Research Collection
Doctoral Thesis
Genetic characterization of soil bacterial communities in the DOK long-term agricultural field experiment Influences of management strategies and crops
Author(s): Hartmann, Martin
Publication Date: 2006
Permanent Link: https://doi.org/10.3929/ethz-a-005335472
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ETH Library Diss. ETHNo. 16963
Genetic characterization of soil bacterial communities in
the DOK long-term agricultural field experiment:
influences of management strategies and crops
A dissertation submitted to the
SWISS FEDERAL INSTITUTE OF TECHNOLOGY ZURICH
for the degree of
DOCTOR OF SCIENCES
presented by
MARTIN HARTMANN
Dipl. Natw. ETH
born 16th February 1977
citizen of Schiers (GR)
accepted on the recommendation of
Prof. Dr. Emmanuel Frossard, examiner
Dr. Franco Widmer, co-examiner
Prof. Dr. Alex Widmer, co-examiner
Prof. Dr. Jakob Pernthaler, co-examiner
2006 TABLE OF CONTENTS
Table of Contents
Table of Contents 1
Summary 5
Zusammenfassung 7
1 General Introduction 10
1.1 Assessing soil quality 10 1.1.1 Definition of soil quality 10 1.1.2 Soil quality monitoring 11
1.2 Agricultural management and ecological impact 12 1.2.1 Trends in agricultural production 12
1.2.2 Agricultural impact on ecosystems 14 1.2.3 Agricultural sustainability and soil quality 15
1.3 Soil characteristics 16
1.4 Microbial soil characteristics and soil quality 18
1.4.1 Microbial roles in soil processes 18 1.4.2 Microbial soil quality indicators 19 1.5 Soil biodiversity 20
1.5.1 Agricultural influence on soil microbiota 21 1.5.2 Soil microbial diversity 21
1.6 Assessing soil microbial community parameters 23
1.6.1 Soil microbial biomass 23
1.6.2 Diversity and community structure 26
1.7 The DOK long-term field experiment 37
1.7.1 Fertilization 39
1.7.2 Plant protection 40
1.7.3 Experimental field, tillage, and crop rotations 41 1.7.4 Soil organic carbon, soil acidity, and phosphorus dynamic 42
1.7.5 Crop yield 44 1.8 Objectives and outline of this thesis 46 1.8.1 Objectives 46
1.8.2 Outline 47
1 TABLE OF CONTENTS
Community structures and substrate utilization of bacteria in soils from organic and conventional farming systems of the DOK long-term field experiment 50
2.1 Abstract 50
2.2 Introduction 51
2.3 Material and Methods 54
2.3.1 Experimental system 54
2.3.2 Soil sampling 55
2.3.3 Soil microbial biomass 56
2.3.4 Community level substrate utilization (CLSU) 56
2.3.5 Soil DNA extraction 57
2.3.6 Quantification of DNA 57
2.3.7 PCR-amplification of bacterial SSU rRNA genes 58
2.3.8 Terminal restriction fragment length polymorphism (T-RFLP) analysis 58
2.3.9 Descriptive and discriminative statistical analyses 59
2.4 Results 60
2.4.1 Soil microbial biomass (Cmic) and colony forming units 60
2.4.2 Total soil DNA content 61
2.4.3 Community level substrate utilization 62
2.4.4 Terminal restriction fragment length polymorphisms analyses 65
2.5 Discussion 69
2.5.1 Soil biomass parameters 69
2.5.2 Soil bacterial community structures 70
2.6 Conclusions 73
2.7 Acknowledgments 74
Ranking the magnitude of crop and farming system effects on soil microbial biomass and genetic structure of bacterial communities 76
3.1 Abstract 76
3.2 Introduction 76
3.3 Material and Methods 79
3.3.1 Experimental system and soil sampling 79
3.3.2 Soil microbial biomass 80
3.3.3 Extraction and quantification of DNA from soil 81
2 TABLE OF CONTENTS
3.3.4 Genetic profiling of soil bacterial populations 81
3.3.5 Statistical analyses 81
3.4 Results 82
3.4.1 Biomass and DNA content 82
3.4.2 Soil bacterial community structures 84
3.5 Discussion 90
3.6 Conclusions 94
3.7 Acknowledgments 94
Community structure analyses are more sensitive to differences in soil bacterial communities than anonymous diversity indices 96
