., 1993). The

degradation of solvent extractable compounds, suberin-derived compounds, cutin-derived

compounds, and lignin has been extensively explored in sediment studies using

geochemical indicators (Goñi and Hedges, 1990; Goñi et al., 1993). However, their

decomposition patterns in a controlled soil environment are less well understood because

studies under natural soil conditions are usually complicated by fresh plant inputs (Otto

and Simpson, 2006a). Thus, it is important to investigate the degradation of specific SOM

compounds and to assess their decomposition rates and temperature sensitivity. It is

especially important to test if structurally recalcitrant SOM components (such as

lignin-derived compounds) have higher Q10 values than more readily degradable

compounds in the ‘free’ form (solvent extractable compounds).

Soil incubation studies are useful techniques to investigate the decomposition and

mineralization of SOM under controlled environmental conditions when interferences

Soil Sample

Residue

Residue

Solvent extractable compounds

Solvent extractionGC/MS

Suberin- and cutin-derived compounds

Base hydrolysisGC/MS

Lignin-derived compounds

CuO oxidationGC/MS

Residue

Sample Procedure SOM fraction Composition Origin Information

Plant waxes & biopolymers,microbes

Plant waxes,suberin,& cutin

Lignin

Source and degradationstage of SOM

Contribution of shoot vs. root& degradation

Source and oxidation stageof lignin

Carbohydrates,n-alkanoic acids,n-alkanols,n-alkanes,steroids

ω-hydroxy acids,α-hydroxy acids,mid-chain substituted acids

Phenols

Soil Sample

Residue

Residue

Solvent extractable compounds

Solvent extractionGC/MS

Suberin- and cutin-derived compounds

Base hydrolysisGC/MS

Lignin-derived compounds

CuO oxidationGC/MS

Residue

Sample Procedure SOM fraction Composition Origin Information

Plant waxes & biopolymers,microbes

Plant waxes,suberin,& cutin

Lignin

Source and degradationstage of SOM

Contribution of shoot vs. root& degradation

Source and oxidation stageof lignin

Carbohydrates,n-alkanoic acids,n-alkanols,n-alkanes,steroids

ω-hydroxy acids,α-hydroxy acids,mid-chain substituted acids

Phenols

59

Figure 3.1: Illustration of the sequential chemical extractions and compositional information of SOM components obtained from the extraction procedure.

from plant carbon input are limited (Dalias et al., 2001; Bol et al., 2003; Leifeld and

Fuhrer, 2005). This study employs geochemical techniques to examine the decomposition

of various SOM components (solvent extractable compounds, suberin-derived compounds,

cutin-derived compounds, and lignin monomers) during a one-year laboratory incubation

at six different temperatures. The investigated SOM components have distinct structures

and specific sources (such as plant waxes, suberin, cutin, and lignin) and are not

considered to be decomposition products of other compounds in the soil. The objectives

of this study are: to investigate the temperature dependence of the decomposition of

various SOM components and to assess the stability of these SOM components by both

geochemical indicators and kinetic modeling. We hypothesize that lignin monomers and

suberin- and cutin-derived compounds are more stable than the solvent extractable

compounds and that the decomposition of lignin monomers is accelerated to a greater

extent by temperature increases (i.e., the Q10 values of lignin monomers are higher than

those of the solvent extractable compounds).

3.3 Materials and Methods

3.3.1 Soil incubation

Surface soil samples were collected from two well-drained, pristine grassland soils in

western Alberta in late August, 2005. The first soil (Soil E) was sampled from the

University of Alberta Ellerslie Research Station, located south of Edmonton, Alberta, and

the second (Soil L) was collected from the Agriculture and Agri-Food Canada Research

Station near Lethbridge, Alberta. Both soils are typical grassland soils in the Prairie

60

Ecozone of Western Canada which contains large reserves of SOM (Janzen et al., 1998),

and have been well characterized in the past (Chapter 2). They are therefore good

candidates for our soil incubation study. The air mean annual temperature (MAT) for Soils

E and L is 1.7°C and 5°C, respectively (Janzen et al., 1998). Details of the sampling site

and soil conditions have been described elsewhere (Chapter 2).

Soils were kept in the dark at 4±1°C for two months after sampling. Soil L had a high

abundance of grass roots during the time of sampling, which were removed before

incubation (the minimum root diameter was 2 mm). Both soils were passed through a

2-mm sieve, homogenized, and then incubated in 450-ml glass jars (~350 g dry soil per

jar) at six different air temperatures (MAT of the original sites, 2, 4, 8, 12, and 20°C

above the MAT, which represented different scenarios of global warming) in the dark. The

water content was kept at ~30% of the soil dry weight (close to field capacity) by

weighing and spraying deionized water at soil surfaces twice a week, so that soil moisture

did not limit microbial activity. At least five jars of soil were incubated at each

temperature, and subsamples (~50 g) were collected before incubation (Day 0) and

randomly from one of the five jars at each incubation temperature on Day 29, 57, 86, 126,

170, 245, and 365, freeze-dried, and ground (< 100 µm) thoroughly prior to chemical

analyses.

3.3.2 Microbial Respiration

Microbial respiration (r), which is equivalent to soil respiration in the absence of

plant roots, was measured in triplicate during the incubation on Days 1, 8, 15, 22, 29, 57,

61

86, 128, 170, 245, and 365, using the alkali absorption method (Winkler et al., 1996).

Respired CO2 was captured by NaOH (1.0 M × 2.0 ml) in small glass vials placed inside

the incubation jars. The jars were sealed and left for 24 h and the vials were then removed

and capped. Excess NaOH was determined by precipitation with BaCl2 and titration with

0.2 M HCl with phenolphthalein as an indicator (Zhang et al., 2005). Microbial

respiration rates (r) were normalized to the dry weight of the soil samples and expressed

in the units of μg CO2 gsoil-1 h-1.

3.3.3 Chemical Analyses

Total carbon, inorganic carbon, and total nitrogen contents of Soils E and L were

determined in triplicate at the start of the incubation using a Shimadzu TOC 5000 total

organic carbon analyzer equipped with a solid sample module capable of analyzing solid

samples such as soils and plant materials (Shimadzu Scientific Instruments, Columbia,

MD, USA). Because inorganic carbon was not detected, soil organic carbon (OC) content

equaled the total carbon content. Soil carbon loss in the one-year incubation is found to be

small relative to the original soil OC content and is consistent with the literature (White et

al., 2002), which may well fall within the precision of the soil TOC measurements (~5%).

Therefore, soil carbon loss (%) during the incubation was estimated by the following

equation:

Soil carbon loss = r×365×24×(12/44)×10-6×100% (3.3)

where r is the measured microbial respiration rate in the units of μg CO2 gsoil-1 h-1.

Chemical extractions (solvent extraction, base hydrolysis, and CuO oxidation) were

62

conducted to produce solvent extractable compounds, suberin- and cutin-derived

compounds, and lignin monomers, respectively (Otto et al., 2005). A diagram illustrating

the chemical extractions and SOM compositional information obtained from the analyses

is shown in Figure 3.1. Briefly, freeze-dried soil samples (5-10 g) were extracted with 30

ml of dichloromethane, dichloromethane:methanol (1:1; v/v) and methanol, respectively.

The combined solvent extractable compounds were filtered through glass fiber filters

(Whatman GF/A and GF/F), concentrated by rotary evaporation, and then dried under

nitrogen gas in 2-ml glass vials. The air-dried soil residues from solvent extraction (2 g)

were then heated at 100°C for 3 h in teflon-lined bombs with 20 ml of 1 M methanolic

KOH. The extracts were acidified to pH 1 with 6 M HCl, and the solvents were removed

by rotary evaporation. Lipids were recovered from the water phase by liquid–liquid

extraction with diethyl ether, concentrated by rotary evaporation, and dried under nitrogen

gas in 2-ml glass vials. The base hydrolysis residues were air-dried and further oxidized

with copper (II) oxide (CuO) to release lignin-derived phenols. Soil residues (2 g) were

extracted with 1 g copper (II) oxide, 100 mg ammonium iron (II) sulfate hexahydrate

[Fe(NH4)2(SO4)2·6H2O] and 15 ml of 2 M NaOH in teflon-lined bombs at 170°C for 2.5 h.

The extracts were acidified to pH 1 with 6 M HCl, and kept for 1 h at room temperature

in the dark to prevent reactions of cinnamic acids. After centrifugation (at 2500 rev min-1

for 30 min), the supernatants were liquid–liquid extracted with diethyl ether. The ether

extracts were concentrated by rotary evaporation, transferred to 2-ml glass vials and dried

under nitrogen gas.

The composition and concentration of chemical extracts were analyzed by gas

63

chromatography/mass spectrometry (GC/MS). Extracts from solvent extraction and CuO

oxidation were converted to trimethylsilyl (TMS) derivatives by reaction with 90 μl

N,O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA) and 10 μl pyridine for 3 h at 70°C

before GC/MS analysis. Base hydrolysis products were first methylated by reacting with

600 μl of diazomethane in ether at 37°C for 1 h, evaporated to dryness under nitrogen,

and then silylated with BSTFA and pyridine as described above. Oleic acid (C18:1 alkanoic

acid) and ergosterol were derivatized in the same method and used as external standards

for solvent-extractable n-alkanes, n-alkanols, n-alkanoic acids and soil steroids,

respectively. Oleic acid methyl ester was used as external standard for base hydrolysis

products, while vanillic acid-TMS was used for CuO oxidation products. GC/MS analysis

was performed on an Agilent model 6890N GC coupled to a Hewlett-Packard model 5973

quadrupole mass selective detector. Separation was achieved on a HP5-MS fused silica

capillary column (30m × 0.25 mm i.d., 0.25 μm film thickness). The GC operating

conditions were as follows: temperature held at 65 °C for 2 min, increased from 65 to 300

°C at a rate of 6 °C min-1 with final isothermal hold at 300 °C for 20 min. Helium was

used as the carrier gas. The sample was injected with a 2:1 split ratio and the injector

temperature was set at 280 °C. The samples (1 μl) were injected with an Agilent 7683

autosampler. The mass spectrometer was operated in the electron impact mode (EI) at 70

eV ionization energy and scanned from 50 to 650 daltons. Data were acquired and

processed with the Chemstation G1701DA software. Individual compounds were

identified by comparison of mass spectra with literature, NIST and Wiley MS library data,

authentic standards, and interpretation of mass spectrometric fragmentation patterns.

64

External quantification standards were used and the response factor was assumed to be 1

for all compound classes. Concentration of individual compound was calculated by

comparison of the peak area of the compound to that of the standard in the total ion

current (TIC) and was then normalized to the sample OC content.

3.3.4 Data Analyses

Enzymatic reactions (such as SOM decomposition by microorganisms) in well-mixed

media under equilibrium conditions are usually fitted with a first-order exponential model:

g(t) = ∑cie-ki×t, where g(t) is the remaining carbon fitted to observational data, ci is the

initial size of carbon ‘pools’ of varying degrees of decomposition, and ki is the decay rate

(Schimel and Weintraub, 2003; Davidson and Janssens, 2006). To simplify the modeling

process, a two-pool exponential model was used to fit the decomposition of SOM

components in this laboratory incubation:

C(t) = Cstable × e-ks×t + Clabile × e-kl×t (3.4)

where C(t) is the concentration of SOM components (mg/g OC) remaining in the soil at

time t (days), Cstable and Clabile are the concentrations of stable and labile SOM pools,

respectively, and ks and kl are the decay rates of stable and labile SOM pools (day-1).

Conceptually, the stable and labile pools of individual SOM components in this study

have the same chemical structures but differ in their interactions with minerals or humic

substances that may limit their decomposition rate due to physical protection (Sollins et

al., 1996; Baldock and Skjemstad, 2000). Because the resistant SOM components

typically have a mean residence time of 20 to 50 years (ks < 1.4×10-4 day-1; Chapin et al.,

65

2002), and are hence not expected to undergo detectable decrease in the one-year

incubation, the model (3.4) is further simplified to:

C(t) = Cstable + Clabile × e-k×t (3.5)

The temperature dependence of the decomposition rate (k) and respiration rate (r)

was modeled for the Arrhenius function (Equation 3.1). Both the a and Ea parameters

were allowed to vary for the model fitting of each single class of SOM components. The

Q10 value was calculated according to Equation 3.2 for a temperature of 15ºC, which is

commonly used as the reference temperature (Reichstein et al., 2002). The decomposition

rate (k) of the ‘labile’ SOM pool was also listed for a similar temperature (close to 15ºC),

i.e., MAT+12ºC for Soil E and MAT+8ºC for Soil L, to compare the stability of individual

SOM components. The model fitting was performed using Origin™ Version 7.0

(Microcal Software, MA, USA) at a confidence level of P ≤ 0.05. The degradation

parameters of cutin, suberin, and lignin, and the modeled values of Cstable, Clabile, and k

were compared against incubation days or temperature increases using linear regression

analysis, and the difference was considered significant at a level of P<0.05. Due to a high

variance associated with the k and Ea values derived from the model fitting, statistical

comparisons of the k and Q10 values were not made between different SOM components.

3.4 Results

3.4.1 Microbial Respiration and Soil Carbon and Nitrogen Contents

Microbial respiration rates (r) were generally higher in Soil L than in Soil E, and r

values decreased in a pseudo-exponential mode with incubation time in both soils (Figure

66

3.2). In Soil E, r values decreased by more than 40% in the first week of incubation and

then slowly decreased to 0.25-0.36 μg CO2 gsoil-1 h-1 at the end of the experiment. In

comparison, r values in Soil L decreased sharply at higher temperatures (MAT+12ºC and

MAT+20ºC) in the first week of incubation and decreased much more slowly at lower

0 50 100 150 200 250 300 350 4000

1

2

3

4

r (μg

CO

2 gso

il-1h-1

)

Days

MAT MAT+2oC MAT+4oC MAT+8oC MAT+12oC MAT+20oC

(a) Soil E

0 50 100 150 200 250 300 350 4000

2

4

6

8

10

12 (b) Soil L

r (μg

CO

2 gso

il-1h-1

)

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4

r (μg

CO

2 gso

il-1h-1

)

Days


(a) Soil E

0 50 100 150 200 250 300 350 4000

2

4

6

8

10

12 (b) Soil L

r (μg

CO

2 gso

il-1h-1

)

Days

Figure 3.2: Microbial respiration rate (r) during soil incubation. MAT: mean annual temperature. Error bars represent standard errors of triplicate measurements.

67

temperatures (MAT-MAT+8ºC) in the first two months of incubation (Figure 3.2b).

Temperature increases significantly enhanced microbial respiration rates during the entire

incubation period in that r values measured on the same day of incubation were positively

correlated to incubation temperatures (P<0.05).

Soil OC content was 4.85% and 2.69% for Soils E and L, respectively. Total nitrogen

content was 0.46% for Soil E and 0.28% for Soil L. Both soils had a similar atomic C/N

ratio of 11-12 at the start of the incubation. Based on the respiration rate on Day 86

(which was close to the average rate), soil carbon loss during the one-year incubation was

estimated to be 0.08-0.12% in Soil E, which accounted for 1.7-2.5% of the original soil

OC content. Similarly, soil carbon loss was about 0.24-0.40% in Soil L, equivalent to

8.9-14.9% of the original OC content. The estimated soil OC loss agrees with the annual

carbon loss in fallow cropping soils and those in short-term soil incubation studies

(Rasmussen et al., 1998; Reichstein et al., 2000; Leifeld and Fuhrer, 2005). The size of

carbon loss in Soil E was small as compared to its OC content. For comparative purpose,

we used 4.85% and 2.69% as the OC content for Soils E and L, respectively, to calculate

the OC-normalized concentration of SOM components in the soil.

3.4.2 Decomposition of Solvent Extractable Compounds

Based on their chemical structures, the solvent extractable compounds of both soils

were grouped into four categories: odd-numbered n-alkanes (in the range of C21-C33),

even-numbered n-alkanols (in the range of C16-C30), even-numbered n-alkanoic acids (in

the range of C12-C28), and steroids (cholesterol, ergosterol, β-sitosterol, stigmasterol,

68

sitosterone, and campesterol). The composition of n-alkanes, n-alkanols, and n-alkanoic

acids reflected a predominant input from plants, i.e., these compounds were primarily

derived from plant sources and not from the decomposition of other SOM components.

Plant steroids (β-sitosterol, stigmasterol, sitosterone, and campesterol) comprised more

than 80% of the steroids detected in both soils with minor inputs of steroids from animals

(cholesterol) and fungi (ergosterol; Otto et al., 2005). Among the identified plant steroids,

sitosterone was the degradation product of the precursor sterols (β-sitosterol and

stigmasterol; Otto and Simpson, 2005) and the ratios of precursor sterols (β-sitosterol and

stigmasterol) to their degradation product (sitosterone) were 6.5 in Soil E and 2.6 in Soil

L at the start of the incubation. Carbohydrates (sucrose, glucose, mannose, and trehalose)

were also detected in the solvent extractable compounds of both soils. However, the

major carbohydrates have multiple sources such as the fungal input of trehalose and both

plant and microbial inputs of glucose (Chapter 2; Otto et al., 2005). Therefore,

carbohydrate distributions are not included here because they have multiple sources and

are difficult to interpret within the context of this study.

The decomposition of solvent extractable compounds was fitted with the first-order

exponential decay model (Equation 3.5; Figure 3.3). Soil E had a lower concentration of

solvent extractable compounds than Soil L, and exhibited a better exponential model fit

(Figures 3.3 a-d) than Soil L (Figures 3.3 e-h). The exponential decay rate (k) and the size

of stable and labile pools (Cstable and Clabile) were derived from model fitting parameters.

The concentrations of stable and labile pools of solvent extractable compounds did not

differ between different incubation temperatures (P>0.05) and the average values were

69

0 50 100 150 200 250 300 350 4000.00

0.01

0.02

0.03

0.04

0.05 MAT (R2=0.85) MAT+2oC (R2=0.77) MAT+4oC (R2=0.69) MAT+8oC (R2=0.86) MAT+12oC (R2=0.88) MAT+20oC (R2=0.71)

mg/

g O

C(a) n-Alkanes: Soil E

0 50 100 150 200 250 300 350 4000.00

0.03

0.06

0.09


mg/

g O

C

(b) n-Alkanols: Soil E

0 50 100 150 200 250 300 350 4000.00

0.05

0.10

0.15

0.20


(c) n-Alkanoic acids: Soil E

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5


(d) Steroids: Soil E

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.00

0.05

0.10

0.15

0.20


(e) n-Alkanes: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4


(f) n-Alkanols: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


(g) n-Alkanoic acids: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4


(h) Steroids: Soil L

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.00

0.01

0.02

0.03

0.04


mg/

g O

C(a) n-Alkanes: Soil E

0 50 100 150 200 250 300 350 4000.00

0.03

0.06

0.09


mg/

g O

C

(b) n-Alkanols: Soil E

0 50 100 150 200 250 300 350 4000.00

0.05

0.10

0.15

0.20


(c) n-Alkanoic acids: Soil E

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5


(d) Steroids: Soil E

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.00

0.05

0.10

0.15

0.20


(e) n-Alkanes: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4


(f) n-Alkanols: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


(g) n-Alkanoic acids: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4


(h) Steroids: Soil L

mg/

g O

C

Days

Figure 3.3: Exponential decomposition of solvent extractable compounds (P<0.05). MAT: mean annual temperature.

70

71

taken for each soil (Table 3.1) to compare the stability of individual classes of compounds.

In Soil E, more than 75% of the solvent extractable compounds were classified into the

‘labile’ pool, with soil steroids comprising the highest percentage in the labile fraction

(95%). By comparison, 64%-85% of the solvent extractable compounds were in the labile

pool in Soil L and the concentration of the labile components was much higher than those

in Soil E (with the exception of steroids). Solvent extractable compounds in the ‘labile’

pool in both soils had similar decay rates (ranging from 0.010 ± 0.007 to 0.026 ± 0.006

day-1 at a similar temperature, i.e., MAT+12ºC for Soil E and MAT+8ºC for Soil L; Table

3.1) except Soil E steroids, which had a faster decay rate of 0.059 ± 0.004 day-1 at

MAT+12ºC. The decay rates increased with increasing incubation temperature for

n-alkanes, n-alkanols, and steroids in Soil E (P<0.05). The temperature-induced

acceleration of decay rates was most pronounced for Soil E steroids and the k value

increased from 0.049 ± 0.005 day-1 at MAT to 0.104 ± 0.019 day-1 at MAT+20ºC.

Temperature dependence was not discernable for the solvent extractable compounds in

Soil L and n-alkanoic acids in Soil E (P>0.05) due to a large variance associated with the

k values.

3.4.3 Decomposition of Suberin- and Cutin-Derived Compounds

Suberin- and cutin-derived compounds were extracted from the soil by base

hydrolysis, which cleaves ester bonds that are dominant in both biomolecules (Riederer et

al., 1993). Suberin- and cutin-derived compounds were summarized and calculated based

on structural parameters developed by Otto and Simpson (2006a). Suberin-derived

72

Table 3.1: Model fitting parameters of SOM components in grassland soils (± standard error)

n-Alkanes n-Alkanols n-Alkanoic acids Steroids Suberin-derived

compounds Lignin V

units Lignin S

units Lignin C

units Soil E Cstable (mg/g OC) 0.01 (0.00) 0.03 (0.00) 0.02 (0.00) 0.03 (0.00) 1.41 (0.15) 0.22 (0.03) 0.17 (0.02) 0.10 (0.01) Clabile (mg/g OC) 0.04 (0.00) 0.08 (0.00) 0.20 (0.00) 0.52 (0.00) 2.10 (0.17) 0.32 (0.03) 0.38 (0.02) 0.22 (0.01) %labilea 78% 75% 89% 95% 60% 59% 69% 68% k (day-1) at MAT+12ºC b

0.024 (0.010) 0.019 (0.007) 0.026 (0.006) 0.059 (0.004) 0.019 (0.011) 0.005 (0.005) 0.006 (0.006) 0.010 (0.008)

Ea (kJ mol-1)c 15.3 (3.8) 19.4 (5.0) na 22.8 (4.3) na 88.3 (18.4) 47.3 (11.4) 34.0 (10.6) Q10 (at 15ºC)d 1.24 1.31 na 1.38 na 3.45 1.94 1.61 Soil L Cstable (mg/g OC) 0.05 (0.01) 0.10 (0.01) 0.11 (0.01) 0.11 (0.01) na 0.69 (0.10) 0.81 (0.10) 0.62 (0.05) Clabile (mg/g OC) 0.09 (0.01) 0.20 (0.02) 0.62 (0.02) 0.21 (0.02) na 1.38 (0.15) 1.01 (0.09) 0.63 (0.06) %labilea 64% 67% 85% 67% na 67% 55% 50% k (day-1) at MAT+8ºC b

0.021 (0.010) 0.015 (0.011) 0.019 (0.003) 0.010 (0.007) na 0.002 (0.007) 0.003 (0.008) 0.004 (0.009)

Ea (kJ mol-1)c na na na na na 49.4 (20.3) 20.3 (18.1) 24.0 (12.4) Q10 (at 15ºC)d na na na na na 2.00 1.33 1.40

a %labile = Clabile /(Clabile + Cstable)×100%; b derived from exponential model fitting, and MAT+12ºC for Soil E and MAT+8ºC for Soil L are close to 15ºC; c derived from Arrhenius function fitting (Equation 3.1); d derived from Equation 3.2. na: not calculated due to poor model fitting.

compounds (∑S) include ω-hydroxyalkanoic acids in the range of C20-C32,

n-alkane-α,ω-dioic acids in the range of C20-C32, and 9,10-epoxy-α,ω-dioic C18 acid.

Cutin-derived compounds (∑C) included mid-chain hydroxyalkanoic C14, C15, C17 acids,

mono- and dihydroxyalkanoic C16 acids and α,ω-dioic acids. Similar to the solvent

extractable compounds, these compounds preserved the structures of their original

biomolecules and are not decomposition products of other SOM components. These

suberin- or cutin-derived compounds have uniform degradation patterns in the

environment because microbial decomposition does not discriminate individual

compounds from the same source (Riederer et al., 1993; Otto and Simpson, 2006a).

Therefore, bulk suberin or cutin can be represented quantitatively by their summed

biomarkers (i.e.: ∑S or ∑C).

The OC-normalized concentrations of suberin- and cutin-derived compounds were

plotted versus time (Figure 3.4). Soil L had a much higher concentration of suberin- and

cutin-derived compounds than Soil E during the incubation. The decomposition of

suberin-derived compounds in Soil E followed the exponential decay model (Figure 3.4a)

with the decay rates ranging from 0.006 ± 0.003 day-1 at MAT to 0.025 ± 0.007 day-1 at

MAT+20ºC, and an average Cstable of 1.41 ± 0.15 mg/g OC and a Clabile of 2.10 ± 0.17

mg/g OC. However, suberin-derived compounds in Soil L and cutin-derived compounds

in both soils did not fit the exponential decay model well, and hence, fitting parameters

were not calculated. The decomposition of suberin- and cutin-derived compounds was

alternatively assessed using geochemical degradation parameters, such as ω-C16/∑C16 and

ω-C18/∑C18, where ∑C16 or ∑C18 includes ω-hydroxyalkanoic acid, n-alkane-α,ω-dioic

73

acid, and mid-chain-substituted acids with 16 or 18 carbons, respectively (Goñi and

Hedges, 1990; Otto and Simpson, 2006a). Both parameters have been reported to increase

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8

1.0 (b) Cutin: Ellerslie

mg/

g O

C

Days

Soil E

0 50 100 150 200 250 300 350 4000

5

10

15

20 (c) Suberin: Lethbridge

mg/

g O

C


Soil L

0 50 100 150 200 250 300 350 4000.0

0.7

1.4

2.1

2.8 (d) Cutin: Lethbridge

mg/

g O

C

Days

Soil L

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

MAT (R2=0.90) MAT+2oC (R2=0.77) MAT+4oC (R2=0.76) MAT+8oC (R2=0.78) MAT+12oC (R2=0.87) MAT+20oC (R2=0.94)

mg/

g O

C

(a) Suberin: Soil E

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8


mg/

g O

C

Days

Soil E

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8


mg/

g O

C

Days

Soil E

0 50 100 150 200 250 300 350 4000

5

10

15


mg/

g O

C


Soil L

0 50 100 150 200 250 300 350 4000

5

10

15


mg/

g O

C


Soil L

0 50 100 150 200 250 300 350 4000.0

0.7

1.4

2.1


mg/

g O

C

Days

Soil L

0 50 100 150 200 250 300 350 4000.0

0.7

1.4

2.1


mg/

g O

C

Days

Soil L

0 50 100 150 200 250 300 350 4000

1

2

3

4

5


mg/

g O

C

(a) Suberin: Soil E

Figure 3.4: The decomposition of suberin- and cutin-derived compounds with time. Suberin-derived compounds in Soil E was fitted with the exponential decay model (P<0.05). MAT: mean annual temperature.

with progressing cutin degradation in marine sediments (Goñi and Hedges, 1990) and

with soil depth (Chapter 2; Otto and Simpson, 2006a) because cutin acids containing

double bonds and more than one hydroxyl group are preferentially degraded compared

74

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7


(e) ω-C16/ΣC16: L soil

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

Rat

io

(c) Suberin/Cutin: E soil

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5 (a) ω-C16/ΣC16: E soil

Rat

io

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8

1.0(b) ω-C18/ΣC18: Soil E

Rat

io

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4 (d) ΣMid/ΣSC: E soil

Rat

io

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4

5

6 (g) Suberin/Cutin: L soil

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8

1.0

1.2 (f) ω-C18/ΣC18: L soil

0 50 100 150 200 250 300 350 4000.00

0.04

0.08

0.12

0.16

0.20

0.24(h) ΣMid/ΣSC: Soil L

Days

(a) Soil E

Rat

io o

f ω-C

16/∑

C16

Rat

io o

f ω-C

18/∑

C18

Rat

io o

f Sub

erin

/Cut

inR

atio

of ∑

Mid

/∑SC

(e) Soil L

(b) Soil E (f) Soil L

(c) Soil E (g) Soil L

(d) Soil E (h) Soil L

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7



0 50 100 150 200 250 300 350 4000

1

2

3

4

5

Rat

io


0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5 (a) ω-C16/ΣC16: E soil

Rat

io

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8


Rat

io

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3


Rat

io

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4

5


0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8

1.0

1.2 (f) ω-C18/ΣC18: L soil

0 50 100 150 200 250 300 350 4000.00

0.04

0.08

0.12

0.16

0.20


Days

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7



0 50 100 150 200 250 300 350 4000

1

2

3

4

5

Rat

io


0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3

0.4

0.5 (a) ω-C16/ΣC16: E soil

Rat

io

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8


Rat

io

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3


Rat

io

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4

5


0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6

0.8

1.0

1.2 (f) ω-C18/ΣC18: L soil

0 50 100 150 200 250 300 350 4000.00

0.04

0.08

0.12

0.16

0.20


Days

(a) Soil E

Rat

io o

f ω-C

16/∑

C16

Rat

io o

f ω-C

18/∑

C18

Rat

io o

f Sub

erin

/Cut

inR

atio

of ∑

Mid

/∑SC

(e) Soil L

(b) Soil E (f) Soil L

(c) Soil E (g) Soil L

(d) Soil E (h) Soil L

75

Figure 3.5: Degradation parameters of suberin- and cutin-derived compounds. (a, b) ω-C16/∑C16 ratio; ∑C16 = ω-hydroxyalkanoic C16 acid + n-alkane-α,ω-dioic C16 acid + mid-chain hydroxy and expoxyalkanoic C16 acids (Goñi and Hedges, 1990). (c, d) ω-C18/∑C18 ratio; ∑C18 = ω-hydroxyalkanoic C18 acid + n-alkane-α,ω-dioic C18 acid + mid-chain hydroxy and expoxyalkanoic C18 acids (Goñi and Hedges, 1990). (e, f) Suberin/cutin ratio = (∑S+∑S∨C)/(∑C+∑S∨C), where ∑S = ω-hydroxyalkanoic acids C20-C32 + n-alkane-α,ω-dioic acids C20-C32 + 9,10-epoxy-α,ω-dioic C18 acid, ∑C = mid-chain hydroxyalkanoic C14, C15, C17 acids + mono- and dihydroxyalkanoic C16 acids and α,ω-dioic acids, ∑S∨C = ω-hydroxyalkanoic C16, C18 acids + di- and trihydroxyalkanoic C18 acids + 9,10-epoxy-ω-hydroxyalkanoic C18 acid + α,ω-dioic C16, C18 acids (Otto and Simpson, 2006a). (g, h) ∑Mid/∑SC ratio; ∑Mid = mid-chain hydroxy and epoxyalkanoic acids, including 7- or 8-hydroxy-1,16-dioic C16 acid, 10,16-dihydroxy C16 acid, 9,10,18-trihydroxy C18 acid, 9,10-dihydroxy-1,18-dioic C18 acid, 9,10-epoxy-18-hydroxy C18 acid; ∑SC = ∑S + ∑C + ∑S∨C. MAT: mean annual temperature.

to ω-hydroxyalkanoic acids. In this study, the ratio of ω-C16/∑C16 stabilized around 0.3

and 0.4 for Soils E and L, respectively (Figures 3.5 a-b). However, the ratio of

ω-C18/∑C18 fluctuated during incubation with no discernable pattern (Figures 3.5 c-d).

Suberin-derived compounds degraded faster than cutin-derived compounds in this study,

and is evidenced by a decreasing ratio of suberin to cutin with time in both soils (Figures

3.5 e-f; suberin/cutin = (∑S+∑S∨C)/(∑C+∑S∨C), where ∑S∨C = ω-hydroxyalkanoic C16,

C18 acids + di- and trihydroxyalkanoic C18 acids + 9,10-epoxy-ω-hydroxyalkanoic C18

acid + n-alkane-α,ω-dioic C16, C18 acids; Otto and Simpson, 2006a). The suberin/cutin

ratios were similar at the start of the incubation study (~4.4), and declined to 2.0 in Soil E

and 2.6 in Soil L by the end of the experiment (Figures 3.5 e-f). The degradation of

suberin- and cutin-derived compounds in soil has been reported to exhibit the preferential

decomposition of mid-chain hydroxy and epoxy acids (∑Mid, including 7- or

8-hydroxy-1,16-dioic C16 acid, 10,16-dihydroxy C16 acid, 9,10,18-trihydroxy C18 acid,

76

9,10-dihydroxy-1,18-dioic C18 acid, 9,10-epoxy-18-hydroxy C18 acid) relative to total

suberin- and cutin-derived acids (∑SC = ∑S + ∑C + ∑S∨C; Otto and Simpson, 2006a). In

this study, the ratio of ∑Mid/∑SC increased with incubation time in Soil E (Figure 3.5g),

which changed from 0.1 to around 0.3 after one year of incubation. This trend was less

prevalent in Soil L (Figure 3.5h).

3.4.4 Decomposition of Lignin-Derived Compounds

Lignin-derived compounds were extracted from the soil by CuO oxidation, which

cleaves aryl ether bonds and releases phenolic monomers from the outer part of the lignin

biopolymer. Lignin monomers are indicative of lignin composition and degree of

oxidation (Hedges and Ertel, 1982; Kögel, 1986; Goñi and Hedges, 1992). Depending on

the number and position of methoxy groups on the phenol ring, lignin monomers

extracted from both soils were categorized as: vanillyls (V; vanillin, acetovanillone, and

vanillic acid), syringyls (S; syringaldehyde, acetosyringone, and syringic acid), and

cinnamyls (C; p-coumaric acid, and ferulic acid). The sum of monomers (VSC) decayed

exponentially in both soils (Figures 3.6 a-f). Similar to the results of the solvent

extractable compounds, the concentrations of stable and labile VSC did not vary between

different incubation temperatures (P>0.05; Table 3.1). A slightly lower percentage of VSC

was classified into the labile pool (50-69%; Table 3.1), compared with solvent extractable

compounds in both soils. The decay rates of VSC also increased with increasing

temperature, but the trend was not significant due to high variance associated with model

parameters (Table 3.1). Nevertheless, VSC in the ‘labile’ pool showed a relatively slow

77

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


mg/

g O

C

(a) Vanillyls: Soil E

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


(b) Syringyls: Soil E

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3


(c) Cinnamyls: Soil E

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.0

0.8

1.6

2.4

3.2

(d) Vanillyls: Soil L

mg/

g O

C


0 50 100 150 200 250 300 350 4000.0

0.6

1.2

1.8

2.4

3.0


(e) Syringyls: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.4

0.8

1.2

1.6

2.0


(f) Cinnamyls: Soil L

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


mg/

g O

C

(a) Vanillyls: Soil E

0 50 100 150 200 250 300 350 4000.0

0.2

0.4

0.6


(b) Syringyls: Soil E

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.1

0.2

0.3


(c) Cinnamyls: Soil E

mg/

g O

C

Days

0 50 100 150 200 250 300 350 4000.0

0.8

1.6

2.4

3.2

(d) Vanillyls: Soil L

mg/

g O

C


0 50 100 150 200 250 300 350 4000.0

0.6

1.2

1.8

2.4

3.0


(e) Syringyls: Soil L

mg/

g O

C

0 50 100 150 200 250 300 350 4000.0

0.4

0.8

1.2

1.6

2.0


(f) Cinnamyls: Soil L

mg/

g O

C

Days

Figure 3.6: Exponential decomposition of lignin monomers (P<0.05). MAT: mean annual temperature.