4.1 Abstract 96
4.2 Introduction 96
4.3 Material and Methods 99
4.3.1 Agricultural management systems 99
4.3.2 Amplification and cloning of bacterial 16S rRNA gene fragments 99
4.3.3 Gene library screening 100
4.3.4 T-RFLP analysis 100
4.3.5 Sequence analysis 100
4.3.6 Diversity analyses 101
4.3.7 Nucleotide sequence accession numbers 102
4.4 Results 102
4.4.1 Phylogenetic affiliation 102
4.4.2 Richness and relative abundance of operational taxonomic units 104
4.4.3 Estimated complexity of the gene libraries 106
4.4.4 Comparison of in silico and experimental T-RF sizes 107
4.4.5 Community structures represented in the gene libraries 108
4.4.6 Potential treatment associated indicator taxa 109
4.5 Discussion 111
4.6 Acknowledgements 116
General Discussion 118
5.1 Potentials and limitations in assessing microbial communities 118
5.1.1 Spatial and temporal heterogeneity of microbial soil characteristics 118 5.1.2 Potential and advantages of molecular analyses 119
3 TABLE OF CONTENTS
5.1.3 Biases and limitations of molecular analyses 122
5.2 Impact of agricultural factors on soil bacterial communities 129
5.2.1 Changes in bacterial community structures 129
5.2.2 Abundances of bacteria in soils 131
5.3 Soil bacterial diversity as soil quality indicator 135 5.3.1 Biodiversity and functional redundancy in microbial communities 135
5.3.2 Limitations of common diversity estimations 138 5.3.3 Novel identity-based similarity estimations 139
5.4 Perspectives and Conclusions 140
5.4.1 The'full-cycle'molecular approach 140
5.4.2 Alternative gene families 143
5.4.3 Active groups 144 5.4.4 Molecular large-scale approaches 147
5.4.5 Final conclusions 149
Appendix: Residual polymerase activity-induced bias in terminal restriction fragment length polymorphism analysis 152
References 159
Curriculum Vitae 194
Publications 195
Published Abstracts and Presentations 196
Acknowledgments 197
4 SUMMARY
Summary
In agriculturally managed ecosystems preservation and improvement of soil fertility and quality is of great importance. As a consequence, strategies and tools are
required to gain detailed information on soil characteristics and which allow for definition and monitoring of soil quality. Soil chemical and soil physical characteristics
have successfully been analyzed for this purpose, whereas soil biological indicators
are less defined. Soil microorganisms directly influence soil structure, nutrient cycles, transformation processes, and plant pathogenesis, and may therefore represent key determinants of soil fertility and high quality crop production. Microbial communities
may respond highly sensitive to environmental and anthropogenic influences such as
pollution, erosion or unsustainable land use and may therefore serve as indicators for
changes in soil quality. Due to technical limitations, analyses of soil microbial
communities have not been well established, but recent advances in molecular
genetic analyses reveal a great potential to close this gap. Therefore, the aims of this thesis were (i) to investigate effects of different agricultural management regimes and
crops on soil bacterial diversity and underlying community structures, and (ii) to evaluate the feasibility of novel molecular techniques to monitor environmental
effects on soil characteristics.
For this purpose, soil bacterial community structures and diversities were analyzed in
the DOK agricultural long-term field experiment. The DOK experiment was established in Therwil (Switzerland) in 1978 designed for evaluation of biodynamic (BIODYN), bio-organic (BIOORG), and conventional (CONFYM) farming practices
along with a minerally (CONMIN) and an unfertilized (NOFERT) control system.
Results obtained in this thesis demonstrated that after 25 years of continuous and
defined agricultural management, different farming systems and crop rotations
significantly and consistently altered microbial biomass and bacterial community
structures as assessed by cultivation-based and molecular techniques. Application of
solid and liquid farmyard manure (FYM) revealed a primary effect on bacterial
communities, whereas crop effects were of secondary magnitude but revealed a
clear short-term influence, which faded after one season. Differences between
organic and conventional farming were partially significant for the biodynamic
system. In contrast, commonly used bacterial diversity indices as assessed by large-
scale sequence analysis of almost 2000 ribosomal RNA genes was highly similar
5 SUMMARY
and among the different farming systems. Different fertilization plant protection regimes did not affect bacterial diversity parameters and the significant differences in results of crop yield were not reflected in diversity estimations. However, large-scale of bacterial gene library screening supported results obtained from analyses community structures. The genetic profiling techniques revealed great potential for consistent, rapid, high-throughput monitoring of changes in microbial soil characteristics. In addition, large-scale sequence analysis allowed the detection of several potential management-specific bacterial indicator taxa, which may help to gain more detailed information on effects of the agricultural management on soil bacterial communities.
In conclusion, detection of changes in microbial community structures may allow for the development of indicator systems for changes in soil condition due to environmental or anthropogenic influences. The combination of genetic profiling techniques with specific identification of potential indicator taxa may provide a further step towards diagnostic of key processes in defined systems. Linking this specific for information on community composition with microbial soil functions may help better understanding of soil quality in the future.
6 ZUSAMMENFASSUNG
Zusammenfassung
Die Erhaltung und Verbesserung der Bodenfruchtbarkeit und -qualität in landwirtschaftlichen Ökosystemen ist von grosser Bedeutung. Strategien und
Methoden, welche detaillierte Informationen über die Bodeneigenschaften liefern und eine Definition sowie Überwachung der Bodenqualität erlauben, sind daher
notwendig. Chemische und physikalische Bodeneigenschaften sind zu diesem Zweck erfolgreich untersucht worden, die biologischen Indikatoren hingegen sind weniger gut etabliert. Bodenmikroorganismen beeinflussen direkt die
Nährstoffkreisläufe und Umwandlungsprozesse im Boden, die Bodenstruktur und die
Pathogenese in Pflanzen. Mikroorganismen sind somit mögliche Schlüsselfaktoren in der Bestimmung von Bodenfruchtbarkeit und der Produktion landwirtschaftlicher
Produkte von hoher Qualität. Mikrobielle Gemeinschaften reagieren sehr sensitiv auf
umweltbedingte und anthropogene Einflüsse wie zum Beispiel
Umweltverschmutzung, Bodenerosion oder nicht-nachhaltige Landnutzung und könnten daher als Indikatoren für Änderungen in der Bodenqualität dienen. Die
Untersuchung von Bodenmikroorganismen war wegen technischer Limitierungen
lange Zeit schlecht etabliert, aber neuste Fortschritte in molekulargenetischen
Methoden zeigen ein grosses Potential auf diese Lücke zu schliessen. Deswegen
waren die Ziele dieser Dissertationsarbeit (i) die Effekte von verschiedenen
landwirtschaftlichen Anbausystemen und Pflanzen auf die Vielfalt und
Zusammensetzung der Bodenbakterien zu untersuchen, und (ii) das Potential der
neuen, molekularen Techniken in der Untersuchung von Umwelteffekten auf
Bodeneigenschaften zu beurteilen.
Zu diesem Zweck wurde die Zusammensetzung und Diversität bakterieller
Gemeinschaften im DOK Langzeitversuch analysiert. Das DOK-Experiment in
Therwil (Schweiz) wurde im Jahre 1978 gestartet und wurde für die Evaluierung von biologisch-dynamischen (BIODYN), biologisch-organischen (BIOORG) und konventionellen Anbaumassnahmen inklusiv mineralisch gedüngter (CONMIN) und
ungedüngter (NOFERT) Kontrollsysteme konzipiert. Die Resultate dieser Arbeit zeigten mittels Kultivierungs- und molekulargenetischen Methoden auf, dass nach 25 Jahren kontinuierlicher Bewirtschaftung die mikrobielle Biomasse und die bakterielle
Gemeinschaftsstrukturen durch die verschiedenen Anbausysteme und Fruchtfolgen
7 ZUSAMMENFASSUNG signifikant und konsistent verändert wurden. Während die Gabe von festem und flüssigem Hofdünger den primären Effekt auf die bakterielle Gemeinschaft hatte, zeigten die Kulturpflanzen den zweitgrössten Einfluss, welcher aber von kurzzeitiger
Charakteristik war und sich nach einer Saison stark abschwächte. Die Unterschiede zwischen biologischem und konventionellem Anbau waren teilweise signifikant und zeigten die Hauptunterschiede im biologisch-dynamischen System. Im Gegensatz zu diesen Parametern, zeigten gebräuchliche Diversitäts-Indizes, welche durch die
Analyse von etwa 2000 ribosomalen RNA Genen ermittelt wurden, eine sehr hohe
Ähnlichkeit zwischen den verschiedenen Anbausystemen auf. Die verschiedenen
Dünge- und Pflanzenschutzmassnahmen beeinflussten die bakterielle
Diversitätsmessungen nicht und die signifikanten Unterschiede im Ernteertrag widerspiegelten sich nicht in den Diversitätsschätzungen. Dennoch unterstützten die Resultate der umfangreichen Genbank-Analyse die Erkenntnisse, die über die
Bakterienzusammensetzung gewonnen wurde. Die genetischen, strukturauflösenden
Methoden zeigten ein grosses Potential um Änderungen der bakteriellen
Zusammensetzung konsistent, schnell und mit grossem Durchsatz zu erfassen.