78

decay rate in both soils (0.002-0.010 day-1 at MAT+12ºC for Soil E and MAT+8ºC for

Soil L; Table 3.1).

0 50 100 150 200 250 300 350 4000

1

2

3

4

5(a) Soil E

(Ad/

Al) v

0 50 100 150 200 250 300 350 4000

1

2

3

4

5(b) Soil E

(Ad/

Al) s

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4 (c) Soil L


0 50 100 150 200 250 300 350 4000.0

0.5

1.0

1.5

2.0 (d) Soil L

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4

5(a) Soil E

(Ad/

Al) v

0 50 100 150 200 250 300 350 4000

1

2

3

4

5(b) Soil E

(Ad/

Al) s

Days

0 50 100 150 200 250 300 350 4000

1

2

3

4 (c) Soil L


0 50 100 150 200 250 300 350 4000.0

0.5

1.0

1.5

2.0 (d) Soil L

Days

Figure 3.7: Degradation parameters of lignin monomers. (a, b) (Ad/Al)v: ratio of vanillic acid/vanillin. (c, d) (Ad/Al)s: ratio of syringic acid/ syringaldehyde. MAT: mean annual temperature.

Lignin degradation was further examined by the acid/aldehyde (Ad/Al) ratios of V

and S units, which has been reported to increase with an increasing degree of lignin

79

oxidation in sediments and soils and hence serves as a geochemical indicator of the stage

of lignin degradation (Hedges et al., 1988; Opsahl and Benner, 1995; Otto et al., 2005).

The ratio of vanillic acid to vanillin, (Ad/Al)v, increased with incubation time from ~2.7

to ~4.2 in Soil E and from ~1.7 to ~2.8 in Soil L (Figures 3.7 a-b; P<0.10), but the ratios

did not differ at varying incubation temperatures (P>0.05). Furthermore, the (Ad/Al)s

ratio did not change during the first half of the incubation period but increased later in the

study (Figures 3.7 c-d). Both soils had the highest (Ad/Al)s ratio with the MAT+20ºC

treatment at the end of the incubation.

3.4.5 Response of SOM Decomposition and Microbial Respiration to Temperature

Changes

The initial microbial respiration rates in both soils are modeled by the Arrhenius

equation (Equation 3.1; R2 = 0.87 for Soil E and R2 = 0.95 for Soil L) for the incubation

temperature range (Figure 3.8a). The decay rates (k) of n-alkanes, n-alkanols, and steroids

in Soil E and VSC units in both soils were also modeled by the Arrhenius equation

(Figures 3.8 b-d). The quality of model fit was highest for Soil E solvent extractable

compounds (0.77<R2<0.87) and VSC (0.68<R2<0.90), and slightly lower for Soil L VSC

(R2=0.64 for V units, 0.55 for C units, and 0.27 for S units; Figure 3.8). The model fitting

produced largely varied a values: 20-1100 for solvent extractable compounds in Soil E

and 100-4,000,000 for VSC in both soils, and no apparent pattern existed for the a values

among different classes of compounds. The activation energy (Ea) derived from model

fitting is listed in Table 3.1, and Q10 values were calculated based on Equation 3.2 for a

80

0.0034 0.0035 0.00360.00

0.03

0.06

0.09

0.12

0.15 n-Alkanes (R2=0.78) n-Alkanols (R2=0.77) Steroids (R2=0.87)

k (d

ay-1)

1/T (oK-1)

(b) Solvent extractable compounds: Soil E

0.0034 0.0035 0.0036

0.00

0.01

0.02

0.03 (d) VSC: Soil L

1/T (oK-1)

k (d

ay-1) V (R2=0.64) S (R2=0.27) C (R2=0.55)

0.0034 0.0035 0.0036

0.00

0.01

0.02

0.03

0.04

0.05

k (d

ay-1)

1/T (oK-1)

V (R2=0.90) S (R2=0.80) C (R2=0.68)

(c) VSC: Soil E

0.0034 0.0035 0.00360

2

4

6

8

10

12Soil E (R2=0.87)Soil L (R2=0.95)

r (μg

CO

2g soil-1

h-1)

1/T (oK-1)

(a) Microbial respiration

0.0034 0.0035 0.00360.00

0.03

0.06

0.09

0.12

0.15 n-Alkanes (R2=0.78) n-Alkanols (R2=0.77) Steroids (R2=0.87)

k (d

ay-1)

1/T (oK-1)

(b) Solvent extractable compounds: Soil E

0.0034 0.0035 0.0036

0.00

0.01

0.02

0.03 (d) VSC: Soil L

1/T (oK-1)

k (d

ay-1) V (R2=0.64) S (R2=0.27) C (R2=0.55)

0.0034 0.0035 0.0036

0.00

0.01

0.02

0.03

0.04

0.05

k (d

ay-1)

1/T (oK-1)

V (R2=0.90) S (R2=0.80) C (R2=0.68)

(c) VSC: Soil E

0.0034 0.0035 0.00360

2

4

6

8

10

12Soil E (R2=0.87)Soil L (R2=0.95)

r (μg

CO

2g soil-1

h-1)

1/T (oK-1)

(a) Microbial respiration

Figure 3.8: Arrhenius relationship between respiration or decomposition rates and temperature. Error bars represent standard errors of triplicate measurements (a) or errors derived from model fitting (b-d).

fixed temperature (15ºC), which was close to the average incubation temperature in this

study and corresponded to the commonly used reference temperature to calculate Q10 in

the literature (Reichstein et al., 2002). VSC in both soils exhibited a higher temperature

sensitivity (Q10 values: 1.33-3.45) than the n-alkanes, n-alkanols, and steroids in Soil E

(Q10 values: 1.24-1.38), and lignin V units exhibited a much higher Q10 value than S and

81

C units in both soils. VSC in Soil L had lower Q10 values than the corresponding lignin

monomers in Soil E. Calculated Q10 values from microbial respiration data were found to

be 1.86 for Soil E and 2.49 for Soil L at 15ºC. Unfortunately, we were unable to calculate

Q10 values for solvent extractable compounds in Soil L and suberin- and cutin-derived

compounds in both soils due to poor model fitting. Considering the high variance

associated with Ea values, we feel inclined not to do statistical comparisons of the Q10

values between different compounds, and the calculated Q10 values here represent an

estimate of the ‘apparent temperature sensitivities’ of various SOM structures partitioned

into the labile pool.

3.5 Discussion

3.5.1 Decompositional Patterns of SOM Components

During the one-year incubation, most of the respired CO2 is likely derived from labile

SOM that is easily accessible to soil microbes (Leifeld and Fuhrer, 2005), such as soluble

carbohydrates, small organic acids, and proteins (Gleixner et al., 2001). By contrast, the

decomposition of the SOM components analyzed in this study (solvent extractable

compounds, cutin- and suberin-derived compounds, and lignin monomers) contributes

only a small fraction to the soil OC loss (only about 0.01-0.02% OC loss based on the

exponential decay curve), and their response to temperature changes may be concealed by

the decomposition of the labile SOM components if only soil respiration is measured. It is

therefore important to monitor the decomposition of those targeted compounds

individually by other techniques.

82

Respired CO2 from soil is derived from the mineralization of readily decomposable

SOM components. The decomposition process of such compounds is predominantly

controlled by microbial enzymatic activities, and hence, SOM decomposition studies that

employ soil respiration data are fitted well by the exponential model (Dalias et al., 2001;

Fang et al., 2005). In comparison, the decomposition of individual SOM components is

not only regulated by enzyme-catalyzed reactions but also associated with the

components’ insolubility, molecular architecture, and/or their interaction with minerals or

humic substances that may limit the efficacies of enzymatic reactions because of physical

protection (Sollins et al., 1996; Baldock and Skjemstad, 2000). The degradation of SOM

components may also be limited at reaction microsites and thus less uniform compared

with bulk soil mineralization. The decomposition of individual SOM components in this

study did not fit the exponential decay model as well as soil respiration data reported in

the literature (Dalias et al., 2001; Fang et al., 2005) and there was a high standard error

associated with k and Ea values derived from the model fitting. The decomposition of Soil

E components shows reasonable fits to the decay model (Figures 3.3 and 3.6). Soil L data

model fitting does not fit as well with the lowest R2 = 0.32 probably due to the high

abundance of grass roots present during the time of sampling. Even though the soil was

sieved and homogenized before incubation, the fine root debris may have complicated the

decay patterns of SOM components through a gradual input of fresh organic matter into

the soil over the course of the incubation. Therefore, the fit of the exponential decay

model was generally poor in Soil L.

Suberin- and cutin-derived compounds did not exhibit exponential decay, probably

83

due to their recalcitrance in the soil (Gleixner et al., 2001). Hence, their decomposition in

the soil was indistinguishable during the incubation period. The fluctuation in the

abundances of cutin-derived compounds was presumably affected by sample

heterogeneity and/or their interactions with mineral surfaces due to selective sorption of

polymethylene carbon (the dominant structure in cutin; Feng et al., 2005), which may

provide physical protection against microbial enzymatic attack for cutin-derived

compounds (Baldock and Skjemstad, 2000). The degradation patterns of suberin- and

cutin-derived compounds in SOM therefore suggest that the decomposition of specific

SOM components is more complicated and may not conform to that of the bulk SOM.

3.5.2 Recalcitrance of SOM Components

To assess the stability or recalcitrance of SOM components, the decay rate (k) derived

from the decomposition data at the same temperature (MAT+12ºC for Soil E and

MAT+8ºC for Soil L) and the percentage of SOM components in the labile pool were

compared (Table 3.1). The decay rate derived from the exponential model (Equation 3.5)

corresponds to a turnover time of 17-100 days for the solvent extractable compounds and

100-500 days for lignin monomers, and reflects the high decomposability or activity of

the ‘labile’ pool of SOM component. The turnover time of the ‘stable’ SOM pool is

assumed to be longer than 20 years (Chapin et al., 2002), and therefore the decay rates of

the ‘stable’ SOM pool were not modeled due to the limited duration of the incubation.

Lignin monomers in the ‘labile’ pool exhibited the lowest decay rates and a slightly lower

percentage of VSCs were classified in the labile pool. This kinetic evidence supports the

84

generally assumed slow turnover of lignin compounds in climate models (Gleixner et al.,

2001; Davidson and Janssens, 2006). However, it is difficult to compare the stability of

individual lignin monomers (ie: vanillyl versus syringyl versus cinnamyl) using the

kinetic parameters due to a high variance associated with the model results (Table 3.1).

Lignin S and C units have been reported to degrade faster than V units in the environment

(Hedges et al., 1988; Opsahl and Benner, 1995; Otto et al., 2005). Hence, V units are

considered to be the most recalcitrant of the lignin monomers. This was corroborated by a

faster degradation of S units in comparison to V units during the incubation, where the

accelerated degradation of S units at higher temperatures, demonstrated by a higher

(Ad/Al)s ratio at MAT+20ºC, is more pronounced than that of V units (Figure 3.7).

Suberin-derived compounds in Soil E have a slower decay rate and a lower percentage

in the labile pool as compared with solvent extractable compounds (Table 3.1), suggesting

that chemically-bound soil lipids are more recalcitrant than soil lipids in the ‘free’ form,

even when they have similar chemical structures, such as aliphatic acids with 20-32

carbons. The solvent extractable compounds have similar decay rates in both soils (except

steroids), but a slightly lower percentage of Soil L lipids are in the labile pool. As

mentioned previously, Soil L contained fine root debris, which may have resulted in an

underestimation of Soil L decomposition by releasing ‘fresh’ organic matter into the soil.

Both soil samples have similar textures and mineralogy (Chapter 2) but Soil L may have

more mineral surfaces available for SOM binding due to its low OC content (2.69%) in

comparison with Soil E (4.85%). Therefore, a larger fraction of SOM may be associated

with minerals in Soil L and partitions into the ‘stable’ pool. Among the solvent extractable

85

compounds, steroids had the highest decay rate in Soil E and the lowest decay rates in

Soil L. Cyclic soil lipids (such as steroids) have been observed to be preferentially

preserved in soils as compared to aliphatic lipids (such as n-alkanes, n-alkanols, and

n-alkanoic acids; Otto and Simpson, 2005). The contrasting decay rates of steroids in the

two soils suggest that environmental factors may play a part in regulating SOM

decomposition in different soils, such as the incorporation of cyclic lipids into humic

substances (van Bergen et al., 1997) and/or interactions with soil minerals (Amblès et al.,

1994; Baldock and Skjemstad, 2000). We hypothesize that a larger fraction of Soil L

steroids may be associated with minerals and hence are protected to a greater extent from

degradation in comparison to steroids in Soil E. Alternatively, steroids in Soil L may be

more difficult to break down because they are in a higher degradation stage at the start of

the incubation as evidenced by the lower ratio of precursor sterols (β-sitosterol and

stigmasterol) to their degradation product (sitosterone) in Soil L (2.6) as compared with

that in Soil E (6.5).

Due to poor fitting of the exponential decay model, cutin-derived compounds are not

included in the comparison of kinetic parameters. However, cutin has been reported to be

an important, recalcitrant component of SOM (Gleixner et al., 2001). The cutin

degradation parameter (ω-C16/∑C16) in both soils confirms the stability of cutin-derived

compounds (Figures 3.5 a-b). The fluctuation in the ω-C18/∑C18 ratio (Figures 3.5 c-d)

was likely due to the preferential degradation of ω-hydroxyalkanoic C18 acid with one

double bond (Otto and Simpson, 2006a) that was detected together with saturated C18

n-alkanoic acid in both soils. Suberin has been reported to be more resistant to

86

degradation than cutin because it has a high content of phenolic units and is embedded in

bark and root tissues (Kolattukudy, 1981; Riederer et al., 1993). However, the aliphatic

components of suberin were observed to degrade faster than cutin-derived compounds in

our soil incubation study, evidenced by a decreasing ratio of suberin/cutin with time in

both soils (Figures 3.5 e-f). Because cutin is only derived from above-ground sources

(Riederer et al., 1993), cutin-derived compounds may have undergone degradation before

they became incorporated into SOM. By contrast, suberin-derived compounds in the soil

mainly originate freshly from root tissues. Consequently, cutin-derived compounds may

be at a higher stage of degradation than suberin-derived compounds in SOM and hence

are more recalcitrant in mineral soils. Therefore, the increasing ratio of ∑Mid/∑SC in Soil

E (Figure 3.5g) likely results from a faster degradation of suberin relative to cutin because

the ratio of ∑Mid/∑SC is low in root tissues and high in fresh vegetation that is rich in

cutin-derived compounds (Otto and Simpson, 2006a). This observation suggests caution

in the interpretation of suberin and cutin degradation data in the soil where suberin

stability may be overestimated by a fresh input from root tissues.

3.5.3 Temperature Sensitivity of SOM Components

As discussed previously, the degradation of individual SOM components is governed

by their interaction with soil minerals and availability to soil microbes as well as their

intrinsic structure. A substantial fraction of SOM may be associated with soil minerals

and partition into the ‘stable’ pool. Therefore, the Q10 values we calculated here are an

estimate of the SOM components’ ‘apparent temperature sensitivities’ that reflect the

87

availability of soil substrates to microbial degraders, rather than the ‘intrinsic temperature

sensitivities’ (Davidson and Janssens, 2006). The applicability of our results therefore

needs to be tested on a broader scale because the SOM-mineral interactions and microbial

communities may differ among different types of soils.

The temperature sensitivity of decomposition varies greatly among the SOM

components in the same soil (Q10 of 1.24-3.45 in Soil E), suggesting heterogeneity in

SOM properties and the varying responses of individual SOM compounds to global

warming. Lignin monomers exhibited higher Q10 values than the solvent extractable

compounds (Table 3.1), which is consistent with the Arrhenius theory that indicates that

the decomposition of more recalcitrant compounds is more sensitive to temperature

(Davidson and Janssens, 2006). In particular, V units that are considered to be the most

recalcitrant among lignin monomers showed a much higher Q10 value than S and C units

in both soils. VSC in Soil L had lower Q10 values than the corresponding units in Soil E,

likely because lignin is at an advanced stage of degradation (in a more recalcitrant form)

in Soil E, evidenced by the higher (Ad/Al) ratios of V and S units (Figure 3.7).

Soil respiration, which results from mineralization of bulk SOM, resulted in a Q10

value of 1.86 for Soil E and 2.49 for Soil L. Because the temperature sensitivity of soil

respiration was measured at the start of the incubation, where labile SOM was

presumably more abundant, temperature responses may have been underestimated (Dalias

et al., 2001). Ideally, Q10 values measured at the end of the incubation are better indicators

of the temperature response of SOM when most of the labile substrates are exhausted

during incubation. Unfortunately, the respiration rates measured at the end of this

88

incubation study are not usable for the calculation of Q10 values because soils incubated

in parallel at different temperatures may have varying amounts of labile SOM after the

one-year incubation. However, Q10 values measured sequentially between 15–35°C over

the course of a long-term incubation (707 days at 25°C) show no significant temporal

change (Leifeld and Fuhrer, 2005). We therefore assume that the Q10 values we calculated

represent a fair estimate of the temperature response of soil respiration over the course of

the experiment. Nevertheless, Q10 values derived from soil respiration data only represent

the average kinetic properties of heterogeneous SOM structures, but not the greatly varied

responses to temperature increases for individual SOM components. Because the reaction

rate of recalcitrant SOM (such as lignin monomers) is much slower than that of the less

stable SOM (such as solvent extractable compounds), changes in k values of recalcitrant

SOM are likely to be concealed by the responses of labile SOM when the mineralization

of both components is measured simultaneously. Notably, the Q10 values for the refractory

lignin units (VSC) are lower (1.33-2.00) than the Q10 value for total respiration (2.49) in

Soil L. Again, the calculated ‘apparent temperature sensitivities’ of VSC units reflect the

high availability of lignin structures in the ‘labile’ pool to soil organisms rather than their

structural recalcitrance.

The Q10 values derived from bulk SOM mineralization are unrelated to the

degradation stage of individual SOM components. In this study, lignin-derived

compounds in Soil E were more oxidized (higher (Ad/Al) ratios) and possessed a higher

Q10 value than those in Soil L. However, Soil L respiration had a much higher Q10 value

(2.49) than Soil E (1.86) probably because recalcitrant SOM such as suberin-derived

89

compounds, cutin-derived compounds, and lignin monomers comprised a smaller fraction

of the identified SOM in Soil E (86%) in comparison to Soil L (92%) at the beginning of

incubation. Even more labile SOM such as proteins and carbohydrates that were not

included in this study may also confound the analysis and the high temperature sensitivity

of the recalcitrant SOM pool in Soil E may be concealed by the respiration data. Similar

temperature sensitivities of SOM mineralization has been reported for soils with

presumably different recalcitrance (Fang et al., 2005). However, the recalcitrance of SOM

is usually assessed by the content of operationally-defined SOM fractions, such as

water-dissolved carbon, or K2SO4-extracted carbon, which contains a mixture of

heterogeneous SOM structures and may not be an accurate indicator of the recalcitrance

of SOM.


We thank Dr. Henry Janzen for assistance with selecting and sampling soil L.

Funding from the Canadian Foundation for Climate and Atmospheric Sciences (GR-520)

is gratefully acknowledged. Leah Nielsen is thanked for conducting part of the chemical

extractions. The Natural Sciences and Engineering Research Council (NSERC) of Canada

is thanked for support via a University Faculty Award (UFA) to M. Simpson and an

undergraduate summer research award (USRA) to L. Neilsen. X. Feng acknowledges

funding from the Ontario Graduate Scholarship (OGS) program.

90

CHAPTER 4

TEMPERATURE AND SUBSTRATE CONTROLS

ON MICROBIAL PHOSPHOLIPID FATTY ACID COMPOSITION

DURING INCUBATION OF GRASSLAND SOILS

CONSTRASTING IN ORGANIC MATTER QUALITY*

* Reprinted from Soil Biology & Biochemistry, 41: 804-812. Authors: Feng, X., Simpson, M.J.,

Copyright (2009), with permissions from Elsevier.

91

4.1 Abstract

Soil incubations are often used to investigate soil organic matter (SOM)

decomposition and its response to increased temperature, but changes in the activity and

community composition of the decomposers have rarely been included. As part of an

integrated investigation into the responses of SOM components in laboratory incubations

at elevated temperatures, fungal and bacterial phospholipid fatty acids (PLFAs) were

measured in two grassland soils contrasting in SOM quality (i.e., lability and availability),

and changes in the microbial biomass and community composition were monitored.

Whilst easily-degradable SOM and necromass released from soil preparation may have

fueled microbial activity at the start of the incubation, the overall activity and biomass of

soil microorganisms were relatively constant during the subsequent one-year soil

incubation, as indicated by the abundance of soil PLFAs, microbial respiration rate (r),

and metabolic quotient (qCO2). PLFAs relating to fungi and Gram-negative bacteria

declined relative to Gram-positive bacteria in soils incubated at higher temperatures,

presumably due to their vulnerability to disturbance and substrate constraints induced by

faster exhaustion of available nutrient sources at higher temperatures. A linear correlation

was found between incubation temperatures and the microbial stress ratios of

cyclopropane PLFA-to-monoenoic precursor (cy17:0/16:1ω7c and cy19:0/18:1ω7c) and

monoenoic-to-saturated PLFAs (mono/sat), as a combined effect of temperature and

temperature-induced substrate constraints. The microbial PLFA decay patterns and ratios

suggest that SOM quality intimately controls microbial responses to global warming.

92

4.2 Introduction

Laboratory incubation of soil or litter under controlled conditions has been used to

investigate soil organic matter (SOM) decomposition and the temperature responses of

microorganisms utilizing SOM (Dalias et al., 2001; Bol et al., 2003). Soil preparation

procedures (such as sieving and mixing) are known to disturb the microbial community

and expose fresh substrates (Petersen and Klug, 1994) while prolonged soil incubation is

reported to induce substrate exhaustion and stress on microbial communities (Joergensen

et al., 1990). The survival rate of soil microorganisms after disturbance or the efficiency

of microbial decomposition of SOM under substrate-limited conditions is documented in

only a few reports (Joergensen et al., 1990; Bossio and Scow, 1998). Furthermore, the

utilization of soil or litter carbon pools is associated with different groups of

microorganisms, typically resulting in microbial succession during decomposition

(McMahon et al., 2005). For instance, fungal growth is found to dominate during early

stages of plant residue decomposition in soil (Chapter 6; Beare et al., 1990), and in the

decomposition of structural materials in pine litter (Berg et al., 1998). Different

Gram-staining groups of bacteria, categorized by their cell wall composition, are also

found to demonstrate various substrate preferences and survival strategies in a changing

environment: Gram-positive bacteria are well adapted to soils with low substrate

availability and in subsoils with lower organic carbon (OC) content (Griffiths et al., 1999;

Fierer et al., 2003), whilst Gram-negative bacteria are more dependent on the input of

fresh organic material to create ‘hot spots’ of decomposition in soils (Griffiths et al., 1999;

Kramer and Gleixner, 2006; Potthoff et al., 2006). Although not strictly related to

93

functional traits, the relative abundance of fungi, Gram-negative and Gram-positive

bacteria is informative of the microbial community composition in soil (Zhang et al.,

2005; Frey et al., 2008), and may be applied to indicate microbial community shifts

during soil incubation as a result of varying degree of vulnerability to substrate

constraints in soil microorganisms.

Profiles of phospholipid fatty acids (PLFAs) have been used to study microbial

biomass activity and community composition in various soil environments because

PLFAs are only found in viable cells and hence are characteristic biomarkers for living

microorganisms (Frostegård and Bååth, 1996; Evershed et al., 2006; Webster et al., 2006).

Based on their chemical structures, such as branching within the molecule or the

occurrence of double bonds, various PLFAs can be used to establish the notional

proportions of fungi, Gram-positive bacteria (including actinomycetes) or Gram-negative

bacteria (Frostegård and Bååth, 1996). Whilst phenotypic profiling techniques (such as

PLFA analysis) that determine microbial membrane composition do not distinguish

between microbial species (Singh et al., 2006), they can be used to determine variations in

the relative abundance of fungi, Gram-negative and Gram-positive bacteria. Shifts

between these microbial groups have been reported with soil warming (Biasi et al., 2005;

Frey et al., 2008) and changes in microbial carbon source (recalcitrant versus labile soil

carbon) are considered to be associated with these different microbial groups (Biasi et al.,

2005; Bardgett et al., 2007). Hence, it is of ecological importance to investigate the

temperature and substrate controls on the fungal and bacterial PLFAs during the course of

soil substrate degradation. PLFAs can also be used to assess the physiological state of soil

94

microorganisms because bacteria are known to alter their membrane fatty acid

components in response to environmental changes, and therefore PLFA composition can

change with respect to an external stress (Bossio and Scow, 1998; Moore-Kucera and

Dick, 2008). For example, the cyclopropane PLFA was shown to be produced once a

bacterial community ran out of easily degradable, soluble carbon in unleached straw and

leachate treatments (Bossio and Scow, 1998). This suggests that increasing ratios of

cyclopropane PLFA-to-monoenoic precursor are potential indicators of microbial

starvation (Guckert et al., 1986; Bossio and Scow, 1998). Monoenoic PLFAs have been

reported to be strongly related to high concentrations of available substrates (Zelles et al.,

1992; Kieft et al., 1994). A decreasing ratio of monoenoic-to-saturated PLFAs (mono/sat)

is typically observed when Gram-negative bacteria are starved (Guckert et al., 1986; Kieft

et al., 1994). Moreover, the ratios of cyclopropane PLFA-to-monoenoic precursor and

mono/sat increase and decline with increasing growth temperatures, respectively (Suutari

and Laakso, 1994) and change under environmental stresses such as water limitations and

metal toxins (Dickens and Anderson, 1999; Li et al., 2007; Moore-Kucera and Dick,

2008).

We report here on a study to investigate the impact of soil incubation on microbial

biomass, activity, and community composition as determined by PLFA analysis at

elevated temperatures in two grassland soils. These soils were shown to differ in the

amount of ‘labile’ SOM and in their ‘oxidation’ stage in our previous study (Chapter 3),

and they therefore represented varied SOM quality for the microbial community. The

objectives of this study were to examine the temporal changes in microbial biomass and

95

activity after soil preparation and during the soil incubation, to investigate the responses

of soil PLFAs to temperature increases in soils with varied SOM quality, and to assess the

efficacy of using PLFA stress indicators to evaluate substrate constraints induced by

temperature increases. We hypothesized that shifts in microbial community structure

might occur due to varying degrees of substrate constraints induced by elevated

incubation temperatures and that the microbial community might respond differently to

temperature increases in soils of varied SOM quality in that the microbial community was

more stable in the soil with higher amounts of ‘labile’ substrates.


4.3.1 Soil Incubation

Surface soil samples were collected from two well-drained, pristine grassland soils in

western Alberta in late August of 2005. The first soil (denoted as Soil E, classified as a

Black Chernozem) was sampled from the University of Alberta Ellerslie Research Station,

located south of Edmonton, Alberta, and the second (Soil L, classified as a Brown

Chernozem) was collected from the Agriculture and Agri-Food Canada Research Station

near Lethbridge, Alberta. Both soils are rich in calcium, have a high base saturation, and a

pH range which varies between 6.4 and 6.75 (Shunthirasingham and Simpson, 2006). Soil

E has a silt loam texture and Soil L is of loam texture with montmorillonite being the

most abundant clay mineral in both soils (Shunthirasingham and Simpson, 2006).

Detailed analyses on the SOM components (such as solvent extractable compounds,

cutin- and suberin-derived compounds, and lignin compounds) in both soils can be found

96

in our previous study (Chapter 3). The mean annual temperature (MAT) for Soils E and L

is 1.7°C and 5°C, respectively (Janzen et al., 1998). Details of the sampling site and soil

conditions have been described elsewhere (Chapter 2).

Soils were kept in the dark at 4±1°C for two months after sampling. Soil L had a high

abundance of grass roots during the time of sampling, which were manually removed

before incubation. Both soils were passed through a 2-mm sieve and homogenized before

incubation to minimize sample heterogeneity before incubation. Pre-conditioning of the

soil was not performed before incubation so that the microbial biomass and activity

changes induced by freshly exposed substrates and necromass could be evaluated. Soil

samples were incubated in 450-mL glass jars (~350 g dry soil per jar in a volume of ~300

mL) at six different temperatures (MAT of the original sites, 2, 4, 8, 12, and 20°C above

the MAT, which represented different scenarios of global warming) in the dark. The water

content was kept at ~30% of the soil dry weight (close to field water holding capacity, and

equivalent to a water filled pore space of 0.48 m3 m-3) by weighing and spraying

deionized water at soil surfaces twice a week, so that soil moisture did not limit microbial

activity (Arnold et al., 1999). At least five jars of soil were incubated at each temperature,

and subsamples (~50 g) were randomly collected with a spatula before incubation (Day 0)

and from one of the five jars at each incubation temperature on Day 29, 57, 86, 126, 170,

245, and 365. Care was taken to minimize disturbing the remaining soil in the jar during

sampling. The sampled soils were freeze-dried, and ground (< 100 µm) thoroughly prior

to chemical analyses.

97


Microbial respiration, which is equivalent to soil respiration in the absence of plant

roots, was measured in triplicate during the incubation on Days 1, 8, 15, 22, 29, 57, 86,

128, 170, 245, and 365, using the alkali absorption method (Winkler et al., 1996).

Respired CO2 was captured by 2 mL of 1.0 M NaOH in small glass vials placed inside the

incubation jars. The jars were sealed and left for 24 h and the vials were then removed

and capped. Excess NaOH was determined by precipitation with BaCl2 and titration with

0.2 M HCl with phenolphthalein as an indicator (Zhang et al., 2005). Microbial

respiration rates (r) were normalized to the dry weight of the soil sample, and expressed

in the units of μg CO2 gsoil-1 h-1. The metabolic quotient (qCO2, = r/Cmic) was calculated

using total PLFAs (see below) as an estimate of microbial biomass (Cmic, μg/gsoil).

4.3.3 Measurements of Soil Carbon and Nitrogen Contents

Total carbon, inorganic carbon, and total nitrogen contents of Soils E and L were

determined in triplicate at the start of the incubation using a Shimadzu TOC 5000

(Shimadzu Scientific Instruments, Columbia, MD, USA). Because inorganic carbon was

not detected, soil organic carbon (OC) content equaled the total carbon content. Soil

carbon loss in the one-year incubation was small relative to the original soil OC content

and consistent with other reports (White et al., 2002), which may well fall within the

precision of the soil TOC measurements (~5%). Therefore, soil carbon loss (%) during the

incubation was estimated by the following equation:

Soil carbon loss = r×365×24×(12/44)×10-6×100% (4.1)

98

where r is the measured microbial respiration rate in the units of μg CO2 gsoil-1 h-1.

4.3.4 PLFA Analysis

Soil PLFAs were extracted from freeze-dried soil samples (~6 g) by a modified

Bligh–Dyer method (Bligh and Dyer, 1959; Frostegård and Bååth, 1996). Briefly, the total

lipid extract was fractionated into neutral lipids, glycolipids, and polar lipids with 10 mL

chloroform, 20 mL acetone, and 10 mL methanol through a silicic acid column,

respectively. The polar lipid fraction containing the phospholipids was evaporated to

dryness under nitrogen, and converted into fatty acid methyl esters (FAMEs) by a mild

alkaline methanolysis reaction (Guckert et al., 1985). The FAMEs were recovered with a

hexane and chloroform mixture (4:1, v/v). The solvents were evaporated to dryness under

nitrogen, and the extracts were re-dissolved in 200 μL hexane. FAMEs were analyzed

with gas chromatography/mass spectrometry (GC/MS) with oleic acid (C18:1 alkanoic acid)

methyl ester as an external standard. GC/MS analysis was performed on an Agilent model

6890N GC coupled to a Hewlett-Packard model 5973 quadrupole mass selective detector.

Separation was achieved on a HP5-MS fused silica capillary column (30 m × 0.25 mm

i.d., 0.25 μm film thickness). The GC operating conditions were as follows: temperature

held at 65°C for 2 min, increased from 65 to 300°C at a rate of 6°C min-1 with final

isothermal hold at 300°C for 20 min. Helium was used as the carrier gas. Samples were

injected in splitless mode and the injector temperature was set at 280°C. The samples (2

μL) were injected with an Agilent 7683 autosampler. The mass spectrometer was operated

in the electron impact mode (EI) at 70 eV ionization energy and scanned from 50 to 650

99

daltons. Data were acquired and processed with the Chemstation G1701DA software.

Individual PLFAs were identified by comparison of mass spectra with literature, National

Institute of Standards and Technology (NIST) and Wiley Mass Spectral library data, and

by comparison of retention times with authentic standards. The concentration of

individual PLFAs was calculated by comparison of its peak area and that of the external

standard in the total ion current (TIC) and was then normalized to the soil OC content.

PLFAs in Soil E were not measured on Day 86 due to sample contamination during the

extraction.

Fatty acids were designated according to the standard PLFA nomenclature (Guckert

et al., 1985). Because PLFA 18:3 (a commonly used indicator of plant lipids; Harwood

and Russell, 1984) was not observed in our soils in the preliminary study, a significant

contribution of plant-derived PLFAs were considered to be negligible. Microbial biomass

was hence calculated by summarizing total PLFAs (C14-C20). PLFAs specific to fungi

(18:2ω6,9c), actinomycetes (10Me18:0), Gram-negative bacteria (16:1ω7c, cy17:0,

18:1ω7c and cy19:0), and Gram-positive bacteria (i14:0, a16:0, i15:0, a15:0, i16:0, i17:0,

and a17:0) were summarized, respectively (Harwood and Russell, 1984). To assess

microbial community changes in the soil incubation, ratios of fungal PLFA to bacterial

PLFAs (the sum of Gram-negative and Gram-positive bacterial PLFAs; F/B) and

Gram-negative to Gram-positive bacterial PLFAs (Gram-negative/Gram-positive) were

calculated.

4.3.5 Statistical Analysis

100

Our preliminary study indicated a minimal time effect on soil PLFAs during

incubation and hence we chose to focus on the temperature control of microbial PLFA

composition. We treated samples taken on different dates as replicates (except Day 0; n=6

in Soil E and n=7 in Soil L) and the standard errors thus represented variation across

sampling dates, assuming that there was not a time × temperature interaction effect on the

PLFA data. The average PLFA ratios were compared against incubation temperatures

using linear regression analysis using Origin™ Version 7.0 (Microal Software, MA, USA),

and the difference was considered significant at a level of P<0.05.

4.4 Results

4.4.1 Microbial Respiration and Soil Carbon Content

Microbial respiration rates were generally higher in Soil L than in Soil E, and r

values decreased in a pseudo-exponential mode with incubation time in both soils (Figure

3.2 in Chapter 3). In Soil E, r values decreased by more than 40% in the first week of

incubation and then slowly decreased to 0.25-0.36 μg CO2 gsoil-1 h-1 at the end of the

experiment. In comparison, r values in Soil L decreased sharply at higher temperatures

(MAT+12ºC and MAT+20ºC) in the first week of incubation and decreased much more

slowly at lower temperatures (MAT-MAT+8ºC) in the first two months of incubation

(Figure 3.2b). Temperature had a significant effect on microbial respiration rates during

the entire incubation period in that r values measured on the same day of incubation were

positively correlated with incubation temperatures (P<0.05) although the correlation

coefficient decreased with time (data not shown).

101

Soil OC content was 4.85% and 2.69% for Soils E and L, respectively. Total nitrogen

content was 0.46% for Soil E and 0.28% for Soil L. Both soils had similar atomic C/N

ratios (11.2-12.3) at the start of the incubation. Based on the respiration rate on Day 86

(which was close to the average rate), soil carbon loss during the one-year incubation was

estimated to be 0.08-0.12% in Soil E, which accounted for 1.7-2.5% of the original soil

OC content. Similarly, soil carbon loss was about 0.24-0.40% in Soil L, equivalent to

8.9-14.9% of the original OC content. For comparative purposes, we used 4.85% and

2.69% as the OC content for Soils E and L, respectively, to calculate the OC-normalized

concentration of PLFAs in the soils.

4.4.2 Microbial PLFA Distribution During Soil Incubation at Elevated Temperatures

Up to 38 PLFAs were identified in the soils, including saturated fatty acids (14:0,

15:0, 16:0, 17:0, 18:0, 19:0, 20:0), monoenoic fatty acids (16:1ω5, 16:1ω6, 16:1ω9,

18:1ω5, 18:1ω9), and the PLFAs specific to fungi, Gram-negative bacteria, and

Gram-positive bacteria. PLFA 10Me18:0 was also detected in both soils but at low

concentrations (5.94 and 40.1 µg g-1 OC on Day 0 in Soil E and Soil L, respectively)

which accounted for only 1% of total PLFAs in both soils. Soil L had a much higher

concentration of total PLFAs than Soil E. The ratio of fungal PLFA: Gram-negative

bacterial PLFAs: Gram-positive bacterial PLFAs was 1:9:10 in Soil E and 1:4:3 in Soil L

at the beginning of the incubation.

Microbial PLFAs in Soil E declined sharply at the beginning of the experiment (until

102

0 100 200 300 4000

5

10

15

20

25

μg/g

OC

Days

(a) Soil E: Fungal PLFA

0 100 200 300 4000

40

80

120

160

200

240

280 (c) Soil E: Gram(+) bacterial PLFAs

μg/g

OC

Days

0 100 200 300 4000

40

80

120

160

200

240 (b) Soil E: Gram(-) bacterial PLFAs

μg/g

OC

0 100 200 300 4000

60

120

180

240 (d) Soil L: Fungal PLFA MAT MAT+2oC MAT+4oC MAT+8oC MAT+12oC MAT+20oC

0 100 200 300 4000

300

600

900

1200

1500 (e) Soil L: Gram(-) bacterial PLFAs

0 100 200 300 4000

200

400

600

800

1000

1200 (f) Soil L: Gram(+) bacterial PLFAs

Days

0 100 200 300 4000

5

10

15

20

25

μg/g

OC

Days

(a) Soil E: Fungal PLFA

0 100 200 300 4000

40

80

120

160

200

240

280 (c) Soil E: Gram(+) bacterial PLFAs

μg/g

OC

Days

0 100 200 300 4000

40

80

120

160

200

240 (b) Soil E: Gram(-) bacterial PLFAs

μg/g

OC

0 100 200 300 4000

60

120

180

240 (d) Soil L: Fungal PLFA MAT MAT+2oC MAT+4oC MAT+8oC MAT+12oC MAT+20oC

0 100 200 300 4000

300

600

900

1200

1500 (e) Soil L: Gram(-) bacterial PLFAs

0 100 200 300 4000

200

400

600

800

1000

1200 (f) Soil L: Gram(+) bacterial PLFAs

Days

Figure 4.1: Changes in microbial PLFAs in grassland soils during incubation. Fungal PLFA: 18:2ω6,9c; Gram-negative (Gram(-)) bacterial PLFAs: 16:1ω7c, cy17:0, 18:1ω7c and cy19:0; Gram-positive (Gram(+)) bacterial PLFAs: i14:0, a16:0, i15:0, a15:0, i16:0, i17:0, and a17:0. MAT: mean annual temperature.

103

Day 57) and then stabilized during the later stages of the incubation, exhibiting a

first-order exponential decay pattern (Figures 4.1 a-c; Müller and Höper, 2004). In

comparison, fungal PLFAs in Soil L followed a similar pattern (Figure 4.1d) while

bacterial PLFAs in Soil L increased in the first three months and then decreased

exponentially (Figures 4.1 e-f). PLFA 10Me18:0 declined in both soils with incubation,

following a similar trend to that observed in the Gram-positive bacterial PLFAs. We did

not observe any correlation between the concentration of 10Me18:0 and temperature (data

not shown).

4.4.3 PLFA Indicators of Microbial Community Structure and Stress

Soil L had a higher F/B ratio (0.14) than Soil E (0.05) at the beginning of the

incubation. In Soil E, the F/B ratio decreased slightly with incubation time from 0.05 to

about 0.03 during the incubation. By comparison, the F/B ratio declined quickly in the

first three months of the incubation and then stabilized around 0.06-0.08 in Soil L. To

assess the temperature effects on fungal and bacterial biomass, we treated the samples

taken on different sampling dates (except Day 0) as replicates (n=6 in Soil E and n=7 in

Soil L) and compared the average F/B ratios against the incubation temperature, assuming

that there was not a time × temperature interaction effect on the F/B ratio. The F/B ratio

decreased with increasing temperature in both soils (Figure 4.2a) and a linear correlation

between the F/B ratio and temperature was found in Soil E (P<0.05). Such a correlation

was also found in Soil L but was not statistically significant (Figure 4.2a). The

Gram-negative/Gram-positive ratio was higher in Soil L (1.43) than in Soil E (0.89) at the

104

beginning of the incubation, and a consistent trend was not observed between the

Gram-negative/Gram-positive ratio and incubation time. Similarly, the average ratio of

0 5 10 15 20 250.00

0.02

0.04

0.06

0.08

0.10

0.12

y=-0.0004x+0.078R2=0.48, P=0.13

Soil E Soil L

Rat

io o

f F/B

Temperature (oC)

y=-0.0006x+0.038R2=0.92, P=0.002

(a)

0 5 10 15 20 250.0

0.4

0.8

1.2

1.6

2.0

y=-0.017x+1.48R2=0.93, P<0.01

Temperature (oC)

Rat

io o

f Gra

m(-

)/Gra

m(+

)

y=-0.014x+1.16R2=0.94, P<0.01

(b)

0 5 10 15 20 250.00

0.02

0.04

0.06

0.08

0.10

0.12

y=-0.0004x+0.078R2=0.48, P=0.13

Soil E Soil L

Rat

io o

f F/B

Temperature (oC)

y=-0.0006x+0.038R2=0.92, P=0.002

(a)

0 5 10 15 20 250.0

0.4

0.8

1.2

1.6

2.0

y=-0.017x+1.48R2=0.93, P<0.01

Temperature (oC)

Rat

io o

f Gra

m(-

)/Gra

m(+

)

y=-0.014x+1.16R2=0.94, P<0.01

(b)

Figure 4.2: Correlation between incubation temperatures and the average ratios of microbial PLFAs across different sampling dates (except Day 0). F/B: ratio of fungal PLFA/bacterial PLFAs; Gram-negative/Gram-positive (Gram(-)/(Gram(+)): ratio of Gram-negative bacterial PLFAs/ Gram-positive bacterial PLFAs. Soil E: n=6; Soil L: n=7.

105

Gram-negative/Gram-positive across different sampling dates (except Day 0) was

calculated for soil samples incubated at different temperatures and was negatively

correlated with incubation temperatures in both soils (Figure 4.2b; P<0.01). The

correlation coefficient was similar in both soils: -0.017 ± 0.002 in Soil E and -0.019 ±

0.003 in Soil L.

Soil E had higher ratios of cy17:0/16:1ω7c (0.76) and cy19:0/18:1ω7c (0.92) than Soil

L (0.65 and 0.49, respectively) and a lower ratio of mono/sat (1.63 in Soil E and 2.06 in

Soil L) at the beginning of the incubation. An increase with time was observed in the

ratios of cy17:0/16:1ω7c (from 0.65 to 1.23) and cy19:0/18:1ω7c (from 0.49 to 0.80) in

Soil L at high incubation temperatures (MAT+12ºC and MAT+20ºC) but not at low

incubation temperatures (MAT-MAT+8ºC) or in Soil E. Similarly, the mono/sat ratio did

not correlate well with incubation time in Soil E or in Soil L at low incubation

temperatures (MAT-MAT+8ºC) but decreased with time in Soil L at high incubation

temperatures (MAT+12ºC and MAT+20ºC) from 2.06 to 1.24. The average values of three

stress indicators across sampling dates (except Day 0) were compared against the

temperature, assuming that there was not a time × temperature interaction effect. The

ratios of cy17:0/16:1ω7c and cy19:0/18:1ω7c showed a minor change at lower

temperature ranges (0-10°C), but were positively correlated with incubation temperatures

(Figures 4.3 a-b; P<0.05) with similar correlation coefficients (0.014-0.018) in both soils

when the data points of MAT+12°C and MAT+20°C are included. The mono/sat ratio also

showed a minor change at lower temperature ranges (0-10°C) but was negatively

correlated with temperatures (Figure 4.3c; P<0.05) with similar correlation coefficients

106

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

y=0.018x+0.40R2=0.97, P<0.001

y=0.014x+0.55R2=0.92, P=0.002

Rat

io o

f cy1

7:0/

16:1ω

7c

Temperature (oC)

Soil E Soil L

(a)

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4 (b)

y=0.016x+0.28R2=0.90, P=0.004

y=0.015x+0.67R2=0.76, P=0.02

Temperature (oC)

Rat

io o

f cy-

19:0

/18:

1ω7c

0 5 10 15 20 250.0

0.6

1.2

1.8

2.4

3.0

3.6 (c)

y=-0.05x+2.81R2=0.89, P=0.004

y=-0.04x+2.10R2=0.86, P=0.008

Temperature (oC)

Rat

io o

f mon

o/sa

t

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

y=0.018x+0.40R2=0.97, P<0.001

y=0.014x+0.55R2=0.92, P=0.002

Rat

io o

f cy1

7:0/

16:1ω

7c

Temperature (oC)

Soil E Soil L

(a)

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4 (b)

y=0.016x+0.28R2=0.90, P=0.004

y=0.015x+0.67R2=0.76, P=0.02

Temperature (oC)

Rat

io o

f cy-

19:0

/18:

1ω7c

0 5 10 15 20 250.0

0.6

1.2

1.8

2.4

3.0

3.6 (c)

y=-0.05x+2.81R2=0.89, P=0.004

y=-0.04x+2.10R2=0.86, P=0.008

Temperature (oC)

Rat

io o

f mon

o/sa

t

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

y=0.018x+0.40R2=0.97, P<0.001

y=0.014x+0.55R2=0.92, P=0.002

Rat

io o

f cy1

7:0/

16:1ω

7c

Temperature (oC)

Soil E Soil L

(a)

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4 (b)

y=0.016x+0.28R2=0.90, P=0.004

y=0.015x+0.67R2=0.76, P=0.02

Temperature (oC)

Rat

io o

f cy-

19:0

/18:

1ω7c

0 5 10 15 20 250.0

0.6

1.2

1.8

2.4

3.0

3.6 (c)

y=-0.05x+2.81R2=0.89, P=0.004

y=-0.04x+2.10R2=0.86, P=0.008

Temperature (oC)

Rat

io o

f mon

o/sa

t

Figure 4.3: Correlation between incubation temperatures and the average PLFA stress indicators across different sampling dates (except Day 0). Ratio of mono/sat: ratio of monoenoic-to-saturated PLFAs. Soil E: n=6; Soil L: n=7.

107

(-0.040 ± 0.008 in Soil E and -0.055 ± 0.009 in Soil L) in both soils when the data points

of MAT+12°C and MAT+20°C were included.

4.4.4 Metabolic Quotient

Soil L had higher qCO2 values than Soil E on the first day of incubation and at high

incubation temperatures (MAT+12ºC and MAT+20ºC; Figure 4.4). However, at low

incubation temperatures (MAT-MAT+8ºC), both soils had similar qCO2 values (15-20 mg

CO2 gmic-1 h-1). The value of qCO2 was elevated at the start of the incubation and stabilized

0 5 10 15 20 250

30

60

90

120

150

Temperature (oC)

qCO

2 (mg

CO

2 gm

ic-1h-1

) Soil E Soil L

Figure 4.4: Metabolic quotient (qCO2) of both grassland soils on Day 1. Points show means (n=3); error bars represent standard error.

after the first month in both soils. When the average values of qCO2 were taken across the

entire incubation period (with the first day excluded, which had considerably higher qCO2

108

values and would bias the average), the average qCO2 value correlated positively with

temperature in both soils (Figure 4.5; P<0.05).

0 5 10 15 20 250

8

16

24

32

40

48

y=0.39x+16.11R2=0.76

Ave

rage

qC

O2

(mg

CO

2 gm

ic-1h-1

)

Temperature (oC)

Soil ESoil L y=1.30x+7.46

R2=0.84

0 5 10 15 20 250

8

16

24

32

40

48

y=0.39x+16.11R2=0.76

Ave

rage

qC

O2

(mg

CO

2 gm

ic-1h-1

)

Temperature (oC)

Soil ESoil L y=1.30x+7.46

R2=0.84

Figure 4.5: Correlation between incubation temperatures and the average metabolic quotient (qCO2) in both grassland soils across different sampling dates (except the Day 1; n=21; P<0.05).

4.5 Discussion

4.5.1 Soil Microbial Biomass and Activity During Soil Incubation

Major changes in the biomass and activity of the decomposer community induced by

soil disturbance and substrate limitation during soil incubation may bias the analysis of

SOM decomposition (Schimel and Weintraub, 2003) which is commonly investigated in

soil incubation studies. Sieving and homogenization of soil samples are known to release

a pool of substrates from disturbed soil and to disrupt the soil microbial community

109

(Hassink, 1992; Petersen and Klug, 1994). Such procedures are often required for sample

preparation. In this study, an increase in the soil bacterial biomass (measured by PLFAs),

likely fueled by the freshly exposed SOM and necromass from soil preparation, was

observed in the first month of incubation in Soil L (Figures 4.1 d-e). However, such an

increase is not as apparent for fungi in Soil L (Figure 4.1f) for two possible reasons: first,

filamentous fungi are more susceptible to disturbances such as sieving (Petersen and Klug,

1994; Moore-Kucera and Dick, 2008) and hence suffer more damage during sample

preparation; and second, bacteria are more adept at exploiting labile resources (Swift et al.,

1979; Petersen and Klug, 1994) and hence experience higher growth in the presence of

fresh substrates. With prolonged soil incubation, such readily available substrates were

exhausted (Joergensen et al., 1990; Petersen and Klug, 1994; Arnold et al., 1999) and both

the fungal and bacterial biomass declined after the first month in Soil L. Microbial

biomass stabilized at a relatively constant level after decreasing during the first two

months in Soil E (Figures 4.1 a-c). The microbial growth induced by freshly-released

SOM and necromass may also be present in Soil E to a much lesser extent because

although Soil E had a higher OC content, SOM is present here in a more ‘oxidized’ or

degraded state than Soil L, as shown by a series of SOM degradation indicators (Chapter

3; Otto et al., 2005; Simpson et al., 2008). Hence, labile SOM constituents were more

readily available for microbial growth at the start of the incubation in Soil L than in Soil E

(this is also evident from the PLFA stress indicators as discussed in Section 4.3 below),

which contributes to the varied biomass found in these grassland soils together with

climate (Haney et al., 2001). Consequently, the decrease of microbial biomass at the

110

beginning of the incubation may be amplified in both soils by the exhaustion of

freshly-exposed substrates. The microbial biomass underwent a longer and slower decline

in Soil L (Figures 4.1 d-f) than in Soil E. Since Soil L had a high abundance of grass roots

and soil aggregates during the time of sampling, some fine root debris may have remained

in Soil L even after soil sieving and provided a gradual release of fresh organic matter

into the soil to sustain the microbial biomass. This hypothesis is supported by the analyses

of major SOM components (solvent-extractable compounds, cutin- and suberin-derived

lipids, and lignin-derived compounds) during the soil incubation, where the labile pool of

SOM components had a generally higher decomposition rate in Soil E than in Soil L

(Chapter 3).

Alternatively, soil microbial activity as indicated by the metabolic quotient was

constant in both soils after the first month of experiment, suggesting that microbial

metabolic activity was not significantly influenced by the prolonged incubation. High

values of qCO2 at the start of incubation were associated with high substrate availability,

which was derived from the freshly exposed SOM and necromass from sieved soil

(Petersen and Klug, 1994). Autochthonous microbial communities are reported to

increase their respiration rate without changing the biomass under an excess of substrate

(Potthoff et al., 2005). Although substrate excess is rare in soil environments where most

SOM is in a mineral-associated or unavailable form (Baldock and Skjemstad, 2000),

easily degradable SOM released from physically disrupted soil aggregates and lysed

microbial cells may have provided enough substrate to induce a short-lived increase in

microbial metabolic activity at the start of the incubation. Similarly, total microbial

111

respiration rate (r) declined only very slowly after the first three months in Soil L and

after the first month in Soil E (Figure 3.2). Overall, soil microbial activity and biomass

was maintained at a relatively constant level during the one-year soil incubation and high

levels of microbial activity at the initial of the experiment were only short-lived.

4.5.2 Effects of Soil Disturbance and Substrate Constraints on Microbial Community

Composition

Two major stresses imposed on the soil microbial community during the controlled

incubation of moist pristine grassland soils include soil disturbance during sample

preparation and substrate constraints induced by prolonged incubation. A decrease in the

F/B ratio was observed in both soils in the first three months of incubation mainly as a

result of soil disturbance, where freshly released substrates and necromass likely fuelled

or sustained bacterial growth while fungi suffered more seriously from the physical

disturbance due to their filamentous nature (Petersen and Klug, 1994; Moore-Kucera and

Dick, 2008). Similar changes in the F/B ratio has been reported in soil incubation studies

(Scheu and Parkinson, 1994). Caution should therefore be used in experimental setup and

sampling procedures to preserve the fungal community. The F/B ratio stabilized during

the later phase of soil incubation with the exhaustion of the freshly exposed substrates and

no consistent change was observed in the ratio of Gram-negative/Gram-positive with

incubation time in both soils. These observations suggest minimal time effect on the

microbial community structure in our study.

On the other hand, increased incubation temperatures significantly enhanced

112

microbial activity throughout the experiment, as indicated by r and qCO2 values (Figures

3.2, 4.4 and 4.5). Consequently, increased microbial activity at higher temperatures

accelerated the exhaustion of easily degradable substrates, and soil microorganisms were

thus subject to higher substrate constraints at higher incubation temperatures. It seems

that the temperature-induced substrate constraints exert a larger influence on the

microbial community composition than those induced by prolonged incubation because

the average Gram-negative/Gram-positive ratios were negatively correlated with

incubation temperatures in both soils (Figure 4.2b). Temperature-induced substrate

constraints rather than the temperature increase itself are suggested to primarily regulate

soil microbial biomass (Zhang et al., 2005; Rinnan et al., 2008), and the fast-growing

Gram-positive bacteria are considered to be more adept at competing for resources at

higher temperatures (Biasi et al., 2005). By comparison, the response of the F/B ratio to

temperature increases was more gradual and only statistically significant in Soil E (Figure

4.2a), suggesting that the F/B ratio is less sensitive to temperature-induced substrate

constraints and that the microbial community structure is more stable in Soil L which has

higher amounts of ‘labile’ substrates. These observations support our hypothesis and are

consistent with the current literature, where warming-induced increases in the relative

abundance of Gram-positive bacteria have been reported (Bardgett et al., 1999; Biasi et

al., 2005; Frey et al., 2008) while the response of the F/B ratio to warming varies

depending on soil properties (Zhang et al., 2005; Rinnan et al., 2007; Frey et al., 2008).

In our study, it remains undetermined whether the reduced percentage of fungi and

Gram-negative bacteria in the soil microbial community had an impact on the degradation

113

pattern of SOM at higher incubation temperatures. It has been argued that the change in

microbial community structure is an artefact associated with ‘parallel’ laboratory

incubations at different temperatures (Leifeld, 2003). However, when the average molar

percentage of PLFAs in the total soil PLFAs was taken across the entire incubation period

(with the first day excluded), Gram-positive bacterial PLFAs increased from 23% at MAT

to 25% at MAT+20ºC in Soil E and from 19% to 21% in Soil L, while the average

percentage of Gram-negative bacterial PLFAs decreased from 25% at MAT to 21% at

MAT+20ºC in Soil E and from 26% to 22% in Soil L. The size of fungal PLFA change

was relatively large compared to the initial size of fungal biomass (from 1.8% at MAT to

1.1% at MAT+20ºC in Soil E and from 3.2% to 2.9% in Soil L). However, these

compositional changes in soil PLFA profiles were not significant from a statistical

perspective and dramatic changes in the SOM decomposition patterns were unlikely

based on the microbial metabolic quotient measurement. Shifts in microbial species due

to substrate constraints and soil disturbance cannot be detected by PLFA analyses.

Complementary techniques such as nucleic acid profiling (Singh et al., 2006; Webster et

al., 2006) may be more informative in this respect.

4.5.3 Temperature and Substrate Effects on PLFA Stress Indicators

Microbial stress indicators such as cy17:0/16:1ω7c, cy19:0/18:1ω7c, and mono/sat

have been extensively studied to monitor changes to microbial cell membrane

composition brought on by temperature changes (Suutari and Laakso, 1994), substrate

availability (Kieft et al., 1994), water limitations (Moore-Kucera and Dick, 2008), and

114

toxins such as metals (Akerblom et al., 2007). In this study pristine grassland soils were

incubated under controlled moisture conditions (equivalent to a water filled pore space of

0.48 m3 m-3 in both soils) and therefore any changes in the microbial PLFA composition

are not due to water limitations or toxins. Temperature and substrate availability are the

main environmental variables influencing PLFA stress indicators in both soils. Increasing

growth temperatures are reported to increase the ratio of cyclopropane

PLFA-to-monoenoic precursor and decrease the ratio of mono/sat (Suutari and Laakso,

1994). However, an opposite trend was observed in our study, where Soil E (with an MAT

of 1.7°C) had higher ratios of cy17:0/16:1ω7c and cy19:0/18:1ω7c and a lower ratio of

mono/sat than Soil L (with an MAT of 5°C) at the start of the incubation. Both soils have

similar mineral composition and pH values, but vary in the amount of labile SOM

substrates (Chapter 3; Otto et al., 2005; Simpson et al., 2008), which contributes to the

varied values of stress indicators in Soils E and L. As suggested by Petersen and Klug

(1994), temperature has a minor influence on stress indicators at low temperatures

(<10°C). Therefore, the high ratios of cy17:0/16:1ω7c and cy19:0/18:1ω7c and the low

ratio of mono/sat in Soil E reflect a control by substrate availability, confirming the

results from SOM component analyses in our previous studies (Chapter 3; Otto et al.,

2005; Simpson et al., 2008).

Consistent with other studies (Petersen and Klug, 1994; Suutari and Laakso, 1994),

stress indicators show a minor change at lower temperature ranges (0-10°C), but a

statistically significant linear correlation was found between the stress indicators and

incubation temperatures when the data points of MAT+12°C and MAT+20°C are included

115

(Figure 4.3). Incubation temperature may affect stress indicators either directly or through

inducing substrate constraints by faster microbial exhaustion of available carbon sources

at higher temperatures (Petersen and Klug, 1994). At the present stage, it is difficult to

separate the interacting effects of incubation temperature and temperature-induced

substrate constraints on PLFA stress indicators. Interestingly, the ratios of cy17:0/16:1ω7c

and cy19:0/18:1ω7c have similar temperature-correlation coefficients (0.014-0.018;

Figures 4.3 a-b) in soils with varied substrate availabilities in the temperature range from

1.7°C to 25°C. And such is the case for the mono/sat ratio (Figure 4.3c). This

phenomenon has not been reported before and implies that temperature may have a

dominant and similar control on PLFA stress indicators at higher temperature ranges in

soils with varied substrate availabilities. Such a correlation should be investigated with a

wider range of soils to test if it is more generally applicable. Alternatively, substrate

availability is an important factor regulating microbial activity and is difficult to

determine in soil studies due to the heterogeneity of soil components. The ratios of

cy17:0/16:1ω7c, cy19:0/18:1ω7c and mono/sat may potentially be used as an indicator of

substrate availability in soil environments with similar physical conditions (such as

average temperature, moisture, and aerobic conditions) and microbial community

composition when no other stressors (such as toxins) are present.

4.5.4 Implications for Global Warming

Distinct decay patterns were observed for Soil E and Soil L PLFAs during the

one-year incubation study, where an apparent stabilization of the microbial biomass in

116

Soil E was not observed in Soil L (Figure 4.1). This varied response of soil microbial

biomass to incubation can be explained by the larger amount of readily-available

substrates in Soil L derived from fine root debris and SOM (Chapter 3; Otto et al., 2005;

Simpson et al., 2008), which sustained microbial biomass in Soil L for a longer time. This

finding supports our hypothesis and highlights the control of SOM quality (lability and

availability) on the microbial response to global warming since temperature increases are

also known to speed up the depletion of soil substrates and thus expose the microbial

community to stress (Arnold et al., 1999). Soil microbial communities with sustaining

‘labile’ carbon resources (such as plant biomass inputs) are more adaptable to temperature

changes. Furthermore, while growth temperature has a significant effect on the microbial

PLFA stress indicators, substrate availability as governed by incubation temperatures has

a strong control on the relative ratios of fungal, Gram-negative and Gram-positive

bacterial biomass (Figure 4.2). Our findings are consistent with the results from field

warming experiments, where warming-induced enhancement of plant or litter inputs has a

stronger control on soil microbial responses than the temperature increase itself (Zhang et

al., 2005; Rinnan et al., 2008).


We sincerely thank the editor and two anonymous reviewers for their insightful

comments which greatly improved the manuscript. We thank Dr. Henry Janzen for

assistance with selecting and sampling Soil L. Funding from the Canadian Foundation for

Climate and Atmospheric Sciences (GR-520) is gratefully acknowledged. L. Nielsen is

117

thanked for conducting part of the chemical extractions. The Natural Sciences and

Engineering Research Council (NSERC) of Canada is thanked for support via a

University Faculty Award (UFA) to M. Simpson and an undergraduate summer research

award (USRA) to L. Nielsen. X. Feng acknowledges funding from the Ontario Graduate

Scholarship (OGS) program.

118

CHAPTER 5

INCREASED CUTICULAR CARBON SEQUESTRATION AND

LIGNIN OXIDATION IN RESPONSE TO SOIL WARMING*

* Reprinted from Nature Geoscience, 1: 836-839. Authors: Feng, X., Simpson, A.J., Wilson, K.P.,

Williams, D.D., Simpson, M.J., Copyright (2008), with permissions from Macmillan Publishers

Limited.

119

5.1 Abstract

Rising temperatures are predicted to accelerate the decomposition of labile soil

organic compounds such as proteins and carbohydrates, while biochemically resistant

compounds, such as lipids from leaf cuticles and roots and lignin from woody tissues, are

expected to remain stable on decadal to centennial timescales (Melillo et al., 2002;

Davidson and Janssens, 2006). However, the extent to which soil warming changes the

molecular composition of soil organic matter is poorly understood (Knorr et al., 2005b;

Davidson et al., 2006). Here we examine the impact of soil warming in a mixed temperate

forest on the molecular make-up of soil organic matter. We show that the abundance of

leaf-cuticle-derived compounds is increased following 14 months of soil warming; we

confirm this with nuclear magnetic resonance spectra of soil organic matter extracts. In

contrast, we find that the abundance of lignin-derived compounds is decreased after the

same treatment, while soil fungi, the primary decomposers of lignin in soil (Carlile et al.,

2001), increase in abundance. We conclude that future warming could alter the

composition of soil organic matter at the molecular level, accelerating lignin degradation

and increasing leaf-cuticle-derived carbon sequestration. With annual litterfall predicted

to increase in the world’s major forests with a 3ºC warming (Liu et al., 2004), we suggest

that future warming may enhance the sequestration of cuticular carbon in soil.

5.2 Introduction

Global warming is predicted to increase vegetation productivity and litterfall in the

northern biomes (Cramer et al., 2001). Yet it remains poorly understood which soil

organic matter (SOM) structures are likely to be accumulated or degraded with such

climate-induced changes in biomass inputs. Plant leaf litter mostly consists of structurally

labile materials (such as carbohydrates; Prescott et al., 2004) which are believed to

120

decompose readily and thus, the long-term preservation of labile plant-derived SOM is

considered to be unlikely (Norby et al., 2007). However, recalcitrant alkyl carbon

structures that are abundant in leaf cuticles can potentially enhance carbon sequestration

in the soil (Lorenz et al., 2007). It is therefore important to study the fate of

cuticle-derived carbon in the soil and to monitor the compositional changes to SOM at

elevated temperatures with increased inputs from leaf litter. Furthermore, global warming

may change SOM decomposition patterns by altering the soil microbial community

structure and activity through increased carbon inputs (referred to as the priming effect;

Pendall et al., 2004). Such changes in SOM composition and microbial community may

be difficult to discern with traditional total soil carbon and nitrogen analysis or soil

respiration measurements (Melillo et al., 2002). In this study, SOM compositional

changes were investigated by molecular-level methods2 after 14 months of soil warming

in a moist mixed forest in southern Ontario, where soil temperature was elevated by an

average of 5°C. Specifically, we targeted phospholipid fatty acids (PLFAs) that are

indicative of bacterial and fungal biomass (Frostegård and Bååth, 1996) and major SOM

components with distinct turnover times (carbohydrates representing the labile SOM and

lignin representing the more slowly-cycling SOM; Gleixner et al., 2001; Melillo et al.,

2002) and from different origins (cutin from leaf cuticles and suberin from roots).

5.3 Results and Discussion

Analysis of samples from both experimental plots shows similar SOM composition

prior to warming (Figure 5.1a), consistent with the even topography and vegetation

distribution in the study area. After soil warming, SOM compositional changes were

observed in the warmed soil (Figure 5.1b; Supplementary Information, Table 5.S1), likely

2 Details of methods are in Section 5.4.

121

resulting from a combination of elevated organic matter inputs and enhanced degradation

induced by higher temperatures and presumably priming effects (Carney et al., 2007). The

soil organic carbon (SOC) content was similar in the control plot after warming but

increased significantly in the warmed plot (P < 0.001; Table 5.S1), suggesting that the

increased organic carbon (OC) input from litter decomposition (and possibly root

exudates as well) with soil warming was higher than the enhanced SOM decomposition in

the short term. SOC content has been observed to increase with mean annual temperature

in only a few well-drained forest soils with a similar coarse textures as the soil in this

study (Liski and Westman, 1997; Callesen et al., 2003) and this increase is predicted to be

0.00.51.01.52.02.53.0

0.00.51.01.52.02.53.0 Treatment plot

Control plot

Rel

ativ

e ab

unda

nce

Before warming

After warming

Carbo-hydrates

Cutin-derived

compounds

Suberin-derived

compounds

Lignin-derived

compounds

Bacterial PLFAs

Fungal PLFA

a

b

Rel

ativ

e ab

unda

nce

*

*

**

0.00.51.01.52.02.53.0

0.00.51.01.52.02.53.0 Treatment plot

Control plot

Rel

ativ

e ab

unda

nce

Before warming

After warming

Carbo-hydrates

Cutin-derived

compounds

Suberin-derived

compounds

Lignin-derived

compounds

Bacterial PLFAs

Fungal PLFA

a

b

Rel

ativ

e ab

unda

nce

*

*

**

Figure 5.1: Relative abundance of major soil organic matter components in the control and treatment plots. All values in the treatment plot are normalized against the corresponding values in the control plot before (a) and after (b) soil warming, respectively. Error bars indicate standard error (n = 3). PLFA, phospholipid fatty acid. * denotes significant difference in the abundance of individual component at the level of P = 0.05. Raw data are listed in Table 5.S1.

122

Table 5.S1: Concentrations and ratios of soil organic matter components before and after soil warming (mean ± s.e.m.)

Pre-warming Post warming

Treatment

plot Control plot

Treatment

plot Control plot

Organic carbon (OC, %) 4.22 ± 0.43 4.28 ± 0.35 6.49 ± 0.23 3.88 ± 0.10

Total nitrogen (N, %) 0.35 ± 0.01 0.36 ± 0.03 0.24 ± 0.00 0.24 ± 0.01

Cutin-derived compounds (mg/g OC) 6.35 ± 1.27 5.68 ± 2.15 15.80 ± 2.47 6.46 ± 2.00

Suberin-derived compounds (mg/g OC) 3.75 ± 1.09 3.60 ± 1.03 4.49 ± 0.35 4.92 ± 0.06

Lignin monomers (mg/g OC) 9.82 ± 1.10 9.80 ± 0.83 4.32 ± 0.03 5.25 ± 0.28

Vanillyls (mg/g OC) 5.32 ± 0.13 5.31 ± 0.35 1.64 ± 0.06 2.27 ± 0.11

Syringyls (mg/g OC) 2.56 ± 0.22 2.54 ± 0.33 0.73 ± 0.01 1.08 ± 0.07

Cinnamyls (mg/g OC) 1.94 ± 0.87 1.95 ± 0.53 1.95 ± 0.07 1.89 ± 0.12

Carbohydrates (mg/g OC) 0.25 ± 0.09 0.22 ± 0.07 0.17 ± 0.03 0.40 ± 0.06

Bacterial PLFAs (mg/g OC) 0.19 ± 0.03 0.19 ± 0.00 0.26 ± 0.03 0.26 ± 0.01

Fungal PLFA (μg/g OC) 7.98 ± 3.68 7.90 ± 1.06 14.90 ± 0.48 9.73 ± 1.00

Ratios

Atomic OC/N ratio 14 14 32 19

C/V 0.36 ± 0.16 0.38 ± 0.12 1.19 ± 0.08 0.83 ± 0.04

S/V 0.48 ± 0.04 0.48 ± 0.06 0.45 ± 0.01 0.48 ± 0.03

Pre-warming values are based on triplicate samples randomly collected in June, 2002 and April and June, 2003 while post warming values are based on triplicate samples collected in May, 2005 that were randomly combined to form a composite sample. Cutin-derived compounds include mid-chain hydroxyalkanoic acids (C14, C15, C17), mono- and dihydroxyhexadecanoic acids, and n-hexadecane-α,ω-dioic acids; suberin-derived compounds include ω-hydroxyalkanoic acids (C20–C32), n-alkane-α,ω-dioic acids (C20–C32), and 9,10-epoxy-octadecane-α,ω-dioic acid; lignin monomers include vanillyls (vanillic acid, vanillin, and acetovanillone), syringyls (syringic acid, syringaldehyde, and acetosyringone), and cinnamyls (ferulic acid, and p-coumaric acid); carbohydrates include mannose, glucose, sucrose, and trehalose; fungal PLFA refers to PLFA 18:2ω6,9c; bacterial PLFAs include PLFAs i15:0, a15:0, i16:0, 16:1ω7c, i17:0, a17:0, cy17:0, 18:1ω7c and cy19:0. Ratios of C/V and S/V refer to lignin monomer ratios of cinnamyls/vanillyls and syringyls/vanillyls, respectively.

gradual (Kirschbaum, 1993). We hypothesize that the observed OC increase is due to

favourable soil moisture contents and temperatures in the study area which facilitated

both the decomposition and transfer of OC from the overlaying litter layer into SOM.