Ausserdem erlaubte es die Genbank-Analyse mehrere potentielle, system¬ spezifische Indikatoren bakterieller Gruppen zu detektieren. Diese Indikatoren können möglicherweise helfen detaillierte Information über die Effekte landwirtschaftlicher Massnahmen auf die bakterielle Bodengemeinschaft zu erhalten.
Die Untersuchung mikrobieller Gemeinschaften kann helfen Indikatoren für
Veränderungen von Bodeneigenschaften durch umweltbedingte und anthropogene
Einflüsse zu entwickeln. Die Kombination genetischer, strukturauflösender Methoden und spezifischer Identifizierung von potentiellen Indikatorgruppen liefert einen weiteren Schritt hin zur Diagnose von Schlüsselprozessen in definierten Systemen.
Die Verknüpfung dieser spezifischen Information der Zusammensetzung von mikrobiellen Gemeinschaften mit deren Funktionen im Boden kann zukünftig zu einem besseren Verständnis der Bodenqualität beitragen.
8 Chapter 1 :
General Introduction
1 1 GENERAL INTRODUCTION
1 General Introduction
1.1 Assessing soil quality
The natural resources air, water, and soil are fundamental to life on planet earth. Air and water quality are routinely controlled and thresholds to prevent disturbances of animal and human health as well as ecosystem damage are provided by legislation.
Protection of soil quality has also become an important goal of environmental policy
(LRV, 1985; GschV, 1998; VBBo, 1998). The soil system represents the primary basis for plant and food production. It serves as reservoir for many essential
nutritional compounds, which are obligate to guarantee life cycle. Furthermore, the soil system contains strong degradation capabilities for wastes and filtering characteristics for water (Karlen et al., 1997). Because soil formation through chemical and biological decomposition of rock lasts geological time spaces, e.g.
approximately 100 to 400 years for 1 cm of topsoil (Jenny, 1980), soil is a natural and
not renewable resource. Therefore, sustainable use of soil is important to maintain
soil functioning. Maintaining, enhancing, and reconstituting soil quality must be a
primary issue in ecological sciences.
1.1.1 Definition of soil quality
Air and water quality definitions are principally based on a maximum concentration
threshold of hazardous and toxic compounds. In contrast, a universally valid
definition of soil quality does not exist and has recently undergone several different
variations (Parr et al., 1992; Doran et al., 1994; Doran étal., 1996; Harris et al., 1996;
Gregorich and Carter, 1997; Karlen et al., 1997; Seybold et al., 1999; Stenberg,
1999; Doran and Zeiss, 2000; Karlen étal., 2001; Wander étal., 2002; Wienhold et
al., 2004). Soil productivity has generally been one of the principle issues, but is
nowadays considered as only one part of soil quality and may be better described as
soil fertility. Soil fertility and soil quality are strongly linked to each other, but may not
be equivalent (Patzel et al., 2000). Soil fertility may be defined as 'the sustainable
capacity of a soil to produce good yields of high quality on the basis of chemical,
physical, and biological quality factors' (Persson and Otabbong, 1994). Beyond soil
10 1 GENERAL INTRODUCTION fertility, the term soil quality is wider to also include environmental aspects. A soil of high quality produces high yields of high quality while simultaneously not harming the environment. Therefore, soil quality may be better described as 'the capacity of a specific kind of soil to function within natural and managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support animal and human health' (Doran et al., 1994; Karlen et al., 1997). In more detail, a soil of high quality should (i) contain locally typical and diverse communities, (ii) reveal undisturbed degradation capabilities, (iii) not influence the quality and development of plant communities, and (iv) not pose a risk to animal and human health (VBBo, 1998). Furthermore, soil quality can be differentiated in (a) its inherent attributes governed by soil formation and climate processes, and (b) the externally induced attributes influenced by environmental and anthropogenic factors. Because inherent attributes may change only slowly, environmental and anthropogenic factors may be the key determinants of soil fertility and quality.