123

The SOC-normalized concentration of carbohydrates and cutin-derived compounds

remained similar in the control plot but was impacted by soil warming in the treatment

plot (Figure 5.1b; Table 5.S1). Major carbohydrates (mannose, glucose, sucrose, and

trehalose) decreased significantly in the treatment plot (P < 0.05) despite higher inputs

from plant biomass. This observation is consistent with model predictions, where

carbohydrates are considered to be amongst the most labile constituents of SOM

(Gleixner et al., 2001; Melillo et al., 2002) and their decomposition is accelerated by

temperature increases in the short term (Knorr et al., 2005b). Cutin-derived compounds,

which originate from the waxy coating of leaves and are believed to be recalcitrant

(Gleixner et al., 2001), increased significantly in the treatment plot (P < 0.05). Increased

vegetation growth at higher temperatures (Melillo et al., 2002) may increase leaf litter

production that contributes to increased cutin inputs into the soil. Soil warming may also

have accelerated the decomposition of leaf litter originally deposited on the soil surface

and therefore increased the inputs of cutin-derived compounds into soil. By contrast,

root-derived components, such as suberin, are considered to be more resistant to

microbial attack than cutin (Riederer et al., 1993). However, the OC-normalized

concentrations of suberin-derived compounds were similar in both plots after soil

warming, suggesting that organic matter inputs from leaf litter (cutin-derived) was more

enhanced than root-derived inputs with soil warming.

The abundance of lignin-derived compounds decreased in both plots as compared to

that before warming (Table 5.S1). However, this decrease was significantly higher in the

warmed soil than in the control soil (P < 0.05; Figure 5.1b) which indicates the

accelerated decomposition of lignin-derived compounds after soil warming.

Lignin-derived monomers are indicative of lignin composition and source. For example,

vanillyl phenols are prevalent in all vascular plants, cinnamyl phenols are specific to

124

non-woody vascular plant tissues, and syringyl phenols are specific to angiosperms (Goñi

et al., 1997; Otto and Simpson, 2006b). The experimental plots had the same ratios of

cinnamyls to vanillyls (C/V) and syringyls to vanillyls (S/V) prior to warming,

confirming a similar SOM composition and source before the experiment (Table 5.S1).

After soil warming, the S/V ratio remained similar but the C/V ratio increased

significantly in both plots (P < 0.05). Vanillyl phenols are reportedly more stable than

cinnamyl phenols in sediments and soils (Hedges et al., 1988; Opsahl and Benner, 1995),

and thus, the increased C/V ratio likely resulted from an increased input of cinnamyl

phenols (i.e. leaves) rather than a selective degradation of vanillyl phenols. The increase

of the C/V ratio in the treatment plot was significantly higher than that in the control plot,

confirming the increased incorporation of leaf-derived carbon into SOM during soil

warming.

To further assess the degradation of lignin in soil warming, we calculated the ratios

of commonly used lignin oxidation parameters: vanillic acid to vanillin and syringic acid

to syringaldehyde, both of which increase with increasing degree of lignin oxidation via

propyl side chain oxidation (Hedges et al., 1988; Opsahl and Benner, 1995; Otto and

Simpson, 2006b). Both ratios were similar prior to warming (Figure 5.2), suggesting a

uniform extent of lignin oxidation across the experimental site. Lignin is not expected to

undergo enhanced degradation with warming in the short term due to its biochemical

recalcitrance (Melillo et al., 2002; Knorr et al., 2005b). However, after 14 months of soil

warming, both lignin oxidation parameters increased significantly in the warmed plot

despite an increased input of fresh OC from plants and litter (P < 0.05; Figure 5.2). Our

results demonstrate that lignin-derived SOM is susceptible to enhanced degradation

induced by global warming. Because only a small group of fungi are able to efficiently

biodegrade lignin in terrestrial environments (Gleixner et al., 2001), increased fungal

125

activity was the most likely cause for the enhanced lignin oxidation in the treatment plot.

To test this hypothesis, we measured fungal and bacterial PLFAs and observed an

increase of fungal PLFA in the warmed soil (P < 0.01), while the concentration of

bacterial PLFAs remained similar in both plots (Figure 5.1b). The increased abundance of

fungi may have promoted lignolytic (lignin-degrading) enzyme activity (Carney et al.,

2007; Drissner et al., 2007) which led to an enhanced oxidation of lignin.

Post warming Warmed soil

Post warmingControl soil

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0

(Ad/Al)v

(Ad/

Al) s

Jun-02Apr-03Jun-03May-05

Pre-warming

Increasing degree of lig

nin oxidation

Ratio of vanillic acid/vanillin

Rat

io o

f syr

ingi

cac

id/

syrin

gald

ehyd

e





0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0

(Ad/Al)v

(Ad/

Al) s

Jun-02Apr-03Jun-03May-05

Pre-warming

Increasing degree of lig

nin oxidation

Ratio of vanillic acid/vanillin

Rat

io o

f syr

ingi

cac

id/

syrin

gald

ehyd

e

Figure 5.2: Differences in lignin degradation parameters from both the control and treatment plots before and after soil warming. White and black symbols represent samples from the control and treatment plots, respectively.

The warming-induced changes in SOM composition were further investigated using

multidimensional 1H-13C solution-state nuclear magnetic resonance (NMR) of soil humic

substances (the base soluble component of SOM). The 13C NMR projections from the 2-D

datasets for the control and warmed soil humic extracts show similar carbon distributions

(Figure 5.3). The relative percentage of chemical structures in the SOM and patterns in

126

the 2-D datasets (not shown) demonstrated that alkyl carbon, mainly originating from

plant cuticles in the soil (Kelleher and Simpson, 2006), increased in the warmed SOM.

Alternatively, the aromatic methoxy carbon, primarily from lignin (Kelleher and Simpson,

2006), decreased, likely due to enhanced lignin oxidation via demethylation/

demethoxylation (Hedges et al., 1988) and/or a decreased concentration of lignin in the

warmed soil. Estimates based on spectral subtraction and deconvolution (data not shown)

indicate that the contribution of cuticular alkyl carbon to soil humic substances increased

by ~25% and lignin methoxy carbon decreased by 12% in the warmed plot relative to the

control. Aromatic and phenolic carbon regions, which include the main structures found

in lignin, decreased only slightly in the warmed soil. Consistent with the previous results,

Chemical Shift (ppm)

Alkyl CO-Alkyl CAromatic & phenolic C

DMSO~

406080100120140 ppm

Higher lignin methoxy carbon content in the control soil

Higher cuticular alkyl carbon content in the warmed soil

Warmed soil

Alkyl CO-Alkyl CAromatic & phenolic C

Control soil

DMSO~

Chemical Shift (ppm)406080100120140 ppm406080100120140 ppm

Higher lignin methoxy carbon content in the control soil

Higher cuticular alkyl carbon content in the warmed soil

Warmed soilControl soil

Figure 5.3: 13C NMR projections from 2-D 1H-13C spectra of humic extracts from the warmed and control soil. The projection shows only protonated carbons which permit the increase in alkyl carbon (mainly cuticle, red arrow) in the warmed soil humic material and decrease of methoxy carbon (lignin, black arrow) to be easily visualized.

127

the NMR data indicate that soil warming enhances lignin oxidation (via side-chain

oxidation and demethylation/demethoxylation) although substantial removal of lignin

aromatic structures from SOM is unlikely in the short term. These results collectively

suggest that in only a relatively short period of time, leaf cuticle-derived carbon is

accumulated in the soil and that lignin oxidation is accelerated with simulated soil

warming.

The distinct SOM compositional changes observed in this environmental setting

highlight the potential changes to SOM quality at the molecular-level and the underlying

mechanisms of SOM degradation in a warmer climate. However, our observations in the

moist sandy loam soil may not be applicable to soil biogeochemical processes in dry areas

or well-developed soils with high clay contents where SOM stabilization through mineral

interaction may dominate and obscure the SOM compositional changes. Nonetheless,

studying SOM at the molecular-level facilitated several novel observations. For example,

the accumulation of leaf-derived cuticular organic matter in the soil suggests that the

resistant cuticular material (Hu et al., 2000) is likely to be preserved in the soil in a

warmer climate. This finding is significant because plant leaf tissues are often ignored in

carbon sequestration studies. However, the recalcitrant alkyl structures typically comprise

~20% of leaf litter (Prescott et al., 2004) and are an important component of the stable

SOM pool (Lorenz et al., 2007). Annual litterfall is estimated to increase by 1.2×1015 g C

in the world’s major (boreal, temperate, and tropical) forests with a 3°C warming (Liu et

al., 2004; refer to Supplementary Information for calculation). Assuming that ~20% of the

litterfall carbon is preserved as alkyl structures in the soil (an optimal scenario), the

sequestration of litterfall carbon in forests will be equivalent to ~2% of annual global

fossil-fuel CO2 emissions. This estimate represents an upper limit of cuticular carbon

sequestration. It may not be suitable for global scale predictions, but can be important in

128

areas where substantial litterfall increases are predicted due to global warming (such as

shrub-encroaching grasslands; Filley et al., 2008). This result demonstrates the potential

for enhanced preservation of cuticular carbon with soil warming and emphasizes the

important role of leaf litterfall in the regulation of SOM composition and soil carbon

sequestration in a changing climate. Lignin-derived SOM, which is hypothesized to

remain stable with global warming (Knorr et al., 2005b), has the potential for enhanced

transformation with soil warming, most likely due to increases in the decomposer (fungi)

community. Increases in fungal biomass with warming has been reported in grassland and

tundra soils (Rillig et al., 2002; Clemmensen et al., 2006), but its significance has not

been explored with relevance to SOM and lignin degradation. If the warming-enhanced

fungal decomposition of lignin is prevalent in other environmental settings, oxidation of

the aromatic SOM pool may accelerate in a warmer world. Our experiment is amongst the

first attempts to explain SOM responses to climatic warming at a molecular-level.

Long-term field studies combined with modern molecular approaches will be critical for

future assessments of SOM responses to climatic change.

5.4 Methods

5.4.1 Soil Warming Experiment

The soil warming experiment was carried out near a small spring-brook in southern

Ontario, Canada (43°45’N, 79°15’W). The experimental site had a good drainage and

evenly-distributed vegetation consisting predominantly of maple (Acer sp.) and cedar

(Thuja occidentalis L.) trees and mixed grasses/shrubs including horsetail (Equisetum

arvense L.), skunk cabbage (Symplocarpus foetidus), quack grass (Agropyron repens),

and watercress (Nasturtium officinal). The experimental site had a homogeneous

topography, and was close to a spring source and hence saturated for part of the year. The

129

soil had a sandy loam texture and slightly high pH values (pH = 7.5 in distilled water) due

to abundant base cations in the groundwater and a high groundwater level (Wilson and

Williams, 2006). The experimental site was separated into a control and a treatment plot,

each 4 m × 4 m. The temperature manipulation was controlled through four variable

transformers each attached to a de-icing cable that was placed into a series of 110 cm long,

4 cm outer diameter, steel pipes that ran along four transects and lasted from March 5th,

2004 to May 5th, 2005 (Wilson and Williams, 2006). The temperature differences

between the control and the treatment plots for the summer, spring, and fall ranged from

3.5 to 4.5°C and in winter from 5 to 6°C. Surface soils (0-20 cm) were collected using a

shovel after clearing the surface litter and plants from a random area of about 20 cm × 20

cm in the study site. Soil samples were taken in areas where large tree roots did not exist

to avoid areas within the plot which may have preferential SOM inputs. The sample size

(~5 kg soil) was large enough to be representative of soil properties in the study site.

Random samples (n=3) were taken in mid-May of 2005 from within the control and

treatment plots and homogenized to make a composite sample to assess ‘average SOM

responses’ across the plot. Pre-warming random soil samples were taken in early June,

2002, late April and early June, 2003, and analyzed separately. This pre-warming analysis

showed that spatial variability between random samples was lower than analytical error

(see Figure 5.1a). All samples were freeze-dried, passed through 2-mm sieve to remove

any stones, twigs, and small plant fragments, and ground thoroughly into a fine powder

before chemical analyses.

5.4.2 Chemical Analyses

SOC and nitrogen contents were determined with a Shimadzu TOC 5000 total

organic carbon analyzer equipped with a solid sample module. Soil samples were also

130

analyzed by the University of Guelph Laboratory Services (Guelph, Ontario, Canada).

Carbohydrates, cutin and suberin compounds, and lignin monomers were extracted by

solvent extraction, base hydrolysis, and copper oxidation, respectively, and analyzed by

gas chromatography/mass spectrometry (GC/MS; Otto et al., 2005). Fungi- and

bacteria-derived compounds (PLFAs) were extracted by a modified Bligh–Dyer method

and analyzed by GC/MS (Frostegård and Bååth, 1996). Details about PLFA nomenclature

are given in the Supplementary Information. Concentrations of individual compounds

were normalized against the OC content of the sample.

5.4.3 NMR Experiments

Soil samples were treated repeatedly with hydrofluoric acid (0.3 M), rinsed with

deionized water, and freeze-dried. Soil humic materials were exhaustively extracted from

the hydrofluoric acid-treated soils by NaOH solution (0.1 M) under nitrogen. The extracts

were filtered through a 0.22-μm Millipore Durapore membrane pressure filter, ion

exchanged with Amberjet 1200(H) ion exchange resin (Sigma-Aldrich), and freeze-dried.

Humic samples (~100 mg) were dissolved in DMSO-d6 (0.75 mL) and transferred to a

5-mm NMR tube for NMR analysis. Solution-state 13C NMR data were acquired on a

Bruker Avance 500 MHz spectrometer using a 5 mm 1H-BB-13C TBI probe fitted with an

actively shielded Z gradient. Heteronuclear Multiple Quantum Coherence (HMQC)

spectra were collected in phase sensitive mode using Echo/Antiecho gradient selection.

512 scans were collected for each of the 256 increments in the F1 dimension. 2 K data

points were collected in F2, a 1J 1H-13C (145 Hz) and a relaxation delay of 2 s was

employed. The F2 planes were multiplied by an exponential function corresponding to a 5

Hz line broadening, while the F1 dimension was processed using sine-squared functions

with a π/2 phase shift and a zero-filling factor of 2. The 13C spectra in Figure 5.3

131

represent the total vertical projection using all columns of the HMQC data. The HMQC

experiment detects all signals from protonated carbons but quaternary carbons are not

observed. The absence of the quaternary carbon simplifies the 13C NMR spectrum

permitting relative changes in lignin methoxyl and cuticular carbon to be more easily

determined.

5.4.4 Statistical Analyses

Student’s t test was used to compare the concentration of SOM components between

the control and the treatment plots after warming, and difference was considered to be

significant at the level of P<0.05.

5.5 Supplementary Information

5.5.1 Supplementary Calculation for Cuticular Carbon Sequestration

Our estimate of potential sequestration of cuticular carbon is based on reports on

forest litterfall production in response to temperature increases (Liu et al., 2004; Raich et

al., 2006). According to Liu et al. (2004), the annual total litterfall (Ltotal, g m−2) in forests

(with both broadleaf and conifer forests considered) is correlated with the regional mean

annual temperature (T, °C) in the following manner:

Ln(Ltotal) = 3.120 + 0.962×Ln(T) (5.1)

Therefore, with a temperature increase of 3°C, forest litterfall will increase by 57-63 g

m−2 depending on the initial mean annual temperature ranging between 1°C and 30°C.

Given the current area of the world’s boreal, temperate, and tropical forests of 1372, 1038,

and 1755 Mha (Dixon et al., 1994), respectively, the increases in the corresponding forest

litterfall are about 0.8×1015 g, 0.6×1015 g, and 1.0×1015 g annually. Assuming that 50% of

litterfall consists of carbon, a 3°C warming will increase the annual litterfall by 0.4×1015

132

g C, 0.3×1015 g C, and 0.5×1015 g C in boreal, temperate, and tropical forests, respectively.

This estimate is slightly higher than that predicted by Raich et al. (2006), where the

annual litterfall in moist tropical evergreen forests will increase by 0.3×1015 g C with a

temperature increase of 3°C. Assuming that ~20% of the litterfall carbon is preserved as

recalcitrant alkyl structures (Prescott et al., 2004) in the soil (this estimate represents the

upper limit of an optimal scenario), a 3°C warming will induce a sequestration of litterfall

carbon of ~0.24×1015 g C in the world’s major forests, equivalent to ~2% of annual global

fossil-fuel CO2 emission.

5.5.2 Supplementary Information for Methods – PLFA Nomenclature

PLFAs were designated based on the number of carbon atoms and number of double

bonds followed by the position of the double bond from the methyl end of the molecule.

The symbol ω indicated that the first carbon-carbon double bond starts on the nth carbon

from CH3 end. The prefixes i- and a- referred to iso-branched and anteiso-branched,

respectively, and the suffix c indicated cis geometry. The designation cy indicated

cyclopropane fatty acids.


We thank three anonymous reviewers for their insightful comments on an earlier

version of this manuscript. Funding from the Canadian Foundation for Climate and

Atmospheric Sciences (GR-520) supported this research. M.J.S also thanks Natural

Sciences and Engineering Research Council of Canada (NSERC) for support via a

University Faculty Award (UFA). X.F. acknowledges funding from the Ontario Graduate

Scholarship (OGS) program. D.D.W. thanks NSERC for support via a Discovery Grant.

133

CHAPTER 6

RESPONSES OF SOIL ORGANIC MATTER AND MICROORGANISMS

TO FREEZE-THAW CYCLES*

* Reprinted from Soil Biology & Biochemistry, 39: 2027-2037. Authors: Feng, X., Nielsen, L.L.,

Simpson, M.J., Copyright (2007), with permissions from Elsevier.

134

6.1 Abstract

Soil organic matter (SOM) biomarker methods were utilized in this study to

investigate the responses of fungi and bacteria to freeze-thaw cycles (FTCs) and to

examine freeze-thaw-induced changes in SOM composition and substrate availability.

Unamended, grass-amended, and lignin-amended soil samples were subject to 10

laboratory FTCs. Three SOM fractions (free lipids, bound lipids, and lignin-derived

phenols) with distinct composition, stability and source were examined with chemolysis

and biomarker Gas Chromatography/Mass Spectrometry methods and the soil microbial

community composition was monitored by phospholipid fatty acid (PLFA) analysis. Soil

microbial respiration was also measured before and during freezing and thawing, which

was not closely related to microbial biomass in the soil but more strongly controlled by

substrate availability and quality. Enhanced microbial mineralization (CO2 flush),

considered to be derived from the freeze-thaw-induced release of easily decomposable

organic matter from microbial cell lyses, was detected but quickly diminished with

successive FTCs. The biomarker distribution demonstrated that free lipids underwent a

considerable size of decrease after repeated FTCs, while bound lipids and lignin

compounds remained stable. This observation indicates that labile SOM may be most

influenced by increased FTCs and that free lipids may contribute indirectly to the

freeze-thaw-induced CO2 flush from the soil. PLFA analysis revealed that fungal biomass

was greatly reduced while bacteria were unaffected through the lab-simulated FTCs.

Microbial community shifts may be caused by freezing stress and competition for

freeze-thaw-induced substrate release. This novel finding may have an impact on carbon

135

and nutrient turnover with predicted increases in FTCs in certain areas, because fungi and

bacteria have different degradation patterns of SOM and the fungi-dominated soil

community is considered to have a higher carbon storage capacity than a

bacteria-dominated community.

6.2 Introduction

Extreme soil temperature conditions such as freeze–thaw fluctuations and hard frost

are a subject of major ecological interest because they can have a large impact on

microbial activity, soil carbon and nitrogen transformation, and plant productivity

(Sulkava and Huhta, 2003; Grogan et al., 2004; Yanai et al., 2004; Sharma et al., 2006).

Temperature increases in late autumn and winter at boreal latitudes and Chinook winds in

North America that result in snow melting may lead to low soil temperatures and increase

freezing-thawing events in these areas (Callaghan et al., 1998; Walker et al., 2006). The

changes introduced to soil by freeze/frost-thaw cycles (FTCs) include the disruption of

soil aggregates, disturbance of microbial community due to stress and/or cell lysis,

increased availability of substrate, and enhanced microbial activity upon thawing

(Edwards and Cresser, 1992; Lipson et al., 2000; Grogan et al., 2004; Sharma et al., 2006).

The enhanced microbial activity upon thawing, detected as a CO2 flush, is considered to

be caused by the freeze-thaw-induced release of easily decomposable organic matter

(Schimel and Clein, 1996). Using 14C-labeled glucose, Herrmann and Witter (2002)

calculated that microbial carbon contributed 65% to the CO2 flush upon thawing. Yet,

little is known about the nature of the remaining 35% carbon source. Also, there is little

136

consensus as to the degree of influence on soil microbial biomass exerted by FTCs. For

instance, a decrease in microbial biomass or cell numbers after thawing has been detected

(Lipson et al., 1999; Pesaro et al., 2003), while other reports have indicated that no effect

on microbial biomass could be observed after FTCs (Grogan et al., 2004; Sharma et al.,

2006). Moreover, freeze-thaw studies have focused on microbial respiration measurement

and these measurements do not differentiate between fungal and bacterial activity. Even

less is known about the vulnerability of varying microbial communities to FTCs. Bacteria

and fungi have very distinct morphology, growth strategy, and ecological niche in the

environment (Boer et al., 2005), hence, they are most likely to respond differently to

FTCs. Consequently, shifts in microbial community structure may follow FTCs. Finally,

by changing microbial activity and possibly microbial community structure in the soil,

FTCs are likely to influence the degradation pattern of soil organic matter (SOM) and

thus modify soil carbon quality and quantity. Yet few studies have investigated the effect

of FTCs on SOM composition and degradation pathways.

This study combines SOM biomarker methods with conventional soil respiration

measurements to examine changes in SOM and microbial community composition with

lab-simulated FTCs. Specifically, three SOM fractions (free lipids, bound lipids, and

lignin-derived phenols) with distinct composition, stability and source are examined with

chemolysis and biomarker Gas Chromatography/Mass Spectrometry (GC/MS) methods

(Otto et al., 2005), while soil microbial community composition is monitored by

phospholipid fatty acid (PLFA) analysis. The objectives of this study are to inspect the

responses of fungi and bacteria to lab-simulated FTCs, to test the use of biomarker

137

method in the examination of freeze-thaw-induced changes in SOM composition and

substrate availability, and to investigate controls on microbial respiration and the

freeze-thaw-induced CO2 flush. We hypothesize that FTCs may have different influences

on fungal and bacterial biomass, and produce varying changes on measurable SOM

fractions.


6.3.1 FTC Treatment of Soil Samples

Soil samples were taken from the organic-rich Ah horizon (0-28 cm) of a

well-drained, pristine grassland soil (classified as Eluviated Black Chernozem) at the

University of Alberta Ellerslie Research Station, located south of Edmonton, Alberta,

Canada. The soil samples were partially air-dried, passed through a 2-mm sieve, and

homogenized after sampling. We acknowledge that the sieving process may disrupt soil

aggregates and microbial responses; however, it was necessary to reduce biomarker

variability. Furthermore, the main objective of this study is to test the use of biomarker

methods, and thus, we made attempts to reduce the analytical error. However, all samples

were handled equally, and thus, handling errors are assumed to be equivalent for each

sample. The native vegetation (Western Wheatgrass, Agropyron smithii) was also sampled,

freeze-dried and ground (< 100 µm) into fine powder before use.

To prepare samples of varying SOM qualities, soils were separated into three

treatment groups: soil only (sample S), soil amended with 2% (dry weight) grass powder

(sample G), and soil amended with 1% (dry weight) alkali lignin (Sigma-Aldrich; sample

138

L). Based on the carbon and nitrogen contents of the grass (carbon: 41%; nitrogen: 2.9%)

and the lignin (carbon: 45%), the grass amendment equaled to an addition of 8 mg

carbon/g soil and 0.6 mg nitrogen/g soil, while the lignin amendment equaled to an

addition of 4.5 mg carbon/g soil. The specific ratio of amendment was determined

through preliminary tests so that by the end of the equilibration incubation sample G and

sample L had a similar carbon contents. The homogenized soil samples (~250 g) were

kept in 250-ml glass jars (four jars for each group, for a total of 1 kg soil for each

treatment), wetted with distilled water to a water content of 30% by weight (similar to the

maximum water holding capacity of the soil), and equilibrated at 17°C for one week

before each freeze-thaw treatment. Two jars of each soil group were then exposed to 10

repeated FTCs with each FTC consisting of freezing at -15°C for 1 d, and thawing at

17°C for 6 d (labeled as Sf, Gf, and Lf samples). The other two jars of each soil group

were kept at 17°C as control samples (labeled as Sc, Gc, and Lc samples). Soils were

sprayed with distilled water at the end of each FTC to maintain constant water content.

Subsamples (~35 g, dry weight) were taken from both the freeze-thaw-treated and the

control samples before FTC (labeled as 0 FTC), at the end of the 1st, 4th, 7th, and 10th FTC,

and from sample Gf on the 1st, 3rd, 7th, and 13th day after its 8th FTC, freeze-dried, and

ground (< 100 µm) thoroughly for chemical analyses. A freezing temperature of -15°C

was selected based on the work of Grogan et al. (2004) and used as a starting point for

this study. The freezing temperature used in this experiment may not necessarily mimic

all soil environments, but is used in this study to determine the efficacy of using GC/MS

biomarker methods for studying changes to SOM with FTCs.

139

6.3.2 Measurement of Microbial Respiration, Carbon and Nitrogen Content

Because of the absence of grass roots, soil respiration was assumed to equal

microbial respiration, which was measured at 17°C in duplicate for all soil samples 4 days

before the 1st FTC and on the 2nd, 4th, and 7th day of the 1st, 4th, and 7th FTC using a

titration method (Zhang et al., 2005). In general, NaOH solutions (1.0 M × 2.0 ml) were

kept in a 10-ml glass vial inside the soil sample jar sealed with plastic cap for 10 h to trap

the respired CO2 (Zhang et al., 2005). Excess NaOH was determined by precipitation with

BaCl2 and titration with 0.2 M HCl with phenolphthalein as an indicator (Zhang et al.,

2005). Microbial respiration was normalized to the dry weight of the soil sample.

Organic carbon (OC, i.e. total carbon subtracted by inorganic carbon) and total

nitrogen contents of soil samples were determined in triplicate using a Shimadzu TOC

5000 total organic carbon analyzer equipped with a solid sample module capable of

analyzing solid samples such as soils and plant materials (Shimadzu Scientific

Instruments, Columbia, MD, USA).

6.3.3 PLFA Analysis

To assess the microbial community composition, PLFAs were extracted in replicate

from freeze-dried soil samples (~6 g) by a modified Bligh–Dyer method (Bligh and Dyer,

1959; Frostegård and Bååth, 1996). PLFAs were analyzed before the freeze-thaw

treatment and after the 1st, 4th, 7th, and 10th FTCs. To investigate the recovery of soil fungi

and bacteria from the freeze-thaw treatment, PLFAs were analyzed for subsamples from

140

sample Gf on the 1st, 3rd, 7th, and 13th day after the 8th FTC. In short, the total lipid extract

was fractionated into neutral lipids, glycolipids, and polar lipids with 10 ml chloroform,

20 ml acetone, and 10 ml methanol through a silicic acid column, respectively. The polar

lipid fraction containing the phospholipids was evaporated to dryness under nitrogen, and

converted into fatty acid methyl esters (FAMEs) by a mild alkaline methanolysis reaction

(Guckert et al., 1985). The FAMEs were recovered with a hexane and chloroform mixture

(4:1, v/v). The solvents were evaporated to dryness under nitrogen, and the extracts were

re-dissolved in 200 μl hexane. FAMEs were analyzed with GC/MS as described below

with oleic acid (C18:1 alkanoic acid) methyl ester as an external standard. Fatty acids were

designated based on the number of carbon atoms and number of double bonds followed

by the position of the double bond from the methyl end of the molecule. The symbol ω

indicated that the first carbon-carbon double bond starts on the nth carbon from CH3 end.

The prefixes i- and a- referred to iso-branched and anteiso-branched, respectively. The

designation cy indicated cyclopropane fatty acids.

6.3.4 Sequential Extractions of SOM

Sequential extractions (solvent extraction, base hydrolysis, and CuO oxidation) were

conducted in duplicate to produce free (solvent extractable) lipids, bound lipids, and

lignin-derived phenols, respectively (Otto et al., 2005). The term ‘lipid’ is used in this

paper to describe a heterogeneous group of organic substances, operationally defined as

being insoluble in water but extractable with organic solvents, which may include

carbohydrates, fatty acids, and steroids. Samples were analyzed prior to the freeze-thaw

141

treatment and after the 1st, 4th, 7th, and 10th FTCs. Briefly, freeze-dried soil samples (5-10

g) were extracted with 30 ml of dichloromethane, dichloromethane:methanol (1:1; v/v)

and methanol, respectively. The combined solvent extracts were filtered through glass

fiber filters (Whatman GF/A and GF/F), concentrated by rotary evaporation, and then

dried under nitrogen gas in 2-ml glass vials. The air-dried soil residues from solvent

extraction were then subject to base hydrolysis to extract ester-linked lipids (Otto and

Simpson, 2006a). The residues (1-2 g) were heated at 100°C for 3 h in teflon-lined bombs

with 20 ml of 1 M methanolic KOH. The extracts were acidified to pH 1 with 6 M HCl,

and the solvents were removed by rotary evaporation. Lipids were recovered from the

water phase by liquid–liquid extraction with diethyl ether, concentrated by rotary

evaporation, and dried under nitrogen gas in 2-ml glass vials. The base hydrolysis

residues were air-dried and further oxidized with CuO to release lignin-derived phenols.

Soil residues (1-2 g) were extracted with 1 g CuO, 100 mg ammonium iron (II) sulfate

hexahydrate [Fe(NH4)2(SO4)2·6H2O] and 15 ml of 2 M NaOH in teflon-lined bombs at

170°C for 2.5 h. The extracts were acidified to pH 1 with 6 M HCl, and kept for 1 h at

room temperature in the dark to prevent reactions of cinnamic acids. After centrifugation

(at 2500 rev min-1 for 30 min), the supernatants were liquid-liquid extracted with diethyl

ether. The ether extracts were concentrated by rotary evaporation, transferred to 2-ml

glass vials and dried under nitrogen gas.

6.3.5 Derivatization and GC/MS Analysis

The extracts of were re-dissolved in dichloromethane:methanol (1:1; v/v), and

142

aliquots (containing ~1 mg extracts) were derivatized for GC/MS analysis. Solvent

extracts and CuO oxidation products were converted to trimethylsilyl (TMS) derivatives

by reaction with 90 μl N,O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA) and 10 μl

pyridine for 3 h at 70°C. After cooling, 100 μl hexane was added to dilute the extracts.

The base hydrolysis products were first methylated by reacting with 600 μl of

diazomethane in ether at 37°C for 1 h, evaporated to dryness under nitrogen, and then

silylated with BSTFA and pyridine as described above. Oleic acid and ergosterol were

derivatized in the same method and used as external standards for solvent extracts

(ergosterol-TMS for steroids and terpenoids). Oleic acid methyl ester was used as external

standard for base hydrolysis products, while vanillic acid-TMS was used for CuO

oxidation products.

GC/MS analysis was performed on an Agilent model 6890N GC coupled to a

Hewlett-Packard model 5973 quadrupole mass selective detector. Separation was

achieved on a HP5-MS fused silica capillary column (30 m × 0.25 mm i.d., 0.25 μm film

thickness). The GC operating conditions were as follows: temperature held at 65°C for 2

min, increased from 65 to 300°C at a rate of 6°C min-1 with final isothermal hold at

300°C for 20 min. Helium was used as the carrier gas. Samples were injected with a 2:1

split ratio (splitless mode was used for PLFA injection) and the injector temperature was

set at 280°C. The samples (1 μl) were injected with an Agilent 7683 autosampler. The

mass spectrometer was operated in the electron impact mode (EI) at 70 eV ionization

energy and scanned from 50 to 650 daltons. Data were acquired and processed with the

Chemstation G1701DA software. Individual compounds were identified by comparison of

143

mass spectra with literature, National Institute of Standards and Technology (NIST) and

Wiley Mass Spectral library data, authentic standards, and interpretation of mass

spectrometric fragmentation patterns. External quantification standards were used and the

response factor was assumed to be 1 for all compound classes. The concentration of

individual compound was calculated by comparison of the peak area of the compound and

that of the standard in the total ion current (TIC) and was then normalized to the organic

carbon content. Research in our laboratory (Otto and Simpson, 2007) has shown that the

biomarker methods are typically reproducible within 5%. Considering the nature of this

study (a lab-simulated freezing-thawing experiment with homogenized soil samples rather

than field measurement with great spatial variability), we carried all measurements in

duplicates to evaluate the use of biomarker methods in investigating the effects of FTCs

on soil components and to discern differences due to treatments versus the analytical error.

All data were reported as the mean value of duplicates and the error bars in figures (where

applicable) represent the original values of the duplicates.

6.4 Results


Microbial respiration decreased sharply with time in all soil samples (Figure 6.1)

such that the respired CO2 was barely detectable with the titration method on the 7th FTC

(data not shown). At the beginning of the experiment, microbial respiration doubled with

the addition of lignin and more than tripled with the addition of grass. While microbial

respiration showed a constant decrease in the control samples, there was a flush of

144

respired CO2 following thawing of the freeze-thaw-treated samples. Consequently,

microbial respiration of the freeze-thaw-treated samples was generally higher in the

middle of the FTCs, but lower than or similar to that of the control samples at the start

and by the end of the FTCs. The CO2 flush was of a similar size and duration in samples

Sf and Lf, and lasted longer in sample Gf. The size of the CO2 flush decreased with

successive FTCs with barely detectable residual flush during the 7th FTC (data not

shown).

4 days Day 2 Day 4 Day 7 Day 2 Day 4 Day 70.0

0.1

0.2

Before FTC After the 1st FTC

Time

Freeze-thaw treatment Control

0.0

0.2

0.4

μmol

CO

2/h/g

soil

(a)

(b)

(c)

0.0

0.1

After the 4th FTC

Figure 6.1: Microbial respiration before and after FTCs (measured at 17°C). Data points represent the mean values of duplicates, and error bars represent the original values of duplicates. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L).

145

6.4.2 Carbon and Nitrogen Content

Inorganic carbon was not detected in the soil, and therefore, OC content equaled the

measured total carbon content. Both OC and total nitrogen contents of the soil samples

did not change throughout the experiment (before the 1st FTC and after the 10th FTC) or

between the control and freeze-thaw-treated samples (data not shown). Therefore, soil OC

and total nitrogen content were assumed to remain the same for the individual soil group

(OC: 4.92% for the S samples, 5.82% for the G samples, and 5.92% for the L samples;

total nitrogen content: 0.62% for the S samples, 0.86% for the G samples, and 0.83% for

the L samples). The resulting atomic C/N ratio declined in samples amended with grass

(7.9) or lignin (8.3) relative to that in the original soil (9.3) after the 7-day equilibration

period prior to the initiation of FTCs, probably due to a priming effect associated with a

burst in microbial growth, where nitrogen was enriched by the amendment and OC was

reduced by microbial mineralization (Fontaine et al., 2004).

6.4.3 PLFAs

A series of PLFAs were detected in the S, G, and L samples at varying concentrations.

Among them, PLFA 18:2ω6 was used as an indicator of fungal biomass, whereas PLFAs

i15:0, a15:0, 15:0, i16:0, 16:1ω7, i17:0, a17:0, cy-17:0, i18:0, 18:1ω7 and cy-19:0

represented the bacterial-derived lipids (Zelles, 1999). Both fungal and bacterial markers

increased in the control samples at the beginning of the experiment (Figure 6.2) likely

because the controlled environment (30% soil moisture content and a soil temperature of

146

0

10

20

30

40

50

60

0 FT C 1s t FT C 4th FT C 7th FTC0

50

100

150

200

250

300

350

400

450

Bac

teria

l mar

kers

(μg/

g O

C)

B ac te ria -FT C B acteria -C on tro l(a )

Fung

al m

arke

r (μg

/g C

)

Fung i-FT C Fung i-C ontro l

0

50

100

150

200

250

300

0 FT C 1st FT C 4th FT C 7th FT C0

100

200

300

400

500

600

700

800

Bac

teria

l mar

kers

(μg/

g O

C)

(b )

Fun

gal m

arke

r (μg

/g C

)

0

10

20

30

40

50

60

0 F T C 1 s t F T C 4 th F T C 7 th F T C0

50

100

150

200

250

300

350

400

450

Bac

teria

l mar

kers

(μg/

g O

C)

T im e

(c )Fu

ngal

mar

ker (μg

/g C

)

0

10

20

30

40

50

60

0 FT C 1s t FT C 4th FT C 7th FTC0

50

100

150

200

250

300

350

400

450

Bac

teria

l mar

kers

(μg/

g O

C)

B ac te ria -FT C B acteria -C on tro l(a )

Fung

al m

arke

r (μg

/g C

)

Fung i-FT C Fung i-C ontro l

0

50

100

150

200

250

300

0 FT C 1st FT C 4th FT C 7th FT C0

100

200

300

400

500

600

700

800

Bac

teria

l mar

kers

(μg/

g O

C)

(b )

Fun

gal m

arke

r (μg

/g C

)

0

10

20

30

40

50

60

0 F T C 1 s t F T C 4 th F T C 7 th F T C0

50

100

150

200

250

300

350

400

450

Bac

teria

l mar

kers

(μg/

g O

C)

T im e

(c )Fu

ngal

mar

ker (μg

/g C

)

Figure 6.2: Microbial responses to FTC. Error bars represent the original values of duplicates. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L). Fungal marker = PLFA 18:2ω6. Bacterial markers = PLFAs α15:0, i15:0, 15:0, i16:0, 16:1ω7, a17:0, i17:0, cy-17:0, i18:0, 18:1ω7, cy-19:0.

147

17°C) favored bacterial and fungal growth. The addition of grass increased microbial

biomarkers relative to the original soil and the ratio of fungi/bacteria markers (F/B)

increased from 0.07 in sample Sc to 0.19 in sample Gc. By comparison, the lignin

amendment did not produce the same response on either the abundance of microbial

biomarkers or the F/B ratio.

Bacterial markers did not change between the FTC and control samples. However,

the fungal marker (PLFA 18:2ω6) was reduced by FTCs and declined with successive

0 FTC 1st FTC 4th FTC 7th FTC0.00

0.05

0.10

0.15

Time

(c)0.000.080.160.24

Rat

io o

f Fu

ngi/B

ater

ia

(b)0.00

0.05

0.10

0.15

Freeze-thaw treatment Control(a)

Figure 6.3: Ratios of fungal marker to bacterial markers (F/B). Error bars represent the original values of duplicates. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L). F/B ratio = the concentration of PLFA 18:2ω6 / concentrations of PLFAs α15:0, i15:0, 15:0, i16:0, 16:1ω7, a17:0, i17:0, cy-17:0, i18:0, 18:1ω7, and cy-19:0.

148

FTCs which resulted in a decreasing F/B ratio in the Sf, Gf, and Lf samples (Figure 6.3).

This trend was most prominent in soil samples amended with grass, where the

concentration of the fungal marker in sample Gf was less than half of that in sample Gc

(Figure 6.2b). The recovery of microbial biomass was monitored in sample Gf after the

8th FTC (Figure 6.4). Both fungi and bacteria were almost recovered from the freezing

after 24 h (i.e., the biomass returned to up to 93% of that before the 8th FTC), and slowly

increased thereafter.

Day 1 Day 3 Day 7 Day 13250

300

350

400

450

500

550

20

30

40

50

60

Bac

teria

l mar

kers

(μg/

g O

C)

Time

Bacterial markers

Before 8th FTC

Fungal marker

Fung

al m

arke

r (μg

/g O

C)

After 8th FTC

Figure 6.4: Microbial recovery from the 8th FTC in samples amended with grass (Gf). Error bars represent the original values of duplicates.

149

150

6.4.4 Free Lipids

Steroids, terpenoids, carbohydrates, phytol, n-alkanoic acids, n-alkanols, and

n-alkanes were identified in the free lipids of the S, G, and L samples. Small amounts of

monomers (such as vanillin, vanillic acid, acetovanillone, and vanillylglyoxalic acid)

derived from lignin were also detected in the L samples (data not shown). The

composition of the free lipids revealed a major input from plants into SOM (evidenced by

the predominance of even-numbered n-alkanoic acids, and even-numbered n-alkanols;

Otto et al., 2005; and references therein). Trehalose, a carbohydrate found in high

abundance in fungi (Smith and Read, 1997; Koide et al., 2000), was also identified in

high concentrations in the samples.

The addition of grass increased the concentrations of free lipids by 5-12 times

relative to those of the original soil, while lignin addition produced a smaller increase

(1.2-4 times; Figure 6.5). Free lipids increased in all samples at the beginning of the

experiment, and then declined. The disruption of soil aggregates, and/or increased

microbial input (Figure 6.2) could contribute to this trend. There was no obvious

difference in the concentrations of free lipids between the FTC and control samples

before and after the 4th FTC. However, the concentrations were lower in the

freeze-thaw-treated samples than those in the control samples after the 7th FTC (the trend

was further confirmed in the S and G samples after the 10th FTC), and was especially

notable in sample Gf.

6.4.5 Bound Lipids

Figure 6.5: Changes in free lipid components with FTC. Error bars represent the original values of duplicates. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L).

01020304050

μg/g

OC

Freeze-thaw treatment Control

050

100150200

0

1224364860

04080

120160

μg/g

OC

0

300

600

900

050

100150200

060

120180240

μg/g

OC

0150300450600

0100200300400

020406080

μg/g

OC

0150

300

450

600

060

120180240

0 FTC1st FTC

4th FTC

7th FTC

10th FTC0

50

100

150

200

μg/g

OC

(a) (b) (c)

0 FTC1st FTC

4th FTC

7th FTC

10th FTC0

200400600800

Treh

alos

e

St

eroi

ds &

n

-Alk

anoi

c ac

ids

n-A

lkan

ols

n-A

lkan

es

Ter

peno

ids

0 FTC1st FTC

4th FTC

7th FTC0

150300450600750

1

2

3

4

5

151

152

Compounds identified in the bound soil lipids included benzyls

(4-hydroxybenzaldehyde, 4-hydroxybenzoic acid), phenols (vanillin, vanillic acid,

m-coumaric acid, p-coumaric acid, syringic acid and ferulic acid), sterol (sitosterol),

ω-hydroxyalkanoic acids, α-hydroxyalkanoic acids, n-alkanoic acids, n-alkane-α,ω-dioic

acids, n-alkanols, and mid-chain substituted acids (x,ω- dihydroxy C15-16 acids, 8-hydroxy

C16 diacids, 9,10,ω-trihydroxy C18 acids, and 9,10-epoxy-ω-hydroxy C18 acid). The

aliphatic lipids showed a predominance of even-numbered molecules originating from

plant biomass. The composition of the bound lipids indicated major inputs from suberin,

cutin, and plant waxes (Otto and Simpson, 2006a).

Based on their structural units and degradation patterns, suberin and cutin markers

were summarized and calculated (Otto and Simpson, 2006a; Figure 6.7). The

concentration of suberin markers (∑S = ω-hydroxyalkanoic acids C20-C32 +

n-alkane-α,ω-dioic acids C20-C32 + 9,10-epoxy-ω-hydroxy C18 acid; after Otto and

Simpson, 2006a) were slightly diluted in the G and L samples in comparison to that in the

S samples by the addition of OC-rich substrates, while cutin markers (∑C = mid-chain

hydroxy C14, C15, C17 acids + C16 mono- and dihydroxy acids and diacids; after Otto and

Simpson, 2006a) were greatly increased with the amendment of grass in the G samples.

Similar to free lipids, suberin and cutin markers slightly increased in all samples at the

beginning of the experiment, and then declined. However, the freeze-thaw treatment did

not make any difference in the concentrations of suberin or cutin markers in the samples

subject to FTCs in comparison to those in the control samples.

Ratios of ω-C16/∑C16 and ω-C18/∑C18 (∑C16 or 18 = ω-hydroxy alkanoic acid C16 or 18 +

Sf0 Sc0 Sf1 Sc1 Sf4 Sc4 Sf7 Sf70

1

2

3

4

5

6

7

8

9

mg/

g O

C

(a)

Gf0 Gc0 Gf1 Gc1 Gf4 Gc4 Gf7 Gc70

1

2

3

4

5

6

7

8

9 (b)

Lf0 Lc0 Lf1 Lc1 Lf4 Lc4 Lf7 Lc70

1

2

3

4

5

6

7

8

9

Suberin ΣS Cutin ΣC Suberin or cutin ΣSvC

(c)

Sf0 Sc0 Sf1 Sc1 Sf4 Sc4 Sf7 Sf70

1

2

3

4

5

6

7

8

9

mg/

g O

C

(a)

Gf0 Gc0 Gf1 Gc1 Gf4 Gc4 Gf7 Gc70

1

2

3

4

5

6

7

8

9 (b)

Lf0 Lc0 Lf1 Lc1 Lf4 Lc4 Lf7 Lc70

1

2

3

4

5

6

7

8

9

Suberin ΣS Cutin ΣC Suberin or cutin ΣSvC

(c)

Figure 6.6: Changes in suberin and cutin markers. (a) Soil samples (S). (b) Soil samples amended with dry grass (G). (c) Soil samples amended with lignin (L). Freeze-thaw treatment indicated by f, control samples by c, followed by the number of FTCs. Suberin markers ∑S = ω-hydroxyalkanoic acids C20-C32 + n-alkane-α,ω-dioic acids C20-C32 + 9,10-epoxy-ω-hydroxy C18 acid. Cutin markers ∑C = mid-chain hydroxy C14, C15, C17 acids + C16 mono- and dihydroxy acids and diacids. Suberin or cutin markers ∑S∨C = ω-hydroxyalkanoic acids C16, C18 + C18 di- and trihydroxy acids + 9,10-epoxy-ω- hydroxy C18 acid + n-alkane-α,ω-dioic acids C16, C18 (Otto and Simpson, 2006a).

153

n-alkane-α,ω-dioic acids C16 or 18 + ∑C16 or 18 mid-chain-substituted acids) are used to

assess the degradation of cutin, which increases with progressing cutin degradation (Goñi

and Hedges, 1990). In this study, ω-C16/∑C16 ratio stabilized at 0.32 for the S and L

samples and at 0.25 for sample G throughout the experiment, while ω-C18/∑C18 ratio

stabilized at 0.64 for the S and L samples and at 0.31 for sample G. Neither the

freeze-thaw treatment nor substrate amendment enhanced the degradation of cutin.

6.4.6 Lignin-Derived Phenols

Benzyls (benzoic acid, 4-hydroxybenzaldehyde, 3-hydroxybenzoic acid,

4-hydroxybenzoic acid, 3,5-dihydroxybenzoic acid, and 1,2,4-benzenetricarboxylic acid),

pyrrol-2-carboxylic acid, and lignin-derived phenols (vanillyls: vanillin, acetovanillone,

vanillic acid, and vanillylglyoxalic acid; syringyls: syringaldehyde, acetosyringone,

syringic acid, and syringylglyoxalic acid; and cinnamyls: p-coumaric acid, and ferulic

acid) were extracted from the soil samples. The concentrations of lignin-derived phenols

were represented by VSC (V: vanillin, acetovanillone, vanillic acid; S: syringaldehyde,

acetosyringone, syringic acid; and C: p-coumaric acid, and ferulic acid). The addition of

grass increased C (cinnamyl) and S (syringyl) units in the soil while the lignin

amendment increased V (vanillyl) units (Figure 6.7a). C and S units are both reported to

degrade faster than V units in the environment (Hedges et al., 1988; Opsahl and Benner,

1995; Otto et al., 2005). Therefore, lignin degradation was expected to be more

discernible in sample G. Indeed, the quantity of VSC in sample G slightly increased at the

beginning of the experiment, and then declined with time (Figure 6.8). By comparison,

154

Sf7 Sc7 Gf7 Gc7 Lf7 Lc70.0

0.5

1.0

1.5

2.0

2.5

VS

C (m

g/g

OC

)

S V C

(a)


0.5

1.0

1.5

2.0 (b)

S/V


0.3

0.6

0.9

1.2 (c)

C/V


0.6

1.2

1.8

2.4

3.0 (d)

(Ad/

Al) v


0.6

1.2

1.8

2.4

3.0 (e)

(Ad/

Al) s

Figure 6.7: Lignin degradation parameters. Error bars represent the original values of duplicates. (a) Concentrations of VSC. VSC = sum of lignin monomers V, S, and C; V = vanillyl phenols: vanillin, acetovanillone, vanillic acid; S = syringyl phenols: syringaldehyde, acetosyringone, syringic acid; C = cinnamyl phenols: p-coumaric acid, ferulic acid. (b) S/V ratio. (c) C/V ratio. (d) (Ad/Al)v = vanillic acid/vanillin. (e) (Ad/Al)s = syringic acid/syringaldehyde.

the quantity of VSC was stable in the S and L samples (data not shown). Lignin

degradation in sample G was further confirmed by the degradation parameters:

acid-to-aldehyde ratios (Ad/Al) of V and S units increased slightly with time in sample

Gc (Figure 6.8), indicating the progressing degradation of lignin (Hedges et al., 1988;

Kögel-Knabner et al., 1991; Opsahl and Benner, 1995). Such a trend was not as obvious

155

in the S and L samples (data not shown), reflecting the recalcitrant nature of V units. By

the end of the 7th FTC, neither VSC nor the degradation parameters differed between the

freeze-thaw-treated and control samples in the S and L samples (Figure 6.7). In the G

samples, however, VSC concentration and ratios of S/V and C/V were lower in the

control sample whereas Ad/Al ratios of S and V units were higher in the control sample

(Figure 6.7). These data suggest inhibited degradation of lignin with the freeze thaw

treatment in samples amended with grass, although more research is needed to confirm

this finding.

0 FTC 1st FTC 4th FTC 7th FTC0.0

0.7

1.4

2.1

2.8

3.5

0.0

0.8

1.6

2.4

3.2

4.0

VSC

VS

C (m

g/g

OC

)

(Ad/

Al)

(Ad/Al)v

(Ad/Al)s

Figure 6.8: Changes in lignin degradation parameters of sample Gc with time. Error bars represent the original values of duplicates. VSC = sum of lignin monomers V, S, and C; V = vanillyl phenols: vanillin, acetovanillone, vanillic acid; S = syringyl phenols: syringaldehyde, acetosyringone, syringic acid; C = cinnamyl phenols: p-coumaric acid, ferulic acid. (Ad/Al)v = vanillic acid/vanillin. (Ad/Al)s = syringic acid/syringaldehyde.

156

6.5 Discussion

6.5.1 Controls on Microbial Respiration

While PLFAs are biomarkers of the viable microbial cells, microbial respiration

indicates carbon mineralization by soil microorganisms or the actual activity of viable

microbes. Microbial respiration is not only controlled by the number of viable

microorganisms but also determined by other environmental factors such as soil moisture,

temperature, substrate availability and dissolution rate (Davidson et al., 2006; Trumbore,

2006). In our study, the lignin amendment did not increase the abundance of microbial

PLFAs in the soil (Figure 6.2) but doubled the rate of microbial respiration in comparison

to that of the control soil (Figure 6.1). Meanwhile, trehalose, found in high abundance in

fungi, increased by three times in the soil amended with lignin (Figure 6.5). As the

principal lignin decomposer in the soil (Gleixner et al., 2001), fungi likely utilized the

carbon in the amended lignin to synthesize the storage carbohydrate (trehalose) but did

not reproduce due to a limitation on nitrogen availability. Therefore, the increase in

microbial respiration was not proportional to the size of the microbial biomass.

Furthermore, the PLFA analysis indicates that the microbial population almost fully

recovered from freezing after incubation at 17°C for 24 h (Figure 6.4). However, carbon

mineralization was still low one day after thawing (Figure 6.1), and the constant level of

PLFAs (Figure 6.2) was not correlated with the observed microbial respiration, which

decreased consistently with time throughout the experiment (Figure 6.1). These data

collectively suggest that microbial respiration is not closely related to microbial biomass,

but may be more strongly controlled by other factors such as substrate availability and

157

quality. For example, at the start of the experiment, the magnitude of microbial respiration

was related to the amount of easily decomposable substrate (free lipids) in the soil:

sample S with the lowest concentration of free lipids had the lowest respiration rate while

sample G with the highest concentration of free lipids (Figure 6.5) had the highest

respiration rate (Figure 6.1). The other two soil fractions did not seem to contribute to the

carbon mineralization rates because sample G and L had similar concentrations of bound

lipids (Figure 6.6) and lignin compounds (Figure 6.7) but different respiration rates

(Figure 6.1).

6.5.2 Source of the CO2 Flush

Numerous studies have reported the freeze-thaw-induced burst of microbial activity

such as carbon or nitrogen mineralization (Schimel and Clein, 1996; Herrmann and Witter,

2002; Sulkava and Huhta, 2003), which is generally attributed to increased levels of labile

substrate in the soil resulting from damage of microorganisms through freezing and

thawing (Schimel and Clein, 1996; Lipson et al., 2000; Herrmann and Witter, 2002). In

our study, microbial biomass was similar in the S and L samples and almost doubled in

the G samples (Figure 6.2). Consequently, the CO2 flush was of the same size and

duration in the S and L samples but lasted longer in sample G (Figure 6.1). Free lipids

(usually considered to be labile) did not seem to directly contribute to the CO2 flush

because the size of CO2 flush was not proportional to the amount of free lipids in the soil:

sample L had more than twice the amount of free lipids than sample S but the CO2 flush

was of the same size in both samples. However, free lipids underwent significant decrease

158

in all samples after repeated FTCs, indicating that the freeze-thaw-induced microbial

activity utilized easily decomposable substrates in the soil, likely transforming free lipids

to microbial carbon. Free lipids may therefore contribute indirectly to the unidentified

source of the CO2 flush (Herrmann and Witter, 2002). This trend was most likely

operative in the first several FTCs as well, but was concealed by the increase of free lipids

released from aggregates disrupted by FTCs (Edwards, 1991), and thus no decline in free

lipids was observed in the freeze-thaw-treated samples at the beginning of the experiment.

Similar to observations from other studies (Schimel and Clein, 1996; Herrmann and

Witter, 2002), freeze-thaw-induced CO2 flush was short-lived and quickly diminished

with successive FTCs even before the decline of free lipids. Considering that bacterial

biomass remained quite stable throughout the experiment and that the decrease in fungal

biomass occurred quite slowly, we hypothesize that there was a shift in microbial species

associated with FTCs which was not delineated by the PLFA analysis. The species shift

may have led to the microbial adaptation to FTCs and thus fewer cell lyses and substrate

release during successive FTCs. The microbial shift likely also changed the substrate use

pattern, which further contributed to the consumption of free lipids.

6.5.3 Responses of Microbial Biomass to Substrate Availability and FTCs

Fungi and bacteria are reported to have different responses to nutrient amendment

(Boer et al., 2005). Fungal growth in the soil dominates in the early stage of plant residue

decomposition (Beare et al., 1990), and in the decomposition of easily degradable

substrates at high substrate loading rates (>1 mg C d-1) probably due to their higher

159

tolerance of osmotic stress in comparison to bacteria (Griffiths et al., 1999). This is

consistent with our findings, where the addition of grass raised the F/B ratio from 0.07 in

sample Sc to 0.19 in sample Gc after a 7-day equilibration period (Figure 6.3). This trend

was not observed with the lignin amendment likely due to the recalcitrance and

degradation characteristics of lignin.

Alternatively, contrasting observations of freeze-thaw conditions on microbial

biomass have been reported: microbial biomass was detected to decrease at high freezing

rates (>1.4°C h-1) and remain unaffected at relatively lower freezing rates (Lipson et al.,

2000) or during moderate freeze-thaw treatment (Grogan et al., 2004). In our study,

different responses to severe freeze-thaw treatments were found in fungi and bacteria:

fungal biomass was greatly reduced by FTCs while bacteria were unaffected (Figure 6.2).

To our knowledge, this observation has not been reported before. We hypothesize that

bacteria compete with fungi for the soluble substrates released from cell lyses and

therefore dominate in the soil after FTCs. Genetic fingerprinting studies have revealed

that the bacterial community structure was more disrupted than the fungal community

after FTCs (Sharma et al., 2006) and suggests that bacteria may be more adaptable to

FTCs through a community shift, and thus predominate in the soil thereafter. The

fungi-dominated soil community is considered to have a higher carbon storage capacity

than the bacteria-dominated community (Six et al., 2006) and thus, freeze-thaw-induced

changes, as revealed by the F/B ratio, may play a part in carbon and nutrient turnover.

Finally, the measured PLFA markers may not represent the entire microbial

community and that the observed changes were based on a community level analysis. The

160

response of individual microbial species to FTCs may therefore vary with different

environments and vary with microbial diversity. Other important microorganisms such as

archaea may play an important role in soil ecosystems and should be considered in soils

that contain this group of organisms.

6.5.4 Stability of SOM Fractions

SOM fractions (free lipids, bound lipids, and lignin-derived phenols) all increased

slightly in sample S at the beginning of the experiment, with the most obvious increase in

the free lipid fraction (Figure 6.5). This increase is considered to be caused by the

disruption of soil aggregates by FTCs (Edwards, 1991) which exposes

physically-protected SOM to microbial attack and chemical extractions. Among the three

SOM fractions examined, free lipids decreased with repeated FTCs, while bound lipids

and lignin compounds did not vary. Thus, consistent with the conventional hypotheses,

the labile SOM pool (free lipids) may be the first to be affected by increased FTCs.

Conversely, in samples amended with grass, lignin appeared more degraded in the control

sample than in the freeze-thaw-treated sample although the difference was not confirmed

statistically. The lack of apparent lignin degradation in the amended samples is likely

associated with the inhibited growth of fungi (the dominant lignin decomposer in the soil)

in these freeze-thaw-treated samples. Our study suggests that FTCs may induce direct or

indirect changes in the molecular composition of SOM; however, further investigation is

warranted.

161

6.6 Conclusions

PLFA analysis revealed that fungal biomass was greatly reduced while bacteria were

unaffected through lab-simulated FTCs. Microbial community shifts may be caused by

freezing stress and competition for freeze-thaw-induced substrate release. This novel

finding may have a large impact on carbon sequestration with predicted increases in FTCs

in certain areas, because fungi and bacteria have different degradation patterns of SOM

and the fungi-dominated soil community is considered to have a higher carbon storage

capacity than the bacteria-dominated community. Meanwhile, the SOM biomarker results

demonstrates that free lipids underwent decrease after repeated FTCs, while bound lipids

and lignin compounds remained quite stable in the soil. This observation indicates that

labile SOM may be most influenced by increased FTCs and that free lipids may

contribute indirectly to the freeze-thaw-induced CO2 flush from the soil. Future research

may apply isotope labeling to trace the source of the CO2 flush and to study the

competition between fungi and bacteria for the freeze-thaw-released substrates.


We thank the two anonymous reviewers whose comments greatly improved the

quality of the manuscript. We also thank Dick Puurveen at the University of Alberta for

providing the soil from the Ellerslie Research Station. Support for this research from the

Canadian Foundation for Climate and Atmospheric Sciences (GR-520) is gratefully

acknowledged. MJS thanks the National Science and Engineering Research Council

(NSERC) of Canada fro support via a University Faculty Award (UFA). NSERC is also

162

acknowledged for supporting L. Nielsen with an Undergraduate Summer Research Award

(USRA).

163

CHAPTER 7

ALTERED MICROBIAL COMMUNITY STRUCTURE AND ORGANIC

MATTER COMPOSITION UNDER ELEVATED CO2 AND N FERTILIZATION

IN THE DUKE FOREST*

* Submitted to Global Change Biology. Authors: Feng, X., Simpson, A.J., Schlesinger, W.H.,

Simpson, M.J.

164

7.1 Abstract

The dynamics and fate of terrestrial organic matter (OM) under elevated CO2 and

nitrogen (N) fertilization are important aspects of long-term carbon sequestration. Despite

numerous studies, questions still remain as to whether the chemical composition of OM

may alter with these global changes. In this study, we employed molecular-level methods

to investigate the composition and degradation of various OM components in the forest

floor and surface soil from the Duke Forest Free Air CO2 Enrichment (FACE) experiment.

We also measured microbial responses to elevated CO2 and N fertilization using

phospholipid fatty acid (PLFA) profiles. While the bulk carbon content of forest floor and

soil did not change significantly by the FACE treatment or N fertilization, increased fresh

carbon inputs into the forest floor under elevated CO2 were observed at the

molecular-level by two degradation parameters of plant-derived steroids and

cutin-derived compounds. N fertilization decreased the ratios of fungal to bacterial

PLFAs and Gram-negative to Gram-positive bacterial PLFAs in the soil, indicating that

microbial community composition was altered. Moreover, the acid to aldehyde ratios of

lignin-derived phenols increased with N fertilization, suggesting enhanced lignin

degradation in the surface soil. 1H nuclear magnetic resonance (NMR) spectra of soil

humic substances revealed an enrichment of leaf-derived alkyl structures with both

elevated CO2 and N fertilization. We hypothesize that both elevated CO2 and N

fertilization promoted microbial decomposition of SOM constituents such as lignin and

hydrolysable lipids, which led to the accumulation of plant-derived recalcitrant structures

(such as alkyl carbon) in the soil.

165

7.2 Introduction

Rising atmospheric CO2 and nitrogen (N) deposition are two major global changes

that affect terrestrial biogeochemical cycles (Jones et al., 1998; Neff et al., 2002).

Elevated CO2 is reported to increase the primary productivity (DeLucia et al., 1999) and

root biomass allocation in plants (Matamala and Schlesinger, 2000; Norby et al., 2004).

However, an increase in soil organic carbon (SOC) is not generally observed (van Kessel

et al., 2006; Lichter et al., 2008) despite elevated carbon inputs from leaf litter (Lichter et

al., 2008) and roots (Heath et al., 2005). This phenomenon is partly attributed to the

priming effect, where an addition of fresh substrates stimulates microbial decomposition

of the native soil organic matter (SOM; Drissner et al., 2007). On the other hand, N

fertilization is known to promote plant growth in N-limited ecosystems (Oren et al., 2001),

and to reduce the priming effect where soil microbes decompose SOM to immobilize N

(van Groenigen et al., 2006). Alternatively, N deposition may also induce an enhanced

decomposition of litter and light soil fractions when microbial activity is N-limited (Neff

et al., 2002; Knorr et al., 2005a). Therefore, the modification of SOC content under both

environmental changes is highly uncertain.

The majority of current studies focus on the carbon storage in plant litter, labile (such

as light, plant detrital, or microbial) and stable (such as mineral-associated or

non-hydrolysable) soil fractions (Jastrow et al., 2005; Hoosbeek et al., 2006; Lichter et al.,

2008). But questions still remain as to whether the chemical composition of organic

matter (OM) is influenced by elevated CO2 or N fertilization. Shifts in microbial

166

community structure and OM-degrading enzymes have been shown under rising CO2 or

N deposition (Jones et al., 1998; Carney et al., 2007; Keeler et al., 2009). For instance,

fungal species that decompose cellulose increased in the soil at elevated CO2 levels (Jones

et al., 1998). Carney et al. (2007) observed a higher activity of phenol oxidase that was

critical to lignin degradation whereas both positive and negative responses of

lignin-degradation enzymes were found with N addition (Norby, 1998; Frey et al., 2004;

Keeler et al., 2009). These findings suggest that various OM structures may have different

decay rates under elevated CO2 and/or N fertilization. In a recent soil warming study,

recalcitrant alkyl carbon derived from plant cuticles was shown to accumulate with

elevated plant inputs into the soil while lignin was preferentially degraded by an increased

fungal community (Chapter 5). Similarly, increased plant inputs and altered microbial

decomposition patterns resulting from elevated CO2 or N fertilization may alter OM

composition in the litter and soil.

Long-term carbon sequestration requires a build-up of recalcitrant or stable SOM,

and it is especially important to investigate the fate of recalcitrant carbon structures in the

soil. There are several lines of evidence suggesting an enhanced carbon sequestration in

the stable SOM under the Free Air CO2 Enrichment (FACE) treatment. For instance,

elevated CO2 promoted soil aggregation (Rillig et al., 1999; Six et al., 2001) and carbon

sequestration in stable soil aggregates (Jastrow et al., 2005) and humified SOM

(Hoosbeek et al., 2007). In the Duke Forest FACE experiment, a significant proportion of

new carbon in the mineral soil (~20%) has accrued in well-protected, stable pools (i.e.,

smallest particle-size and non-hydrolysable fractions) under elevated CO2 over the last 9

167

years (Lichter et al., 2008). But it remains to be revealed as to the chemical composition

and source of these stable SOM pools.

The Duke Forest FACE experiment was one of the earliest forest FACE experiments

in the world. In 2005, nitrate was applied to the experimental plots, allowing the study of

both elevated CO2 and N fertilization impacts on plant growth and SOC dynamics. Here

we report on a detailed investigation on the chemical composition and degradation of OM

in the forest floor and surface soil from the Duke Forest FACE experiment using two

complementary molecular-level methods. Major OM biomarkers (such as carbohydrates,

extractable and hydrolysable lipids, and lignin-derived phenols) were measured using gas

chromatography/mass spectrometry (GC/MS) to assess the inputs and decomposition of

source-specific compounds (Otto et al., 2005), whereas the soil humic substance

composition was examined by 1H nuclear magnetic resonance (NMR) spectroscopy

(Simpson et al., 2003; Kelleher and Simpson, 2006). Microbial biomass and community

composition were also measured using phospholipid fatty acid (PLFA) profiles and

ergosterol content. The objective of this study was to investigate the responses of

microbial community and OM composition to elevated CO2 and N fertilization at the

molecular-level. In particular, we focused on the OM components that exhibited high

recalcitrance in soils (such as alkyl structures in humic substances) and degradation

indicators of the compounds that may contribute to the stable SOM pool (such as lignin,

cutin originating from leaf cuticles, and suberin from barks and roots). We hypothesize

that higher substrate or N availability under elevated CO2 and/or N fertilization may alter

microbial community structure such that labile carbon structures are favored during

168

decomposition. This shift in microbial decomposition patterns, together with higher plant

inputs, may lead to an accumulation of plant-derived recalcitrant structures (such as alkyl

carbon) in SOM.


7.3.1 Site Description and Sample Collection

The Duke Forest FACE experiment is located in a pine forest near Chapel Hill, NC,

USA (35°58’N 79°05’W). Loblolly pine (Pinus taeda L.) is the dominant vegetation in

the forest, accompanied by deciduous trees such as sweet gum (Liquidambar styraciflua

L.), red maple (Acer rubrum L.), red bud (Cercis Canadensis L.), and dogwood (Cornus

florida L.). The soil in the study area has a clay loam texture, is slightly acidic (pH = 5.75)

and is limited in N and phosphorus (P; Lichter et al., 2005). The mean annual

temperature is 15.5°C and the mean annual precipitation is 1140 mm.

The FACE experiment consists of four ambient and four elevated CO2 plots with a

diameter of 30 m each. The elevated CO2 plots were fumigated with CO2 to maintain an

atmospheric CO2 level that was 200 µmol mol-1 above ambient (an average of 565 µmol

mol-1 total atmospheric CO2) while the CO2 level in the ambient plots was maintained at

approximately 365 µmol mol-1 (Hendrey et al., 1999). The experiment began in the

prototype plot and its corresponding control plot in June, 1994 and in the other six plots

in August, 1996. Each plot was further separated into four quadrants and ammonium

nitrate was applied starting from early 2005 in two of the quadrants at a rate of 11.2 g N

m-2 y-1 (Oren et al., 2001). Consequently, four experimental treatments were included in

169

the experiment: elevated CO2 level without N fertilization (EU) or with N fertilization

(EF), and ambient CO2 level without N fertilization (AU) or with N fertilization (AF).

Samples of the surface soil (0-15 cm) were collected randomly from each quadrant

(32 cores in total) in April 2007 using a corer (5 cm in diameter) after over 10 years of

FACE treatment (12 years in the prototype plot) and 2 years of N fertilization.

Undecomposed pine needles and the forest floor litter layer (O horizon) overlaying the

soil cores were collected separately. All samples were shipped back to the laboratory

within two days and kept frozen until freeze-dried. Composite samples were made for the

forest floor and soil samples from the same plot under the same treatment, respectively (n

= 4 for each treatment). The undecomposed needles under the same treatment were

combined to create a composite sample. The forest floor samples and undecomposed

pine needles were ground into a fine powder using a grinder, and the soil samples were

sieved through a 2-mm sieve and ground thoroughly using a mortar and pestle. Plant

roots (1-5 mm in diameter) were manually picked out from the soil using tweezers,

rinsed with deionized water, freeze-dried, ground, and combined into a composite sample

under the same treatment.

7.3.2 Chemical Extractions and GC/MS Analysis

The organic carbon (OC) content of pine needles, roots, forest floor and soil samples

was determined by combustion on an LECO analyzer (at the University of Guelph

Laboratory Services, Guelph, Ontario, Canada). Sequential chemical extractions (solvent

extraction, base hydrolysis, and CuO oxidation) were conducted to isolate

170

solvent-extractable compounds, hydrolysable lipids including ester-bound fatty acids

(FAs), cutin- and suberin-derived compounds, and lignin-derived phenols, respectively

(Chapter 3; Otto et al., 2005). Briefly, freeze-dried samples (~1 g plant or forest floor

materials or ~8 g soil) were extracted with 30 mL of dichloromethane,

dichloromethane:methanol (1:1; v/v) and methanol, respectively. The combined solvent

extracts were filtered through glass fiber filters (Whatman GF/A and GF/F), and

concentrated by rotary evaporation. The air-dried residues from solvent extraction (~100

mg plant or forest floor materials or ~2 g soil) were heated at 100°C for 3 h in

teflon-lined bombs with 20 mL of 1 M methanolic KOH to extract hydrolysable lipids.

The extracts were then acidified to pH 1 with 6 M HCl. Hydrolysable lipids were

recovered by liquid-liquid extraction with diethyl ether, concentrated by rotary

evaporation, and methylated with diazomethane. Base hydrolysis residues were air-dried

and further extracted with 1 g copper (II) oxide (CuO), 100 mg ammonium iron (II)

sulfate hexahydrate [Fe(NH4)2(SO4)2·6H2O] and 15 mL of 2 M NaOH in teflon-lined

bombs at 170°C for 2.5 h to isolate lignin-derived phenols. The extracts were acidified to

pH 1 with 6 M HCl, and kept for 1 h at room temperature in the dark to prevent reactions

of cinnamic acids. After centrifugation (2500 rev min-1 for 30 min), lignin-derived

phenols were liquid-liquid extracted from the clear supernatant with diethyl ether,

concentrated by rotary evaporation, and dried under nitrogen gas.