1.1.2 Soil quality monitoring
The effort to develop suitable programs for soil quality has increased in the last years
(OECD, 2003). Three key questions may be central when setting up programs to ensure soil quality. What is soil quality? How do we measure and evaluate it? How do we improve or preserve it? These three questions imply a defined order of how to approach soil quality monitoring. Potential indicators have to be defined, followed by the development of suitable monitoring techniques of these indicators, and the generation of defined management and remediation strategies to protect and improve soil quality. Due to the lack of a universally valid definition, suitable soil parameters and thresholds are required that sensitively and consistently indicate changes in soil condition. High redundancy in soil processes and many unknown soil biological characteristics make it difficult to predict soil functioning. Before we can gain detailed information about functions and performance, and thereby quality of a soil, we first have to know which factors affect soil characteristics in which magnitude.
The relative strength of effects and the sensitivity of soil parameters analyzed may be crucial factors to assess changes in soil quality.
11 1 GENERAL INTRODUCTION
1.2 Agricultural management and ecological impact
An ecologically sustainable use of soil is essential for maintaining the soils capacity in providing resources for life (Stenberg, 1999). Sustaining soil quality is strongly linked to sustainable agriculture, because agricultural land reflects major areas of the global terrestrial surface. Therefore, sustainable agriculture is a primary issue for the protection of the global ecosystems.
1.2.1 Trends in agricultural production
To feed human populations worldwide, agricultural production has been tremendously increased in the last decades and has to be pushed further, if we want to feed the fast growing global population in the near future (Fig. 1.1). The global population has increased form 3 to 6.5 billion people in the last 45 years (Fig. 1.1a) and is assumed to increase further before leveling off at about 10 to 11 billion people
(Tilman, 1999b). This immense population growth may at least require a doubling of global food production. Areas of agriculturally used land has increased from 34 to 38% of total land between 1960 and today, highly correlating with population growth (Fig. 1.1a). Global food production has also drastically increased in the last decades and in particular demands for meat increase the pressure on agricultural food production (Fig. 1.1b). What impact will this increased food production have on the agricultural and non-agricultural ecosystems?
The green revolution, which has led to higher agricultural productivity in the last century has based on three different criteria; (i) modification of crops and their genetics, (ii) improvement of soil fertility by fertilizers and water, and (iii) enhanced regulation of pests (Tilman, 1999b). In the last decades, greater input of fertilizer, water, and pesticides (Fig. 1.1c and d), as well as new crops and modern technologies have been applied to increase the global yield (Tilman et al., 2002).
Agricultural science will therefore have to focus on future developments to guarantee sufficient food production, while preserving ecosystem health to sustain food production.