Fungal and bacterial PLFAs were also extracted from freeze-dried soil samples by a

modified Bligh-Dyer method (Chapter 4; Bligh and Dyer, 1959; Frostegård and Bååth,

1996). Briefly, the total lipid extract was fractionated into neutral lipids, glycolipids, and

171

polar lipids with 10 mL chloroform, 20 mL acetone, and 10 mL methanol through a

silicic acid column, respectively. The polar lipid fraction containing the phospholipids

was evaporated to dryness under N2, and converted into fatty acid methyl esters (FAMEs)

by a mild alkaline methanolysis reaction (Guckert et al., 1985). The FAMEs were

recovered with a hexane and chloroform mixture (4:1, v/v), dried under N2, and

re-dissolved in 200 μL hexane for GC/MS analysis.

Aliquots of extracts from the previous extractions (except PLFAs) were converted to

trimethylsilyl (TMS) derivatives by reaction with N,O-bis-(trimethylsilyl)

trifluoroacetamide (BSTFA) and pyridine. The derivatized compounds and PLFAs were

analyzed on an Agilent model 6890N GC coupled to a Hewlett-Packard model 5973

quadrupole mass selective detector. Separation was achieved on a HP5-MS fused silica

capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness). The GC operating

conditions were as follows: temperature held at 65 °C for 2 min, increased from 65 to

300 °C at a rate of 6 °C min-1 with final isothermal hold at 300 °C for 20 min. Helium

was used as the carrier gas. The sample was injected with an Agilent 7683 autosampler

and the injector temperature was set at 280 °C. The mass spectrometer was operated in

the electron impact mode (EI) at 70 eV ionization energy and scanned from 50 to 650

daltons. Data were acquired and processed with the Chemstation G1701DA software.

Individual compounds were identified by comparison of mass spectra with literature,

NIST and Wiley MS library data, authentic standards, and interpretation of mass

spectrometric fragmentation patterns (Chapters 3 and 4). Quantification was performed

using external standards (oleic acid and ergosterol-TMS for solvent-extractable

172

compounds, oleic acid methyl ester for hydrolysable lipids and PLFAs, and vanillic

acid-TMS for lignin-derived phenols) in the total ion current (TIC). The concentration of

individual compounds was normalized to the sample OC content.

7.3.3 Compound Groupings and Parameters

Compounds of interest were categorized according to their structural origin.

Solvent-extractable compounds were grouped into carbohydrates (including glucose,

mannose, sucrose and trehalose), ergosterol (a characteristic indicator of fungi;

Frostegård and Bååth, 1996; Otto et al., 2005), extractable short-chain fatty acids (SFAs;

C12-C18 n-alkanoic and n-alkenoic acids), and a series of extractable plant-derived lipids

including wax lipids (C29 and C31 n-alkanes, and C20-C32 even-numbered n-alkanoic

acids and n-alkanols), steroids (β-sitosterol, stigmasterol, sitosterone,

stigmasta-3,5-dien-7-one, and campesterol), and terpenoids (isopimaric acid, pimaric

acid, abietic acid, dehydroabietic acid, and methyl dehydroabietate). The degradation

stage of plant-derived steroids was assessed by calculating the ratio of precursor steroids

(β-sitosterol and stigmasterol) to corresponding degradation products (sitosterone and

stigmasta-3,5-dien-7-one; Otto and Simpson, 2005).

Base hydrolysis released cutin- and suberin-derived compounds and bound FAs,

including SFAs (C12-C18 n-alkanoic and n-alkenoic acids) and long-chain fatty acids

(LFAs; C20-C32 even-numbered n-alkanoic acids; Otto and Simpson, 2006a).

Cutin-derived compounds (∑C) included mid-chain hydroxyalkanoic C14, C15, C17 acids,

mono- and dihydroxyalkanoic C16 acids and α,ω-dioic acids, while suberin-derived

173

compounds (∑S) included ω-hydroxyalkanoic acids in the range of C20-C32,

n-alkane-α,ω-dioic acids in the range of C20-C32, and 9,10-epoxy-α,ω-dioic C18 acid. To

assess the degradation of cutin and suberin, the ratios of ω-C16/∑C16 and ω-C18/∑C18

were calculated, where ∑C16 or ∑C18 includes ω-hydroxyalkanoic acid,

n-alkane-α,ω-dioic acid, and mid-chain-substituted acids with 16 or 18 carbons,

respectively (Goñi and Hedges, 1990; Otto and Simpson, 2006a). The relative ratio of

suberin to cutin (suberin/cutin) was also assessed by (∑S+∑S∨C)/(∑C+∑S∨C), where

∑S∨C was compounds common in both cutin and suberin, including ω-hydroxyalkanoic

C16, C18 acids, di- and trihydroxyalkanoic C18 acids, 9,10-epoxy-ω-hydroxyalkanoic C18

acid, and n-alkane-α,ω-dioic C16, C18 acids (Otto and Simpson, 2006a).

Lignin-derived phenols included vanillyls (vanillin, acetovanillone, and vanillic

acid), syringyls (syringaldehyde, acetosyringone, and syringic acid), and cinnamyls

(p-coumaric acid, and ferulic acid). The ratios of vanillic acid/vanillin, i.e., (Ad/Al)v, and

syringic acid/syringaldehyde, i.e., (Ad/Al)s, were used to assess lignin degradation, which

has been observed to increase with increasing lignin degradation (Hedges et al., 1988;

Opsahl and Benner, 1995; Otto and Simpson, 2006b).

PLFAs were designated according to the standard PLFA nomenclature (Guckert et al.,

1985). PLFAs specific to fungi (18:2ω6,9c), Gram-negative bacteria (16:1ω7c, cy17:0,

18:1ω7c and cy19:0), and Gram-positive bacteria (i14:0, i15:0, a15:0, i16:0, i17:0, and

a17:0) were quantified (Harwood and Russell, 1984). The microbial community

composition was assessed by the ratios of fungal PLFA to bacterial PLFAs (the sum of

Gram-negative and Gram-positive bacterial PLFAs; F/B) and Gram-negative to

174

Gram-positive bacterial PLFAs (Gram-negative/Gram-positive).

7.3.4 NMR Analysis

SOM composition was further investigated using solution-state 1H NMR of

NaOH-extractable OM (Simpson et al., 2003; Kelleher and Simpson, 2006).

Approximately 15 g of soil from each plot was combined into one composite sample,

representing the average soil properties under the same experimental treatment (AU, AF,

EU, and EF, respectively). The samples were treated repeatedly with HF (0.3 M), rinsed

with deionized water, and freeze-dried (Goncalves et al., 2003). Approximately 100 mg

of the freeze-dried HF-treated soil samples were preliminarily analyzed by 13C cross

polarization magic angle spinning (CP/MAS) NMR on a Bruker BioSpin Avance 200

MHz NMR spectrometer using ramp-CP. The solid-state 13C NMR spectra did not reveal

major differences in the chemical composition of bulk SOM between different treatments

(data not shown). 13C NMR may not detect small changes in SOM because the chemical

heterogeneity of SOM combined with relatively low resolution of solid-state NMR may

mask small differences. To further investigate SOM composition, solution-state 1H NMR

of soil humic substances was employed. Humic substances were exhaustively extracted

from the HF-treated soils with NaOH solution (0.1 M) under nitrogen gas. The extracts

were filtered through a 0.22-μm Millipore Durapore PVDF membrane, ion exchanged

with Amberjet 1200(H) ion exchange resin (Sigma-Aldrich), and freeze-dried. Humic

samples (~100 mg) were dissolved in DMSO-d6 (0.75 mL) and transferred to a 5-mm

NMR tube for analysis on a Bruker Avance 500 MHz spectrometer fitted with a 5 mm

175

176

1H-BB-13C TBI probe. 1-D solution-state 1H NMR experiments were performed with 512

scans, a recycle delay of 2 s, 16,384 time domain points, and a sample temperature of

298 ºK. Spectra were apodized by multiplication with an exponential decay

corresponding to 1 Hz line broadening in the transformed spectrum, and a zero filling

factor of 2. Chemical shift assignments were made using a range of 2-D experiments as

perviously discussed (Simpson et al., 2003; Kelleher and Simpson 2006). The 1-D

spectra were labeled with general regions that were dominated by the following

categories of chemical components: aliphatic (0.6-2.8 ppm), carbohydrates and amino

acids (2.8-5.6 ppm), amide and aromatics (6.2-9 ppm; Simpson et al., 2007a).

7.3.5 Statistical Analysis

Two-way ANOVA was used to assess the effect of elevated CO2 and N fertilization

with a covariate to account for site differences by General Linear Model in SPSS (v 10.0).

Differences were considered significant when the P-value of the F-test was <0.05.

Significant interaction effect was not observed on the chemical composition of forest

floor or SOM, and hence the corresponding P values were not reported, which also had a

limited degree of freedom.

7.4 Results

7.4.1 Chemical Composition of the Forest Floor OM

The OC content, chemical composition and degradation parameters of pine needles,

fine roots, and OM in the forest floor are listed in Table 7.1. Carbohydrates were slightly

Table 7.1: Chemical composition and organic matter degradation parameters of the Duke forest floor under elevated CO2 and N fertilization†

Ambient CO2 Elevated CO2 P values from

two-way ANOVA Pine needles Roots

Unfertilized Fertilized Unfertilized Fertilized CO2 effect

N effect

OC (%) 52.0 47.7 23.9 ± 3.9 17.3 ± 2.7 25.6 ± 2.4 28.8 ± 2.8 0.06 0.59 Abundance of Compounds (mg g-1OC)

Solvent-extractable compounds Ergosterol 0.00 0.00 0.085 ± 0.015 0.041 ± 0.011 0.082 ± 0.023 0.072 ± 0.014 0.42 0.13

Extractable SFAs 2.59 0.00 1.01 ± 0.16 0.51 ± 0.07 1.66 ± 0.57 0.98 ± 0.25 0.10 0.09 Extractable plant-derived lipids 15.83 0.50 7.71 ± 1.47 5.53 ± 0.67 6.07 ± 1.28 5.92 ± 1.26 0.63 0.38

Carbohydrates 18.05 0.42 8.95 ± 0.70 12.20 ± 2.70 11.96 ± 2.77 9.68 ± 1.09 0.90 0.81 Hydrolysable lipids

Bound SFAs 2.36 0.49 1.47 ± 0.09 1.84 ± 0.33 2.07 ± 0.48 1.74 ± 0.16 0.45 0.94 Bound LFAs 1.01 1.87 1.52 ± 0.06 2.25 ± 0.56 1.81 ± 0.35 2.45 ± 0.26 0.53 0.09

Cutin-derived compounds (∑C) 23.19 0.00 8.16 ± 0.31 12.26 ± 1.84 10.20 ± 1.49 10.44 ± 1.14 0.94 0.14 Suberin-derived compounds (∑S) 0.68 5.34 2.10 ± 0.07 3.65 ± 0.69 2.85 ± 0.44 3.80 ± 0.61 0.41 0.04*

Lignin-derived phenols Vanillyls 4.42 3.69 10.19 ± 2.10 13.99 ± 1.77 10.94 ± 1.69 9.82 ± 1.03 0.35 0.46

Cinnamyls 0.43 0.16 1.05 ± 0.21 1.23 ± 0.12 1.06 ± 0.16 1.00 ± 0.12 0.53 0.73 Syringyls 0.00 2.20 0.39 ± 0.08 1.08 ± 0.36 0.57 ± 0.05 0.72 ± 0.30 0.73 0.13

Parameters steroid ratio 5.12 na 3.44 ± 0.07 3.75 ± 0.16 4.06 ± 0.19 4.42 ± 0.21 0.003* 0.08 ω-C16/∑C16 0.43 0.54 0.29 ± 0.01 0.28 ± 0.01 0.30 ± 0.01 0.31 ± 0.01 0.02* 0.88 ω-C18/∑C18 0.05 0.42 0.21 ± 0.01 0.22 ± 0.02 0.23 ± 0.01 0.23 ± 0.03 0.52 0.86

suberin/cutin 0.56 1.42 0.62 ± 0.01 0.64 ± 0.02 0.66 ± 0.02 0.72 ± 0.06 0.14 0.27 (Ad/Al)v 0.33 0.27 0.81 ± 0.07 0.79 ± 0.07 0.79 ± 0.05 0.89 ± 0.08 0.56 0.56 (Ad/Al)s na 0.16 0.54 ± 0.08 0.53 ± 0.08 0.57 ± 0.03 0.51 ± 0.04 0.88 0.47

177

178

† Values for pine needles and roots were determined from one composite sample collected from the AU plots. All values for the forest floor samples were reported as mean ± standard error (n=4).

* denotes statistical significance (P<0.05). Compounds within each category are defined in the Materials and Methods. na: not applicable.

higher (8.95-12.20 mg g-1OC) than plant-derived lipids (5.53-7.71 mg g-1OC) in the

solvent-extractable compounds and ergosterol ranged from 0.041-0.085 mg g-1OC in the

forest floor. Cutin-derived compounds were the dominant component of hydrolysable

lipids in the forest floor (8.16-12.26 mg g-1OC) while suberin-derived compounds, bound

SFAs, and LFAs were in a much lower concentration (1.47-3.80 mg g-1OC). Vanillyls

were the most abundant lignin phenols in the forest floor (9.82-13.99 mg g-1OC), and

syringyls originating from angiosperm species were detected in minor abundance

(0.39-1.08 mg g-1OC). Pine needles contained twice the amount of carbohydrates,

plant-derived lipids, and cutin-derived compounds as in the forest floor but only half the

amount of lignin vanillyls and cinnamyls. In comparison, roots were dominated by

suberin-derived compounds with low concentrations of solvent-extractable compounds.

The presence of syringyls suggested contributions from angiosperms in the roots

collected from the surface soil. The steroid and ω-C16/∑C16 ratios were higher in pine

needles than in the forest floor while the ratios of ω-C18/∑C18 and (Ad/Al)v were much

lower. The ratios of (Ad/Al)v and (Ad/Al)s were much lower in roots than in the forest

floor but the ratios of ω-C16/∑C16 and ω-C18/∑C18 were higher. Degradation products of

plant steroids were not detected in roots and hence the steroid ratio was not reported.

Elevated CO2 marginally increased the OC content of the forest floor (P = 0.06) and

extractable SFAs (P = 0.10) without changing the abundance of the other analyzed

components. Alternatively, a significant increase was observed in the steroid ratio (P =

0.003) and the ratio of ω-C16/∑C16 (P = 0.02) at the elevated CO2 level while the ratios of

ω-C18/∑C18 and Ad/Al remained similar to those at the ambient CO2 level. By

179

180

comparison, N fertilization did not change the OC content of the forest floor but

increased the abundance of plant-derived lipids in the hydrolysable lipids (i.e., bound

LFAs, cutin- and suberin-derived compounds) although the increase was only significant

for suberin-derived compounds (P = 0.04). N fertilization also decreased the abundance

of ergosterol and extractable SFAs, and increased the steroid ratio in the forest floor, but

these trends were not statistically significant.

7.4.2 Microbial and SOM Composition in the Surface Soil

The SOC content, microbial PLFAs and SOM composition in the surface soil are

listed in Table 7.2. The PLFA ratio of fungi: Gram-negative bacteria: Gram-positive

bacteria averaged to 1:3:7. The concentration of ergosterol decreased in the surface soil

as compared to that in the forest floor. Bound SFAs, LFAs, and suberin-derived

compounds increased in SOM as compared to those in the forest floor, whereas

lignin-derived phenols, cutin-derived compounds, carbohydrates, extractable SFAs and

plant-derived lipids decreased. A slightly higher steroid ratio was observed in the surface

soil than in the forest floor, indicating a higher level of steroid oxidation in the forest

floor than in the mineral horizon. The other OM degradation parameters, i.e., ratios of

ω-C16/∑C16, ω-C18/∑C18, and Ad/Al of lignin-derived phenols, increased from forest

floor to soil, indicating progressive degradation of OM with depth.

Elevated CO2 or N fertilization did not induce any significant change in the SOC

content in the Duke Forest. A marginal increase was observed in the concentration of

ergosterol at the elevated CO2 level (P = 0.08), but microbial PLFA profiles or ratios did

Table 7.2: Microbial and SOM composition in the Duke Forest surface soil under elevated CO2 and N fertilization†

Ambient CO2 Elevated CO2 P values from

two-way ANOVA Unfertilized Fertilized Unfertilized Fertilized CO2

effect N

effect OC (%) 1.18 ± 0.05 1.11 ± 0.13 1.32 ± 0.17 1.08 ± 0.16 0.60 0.14

Abundance of Compounds (mg g-1OC) Microbial PLFAs

Fungal PLFA 0.027 ± 0.004 0.020 ± 0.007 0.022 ± 0.004 0.022 ± 0.006 0.75 0.58 Gram-positive bacterial PLFAs 0.072 ± 0.007 0.082 ± 0.013 0.067 ± 0.008 0.090 ± 0.006 0.88 0.11 Gram-negative bacterial PLFAs 0.14 ± 0.01 0.16 ± 0.02 0.15 ± 0.01 0.17 ± 0.01 0.66 0.26

Solvent-extractable compounds Ergosterol 0.045 ± 0.009 0.016 ± 0.006 0.054 ± 0.007 0.035 ± 0.007 0.08 0.01*

Extractable SFAs 0.41 ± 0.04 0.32 ± 0.06 0.55 ± 0.07 0.29 ± 0.02 0.19 0.001* Extractable plant-derived lipids 1.54 ± 0.06 1.61 ± 0.19 2.23 ± 0.55 1.41 ± 0.08 0.44 0.25

Carbohydrates 3.17 ± 0.13 4.82 ± 0.52 4.52 ± 0.60 4.10 ± 0.29 0.50 0.20 Hydrolysable lipids

Bound SFAs 4.98 ± 0.32 4.13 ± 0.70 5.20 ± 0.24 4.65 ± 0.49 0.23 0.04* Bound LFAs 2.27 ± 0.15 2.72 ± 0.30 3.28 ± 0.65 2.63 ± 0.21 0.19 0.78

Cutin-derived compounds (∑C) 2.96 ± 0.24 3.84 ± 0.57 5.15 ± 1.57 2.81 ± 0.48 0.54 0.44 Suberin-derived compounds (∑S) 6.76 ± 0.81 8.32 ± 1.11 13.60 ± 5.42 8.43 ± 1.63 0.27 0.55

Lignin-derived phenols Vanillyls 2.16 ± 0.44 2.68 ± 0.42 3.00 ± 0.71 2.20 ± 0.36 0.72 0.78

Cinnamyls 0.38 ± 0.05 0.42 ± 0.05 0.54 ± 0.04 0.34 ± 0.03 0.33 0.08 Syringyls 0.25 ± 0.04 0.39 ± 0.12 0.36 ± 0.06 0.30 ± 0.05 0.94 0.58

Parameters F/B 0.13 ± 0.02 0.08 ± 0.02 0.10 ± 0.02 0.08 ± 0.02 0.55 0.09

181

182

Gram-negative/Gram-positive 2.01 ± 0.08 1.97 ± 0.06 2.30 ± 0.17 1.86 ± 0.06 0.41 0.03* steroid ratio 5.86 ± 1.35 6.84 ± 0.84 4.51 ± 0.50 5.85 ± 1.53 0.59 0.30 ω-C16/∑C16 0.40 ± 0.02 0.40 ± 0.03 0.37 ± 0.02 0.41 ± 0.03 0.83 0.47 ω-C18/∑C18 0.28 ± 0.09 0.33 ± 0.11 0.35 ± 0.11 0.38 ± 0.10 0.57 0.70

suberin/cutin 1.34 ± 0.06 1.33 ± 0.04 1.39 ± 0.06 1.57 ± 0.14 0.13 0.31 (Ad/Al)v 1.39 ± 0.11 1.60 ± 0.15 1.44 ± 0.09 1.56 ± 0.17 0.97 0.07 (Ad/Al)s 0.78 ± 0.06 0.84 ± 0.08 0.72 ± 0.05 0.92 ± 0.08 0.84 0.05

† All values were reported as mean ± standard error (n=4). * denotes statistical significance (P<0.05). Compounds within each category are defined in the Materials and Methods. na: not applicable.

not change. Significant changes in the other SOM components or degradation parameters

were not observed with elevated CO2. However, plant-derived lipids (extractable lipids,

bound LFAs, cutin- and suberin-derived compounds) increased with elevated CO2 in the

unfertilized soil although the same trend was not observed in the N-fertilized soil. A

non-significant increase in the suberin/cutin ratio was observed. By comparison, N

fertilization significantly decreased the concentration of ergosterol in the soil (P = 0.01).

Although changes in microbial PLFA concentrations were not observed, the ratios of

Gram-negative/Gram-positive and F/B decreased with N addition significantly (P = 0.03)

and marginally (P = 0.09), respectively. SOM components did not change in abundance

except extractable and bound SFAs, which significantly decreased (P = 0.001 and 0.04,

respectively). The Ad/Al ratios of both vanillyls (P = 0.07) and syringyls (P = 0.05)

increased marginally in the N-fertilized soils, indicating an enhanced oxidation of lignin.

The 1H NMR spectra of soil humic substances are shown in Figure 7.1. Amino acids,

carbohydrates, and aliphatics were the dominating structures in the soil humic substances.

Similar distributions of carbohydrates and aromatic structures were observed in all

treatments. However, the intensity of terminal CH3 peaks (characteristic of microbial

proteins; Simpson et al., 2007) slightly increased in soils under elevated CO2 and was

greatest in the EF plot. Furthermore, alkyl carbon (the CH2 peaks) in the aliphatic region

(characteristic of lipids and cuticular components; Simpson et al., 2003) increased with

both elevated CO2 and N fertilization relative to the terminal CH3 peaks.

183

23456789 ppm23456789 ppm

1H Chemical Shift (ppm)1H Chemical Shift (ppm)

(a) Ambient CO2, Unfertilized with N

(b) Ambient CO2, Fertilized with N

(c) Elevated CO2, Unfertilized with N

(d) Elevated CO2, Fertilized with N

*~

*~

*~

CH2

CH3

*~

CH2

CH3

CH2

CH3

CH2

CH3

AliphaticsAmino acids &Carbohydrates

Amide &Aromatics


Amide &Aromatics

23456789 ppm23456789 ppm23456789 ppm

1H Chemical Shift (ppm)1H Chemical Shift (ppm)

(a) Ambient CO2, Unfertilized with N

(b) Ambient CO2, Fertilized with N

(c) Elevated CO2, Unfertilized with N

(d) Elevated CO2, Fertilized with N

*~

*~

*~

CH2

CH3

*~

CH2

CH3

CH2

CH3

CH2

CH3


Amide &Aromatics


Amide &Aromatics


Amide &Aromatics


Amide &Aromatics

Figure 7.1: 1H NMR spectra of soil humic substances from the Duke Forest soil. * denotes DMSO-d6 (solvent).

7.5 Discussion

7.5.1 Microbial Responses to Elevated CO2 and N Fertilization

Microbial activity and community composition are essential in regulating SOM

decomposition rates and patterns, and microbial responses to elevated CO2 and N

fertilization varies greatly among different ecosystems. Elevated CO2 has been reported to

increase fungal abundance in soils (Rillig et al., 1999; Lipson et al., 2005; Carney et al.,

2007) partly as a result of a higher carbon assimilation efficiency. In the Duke Forest,

184

over 10 years of FACE treatment marginally increased the soil content of ergosterol

(Table 7.2), a common indicator of fungal biomass (Frostegård and Bååth, 1996; Otto et

al., 2005). This trend was consistent with an increase in ectomycorrhizal root colonization

(Garcia et al., 2008; Pritchard et al., 2008) under elevated CO2 reported at this site.

However, elevated CO2 did not alter microbial PLFA profiles or ratios in the surface soil

(Table 7.2). Although a positive linear correlation between ergosterol and fungal PLFA is

commonly reported (Frostegård and Bååth, 1996), both compounds vary in abundance

with different fungal species, cell age, and environmental conditions (Stahl and Klug,

1996; Pasanen et al., 1999). Therefore, the varied responses of ergosterol and fungal

PLFA to elevated CO2 may reflect a difference in their contents among different fungal

species and a change in the fungal community composition, which was previously

detected in the Duke Forest by DNA analysis (Parrent et al., 2006). Similarly, the fungal

PLFA content remained unchanged while ergosterol decreased in both the forest floor

(Table 7.1) and surface soil under N fertilization (Table 7.2). A marginal decrease in the

F/B ratio (Table 7.2) confirms the negative response of fungi to N fertilization, which is

considered to relate to a reduced carbon allocation to fine roots for fungal colonization

(Albaugh et al., 1998; van Diepen et al., 2007) or a preferential growth of fungal species

with a low carbon assimilation efficiency (Johnson, 1993).

In comparison to fungi, there are fewer reports on the variation of Gram-staining

bacterial groups with N fertilization. Billings and Ziegler (2008) observed an increased

activity of Gram-negative bacteria after one-year N addition to the Duke Forest soil and a

reduced activity of Gram-positive bacteria, while changes in microbial biomass were not

185

detected. Their observation indicated an increased role for Gram-negative bacteria in

transforming recently formed SOM with N addition. Billings and Ziegler further

suggested that progressive N limitation (see discussions in Finzi et al., 2006) in the Duke

Forest may increase the activity of actinomycetes and other Gram-positive bacteria

responsible for mineralizing relatively recalcitrant substrates in the long term. After two

years of N fertilization, we observed a significant decrease of Gram-negative bacterial

PLFAs relative to Gram-positive bacterial PLFAs at the same site (Table 7.2).

Gram-negative bacteria have been shown to favor easily available and degradable carbon

substrates from rhizodeposition (Treonis et al., 2004; Drissner et al., 2007). N fertilization

may have decreased the relative abundance of Gram-negative bacteria through reducing

belowground carbon allocation by plants (Albaugh et al., 1998; van Diepen et al., 2007).

Our data imply an increased role of Gram-positive bacteria in the microbial community as

hypothesized by Billings and Ziegler (2008) and suggest that the response of bacterial

community to N fertilization may be different in the long term when labile substrates that

fueled Gram-negative bacterial activity become exhausted.

Finally, a concomitant decrease in the extractable and bound SFAs was detected in

the N-fertilized soil (Table 7.2). Despite their presence in plant materials (Table 7.1),

SFAs in the soil were considered to be derived from a microbial origin as well (Otto and

Simpson, 2006a). Since the response of SFAs mimicked that of ergosterol in the soil, the

decline of SFAs was most likely to relate to a decrease of certain microbial groups under

N fertilization. Collectively, these data suggest an altered microbial community structure

resulting from elevated CO2 and N fertilization, which may cause a shift in the microbial

186

function.

7.5.2 Molecular Indicators of Increased OM Inputs at Elevated CO2 Levels

Despite an increase in fresh carbon inputs that led to a marginal increase in the forest

floor OC content, significant changes in the investigated components (carbohydrates,

extractable and hydrolysable lipids, and lignin-derived phenols) were not observed in the

forest floor under elevated CO2 (Table 7.1). This result is consistent with a previous

investigation using tetramethylammonium hydroxide (TMAH) thermochemolysis method,

where the FACE treatment did not change the chemical signatures of carbohydrate-,

lignin-, or FA-derived compounds in the forest floor at this site (Lichter et al., 2008).

However, an increased steroid ratio provides the molecular-level evidence for enhanced

fresh plant inputs into the forest floor under elevated CO2, because the ratio was higher in

the undecomposed pine needles than in the forest floor or surface soil (Table 7.1). The

cutin degradation parameter (the ω-C16/∑C16 ratio) also increased in the forest floor with

the FACE treatment while the ω-C18/∑C18 ratio remained unchanged (Table 7.1). Two

possible processes may be responsible for the increase in the ω-C16/∑C16 ratio: an

enhanced cutin degradation in the FACE-treated forest floor where cutin acids containing

double bonds and more than one hydroxyl groups were preferentially degraded as

compared to ω-hydroxyalkanoic acids (Chapter 2; Goñi and Hedges, 1990; Otto and

Simpson, 2006a); or increased inputs of pine needles into the forest floor, which yielded a

higher ratio of ω-C16/∑C16 (Table 7.1). Because the forest floor turnover rates did not

increase with elevated CO2 in the Duke Forest (Lichter et al., 2008) and a concurrent

187

increase in the ω-C18/∑C18 ratio with enhanced cutin degradation was not observed, the

increase in the ω-C16/∑C16 ratio was most likely to result from the increased fresh carbon

inputs at elevated CO2 levels. By comparison, the ω-C18/∑C18 ratio was too low in the

undecomposed pine needles to significantly change the ω-C18/∑C18 ratio of the forest

floor despite increased inputs of plant litter.

7.5.3 Enrichment of Refractory Alkyl Carbon in SOM at Elevated CO2 Levels

An enrichment of alkyl structures that mainly originated from plant cuticles (Kelleher

and Simpson, 2006) was detected in the humic substances under elevated CO2 (Figure

7.1), which confirmed our previous hypothesis. However, this trend was not detected for

cutin-derived compounds in the N-fertilized soil (Table 7.2). Despite their common

source, cutin-derived compounds consisted of monomers in the hydrolysable fraction of

plant cuticles whereas the alkyl carbon in humic substances included contributions from

the non-hydrolysable (‘cutan’; Rumpel et al., 2005; Winkler et al., 2005) and

mineral-protected cuticular materials that were released upon HF treatment. The

hydrolysable lipids (including cutin-derived compounds) have decadal turnover times in

the soil based on a compound-specific isotopic analysis (Feng et al., unpublished results),

indicating their labile nature in the SOM. By contrast, the non-hydrolysable alkyl

structures are considered to be more refractory through mineral interactions and/or

cross-linking during humification processes (Kögel-Knabner et al., 1992; Lorenz et al.,

2007). Fertilization-induced changes to microbial decomposition patterns of labile versus

refractory SOM may have contributed to the varied responses of hydrolysable lipids and

188

alkyl carbon to elevated CO2.

During SOM decomposition, microbes are believed to preferentially use labile

structures including hydrolysable lipids with decadal turnover times, while the recalcitrant

alkyl structures were selectively preserved and incorporated into the stable SOM pool.

Such processes may be intensified under elevated CO2, where both inputs of alkyl carbon

from plants and the microbial utilization of labile carbon were promoted (Drissner et al.,

2007). This is supported by the NMR data which show an increase in the total

contribution of microbial protein in the both samples under elevated CO2 (Figure 7.1).

This is best gauged from the broad CH3 signal which mainly arises from methyl rich

amino acid side chains in microbial proteins (Simpson et al., 2007). The increase in CH3

signal intensity is greatest in the CO2 and N fertilized plot suggesting that microbial

growth under these conditions is most prolific. We hypothesize that the microbes degrade

the labile materials leaving an organic signature enriched in microbial cells (hence higher

microbial proteins) and recalcitrant alkyl materials (hence higher CH2 resonance). Our

hypothesis is supported by a significant incorporation of fresh carbon into the

non-hydrolysable (stable) SOM following 9 years of FACE experiment in the Duke Forest

(Lichter et al., 2008), which presumably consisted of a high contribution from alkyl

structures. As for the labile (extractable and hydrolysable) plant lipids or free light soil

fraction (Lichter et al., 2008), their decomposition is most likely to accelerate with an

increased microbial activity resulting from greater nutrient availabilities under N

fertilization, and their increased inputs under elevated CO2 were reduced. This

explanation is consistent with the observed microbial community shifts and enhanced

189

activity of Gram-negative bacteria in the Duke Forest with N addition (Billings and

Ziegler, 2008), which favored more recently formed labile carbon.

7.5.4 Fertilization-Induced Changes in OM Composition and Degradation

Compared with the FACE treatment, N fertilization only lasted two years before our

sample collection. Yet elevated fresh carbon inputs into the forest floor in the N-limited

Duke Forest were evident by a marginal increase in the steroid ratio (Table 7.1),

suggesting that the steroid ratio is a good indicator of fresh carbon inputs. Furthermore,

plant-derived hydrolysable lipids increased in the forest floor, although the trend was only

significant for suberin-derived compounds. Such a pattern was not detected with only the

FACE treatment, likely resulting from the greater impact of N fertilization on the

microbial community that led to the preservation of plant lipids in the forest floor. N

addition is generally found to stimulate cellulose-decomposing enzymes in N-limited

ecosystems (Carreiro et al., 2000; Sinsabaugh et al., 2005; Keeler et al., 2009).

Consequently, cellulose decomposed more rapidly under N fertilization (Carreiro et al.,

2000; Sjöberg et al., 2004) whereas plant lipids accumulated in the decomposing material

(Sjöberg et al., 2004). This mechanism was not apparent in the extractable fraction of the

forest floor OM or SOM, probably due to its labile nature and higher accessibility to

microbial degraders (Chapter 3).

It is noteworthy that N fertilization enhanced lignin oxidation in the surface soil

(Table 7.2) but the same trend was not observed in the forest floor (Table 7.1). Both

positive and negative responses of lignin decomposition to N addition have been reported,

190

depending on the OM chemistry, nutrient availability, and microbial community

composition at the specific site (Norby, 1998; Frey et al., 2004; Keeler et al., 2009). As

discussed previously, changes in the soil fungal community composition was detected

following N fertilization in the Duke Forest (Parrent et al., 2006), which may have

promoted fungal species that were efficient in lignin degradation and contributed to the

elevated degradation of lignin in soil. Alternatively, N addition was found to accelerate

the decomposition of labile or light SOM fractions with decadal turnover times (Neff et

al., 2002; Hoosbeek et al., 2006). Lignin-derived phenols extracted by CuO oxidation

from the Duke Forest soil had decadal turnover times, based on a compound-specific

isotopic analysis (Feng et al., unpublished results), and their decomposition was very

likely to be stimulated under N fertilization. By contrast, mineral-protected or refractory

SOM that was less accessible to enzymatic attack was shown to accumulate under N

fertilization (Neff et al., 2002; Hagedorn et al., 2003). Consistently, we observed an

increase in alkyl structures in the humic substances in the N-fertilized soils (Figure 7.1),

which are considered to be refractory (Kögel-Knabner et al., 1992; Lorenz et al., 2007).

This mechanism may not apply to the forest floor partly due to a lack of mineral

interactions and hence the preferential degradation of lignin was not detected.

7.6 Conclusions

Compositional changes in OM with environmental changes such as elevated CO2 or

N fertilization are usually difficult to detect due to the chemical heterogeneity of OM

components and a large spatial variability. By analyzing source-specific compounds in the

191

forest floor and surface soil in the Duke Forest FACE experiment, we show

molecular-level evidence for the increased fresh carbon inputs into the forest litter at

elevated CO2 levels, i.e., an elevated steroid ratio and an increased ratio of ω-C16/∑C16,

both of which had higher values in the undecomposed plant litter. Furthermore, higher

substrate or nutrient availabilities with elevated CO2 or N fertilization changed microbial

community composition and activity, leading to a stimulated decomposition of labile

structures, including lignin and hydrolysable lipids. This trend was more pronounced with

N fertilization in this N-limited forest, where significant shifts in fungal community were

detected in both the forest floor and surface soil. An altered microbial decomposition

pattern, together with elevated plant inputs, contributed to the enrichment of plant-derived

recalcitrant structures (non-hydrolysable alkyl carbon) in the soil humic substances.