12 1 GENERAL INTRODUCTION
7.0 39.0 a • Population 6.5 38.5 c O Agricultural Land oOoooooO^S0' 38.0 T> o 6.0 CO CO 37.5 5.5 CO 5 "w" 37.0 _l 9- c 5.0 m O k^ o 36.5 1 CO CL = 4.5 u S '& 36.0 3 ra — «.«s*»" U 40 o° fc 80 35.5 ^ 3.5 • •ft88 35.0 < X 3.Ü 34.5
' ' * 2.5 1_ 34.0
2.4 0.28 O Cereals 8e. c ?? 0.26 o • Meat o Vi) oooocjggc? 0.24 D Fruits o CJ Vegetables & o°o°o0o 0.22 ^ 1.8 .•• A -— "a -—- Roots & Tubers o°o°00°0 0.20 "O o w 1.b 5 8 £: c 00 0.18 £ Cl o 1.4 CL O -*—• 0.16 "O oj 1 ? ,ooo ,a°L "O o) O O nQQ 0.14 O o i- O i- O 1 (I .OO "—' 0.12 LL nnacinn U.8 0.10 •••" CÖ • ?A\flAAnûAAAAûÛAAUAA cO 0.6 • QâQHBBa"AAAAÛÛÛÛÛÛAA^ 0.08 CD CL 0.4 SSâ 0.06 0.2 0.04
160 Total C 140 o "O Nitrogen .•** •4—r .-**%\ Q. 120 A Phosphorus F ..- 100 CO c
o 9 00o000O° an Oooo°000c°oo 60 - o ri. .•* Oo°°° N *" 40 0ooou ûflÛAAûAA •e AAAAAÛÛAÛÛÛAAAAûû <15 ?n LL 0
18 0.30 D Irrigation 16 DDdDDi 0.28 Pesticides -, 14 nQÜL 0.26 D 9-^ 12 0.24 noo O co tOT m ,DÛL 0.22 '5 -^ m -> -,' C35 "O m H 0.20 o ö o 6 0.18 to CD 4 0.16 CL 1 na°L 0.14
'- ' ' ' ' '-, 0 L-""=-- J 0.12
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Figure 1.1. Global agricultural trends over the past 45 years, a) global human population and agricultural land use, b) global plant and meat food production, c) global total, nitrogenous, and phosphorous fertilizer use, d) global pesticide imports and irrigated areas (data source: statistical database of the Food and Agriculture
Organisation of the United Nations, www.faostat.fao.org).
13 1 GENERAL INTRODUCTION
1.2.2 Agricultural impact on ecosystems
The fast development of agricultural practices to increase food production has led to severe environmental impacts. Main impacts come from conversion of natural ecosystems to agriculture, from leaching of nutrients that pollute aquatic and terrestrial habitats, from bio-accumulation of persistent pesticides, and from soil erosion (Stenberg, 1999; Tilman, 1999b; Tilman et ai, 2002). The doubling of food production between 1961 and 1996 was based on 6.9-fold increase in nitrogen application, 3.5-fold increase in phosphorus application, 1.7-fold increase in irrigated areas, and 1.1-fold increase of cultivated land in only 35 years (Tilman, 1999b). High applications of fertilizers and pesticides can increase nutrients and toxins in the ground and surface waters, representing a threat to human and animal health and leading to high costs for water purification. As a consequence of soil damages, higher input of fertilizer, irrigation and energy may be required to maintain the same production level on degraded soils. This vicious circle may result in irreversible damages of soils.
These concerns have led to stronger restrictions of manure, fertilizer, and pesticide application by national governments such as Switzerland (LWG, 1998). New agricultural management systems such as organic farming have been developed, which may protect agricultural areas from damages due to unsustainable farming.
However, little is known about the effective sustainability of organic farming practices
(Edwards-Jones and Howells, 2001). Conservative use of fertilizer and pesticides in organic systems may lead to a decrease in yield that in turn may lead to an increased demand for agricultural land and the conversion of natural ecosystems into agriculture areas to maintain productivity (Tilman et al., 2002). It was reported that
25% more agricultural land would have to be managed to compensate for 20% reduction in yield as observed in a long-term organic farming experiment (Goklany, 2002). Therefore, benefits emerging from the organic farming practices with regards to soil quality, e.g. reduced fertilizer and pesticide application, reduced energy input,
and increased soil organic carbon, are confronted with the detrimental effect of yield
loss.