Because long-term carbon sequestration depends on a build-up of recalcitrant or stable

SOM, our findings suggest that there is a potential for enhanced carbon sequestration in

the stable SOM with a similar SOC content under elevated CO2 or N fertilization.


Dr. Ram Oren is greatly acknowledged for facilitating collaboration and sample

collection from the Duke Forest FACE experiment. We thank Pui Sai Lau and Jennifer

Heidenheim for help with chemical extractions and Jeff Pippen for field assistance.

Funding from the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS)

supported this research. MJS thanks Natural Sciences and Engineering Research Council

of Canada (NSERC) for a University Faculty Award. XF thanks the Centre for Global

192

Change Science at the University of Toronto for a Graduate Student Award and the

Ontario Graduate Scholarship. The FACE research was supported by the Office of

Science (BER), U.S. Department of Energy, Grant No. DE-FG02-95ER62083, and

through its Southeast Regional Center (SERC) of the National Institute for Global

Environmental Change (NIGEC) under Cooperative Agreement No.

DE-FC02-03ER63613.

193

CHAPTER 8

CONCLUSIONS

194

8.1 Summary

This thesis utilized two complementary analytical techniques, i.e., biomarker GC/MS

methods and NMR spectroscopy, and investigated the composition, origin, and

degradation of grassland and forest SOM at the molecular-level. The response of various

SOM components to the altered plant inputs and microbial community structure resulting

from soil warming and elevated atmospheric CO2 levels was examined through five

laboratory experiments or field simulations: (1) assessment of the composition, source

and degradation stage of SOM in the Canadian Prairie grassland soil profiles (Chapter 2);

(2) investigation of soil microbial community changes and the decomposition of various

SOM components at six different temperatures during a one-year laboratory incubation

(Chapters 3 and 4); (3) examination of SOM compositional changes in an in situ soil

warming experiment in a mixed forest (Chapter 5); (4) study of microbial and SOM

responses to laboratory-simulated FTCs (Chapter 6); and (5) analysis of SOM

compositional and microbial community structural changes under elevated CO2 and N

fertilization in the Duke Forest FACE experiment (Chapter 7). Throughout those

experiments, three hypotheses were tested and confirmed:

1. As the most active component in soil, microbial communities were sensitive to

substrate changes resulting from prolonged soil incubation, freeze-thaw-induced cell lyses,

N fertilization and increased plant inputs under elevated CO2 or soil warming. For

instance, microbial biomass (measured by PLFAs) demonstrated distinct decay patterns in

two grassland soils contrasting in SOM quality, suggesting that SOM lability and

availability had a strong control on the microbial response to global warming (Chapter 4).

195

The fungal community showed a positive response to soil warming, likely resulting from

the elevated fresh carbon inputs (Chapter 5), whereas FTCs and N fertilization reduced

fungal biomass or changed fungal community structure (Chapters 6 and 7). Microbial

activity and community composition are essential in regulating SOM decomposition rate

and patterns, and the observed responses of microbial community to the simulated global

changes may have direct impacts on SOM decomposition patterns. For instance, the

increased fungal community may have contributed to the enhanced lignin oxidation in the

in situ soil warming experiment because fungi were the primary degrader of lignin in

terrestrial environments (Chapter 5). These findings confirm Hypothesis 1.

2. Solvent-extractable (free) soil lipids were more easily degraded during soil

incubation (Chapter 3) and FTCs (Chapter 6). By comparison, lignin-derived phenols and

bound aliphatic lipids primarily originating from plant cutin and suberin demonstrated a

higher stability during soil incubation (Chapter 3) and FTCs (Chapter 6). This finding is

consistent with Hypothesis 2. More importantly, contrast to the conventional belief that

aromatic SOM was recalcitrant and stable in soils, bound aliphatic lipids were

preferentially preserved in the Canadian Prairie grassland soil profiles as compared with

lignin-derived phenols (Chapter 2). Furthermore, lignin-derived phenols underwent an

enhanced oxidation after only one year of soil warming (Chapter 5) and two years of N

fertilization (Chapter 7), suggesting that the degradation of aromatic SOM may be faster

than estimated.

3. An accumulation of recalcitrant alkyl structures that were most abundant in leaf

cuticles was observed in both the soil warming (Chapter 5) and Duke Forest FACE

196

experiments (Chapter 7), with an increased litter production under global warming,

elevated CO2, and/or N fertilization. This finding indicated a positive response of the

recalcitrant SOM pool to the above-mentioned global changes and highlighted the role of

plant litter in promoting carbon sequestration over the long term. This finding confirms

Hypothesis 3.

Overall, this thesis represents the first of its kind to employ comprehensive

molecular-level techniques in the investigation of SOM structural alterations under

climate change. While changes in the bulk SOC content following global warming or

elevated CO2 are usually difficult to detect due to a large spatial variability, the detailed

examination of molecular parameters of targeted compounds offers great insights into

SOM composition and transformation in a changing environment. In particular, various

SOM components with distinct structures and origins were shown to vary in their

abundance and decomposition rates under global changes such as warming or elevated

CO2. For instance, lignin oxidation was promoted under soil warming (Chapter 5) or N

fertilization (Chapter 7) accompanied with an altered microbial community structure,

whereas alkyl structures mainly originating from plant leaf cuticles accumulated in the

soil with elevated plant inputs under soil warming, elevated CO2 or N fertilization. These

results are complementary to the current literature and have significant implications for

carbon sequestration and terrestrial biogeochemistry, suggesting an underestimation of

lignin degradation and carbon sequestration through leaf litter accumulation in a changing

climate.

Some cautions are warranted in the interpretation of the findings from this thesis. Due

197

to the complexity and high expense of field simulation experiments, the in situ soil

warming study was not replicated and conducted in a single forest (Chapter 5). Hence, the

results from this specific site (coarse-textured forest soil with high moisture content) need

to be tested in replicated experiments in other systems (such as dry upland soils,

well-developed soils with fine textures, or grassland soils with lower litter inputs). It is

well known that the response of microbial and plant communities to warming varies

among different ecosystems due to varying moisture and nutrients limitations and

physical properties that are site-specific (see reviews in Pendall et al., 2004; Hyvönen et

al., 2006). Therefore, the warming-induced alteration in SOM composition as observed in

this thesis may not be universal in other ecosystems although the underlying mechanism

of SOM stability and changes may be widely applicable. Similarly, the observed changes

in microbial community structure and OM composition in the Duke Forest FACE

experiment (Chapter 7) are limited to the young temperate pine forest developed on an

N-limited acidic soil. Caution is urged to extrapolate the findings from this study to other

systems with different vegetation types and nutrient availabilities. Furthermore,

climate-change-induced shifts in microbial community structure and SOM composition

may vary on different timescales. For instance, the increase in microbial activity or plant

growth resulting from warming or elevated CO2 is likely to diminish with time with the

exhaustion of newly-released nutrients or labile substrates in the soil (discussions in

Norby and Luo, 2004). Consequently, the long-term response of microbial communities

and SOM composition to global warming and elevated CO2 merits further investigation.

198

8.2 Recommended Future Research

It should be pointed out that the majority of SOM still remains uncharacterized at the

molecular-level and the microbe-SOM-mineral interactions remain elusive despite

numerous research efforts (as highlighted in Baldock and Skjemstad, 2000; Davidson and

Janssens, 2006). Future research may focus on the following areas to improve our

understanding and prediction of soil carbon transformation and dynamics in a changing

climate.

a. Assessing SOM turnover with compound-specific isotopic analysis (CSIA).

Biomarker GC/MS and NMR methods are powerful tools to study the structural

composition and abundance of individual molecules in SOM. Another important trait

preserved in organic molecules is their radiocarbon content and stable carbon isotopic

composition, which can shed light on their carbon sources (modern versus ancient; C3

versus C4 plants) and residence time in the soil (Balesdent et al., 1988; Bol et al., 1996).

Compound-specific radiocarbon and stable carbon analysis, in particular, has recently

been used in organic geochemical applications and holds great promise to evaluate the

origin and turnover of individual SOM component in a changing environment (Eglinton

et al., 1996; Glaser, 2005; Amelung et al., 2008). For instance, the fresh plant biomass

produced under the fumigated CO2 (derived from natural gas) in the Duke Forest FACE

experiment carries a distinct stable carbon isotopic signature from the old SOM (Lichter

et al., 2008). This isotopic signature is transferred into SOM from plants and can be

utilized to estimate the turnover rates of bulk SOM and individual organic molecules,

such as cutin- and suberin-derived compounds whose turnover rates are largely unknown

199

in the current literature. These innovative techniques are complementary to the analytical

techniques used in this thesis and will contribute novel perspectives to the study of SOM

dynamics and terrestrial carbon cycling.

b. Investigating the chemical composition and origin of mineral-protected SOM.

Mineral protection of SOM is known to play a key role in soil carbon preservation (Torn

et al., 1997; Baldock and Skjemstad, 2000). In the Duke Forest FACE experiment,

SOM-mineral interaction was considered to contribute to the enrichment of alkyl

structures in soil humic substances under the elevated CO2 levels (Chapter 7). Yet the

mechanisms and extend of mineral protection remained an enigma. Mineral-associated

SOM can be separated from bulk soil using physical fractionation techniques (Six et al.,

2001). Alternatively, mineral-protected SOM may be released for analysis upon HF

treatment (Mead and Goñi, 2008). The chemical and isotopic composition of this SOM

fraction may be examined by analytical methods such as GC/MS, NMR, and isotope

analysis and may provide insights into the soil carbon preservation mechanisms. As an

example, the results of a preliminary GC/MS analysis of the mineral-protected

non-hydrolysable soil lipids from the Duke Forest FACE experiment were compiled in

Appendix 1.

c. Linking microbial degraders to specific SOM components. It has been shown in

this thesis that the response of various SOM components to soil warming and elevated

CO2 is related to the community composition of microbial degraders. To facilitate

improved understanding and prediction of soil carbon transformation, it is essential to

study the direct linkages between individual SOM components and their specific

200

microbial degraders in the soil. The microbial community information obtained from

PLFA analysis is based on a phenotypical classification (Gram-staining groups of bacteria)

and is not strictly related to microbial functions or species in the soil. Complementary

techniques such as nucleic acid profiling (Singh et al., 2006; Webster et al., 2006) may

provide information on the microbial community structural changes at the species-level.

Furthermore, a considerable fraction of the living microbial community is inactive or

dormant in the soil that does not actively participate in the SOM decomposition. Isotopic

labeling techniques combined with microbial PLFA or DNA extractions allows the

detection and quantification of the active soil microbial communities (Radajewski et al.,

2000; Treonis et al., 2004; Evershed et al., 2006). These novel techniques may be useful

in investigating the fate of SOM of specific origins (old versus freshly-labeled) and

microbial functions in substrate-constrained or nutrient-spiked soils.

201

APPENDIX 1

PRELIMINARY GC/MS ANALYSIS OF MINERAL-PROTECTED SOIL LIPIDS

FROM THE DUKE FOREST FACE EXPERIMENT

202

Methods

The non-hydrolysable soil lipids that were protected by mineral matrix and hence not

detected in the extractable or hydrolysable lipids (Chapter 7) were extracted by a

modified method (Figure A1.1) after Mead and Goñi (2008). Briefly, composite samples

were made by mixing equal amounts of soil (10 g) from the four plots under the same

experimental treatment from the Duke Forest FACE experiment (refer to Materials

soil sample

solvent extractionwith CH3OH/CH2Cl2

extractable lipids (F1)

Residue 1

base hydrolysis

hydrolysable lipids (F2)

Residue 2

HF treatment&

base hydrolysis

“mineral-protected” lipids (F3)

Residue 3

soil sample

solvent extractionwith CH3OH/CH2Cl2

extractable lipids (F1)

Residue 1

base hydrolysis

hydrolysable lipids (F2)

Residue 2

HF treatment&

base hydrolysis

“mineral-protected” lipids (F3)

Residue 3

Figure A1.1: Extraction scheme to assess the ‘mineral-protected’ soil lipids.

and Methods in Chapter 7). The composite sample was first subject to solvent extraction

and base hydrolysis to remove extractable (F1) and hydrolysable (F2) lipids, respectively.

203

The residue was treated with concentrated HF (48%) twice for 48 h to remove mineral

matrix and thoroughly rinsed with deionized water. SOM was separated from soil

minerals by floating in a saturated NaCl solution (1.2 g cm-3), rinsed, recovered by

centrifugation, and freeze-dried. The treated SOM was then extracted in triplicates by

base hydrolysis and compounds recovered from this fraction were termed the

‘mineral-protected’ lipids (F3).

Results

The GC/MS chromatograms of the major soil lipids extracted from the Duke Forest

soil under ambient CO2, N-fertilized treatment were shown in Figure A1.2. Base

hydrolysis was efficient in extracting cutin- and suberin-derived compounds from soil

because only minor amounts (<8%) of those compounds were detected in the

‘mineral-protected’ lipids (Table A1.1). ‘Mineral-protected’ lipids had a higher

contribution from non-plant sources (microbial origin or degradation products from

primary carbon structures), indicated by the presence of even-numbered alkanes,

odd-numbered fatty acids (FAs), short-chain FAs and alkanols. Furthermore, a

considerable amount of alkanes became extractable after demineralization by HF

treatment, suggesting evidence of mineral protection of SOM.

204

20 25 30 35 40 45 50

22ω

24ω

18:1+16

+

p-Cd 18+

α

20ω22

+

24+20

▽

18▽

22▽

16ω

26ω

20+

16++

Vd

4-OH

Bd

26+

22++

▽

24++ 28

+30+

20++14

ω

x,ω-O

H C

16

19:1+

Fd12ω

9,10-ep C18

18:1ω

20 25 30 35 40 45

22ω

24▽

24ω

24α

18:1+

16+

p-Cd

18+

α α

20ω22

+24+

u1

20▽

18▽

22▽

16ω

26ω

20+16

++ u

xω-O

H C

16

Vd

4-OH

Bd

β-sitosterol

21+

23+

22++

°

26▽

u1 24++

28+

30+

32+▽

+20++

°°14ω

(a) Hydrolysable lipids (F2)

(b) “Mineral-protected” lipids (F3)

Retention Time (min)

Rel

ativ

e Ab

unda

nce

20 25 30 35 40 45 50

22ω

24ω

18:1+16

+

p-Cd 18+

α

20ω22

+

24+20

▽

18▽

22▽

16ω

26ω

20+

16++

Vd

4-OH

Bd

26+

22++

▽

24++ 28

+30+

20++14

ω

x,ω-O

H C

16

19:1+

Fd12ω

9,10-ep C18

18:1ω

20 25 30 35 40 45

22ω

24▽

24ω

24α

18:1+

16+

p-Cd

18+

α α

20ω22

+24+

u1

20▽

18▽

22▽

16ω

26ω

20+16

++ u

xω-O

H C

16

Vd

4-OH

Bd

β-sitosterol

21+

23+

22++

°

26▽

u1 24++

28+

30+

32+▽

+20++

°°14ω



20 25 30 35 40 45 5020 25 30 35 40 45 50

22ω

24ω

18:1+16

+

p-Cd 18+

α

20ω22

+

24+20

▽

18▽

22▽

16ω

26ω

20+

16++

Vd

4-OH

Bd

26+

22++

▽

24++ 28

+30+

20++14

ω

x,ω-O

H C

16

19:1+

Fd12ω

9,10-ep C18

18:1ω

20 25 30 35 40 45

22ω

24▽

24ω

24α

18:1+

16+

p-Cd

18+

α α

20ω22

+24+

u1

20▽

18▽

22▽

16ω

26ω

20+16

++ u

xω-O

H C

16

Vd

4-OH

Bd

β-sitosterol

21+

23+

22++

°

26▽

u1 24++

28+

30+

32+▽

+20++

°°14ω



Retention Time (min)

Rel

ativ

e Ab

unda

nce

Figure A1.2: GC/MS chromatograms (TIC) of the major soil lipids extracted from the Duke Forest soil under ambient CO2, N-fertilized treatment. (a) Methylated and silylated hydrolysable lipids (F2). (b) Methylated and silylated ‘mineral-protected’ lipids (F3). + = n-alkanoic acids, = ▽ n-alkanols, o = n-alkanes, ω = ω-hydroxyalkanoic acids, ++ = n-alkanedioic acids, α = α-hydroxyalkanoic acids, 4-OH Bd = 4-hydroxy benzoic acid, Vd = vanillic acid, p-Cd = p-coumaric acid, Fd = ferulic acid, x, ω-OH C16 = 10,16-Dihydroxy C16 acid, 9,10-ep C18 = methoxy, chlorohydrine and 9,10-dihydroxy derivatives of C18 acid, u = unknowns. Numbers refer to total carbon numbers in aliphatic lipid series.

205

Table A1.1: The composition and abundance of soil lipids in the Duke Forest soil (mg/g

OC)

mean s.e.m. mean s.e.m. mean s.e.m. mean s.e.m.Extractable lipids (F1)

alkanes (C29, C31) 0.05 0.01 0.05 0.00 0.06 0.01 0.05 0.00short-chain FAs (C12-C18) 0.41 0.04 0.32 0.06 0.55 0.07 0.29 0.02

long-chain FAs (C20-C32 even-numbered) 0.35 0.02 0.38 0.10 0.36 0.07 0.33 0.05short-chain alkanols (C12-C18) 0.12 0.02 0.12 0.02 0.14 0.02 0.11 0.01

long-chain alkanols (C20-C32 even-numbered) 0.31 0.01 0.37 0.04 0.48 0.08 0.36 0.03Hydrolysable lipids (F2)

short-chain FAs (C12-C18) 4.98 0.32 4.13 0.70 5.20 0.24 4.65 0.49long-chain FAs (C20-C32 even-numbered) 2.27 0.15 2.72 0.30 3.28 0.65 2.63 0.21

short-chain alkanols (C12-C18) 0.47 0.05 0.57 0.06 0.62 0.09 0.48 0.03long-chain alkanols (C20-C32 even-numbered) 1.86 0.17 2.33 0.36 3.83 1.75 2.16 0.42

α-hydroxy FAs (C23-C26) 0.80 0.10 0.84 0.11 1.08 0.07 1.07 0.05cutin-derived compounds (∑C) 2.96 0.24 3.84 0.57 5.15 1.57 2.81 0.48

suberin-derived compounds (∑S) 6.76 0.81 8.32 1.11 13.60 5.42 8.43 1.63"Mineral-protected" lipids (F3)

alkanes (C23-C33 odd-numbered) 0.03 0.02 0.21 0.05 0.37 0.06 0.30 0.04alkanes (C24-C32 even numbered) 0.03 0.02 0.17 0.04 0.38 0.02 0.28 0.01

short-chain FAs (C12-C18) 0.08 0.04 0.96 0.26 1.42 0.37 1.61 0.37long-chain FAs (C21-C29 odd-numbered) 0.01 0.01 0.12 0.02 0.16 0.02 0.12 0.01

long-chain FAs (C20-C32 even-numbered) 0.05 0.03 0.56 0.11 0.95 0.03 0.69 0.04short-chain alkanols (C12-C18) 0.01 0.01 0.23 0.06 0.27 0.00 0.27 0.04

long-chain alkanols (C20-C32 even-numbered) 0.04 0.02 0.70 0.18 0.82 0.11 0.56 0.09α-hydroxy FAs (C16-C26) 0.01 0.01 0.33 0.06 0.27 0.03 0.30 0.04

cutin-derived compounds (∑C) 0.00 0.00 0.10 0.03 0.00 0.00 0.09 0.05suberin-derived compounds (∑S) 0.03 0.02 0.80 0.12 0.89 0.04 0.86 0.18

Ambient CO2 Elevated CO2

Unfertilized Fertilized Unfertilized Fertilized

FA: fatty acid; cutin-derived compounds include mid-chain hydroxyalkanoic acids (C14, C15, C17), mono- and dihydroxyhexadecanoic acids, and n-hexadecane-α,ω-dioic acids; suberin-derived compounds include ω-hydroxyalkanoic acids (C20–C32), n-alkane-α,ω-dioic acids (C20–C32), and 9,10-epoxy-octadecane-α,ω-dioic acid.

206

References

Ågren, G.I., Kirschbaum, M.U.F., Johnson, D.W., Bosatta, E., 1996. Ecosystem physiology- soil organic matter. In: Breymeyer, A.I., Hall, D.O., Melillo, J.M., Ågren, G.I. (Eds.), Global change: effects on coniferous forests and grasslands. John Wiley & Sons Ltd, New York, pp. 205-228.

Akerblom, S., Bååth, E., Bringmark, L., Bringmark, E., 2007. Experimentally induced effects of heavy metal on microbial activity and community structure of forest mor layers. Biology and Fertility of Soils 44, 79-91.

Albaugh, T.J., Allen, H.L., Doughtery, P.M., Kress, L.W., King, J.S., 1998. Leaf area and above- and belowground growth responses of loblolly pine to nutrient and water additions. Forest Science 44, 317-328.

Amblès, A., Jambu, P., Parlanti, E., Joffre, J., Riffe, C., 1994. Incorporation of natural monoacids from plant residues into an hydromorphic forest podzol. European Journal of Soil Science 45, 175-182.

Amelung, W., Brodowski, S., Sandhage-Hofmann, A., Bol, R., 2008. Combining biomarker with stable isotope analyses for assessing the transformation and turnover of soil organic matter. Advances in Agronomy 100, 155-250.

Anderson, J.M., 1991. The effects of climate change on decomposition processes in grassland and coniferous forests. Ecological Applications 1, 326-347.

Anderson, T.H., Domsch, K.H., 1989. Ratios of microbial biomass carbon to total organic carbon in arable soils. Soil Biology & Biochemistry 21, 471-479.

Arnold, S.S., Fernandez, I.J., Rustad, L.E., Zibilske, L.M., 1999. Microbial response of an acid forest soil to experimental soil warming. Biology and Fertility of Soils 30, 239-244.

Augris, N., Balesdent, J., Mariotti, A., Derenne, S., Largeau, C., 1998. Structure and origin of insoluble and non-hydrolyzable, aliphatic organic matter in a forest soil. Organic Geochemistry 28, 119-124.

Bahri, H., Dignac, M.-F., Rumpel, C., Rasse, D.P., Chenu, C., Mariotti, A., 2006. Lignin turnover kinetics in an agricultural soil is monomer specific. Soil Biology & Biochemistry 38, 1977-1988.

Baldock, J.A., Skjemstad, J.O., 2000. Role of the soil matrix and minerals in protecting natural organic materials against biological attack. Organic Geochemistry 31, 697-710.

Balesdent, J., Wagner, G.H., Mariotti, A., 1988. Soil organic matter turnover in long-term field experiments as revealed by carbon-13 natural abundance. Soil Science Society of America Journal 52, 118-124.

Balser, T.C., Firestone, M.K., 2005. Linking microbial community composition and soil processes in a California annual grassland and mixed-conifer forest. Biogeochemistry 73, 395-415.

Bardgett, R.D., Kandeler, E., Tscherko, D., Hobbs, P.J., Bezemer, T.M., Jones, T.H., Thompson, L.J., 1999. Below-ground microbial community development in a high temperature world. Oikos 85, 193-203.

Bardgett, R.D., Richter, A., Bol, R., et al., 2007. Heterotrophic microbial communities use

207

ancient carbon following glacial retreat. Biology Letters 3, 487-490. Batjes, N.H., 1996. Total carbon and nitrogen in the soils of the world. European Journal

of Soil Science 47, 151-163. Beare, M.H., Neely, C.L., Coleman, D.C., Hargrove, W.L., 1990. A substrate-induced

respiration (SIR) method for measurement of fungal and bacterial biomass on plant residues. Soil Biology & Biochemistry 22, 585-594.

Berg, B., 2000. Litter decomposition and organic matter turnover in northern forest soils. Forest Ecology and Management 133, 13-22.

Berg, M.P., Kniese, J.P., Verhoef, H.A., 1998. Dynamics and stratification of bacteria and fungi in the organic layers of a Scots pine forest soil. Biology and Fertility of Soils 26, 313-322.

Biasi, C., Rusalimova, O., Meyer, H., et al., 2005. Temperature-dependent shift from labile to recalcitrant carbon sources of arctic heterotrophs. Rapid Communications in Mass Spectrometry 19, 1401-1408.

Billings, S.A., Ziegler, S.E., 2008. Altered patterns of soil carbon substrate usage and heterotrophic respiration in a pine forest with elevated CO2 and N fertilization. Global Change Biology 14, 1025-1036.

Bligh, E.G., Dyer, W.J., 1959. A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology 37, 911-917.

Boer, W.d., Folman, L.B., Summerbell, R.C., Boddy, L., 2005. Living in a fungal world: Impact of fungi on soil bacterial niche development. FEMS Microbiology Reviews 29, 795-811.

Bol, R., Bolger, T., Cully, R., Little, D., 2003. Recalcitrant soil organic materials mineralize more efficiently at higher temperatures. Journal of Plant Nutrition and Soil Science 166, 300-307.

Bol, R., Huang, Y., Meridith, J.A., Eglinton, G., Harkness, P., Ineson, P., 1996. The 14C age and residence time of organic matter and its lipid constituents in a stagnohumic gley soil. European Journal of Soil Science 47, 215-222.

Bosatta, E., Ågren, G.I., 1999. Soil organic matter quality interpreted thermodynamically. Soil Biology & Biochemistry 31, 1889-1891.

Bossio, D.A., Scow, K.M., 1998. Impacts of carbon and flooding on soil microbial communities: Phospholipid fatty acid profiles and substrate utilization patterns. Microbial Ecology 35, 265-278.

Bottomley, P.J., Yarwood, R.R., Kageyama, S.A., Waterstripe, K.E., Williams, M.A., Cromack, K.J., Myrold, D.D., 2006. Responses of soil bacterial and fungal communities to reciprocal transfers of soil between adjacent coniferous forest and meadow vegetation in the Cascade Mountains of Oregon. Plant and Soil 289, 35-45.

Bull, I.D., Nott, C.J., van Bergen, P.F., Poulton, P.R., Evershed, R.P., 2000. Organic geochemical studies of soils from the Rothamsted classical experiments--VI. The occurrence and source of organic acids in an experimental grassland soil. Soil Biology & Biochemistry 32, 1367-1376.

Callaghan, T.V., Korner, C., Heal, O.W., Lee, S.E., Cornelissen, J.H.C., 1998. Scenarios for ecosystem responses to global change. In: Heal, O.W., Callaghan, T.V.,

208

Cornelissen, J.H.C., Korner, C., Lee, S.E. (Eds.), Global Change in Europe's Cold Regions-Ecosystems Research Report, Luxembourg, pp. 65-134.

Callesen, I., Liski, J., Raulund-Rasmussen, K., Olsson, M.T., Tau-Strand, L., Vesterdal, L., Westman, C.J., 2003. Soil carbon stores in Nordic well-drained forest soils-relationships with climate and texture class. Global Change Biology 9, 358-370.

Cambardella, C.A., Elliott, E.T., 1993. Carbon and nitrogen distribution in aggregates from cultivated and native grassland soils. Soil Science Society of America Journal 57, 1071-1076.

Carlile, M.J., Watkinson, S.C., Gooday, G.W., 2001. The fungi. Academic Press, San Diego.

Carney, K.M., Hungate, B.A., Drake, B.G., Megonigal, J.P., 2007. Altered soil microbial community at elevated CO2 leads to loss of soil carbon. Proceedings of the National Academy of Sciences of the United States of America 104, 4990-4995.

Carreiro, M.M., Sinsabaugh, R.L., Repert, D.A., Parkhurst, D.F., 2000. Microbial enzyme shifts explain litter decay responses to simulated nitrogen deposition. Ecology 81, 2359-2365.

Challinor, J.M., 1995. Characterisation of wood by pyrolysis derivatisation - gas chromatography/mass spectrometry. Journal of Analytical and Applied Pyrolysis 35, 93-107.

Challinor, J.M., 2001. Review: the development and applications of thermally assisted hydrolysis and methylation reactions. Journal of Analytical and Applied Pyrolysis 61, 3-34.

Chapin, F.S.I., Matson, P.A., Mooney, H.A., 2002. Principles of terrestrial ecosystem ecology. Springer-Verlag, New York.

Chefetz, B., Chen, Y., Clapp, C.E., Hatcher, P.G., 2000. Characterization of organic matter in soils by thermochemolysis using tetramethylammonium hydroxide (TMAH). Soil Science Society of America Journal 64, 583-589.

Christensen, J.H., Hewitson, B., Busuioc, A., et al., 2007. Regional climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 847-940.

Clemmensen, K.E., Michelsen, A., Jonasson, S., Shaver, G.R., 2006. Increased ectomycorrhizal fungal abundance after long-term fertilization and warming of two arctic tundra ecosystems. New Phytologist 171, 391-404.

Cox, P.M., Betts, R.A., Jones, C.D., Spall, S.A., Totterdell, I.J., 2000. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184-187.

Cramer, W., Bondeau, A., Woodward, F.I., et al., 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology 7, 357-373.

Dalias, P., Anderson, J.M., Bottner, P., Couteaux, M.-M., 2001. Temperature responses of

209

carbon mineralization in conifer forest soils from different regional climates incubated under standard laboratory conditions. Global Change Biology 7, 181-192.

Davidson, E.A., Janssens, I.A., 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165-173.

Davidson, E.A., Janssens, I.A., Luo, Y., 2006. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biology 12, 154-164.

DeLucia, E.H., Hamilton, J.G., Naidu, S.L., et al., 1999. Net primary production of a forest ecosystem with experimental CO2 enrichment. Science 284, 1177-1179.

Derenne, S., Largeau, C., 2001. A review of some important families of refractory macromolecules: composition, origin, and fate in soils and sediments. Soil Science 166, 833-847.

Dickens, H.E., Anderson, J.M., 1999. Manipulation of soil microbial community structure in bog and forest soils using chloroform fumigation. Soil Biology & Biochemistry 31, 2049-2058.

Dinel, H., Schnitzer, M., Mehuys, G.R., 1990. Soil lipids: origin, nature, content, decomposition, and effect on soil physical properties. In: Bollag, J.-M., Stotzky, G. (Eds.), Soil Biochemistry. Marcel Dekker, New York, pp. 397-427.

Dixon, R.K., Brown, S., Houghton, R.A., Solomon, A.M., Trexler, M.C., Wisniewski, J., 1994. Carbon pools and flux of global forest ecosystems. Science 263, 185-190.

Drissner, D., Blum, H., Tscherko, D., Kandeler, E., 2007. Nine years of enriched CO2 changes the function and structural diversity of soil microorganisms in a grassland. European Journal of Soil Science 58, 260-269.

Dudas, M.J., Pawluk, S., 1969. Chernozem soils of the Alberta Parklands. Geoderma 3, 19-36.

Edwards, A.C., Cresser, M.S., 1992. Freezing and its effect on chemical and biological properties of soil. Advances in Soil Science 18, 59-79.

Edwards, L.M., 1991. The effect of alternate freezing and thawing on aggregate stability and aggregate size distribution of some Prince Edward Island soils. Journal of Soil Science 42, 193-204.

Eglinton, T.I., Aluwihare, L.I., Bauer, J.E., Druffel, E.R.M., McNichol, A.P., 1996. Gas chromatographic isolation of individual compounds from complex matrices for radiocarbon dating. Analytical Chemistry 68, 904-912.

Evershed, R.P., Crossman, Z.M., Bull, I.D., Mottram, H., Dungait, J.A., Maxfield, P.J., Brennand, E.L., 2006. 13C-Labelling of lipids to investigate microbial communities in the environment. Current Opinion in Biotechnology 17, 72-82.

Fang, C., Smith, P., Moncrieff, J.B., Smith, J.U., 2005. Similar response of labile and resistant soil organic matter pools to changes in temperature. Nature 433, 57-59.

Feng, X., Simpson, A.J., Simpson, M.J., 2005. Chemical and mineralogical controls on humic acid sorption to clay mineral surfaces. Organic Geochemistry 36, 1553-1566.

Feng, X., Simpson, A.J., Simpson, M.J., 2006. Investigating the role of mineral-bound humic acid in phenanthrene sorption. Environmental Science & Technology 40, 3260-3266.

210

Fierer, N., Craine, J.M., McLauchlan, K., Schimel, J.P., 2005. Litter quality and the temperature sensitivity of decomposition. Ecology 86, 320-326.

Fierer, N., Schimel, J.P., Holden, P.A., 2003. Variations in microbial community composition through two soil depth profiles. Soil Biology & Biochemistry 35, 167-176.

Filley, T.R., Boutton, T.W., Liao, J.D., Jastrow, J.D., Gamblin, D.E., 2008. Chemical changes to nonaggregated particulate soil organic matter following grassland-to-woodland transition in a subtropical savanna. Journal of Geophysical Research 113, G03009, doi:10.1029/2007JG000564.

Finzi, A.C., Moore, D.J., DeLucia, E.H., et al., 2006. Progressive nitrogen limitation of ecosystem processes under elevated CO2 in a warm-temperate forest. Ecology 87, 15-25.

Fontaine, S., Bardoux, G., Benest, D., Verdier, B., Mariotti, A., Abbadie, L., 2004. Mechanisms of the priming effect in a savannah soil amended with cellulose. Soil Biology & Biochemistry 68, 125-131.

Fox, C.A., Preston, C.M., Fyfe, C.A., 1994. Micromorphological and 13C NMR characterization of a humic, lignic, and histic folisol from British Columbia. Canadian Journal of Soil Science 74, 1-15.

Freeman, C., Ostle, N.J., Fenner, N., Kang, H., 2004. A regulatory role for phenol oxidase during decomposition in peatlands. Soil Biology & Biochemistry 36, 1663-1667.

Frey, S.D., Drijber, R., Smith, H., Melillo, J., 2008. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biology & Biochemistry 40, 2904-2907.

Frey, S.D., Knorr, M., Parrent, J.L., Simpson, R.T., 2004. Chronic nitrogen enrichment affects the structure and function of the soil microbial community in temperate hardwood and pine forests. Forest Ecology and Management 196, 159-171.

Frostegård, A., Bååth, E., 1996. The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biology and Fertility of Soils 22, 59-65.

Garcia, M.O., Ovasapyan, T., Greas, M., Treseder, K.K., 2008. Mycorrhizal dynamics under elevated CO2 and nitrogen fertilization in a warm temperate forest. Plant and Soil 303, 301-310.

Gaudinski, J.B., Trumbore, S.E., Davidson, E.A., Zheng, S., 2000. Soil carbon cycling in a temperate forest: radiocarbon-based estimates of residence times, sequestration rates and partitioning of fluxes. Biogeochemistry 51, 33-69.

Giardina, C.P., Ryan, M.G., 2000. Evidence that decomposition rates of organic carbon in mineral soil do not vary with temperature. Nature 404, 858-861.

Glaser, B., 2005. Compound-specific stable-isotope (d13C) analysis in soil science. Journal of Plant Nutrition and Soil Science 168, 633-648.

Gleixner, G., Czimczki, C., Kramer, K., Luhker, B.M., Schmidt, M.W.I., 2001. Plant compounds and their turnover and stabilization as soil organic matter. In: Schulze, E.D., Heimann, M., Harrison, S., Holland, E., Lloyd, J., Prentice, I.C., Schimel, D. (Eds.), Global Biogeochemical Cycles in the Climate System. Academic Press, San Diego, pp. 201-215.

Goncalves, C.N., Dalmolin, R.S.D., Dick, D.P., Knicker, H., Klamt, E., Kögel-Knabner, I.,

211

2003. The effect of 10% HF treatment on the resolution of CPMAS 13C NMR spectra and on the quality of organic matter in Ferralsols. Geoderma 116, 373-392.

Goñi, M.A., Hedges, J.I., 1990. The diagenetic behaviour of cutin acids in buried conifer needles and sediments from a coastal marine environment. Geochimica et Cosmochimica Acta 54, 3083-3093.

Goñi, M.A., Hedges, J.I., 1992. Lignin dimers: Structures, distribution and potential geochemical applications. Geochimica et Cosmochimica Acta 56, 4025-4043.

Goñi, M.A., Nelson, B., Blanchette, R.A., Hedges, J.I., 1993. Fungal degradation of wood lignins: Geochemical perspectives from CuO-derived phenolic dimers and monomers. Geochimica et Cosmochimica Acta 57, 3985-4002.

Goñi, M.A., Ruttenberg, K.C., Eglinton, T.I., 1997. Sources and contribution of terrigenous organic carbon to surface sediments in the Gulf of Mexico. Nature 389, 275-278.

Goñi, M.A., Yunker, M.B., Macdonald, R.W., Eglinton, T.I., 2000. Distribution and sources of organic biomarkers in arctic sediments from the Mackenzie River and Beaufort Shelf. Marine Chemistry 71, 23-51.

Gorham, E., 1991. Northern peatlands: role in the carbon cycle and probable response to climatic warming. Ecological Applications 1, 182-195.

Grant, W.D., West, A.W., 1986. Measurement of ergosterol, diaminopimelic acid and glucosamine in soil: evaluation as indicators of soil microbial biomass. Journal of Microbiological Methods 6, 47-53.

Gregorich, E.G., Monreal, C.M., Schnitzer, M., Schulten, H.-R., 1996. Transformation of plant residues into soil organic matter: Chemical characterization of plant tissue, isolated soil fractions, and whole soils. Soil Science 161, 680-693.

Griffiths, B.S., Ritz, K., Ebblewhite, N., Dobson, G., 1999. Soil microbial community structure: Effects of substrate loading rates. Soil Biology & Biochemistry 31, 145-153.

Grogan, P., Michelsen, A., Ambus, P., Jonasson, S., 2004. Freeze-thaw regime effects on carbon and nitrogen dynamics in sub-arctic heath tundra mesocosms. Soil Biology & Biochemistry 36, 641-654.

Guckert, J.B., Antworth, C.P., Nichols, P.D., White, D.C., 1985. Phospholipid, ester-linked fatty acid profiles as reproducible assays for changes in prokaryotic community structure of estuarine sediments. FEMS Microbial Ecology 31, 147-158.

Guckert, J.B., Hood, M.A., White, D.C., 1986. Phospholipid ester-linked fatty acid profile changes during nutrient deprivation of Vibrio cholerae: Increases in the trans/cis ratio and proportions of cyclopropyl fatty acids. Applied and Environmental Microbiology 52, 794-801.

Guggenberger, G., Frey, S.D., Six, J., Paustian, K., Elliott, E.T., 1999. Bacterial and fungal cell-wall residues in conventional and no-tillage agroecosystems. Soil Science Society of America Journal 63, 1188-1198.

Guignard, C., Lemee, L., Ambles, A., 2005. Lipid constituents of peat humic acids and humin. Distinction from directly extractable bitumen components using TMAH and TEAAc thermochemolysis. Organic Geochemistry 36, 287-297.

212

Hagedorn, F., Spinnler, D., Siegwolf, R., 2003. Increased N deposition retards mineralization of old soil organic matter. Soil Biology & Biochemistry 35, 1683-1692.

Haney, R.L., Honeycutt, C.W., Arshad, M.A., Schomberg, H.H., Hons, F.M., 2001. Climatic influences on active fractions of soil organic matter. Soil Biology & Biochemistry 33, 1103-1111.

Harwood, J.L., Russell, N.J., 1984. Lipids in Plants and Microbes. G. Allen & Unwin, London.

Hassink, J., 1992. Effects of soil texture and structure on C and N mineralization in grassland soils. Biology and Fertility of Soils 14, 126-134.

Heath, J., Ayres, E., Possell, M., et al., 2005. Rising atmospheric CO2 reduces sequestration of root-derived soil carbon. Science 309, 1711-1713.

Hedges, J.I., Blanchette, R.A., Weliky, K., Devol, A.H., 1988. Effects of fungal degradation on the CuO oxidation products of lignin: A controlled laboratory study. Geochimica et Cosmochimica Acta 52, 2717-2726.

Hedges, J.I., Eglinton, G., Hatcher, P.G., et al., 2000. The molecularly-uncharacterized component of nonliving organic matter in natural environments. Organic Geochemistry 31, 945-958.

Hedges, J.I., Ertel, J.R., 1982. Characterization of lignin by gas capillary chromatography of cupric oxide oxidation products. Analytical Chemistry 54, 174-178.

Hedges, J.I., Mann, D.C., 1979. The characterization of plant tissues by their lignin oxidation products. Geochimica et Cosmochimica Acta 43, 1803-1807.

Hendrey, G.R., Ellsworth, D.S., Lewin, K.F., Nagy, J., 1999. A free-air enrichment system for exposing tall forest vegetation to elevated atmospheric CO2. Global Change Biology 5, 293-309.

Herrmann, A., Witter, E., 2002. Sources of C and N contributing to the flush in mineralization upon freeze-thaw cycles in soils. Soil Biology & Biochemistry 34, 1495-1505.

Holloway, P.J., Deas, A.H.B., 1973. Epoxyoctadecanoic acids in plant cutins and suberins. Phytochemistry 12, 1721-1735.

Hoosbeek, M.R., Li, Y., Scarascia-Mugnozza, G.E., 2006. Free atmospheric CO2 enrichment (FACE) increased labile and total carbon in the mineral soil of a short rotation Poplar plantation. Plant and Soil 281, 247-254.

Hoosbeek, M.R., Vos, J.M., Meinders, M.B.J., Velthorst, E.J., Scarascia-Mugnozza, G.E., 2007. Free atmospheric CO2 enrichment (FACE) increased respiration and humification in the mineral soil of a poplar plantation. Geoderma 138, 204-212.

Houghton, R.A., 1994. The worldwide extent of land-use change. Bioscience 44, 305-313.

Hu, W.-G., Mao, J., Xing, B., Schmidt-Rohr, K., 2000. Poly(methylene) crystallites in humic substances detected by nuclear magnetic resonance. Environ. Sci. Technol. 34, 530-534.

Hyvönen, R., Ågren, G.I., Linder, S., et al., 2006. The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. New

213

Phytologist 173, 463-480. Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D.,

1996. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389-411.

Janzen, H.H., 2004. Carbon cycling in earth systems-a soil science perspective. Agriculture, Ecosystems & Environment 104, 399-417.

Janzen, H.H., Campbell, C.A., Izaurralde, R.C., Ellert, B.H., Juma, N., McGill, W.B., Zentner, R.P., 1998. Management effects on soil C storage on the Canadian prairies. Soil and Tillage Research 47, 181-195.

Jastrow, J.D., Miller, R.M., Matamala, R., Norby, R.J., Boutton, T.W., Rice, C.W., Owensby, C.E., 2005. Elevated atmospheric carbon dioxide increases soil carbon. Global Change Biology 11, 2057-2064.

Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecological Applications 10.

Joergensen, R.G., Brookes, P.C., Jenkinson, D.S., 1990. Survival of the soil microbial biomass at elevated temperatures. Soil Biology & Biochemistry 22, 1129-1136.

Johnson, N.C., 1993. Can fertilization of soil select less mutualistic mycorrhizae? Ecological Applications 3, 749-757.

Jones, T.H., Thompson, L.J., Lawton, J.H., et al., 1998. Impacts of rising atmospheric carbon dioxide on model terrestrial ecosystems. Science 280, 441-443.

Keeler, B.L., Hobbie, S.E., Kellogg, L.E., 2009. Effects of long-term nitrogen addition on microbial enzyme activity in eight forested and grassland sites: Implications for litter and soil organic matter decomposition. Ecosystems 12, 1-15.

Kelleher, B.P., Simpson, A.J., 2006. Humic substances in soils: Are they really chemically distinct? Environmental Science & Technology 40, 4605-4611.

Kelleher, B.P., Simpson, M.J., Simpson, A.J., 2006. Assessing the fate and transformation of plant residues in the terrestrial environment using HR-MAS NMR spectroscopy. Geochimica et Cosmochimica Acta 70, 4080-4094.

Kieft, T.L., Ringelberg, D.B., White, D.C., 1994. Changes in ester-linked phospholipid fatty acid profiles of subsurface bacteria during starvation and desiccation in a porous medium. Applied and Environmental Microbiology 60, 3292-3299.

Kirschbaum, M.U.F., 1993. A modelling study of the effects of changes in atmospheric CO2 concentration, temperature and atmospheric nitrogen input on soil organic carbon storage. Tellus 45B, 321-334.

Knorr, M., Frey, S.D., Curtis, P.S., 2005a. Nitrogen additions and litter decomposition: A meta-analysis. Ecology 86, 3252-3257.

Knorr, W., Prentice, I.C., House, J.I., Holland, E.A., 2005b. Long-term sensitivity of soil carbon turnover to warming. Nature 433, 298-301.

Kögel-Knabner, I., Hatcher, P.G., Zech, W., 1991. Chemical structural studies of forest soil humic acids: Aromatic carbon fraction. Soil Science Society of America Journal 55, 241-247.

Kögel, I., 1986. Estimation and decomposition pattern of the lignin component in forest humus layers. Soil Biology & Biochemistry 18, 589-594.

Kögel-Knabner, I., 2000. Analytical approaches for characterizing soil organic matter.

214

Organic Geochemistry 31, 609-625. Kögel-Knabner, I., 2002. The macromolecular organic composition of plant and microbial

residues as inputs to soil organic matter (review). Soil Biology & Biochemistry 34, 139-162.

Kögel-Knabner, I., De Leeuw, J.W., Hatcher, P.G., 1992. Nature and distribution of alkyl carbon in forest soil profiles: implications for the origin and humification of aliphatic biopolymers. Science of the Total Environment 117/118, 175-185.

Kögel-Knabner, I., Ziegler, F., Riederer, M., Zech, W., 1989. Distribution and decomposition pattern of cutin and suberin in forest soils. Zeitschrift fur Pflanzenernahrung und Bodenkunde 152, 409-413.

Koide, R.T., Shumway, D.L., Stevens, C.M., 2000. Soluble carbohydrates of red pine (Pinus resinosa) mycorrhizas and mycorrhizal fungi. Mycological Research 104, 834-840.

Kolattukudy, P.E., 1981. Structure, biosynthesis, and biodegradation of cutin and suberin. Annual Review of Plant Physiology 32, 539-567.

Kramer, C., Gleixner, G., 2006. Variable use of plant- and soil-derived carbon by microorganisms in agricultural soils. Soil Biology & Biochemistry 38, 3267-3278.

Lal, R., 2004. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1-22. Lam, T.B.T., Kadoya, K., Iiyama, K., 2001. Bonding of hydroxycinnamic acids to lignin:

ferulic and p-coumaric acids are predominantly linked at the benzyl position of lignin, not the b-position, in grass cell walls. Phytochemistry 57, 987-992.

Leifeld, J., 2003. Comments on "Recalcitrant soil organic materials mineralize more efficiently at higher temperatures". Journal of Plant Nutrition and Soil Science 166, 777-779.

Leifeld, J., Fuhrer, J., 2005. The temperature response of CO2 production from bulk soils and soil fractions is related to soil organic matter quality. Biogeochemistry 75, 433-453.

Li, Y.-L., Peacock, A.D., White, D.C., Geyer, R., Zhang, C.L., 2007. Spatial patterns of bacterial signature biomarkers in marine sediments of the Gulf of Mexico. Chemical Geology 238, 168-179.

Lichter, J., Barron, S.H., Bevacqua, C.E., Finzi, A.C., Irving, K.F., Stemmler, E.A., Schlesinger, W.H., 2005. Soil carbon sequestration and turnover in a pine forest after six years of atmospheric CO2 enrichment. Ecology 86, 1835-1847.

Lichter, J., Billings, S.A., Ziegler, S., et al., 2008. Soil carbon sequestration in a pine forest after 9 years of atmospheric CO2 enrichment. Global Change Biology 14, 2910-2922.

Lipson, D.A., Schmidt, S.K., Monson, R.K., 1999. Links between microbial population dynamics and nitrogen availability in an alpine ecosystem. Ecology 80, 1623-1631.

Lipson, D.A., Schmidt, S.K., Monson, R.K., 2000. Carbon availability and temperature control the post-snowmelt decline in alpine soil microbial biomass. Soil Biology & Biochemistry 32, 441-448.

Lipson, D.A., Wilson, R.F., Oechel, W.C., 2005. Effects of elevated atmospheric CO2 on soil microbial biomass, activity, and diversity in a chaparral ecosystem. Applied

215

and Environmental Microbiology 71, 8573-8580. Liski, J., Westman, C.J., 1997. Carbon storage in forest soil of Finland. 1. Effect of

thermoclimate. Biogeochemistry 36, 239-260. Liu, C., Westman, C.J., Berg, B., Kutsch, W., Wang, G.Z., Man, R., Ilvesniemi, H., 2004.

Variation in litterfall-climate relationships between coniferous and broadleaf forests in Eurasia. Global Ecology and Biogeography 13, 105-114.

Lorenz, K., Lal, R., 2005. The depth distribution of soil organic carbon in relation to land use and management and the potential of carbon sequestration in subsoil horizons. Advances in Agronomy 88, 35-66.

Lorenz, K., Lal, R., Preston, C.M., Nierop, K.G.J., 2007. Strengthening the soil organic carbon pool by increasing contributions from recalcitrant aliphatic bio(macro)molecules. Geoderma 142, 1-10.

Matamala, R., Schlesinger, W.H., 2000. Effects of elevated atmospheric CO2 on fine root production and activity in an intact temperate forest ecosystem. Global Change Biology 6, 967-979.

Matzner, E., Borken, W., 2008. Do freeze-thaw events enhance C and N losses from soils of different ecosystems? A review. European Journal of Soil Science 59, 274-284.

McKinley, V.L., Peacock, A.D., White, D.C., 2005. Microbial community PLFA and PHB responses to ecosystem restoration in tallgrass prairie soils. Soil Biology & Biochemistry 37, 1946-1958.

McMahon, S.K., Williams, M.A., Bottomley, P.J., Myrold, D.D., 2005. Dynamics of microbial communities during decomposition of carbon-13 labeled ryegrass fractions in soil. Soil Science Society of America Journal 69, 1238-1247.

Mead, R.N., Goñi, M.A., 2008. Matrix protected organic matter in a river dominated margin: A possible mechanism to sequester terrestrial organic matter? Geochimica et Cosmochimica Acta 72, 2673-2686.

Meehl, G.A., Stocker, T.F., Collins, W.D., et al., 2007. Global climate projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 747-845.

Melillo, J.M., Aber, J.D., Linkins, A.E., Ricca, A., Fry, B., Nadelhoffer, K.J., 1989. Carbon and nitrogen dynamics along the decay continuum: Plant litter to soil organic matter. Plant and Soil 115, 189-198.

Melillo, J.M., Aber, J.D., Muratore, J.F., 1982. Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 63, 621-626.

Melillo, J.M., Steudler, P.A., Aber, J.D., et al., 2002. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173-2176.

Mikutta, R., Kleber, M., Torn, M.S., Jahn, R., 2006. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 77, 25-56.

Monson, R.K., Lipson, D.L., Burns, S.P., Turnipseed, A.A., Delany, A.C., Williams, M.W., Schmidt, S.K., 2006. Winter forest soil respiration controlled by climate and microbial community composition. Nature 439, 711-714.

216

Moore-Kucera, J., Dick, R.P., 2008. PLFA profiling of microbial community structure and seasonal shifts in soils of a Douglas-fir chronosequence. Microbial Ecology 55, 500-511.

Müller, J., Boller, T., Wiemken, A., 1995. Trehalose and trehalase in plants: Recent developments. Plant Science 112, 1-9.

Müller, T., Höper, H., 2004. Soil organic matter turnover as a function of the soil clay content: consequences for model applications. Soil Biology & Biochemistry 36, 877-888.

Neff, J.C., Townsend, A.R., Gleixner, G., Lehman, S.J., Turnbull, J., Bowman, W.D., 2002. Variable effects of nitrogen additions on the stability and turnover of soil carbon. Nature 419, 915-917.

Nierop, G.J.K., 1998. Origin of aliphatic compounds in a forest soil. Organic Geochemistry 29, 1009-1016.

Nierop, K.G.J., Naafs, D.F.W., Verstraten, J.M., 2003. Occurrence and distribution of ester-bond lipids in Dutch coastal dune soils along a pH gradient. Organic Geochemistry 34, 719-729.

Norby, R.J., 1998. Nitrogen deposition: a component of global change analyses. New Phytologist 139, 189-200.

Norby, R.J., Ledford, J., Reilly, C.D., Miller, N.E., O'Neill, E.G., 2004. Fine-root production dominates response of a deciduous forest to atmospheric CO2 enrichment. Proceedings of the National Academy of Sciences of the United States of America 101, 9689-9693.

Norby, R.J., Luo, Y., 2004. Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi-factor world. New Phytologist 162, 281-293.

Norby, R.J., Rustad, L.E., Dukes, J.S., et al., 2007. Ecosystem responses to warming and interacting global change factors. In: Canadell, J.G., Pataki, D.E., Pitelka, L.F. (Eds.), Terrestrial Ecosystems in a Changing World. Springer-Verlag, Berlin, pp. 23-36.

Opsahl, S., Benner, R., 1995. Early diagenesis of vascular plant tissues: Lignin and cutin decomposition and biogeochemical implications. Geochimica et Cosmochimica Acta 59, 4889-4904.

Oren, R., Ellsworth, D.S., Johnsen, K.H., et al., 2001. Soil fertility limits carbon sequestration by forest ecosystems in a CO2-enriched atmosphere. Nature 411, 469-472.

Otto, A., Shunthirasingham, C., Simpson, M.J., 2005. A comparison of plant and microbial biomarkers in grassland soils from the Prairie Ecozone of Canada. Organic Geochemistry 36, 425-448.

Otto, A., Simpson, M.J., 2005. Degradation and preservation of vascular plant-derived biomarkers in grassland and forest soils from western Canada. Biogeochemistry 74, 377-409.

Otto, A., Simpson, M.J., 2006a. Sources and composition of hydrolysable aliphatic lipids and phenols in soils from western Canada. Organic Geochemistry 37, 385-407.

Otto, A., Simpson, M.J., 2006b. Evaluation of CuO oxidation parameters for determining the source and stage of lignin degradation in soil. Biogeochemistry 80, 121-142.

217

Otto, A., Simpson, M.J., 2007. Analysis of soil organic matter biomarkers by sequential chemical degradation and gas chromatography - mass spectrometry. Journal of Separation Science 30, 1-11.

Parrent, J.L., Morris, W.F., Vilgalys, R., 2006. CO2-enrichment and nutrient availability alter ectomycorrhizal fungal communities. Ecology 87, 2278-2287.

Parton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S., 1987. Analysis of factors controlling soil organic matter levels in Great Plain grasslands. Soil Science Society of America Journal 51, 1173-1179.

Pasanen, A.-L., Yli-Pietila, K., Pasanen, P., Kalliokoski, P., Tarhanen, J., 1999. Ergosterol content in various fungal species and biocontaminated building materials. Applied and Environmental Microbiology 65, 138-142.

Pendall, E., Bridgham, S., Hanson, P.J., et al., 2004. Below-ground process responses to elevated CO2 and temperature: A discussion of observations, measurement methods, and models. New Phytologist 162, 311-322.

Pesaro, M., Widmer, F., Nicollier, G., Zeyer, J., 2003. Effects of freeze-thaw stress during soil storage on microbial communities and methidathion degradation. Soil Biology & Biochemistry 35, 1049-1061.

Petersen, S.O., Klug, M.J., 1994. Effects of sieving, storage, and incubation temperature on the phospholipid fatty acid profile of a soil microbial community. Applied and Environmental Microbiology 60, 2421-2430.

Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G., 1982. Soil carbon pools and world life zones. Nature 298, 156-159.

Potthoff, M., Dyckmans, J., Flessa, H., Muhs, A., Beese, F., Joergensen, R.G., 2005. Dynamics of maize (Zea mays L.) leaf straw mineralization as affected by the presence of soil and the availability of nitrogen. Soil Biology & Biochemistry 37, 1259-1266.

Potthoff, M., Steenwerth, K.L., Jackson, L.E., Drenovsky, R.E., Scow, K.M., Joergensen, R.G., 2006. Soil microbial community composition as affected by restoration practices in California grassland. Soil Biology & Biochemistry 38, 1851-1860.

Prescott, C.E., Vesterdal, L., Preston, C.M., Simard, S.W., 2004. Influence of initial chemistry on decomposition of foliar litter in contrasting forest types in British Columbia. Canadian Journal of Forest Research 34, 1714-1729.

Preston, C.M., 1996. Applications of NMR to soil organic matter analysis: history and prospects. Soil Science 161, 144-166.

Pritchard, S.G., Strand, A.E., McCormack, M.L., et al., 2008. Fine root dynamics in a loblolly pine forest are influenced by free-air-CO2-enrichment: a six-year minirhizotron study. Global Change Biology 14, 588-602.

Quénéa, K., Derenne, S., Gonzalez-Vila, F.J., Mariotti, A., Rouzaud, J.-N., Largeau, C., 2005. Study of the composition of the macromolecular refractory fraction from an acidic sandy forest soil (Landes de Gascogne, France) using chemical degradation and electron microscopy. Organic Geochemistry 36, 1151-1162.

Quénéa, K., Derenne, S., Largeau, C., Rumpel, C., Mariotti, A., 2004. Variation in lipid relative abundance and composition among different particle size fractions of a forest soil. Organic Geochemistry 35, 1355-1370.

218

Radajewski, S., Ineson, P., Parekh, N.R., Murrell, J.C., 2000. Stable-isotope probing as a tool in microbial ecology. Nature 403, 646-649.

Raich, J.W., Russel, A.E., Kitayama, K., Parton, W.J., Vitousek, P.M., 2006. Temperature influences carbon accumulation in moist tropical forests. Ecology 87, 76-87.

Rasmussen, P.E., Albrecht, S.L., Smiley, R.W., 1998. Soil C and N changes under tillage and cropping systems in semi-arid Pacific Northwest agriculture. Soil and Tillage Research 47, 197-205.

Rasse, D.P., Rumpel, C., Dignac, M.-F., 2005. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant and Soil 269, 341-356.

Reichstein, M., Bednorz, F., Broll, G., Katterer, T., 2000. Temperature dependence of carbon mineralisation: conclusions from a long-term incubation of subalpine soil samples. Soil Biology & Biochemistry 32, 947-958.

Reichstein, M., Tenhunen, J.D., Roupsard, O., Ourcival, J.-M., Rambal, S., Dore, S., Valentini, R., 2002. Ecosystem respiration in two Mediterranean evergreen holm oak forests: drought effects and decomposition dynamics. Functional Ecology 16, 27-39.

Riederer, M., Matzke, K., Ziegler, F., Kögel-Knabner, I., 1993. Occurrence, distribution and fate of the lipid plant biopolymers cutin and suberin in temperate forest soils. Organic Geochemistry 20, 1063-1076.

Rillig, M.C., Wright, S.F., Allen, M.F., Field, C.B., 1999. Rise in carbon dioxide changes soil structure. Nature 400, 628-628.

Rillig, M.C., Wright, S.F., Shaw, M.R., Field, C.B., 2002. Artificial climate warming positively affects arbuscular mycorrhizae but decreases soil aggregate water stability in an annual grassland. Oikos 97, 52-58.

Rinnan, R., Michelsen, A., Bååth, E., Jonasson, S., 2007. Fifteen years of climate change manipulations alter soil microbial communities in a subarctic heath ecosystem. Global Change Biology 13, 28-39.

Rinnan, R., Michelsen, A., Jonasson, S., 2008. Effects of litter addition and warming on soil carbon, nutrient pools and microbial communities in a subarctic heath ecosystem. Applied Soil Ecology 39, 271-281.

Rumpel, C., Eusterhues, K., Kögel-Knabner, I., 2004. Location and chemical composition of stabilized organic carbon in topsoil and subsoil horizons of two acid forest soils. Soil Biology & Biochemistry 36, 177-190.

Rumpel, C., Kögel-Knabner, I., Bruhn, F., 2002. Vertical distribution, age, and chemical composition of organic carbon in two forest soils of different pedogenesis. Organic Geochemistry 33, 1131-1142.

Rumpel, C., Seraphin, A., Dignac, M.-F., Michaelis, W., Eusterhues, K., Kögel-Knabner, I., 2005. Effect of base hydrolysis on the chemical composition of organic matter of an acid forest soil. Organic Geochemistry 36, 239-249.

Rustad, L.E., Huntington, T.G., Boone, R.D., 2000. Controls on soil respiration: Implications for climate change. Biogeochemistry 48, 1-6.

Saiz-Jimenez, C., 1994. Analytical pyrolysis of humic substances: Pitfalls, limitations, and possible solutions. Environmental Science & Technology 28, 1773-1780.

Scheu, S., Parkinson, D., 1994. Changes in bacterial and fungal biomass C, bacterial and

219

fungal biovolume and ergosterol content after drying, remoistening and incubation of different layers of cool temperate forest soils. Soil Biology & Biochemistry 26, 1515-1525.

Schimel, J.P., Clein, J.S., 1996. Microbial responses to freeze-thaw cycles in tundra and taiga soils. Soil Biology & Biochemistry 28, 1061-1066.

Schimel, J.P., Mikan, C., 2005. Changing microbial substrate use in Arctic tundra soils through a freeze-thaw cycle. Soil Biology & Biochemistry 37, 1411-1418.

Schimel, J.P., Weintraub, M.N., 2003. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biology & Biochemistry 35, 549-563.

Schindler, D.W., 1999. The mysterious missing sink. Nature 398, 105-106. Schlesinger, W.H., 1991. Biogeochemistry: An analysis of global change. Academic Press,

San Diego. Schlesinger, W.H., Andrews, J.A., 2000. Soil respiration and the global carbon cycle.

Biogeochemistry 48, 7-20. Sharma, S., Szele, Z., Schilling, R., Munch, J.C., Schloter, M., 2006. Influence of

freeze-thaw stress on the structure and function of microbial communities and denitrifying populations in soil. Applied and Environmental Microbiology 72, 2148-2154.

Sheldrick, B.H., Wang, C., 1993. Particle size distribution. In: Carter, M.R. (Ed.), Soil sampling and methods of analysis. Lewis Publishers, Boca Raton, FL, pp. 499-511.

Shunthirasingham, C., Simpson, M.J., 2006. Investigation of bacterial hopanoid inputs to soils from Western Canada. Applied Geochemistry 21, 964-976.

Simpson, A., 2001. Multidimensional solution state NMR of humic substances: A practical guide and review. Soil Science 166, 795-809.

Simpson, A.J., Kingery, W.L., Hatcher, P.G., 2003. The identification of plant derived structures in humic materials using three-dimensional NMR spectroscopy. Environmental Science & Technology 37, 337-342.

Simpson, A.J., Salloum, M.J., Kingery, W.L., Hatcher, P.G., 2002. Improvements in the two-dimensional nuclear magnetic resonance spectroscopy of humic substances. Journal of Environmental Quality 31, 388-392.

Simpson, A.J., Simpson, M.J., Smith, E., Kelleher, B.P., 2007a. Micorbially derived inputs to soil organic matter: Are current estimates too low? Environmental Science & Technology 41, 8070-8076.

Simpson, A.J., Song, G., Smith, E., Lam, B., Novotny, E.H., Hayes, M.H.B., 2007b. Unraveling the structural components of soil humin by use of solution-state nuclear magnetic resonance spectroscopy. Environmental Science & Technology 41, 876-883.

Simpson, M.J., Otto, A., Feng, X., 2008. Comparison of solid-state carbon-13 nuclear magnetic resonance and organic matter biomarkers for assessing soil organic matter degradation. Soil Science Society of America Journal 72, 268-276.

Singh, B.K., Munro, S., Reid, E., Ord, B., Potts, J.M., Paterson, E., Millard, P., 2006. Investigating microbial community structure in soils by physiological,

220

biochemical and molecular fingerprinting methods. European Journal of Soil Science 57, 72-82.

Sinsabaugh, R.L., Gallo, M.E., Lauber, C., Waldrop, M.P., Zak, D.R., 2005. Extracellular enzyme activities and soil organic matter dynamics for northern hardwood forests receiving simulated nitrogen deposition. Biogeochemistry 75, 201-215.

Six, J., Carpentier, A., van Kessel, C., Merckx, R., Harris, D., Horwath, W.R., Luscher, A., 2001. Impact of elevated CO2 on soil organic matter dynamics as related to changes in aggregate turnover and residue quality. Plant and Soil 234, 27-36.

Six, J., Frey, S.D., Thiet, R.K., Batten, K.M., 2006. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Science Society of America Journal 70, 555-570.

Sjöberg, G., Nilsson, S.I., Persson, T., Karlsson, P., 2004. Degradation of hemicellulose, cellulose and lignin in decomposing spruce needle litter in relation to N. Soil Biology & Biochemistry 36, 1761-1768.

Smith, S.E., Read, D.J., 1997. Mycorrhizal symbiosis. Academic Press, London. Sollins, P., Homann, P., Caldwell, B.A., 1996. Stabilization and destabilization of soil

organic matter: mechanisms and controls. Geoderma 74, 65-105. Stahl, P.D., Klug, M.J., 1996. Characterization and differentiation of filamentous fungi

based on fatty acid composition. Applied and Environmental Microbiology 62, 4136-4146.

Sulkava, P., Huhta, V., 2003. Effects of hard frost and freeze-thaw cycles on decomposer communities and N mineralisation in boreal forest soil. Applied Soil Ecology 22, 225-239.

Suutari, M., Laakso, S., 1994. Microbial fatty acids and thermal adaptation. Critical Reviews in Microbiology 20, 285-328.

Swift, M.J., Heal, O.W., Anderson, J.M., 1979. Decomposition in Terrestrial Ecosystems. Blackwell Scientific, Oxford.

Swift, R.S., 2001. Sequestration of carbon by soils. Soil Science 166, 858-871. Torn, M.S., Trumbore, S.E., Chadwick, O.A., Vitousek, P.M., Hendricks, D.M., 1997.

Mineral control of soil organic carbon storage and turnover. Nature 389, 170-174. Treonis, A.M., Ostle, N.J., Stott, A.W., Primrose, R., Grayston, S.J., Ineson, P., 2004.

Identification of groups of metabolically-active rhizosphere microorganisms by stable isotope probing of PLFAs. Soil Biology & Biochemistry 36, 533-537.

Treseder, K.K., 2008. Nitrogen additions and microbial biomass: a meta-analysis of ecosystem studies. Ecology 11, 1111-1120.

Trumbore, S., 2006. Carbon respired by terrestrial ecosystems-recent progress and challenges. Global Change Biology 12, 141-153.

Trumbore, S.E., 1997. Potential response of soil organic carbon to global environmental change. Proceedings of the National Academy of Sciences of the United States of America 94, 8284-8291.

Ussiri, D.A.N., Johnson, C.E., 2003. Characterization of organic matter in a northern hardwood forest soil by 13C NMR spectroscopy and chemical methods. Geoderma 111, 123-149.

van Bergen, P.F., Bull, I.D., Poulton, P.R., Evershed, R.P., 1997. Organic geochemical

221

studies of soils from the Rothamsted Classical Experiments--I. Total lipid extracts, solvent insoluble residues and humic acids from Broadbalk Wilderness. Organic Geochemistry 26, 117-135.

van Diepen, L.T.A., Lilleskov, E.A., Pregitzer, K.S., Miller, R.M., 2007. Decline of arbuscular mycorrhizal fungi in northern hardwood forests exposed to chronic nitrogen additions. New Phytologist 176, 175-183.

van Groenigen, K.-J., Six, J., Hungate, B.A., de Graaff, M.-A., van Breemen, N., van Kessel, C., 2006. Element interactions limit soil carbon storage. Proceedings of the National Academy of Sciences of the United States of America 103, 6571-6574.

van Kessel, C., Boots, B., de Graaff, M.-A., Harris, D., Blum, H., Six, J., 2006. Total soil C and N sequestration in a grassland following 10 years of free air CO2 enrichment. Global Change Biology 12, 2187-2199.

Walker, V.K., Palmer, G.R., Voordouw, G., 2006. Freeze-thaw tolerance and clues to the winter survival of a soil community. Applied and Environmental Microbiology 72, 1784-1792.

Wardle, D.A., 1992. A comparative assessment of factors which influence microbial biomass carbon and nitrogen levels in soil. Biological Reviews 67, 321-358.

Webster, G., Watt, L.C., Rinna, J., Fry, J.C., Evershed, R.P., Parkes, R.J., Weightman, A.J., 2006. A comparison of stable-isotope probing of DNA and phospholipid fatty acids to study prokaryotic functional diversity in sulfate-reducing marine sediment enrichment slurries. Environmental Microbiology 8, 1575-1589.

White, D.M., Garland, D.S., Dai, X., Ping, C.-L., 2002. Fingerprinting soil organic matter in the arctic to help predict CO2 flux. Cold Regions Science and Technology 35, 185-194.

Wilson, K.P., Williams, D.D., 2006. Response of dissolved organic carbon in a shallow groundwater ecosystem to a simulated global warming experiment. Geo-Environment and Landscape Evolution II 89, 163-174.

Wingler, A., 2002. The function of trehalose biosynthesis in plants. Phytochemistry 60, 437-440.

Winkler, A., Haumaier, L., Zech, W., 2005. Insoluble alkyl carbon components in soils derive mainly from cutin and suberin. Organic Geochemistry 36, 519-529.

Winkler, J.P., Cherry, R.S., Schlesinger, W.H., 1996. The Q10 relationship of microbial respiration in a temperate forest soil. Soil Biology & Biochemistry 28, 1067-1072.

Yanai, Y., Toyota, K., Okazaki, M., 2004. Effects of successive soil freeze-thaw cycles on soil microbial biomass and organic matter decomposition potential of soils. Soil Science and Plant Nutrition 50, 821-829.

Zelles, L., 1999. Fatty acid patterns of phospholipids and lipopolysaccharides in the characterisation of microbial communities in soil: a review. Biology and Fertility of Soils 29, 111-129.

Zelles, L., Bai, Q.Y., Beck, T., Beese, F., 1992. Signature fatty acids in phospholipids and lipopolysaccharides as indicators of microbial biomass and community structure in agricultural soils. Soil Biology & Biochemistry 24, 317-323.

Zhang, W., Parker, K.M., Luo, Y., Wan, S., Wallace, L.L., Hu, S., 2005. Soil microbial

222

responses to experimental warming and clipping in a tallgrass prairie. Global Change Biology 11, 266-277.

223

the molecular composition of soil organic matter … · 2013-11-07 · plant inputs under elevated...

Documents