How can we ensure sufficient food production while keeping land use to a certain
extent and preserving ecosystems? Increasing the efficiency in nutrient use, water
use and pest control may be the only way to decrease the environmental impact of
14 1 GENERAL INTRODUCTION
agricultural land use. Today, about 30 to 50% of nitrogen and phosphorus fertilizers
are taken up by the crops, while a significant amount is leaching from the agricultural
systems and harm off-site ecosystems and water quality as well as causing
atmospheric pollution (Tilman et al., 2002). Breeding of high-efficiency crops, use of
cover crops, reduction of soil tillage, improved timing of fertilizer application, and
investments in research and education may reduce fertilizer losses by leaching and
decrease the amount of fertilizers required for high crop yields. In addition,
application of organic fertilizer can also reduce the leaching process, because of its
slower release of nutrients. However, it is questionable if such fertilizer sources alone
are sufficient to ensure the nutrient demand of the crops during high-yield production
(Tilman et al., 2002). Water use efficiency may be improved by development of new
irrigation systems, increased water-holding capacity of soils by increased soil organic
matter contents and reduced tillage, as well as cultivation of crops with high water-
uptake efficiency and high drought tolerance. Finally, crop rotation and intercropping
systems, as well as breeding of highly resistant crops may improve pest control by
reducing the amount of pesticides required. Non-intensive management will
furthermore result in reduced soil erosion. All these strategies may help to maintain
sufficient food production while preserving agricultural soil ecosystems. However, the
optimal balance of all these forces to obtain sustainable agricultural areas is not
entirely understood yet.
1.2.3 Agricultural sustainability and soil quality
Sustainable use of soil in agricultural areas will determine soil quality. In principal,
maintenance of soil quality depends on a long-term balance between degrading and conserving forces (Fig. 1.2). Whereas balanced fertilizer application, improved
cropping, alternative plant protection strategies, and conserved soil tillage may
enhance soil quality, leaching of nutrients, soil erosion, contamination, and loss of organic matter may decrease soil quality and functionality. It is important to know, which of these factors are influencing particular soil parameters at which level of strength. These influences have to be evaluated on several different soil characteristics in order to assess their influence on soil quality. Only then we may gain a more detailed understanding of agricultural sustainability and may be able to predict thresholds and evaluate changes in soil quality.
15 1 GENERAL INTRODUCTION
Agricultural Ecological Sustainability
Quality ) CONSERVING FORCES DEGRADING FORCES • Residue management • Nutrient runoff • Fertilizer application balance • Erosion • Crop cultivation • Organic matter loss • Crop rotation and intercropping • Acidification • Conservation tillage • Toxicant accumulation • Improved drainage • Compaction • Water conservation • Crusting • Pest management • Denitrification • Salinization Figure 1.2. Factors influencing soil quality and their impacts on agricultural sustainability (modified from Kennedy and Papendick, 1995). 1.3 Soil characteristics Agroecosystem sustainability comprises environmental, economic and social aspects. The environmental quality is reflected in the relative quality of soil, water, and air. Soil characteristics can be divided into three major parts, addressing physical, chemical, and biological properties (Stenberg et ai, 1998). Therefore, knowledge about sustainability of agriculture is directly linked to these three soil characteristics (Fig. 1.3). For a detailed description of soil quality and soil fertility, suitable parameters from all three parts are required. Chemical and physical parameters have been in the focus of applied soil analysis (Stenberg et ai, 1998; Stenberg, 1999). Physical characteristics such as soil texture, aggregate stability, bulk soil density, water holding capacity, soil temperature, and field capacity and chemical characteristics such as pH value, base saturation, cation-exchange capacity, mineral nutrients, heavy metals, and trace elements were intensively studied (Stenberg et ai, 1998). 16 1 GENERAL INTRODUCTION Agricultural Sustainability Ecological Economie Social Sustainability Sustainability Sustainability 3^2: Environmental Quality Soil Quality Water Quality Air Quality ^2: Soil Quality Biological Chemical Physical Characteristics Characteristics Characteristics Biomass Nutrient availability Texture & structure Respiration pH Compaction/porosity Community structure Base saturation Depth Substrate utilization Toxic materials Aggregate stability Enzymatic activity Salinity Electrical conductivity Nutrient cycling Cation exchange capacity Water holding Pathogenic & capacity beneficial organisms Figure 1.3. Relation between agroecosystem sustainability and soil quality as a function of biological, chemical, and physical characteristics (modified from Andrews et al., 2002). However, legislation and science still struggle with defining optimal and threshold values for soil parameters. Regulations mainly focus on chemical parameters such as heavy metals and toxic organic compounds and on soil erosion (VBBo, 1998). Biological components of soil quality are still widely unassessed (Stenberg et ai, 1998), although the importance of biological processes for soil functioning as mediated by organisms like arthropods, earthworms, or microorganisms, has been recognized. Biological parameters often lack a standardized methodology and, in addition, important components of soil biology, particularly soil microbiology, are simply unknown. The value of the biological component regarding soil quality is 17 1 GENERAL INTRODUCTION therefore hardly predictable up to date (Parr et ai, 1992; Jordan et ai, 1995; Doran and Zeiss, 2000; Bending et ai, 2004). Physical and chemical parameters are mainly funded in the soil formation process, and are inherently linked to the on-site conditions. These parameters may change slowly as long as drastic impacts such as soil erosion, compaction or pollution are not occurring. Biological soil components, including plant roots and soil organisms, may be more sensitive and faster changing with regards to environmental and anthropogenic influences (Dick, 1992). Therefore, biological properties may comprise suitable parameters to assess changes at a high sensitivity (Elliott, 1997) and may serve as early warning system in soil monitoring programs (Jordan et ai, 1995; Pankhurst et ai, 1995; Nielsen and Winding, 2002). 1.4 Microbial soil characteristics and soil quality 1.4.1 Microbial roles in soil processes Soil microorganisms are the predominant part of soil biological components, and are responsible for many important processes that support soil functioning. Soil microorganisms are playing important roles in maintaining, enhancing, and reconstituting soil quality and plant health (Nielsen and Winding, 2002; Nannipieri et ai, 2003). Microbial communities perform essential functions in decomposing organic matter, mineralizing nutrients, providing soil structure and degrading toxic compounds. It is assumed that 80 to 90% of all processes in soil are mediated by microorganisms (Coleman and Crossley, 1996; Nannipieri and Badalucco, 2003). They are strongly involved in nutrient cycling processes such as decomposition of organic materials and mineralization of residues (Balloni and Favilli, 1987; Bloem et ai, 1997; Doran and Zeiss, 2000). Microorganisms are involved in transformation, degradation, and immobilization processes of various hazardous compounds such as pesticides or heavy metals (Nakajima and Sakaguchi, 1986; Mulbry and Kearney, 1991; Aislabie and Lloydjones, 1995; Kumar et ai, 1996; Tsekova et ai, 1998; Kotrba and Ruml, 2000; Zhang and Qiao, 2002). Furthermore, they significantly influence soil structure and soil aggregate stability by the production of extra-cellular compounds and therefore affect physical parameters such as water holding capacity, 18 1 GENERAL INTRODUCTION infiltration rate, crusting, erodibility, and susceptibility to compaction (Lynch, 1984; Lynch and Bragg, 1985; Gupta and Germida, 1988; Elliott et ai, 1996). They are capable to suppress soil-borne pathogens through antagonism, synthesize various enzymes, vitamins, and hormones that regulate population processes, and they directly interact with plant functioning, by providing nutrients and promoting nutrient uptake, or inducing competition as well as pathogenesis (Altieri, 1999). 1.4.2 Microbial soil quality indicators Based on all the aspects of soil processes mentioned above, soil microbiology holds the potential to give an integrated measurement of soil quality. Various methods are available to assess specific components, e.g. enzymes, involved in processes such as C- and N-cycling. However, different microbial species may carry out the same processes in soil, which is defined as functional redundancy, and therefore may undergo changes without apparent loss of functioning (Visser and Parkinson, 1992; Yin et ai, 2000). Therefore indicators such as enzyme activities that are shared by different species may be less sensitive to disturbances (Stenberg, 1999). More emphasis should therefore be given to redundancy-independent indicators like microbial community compositions, because these attributes may be more sensitive to environmental influences (Kennedy and Smith, 1995). Rapid response of microorganisms to changes of environmental conditions such as nutrient availability favor microbial parameters for use in sensitive soil quality assessments (Kennedy and Papendick, 1995; Pankhurst et ai, 1995). Changes in microbial community structures or activities may precede other changes, for instance in physical or chemical characteristics and therefore may provide a suitable early indicator for alterations in soil quality. The multi-functionality of microbial communities in soil ecosystem processes allows for the definition of many potential indicators of soil quality (Nielsen and Winding, 2002). Parameters such as basal respiration rate (Anderson, 1982), microbial biomass (Anderson and Domsch, 1978; Vance er ai, 1987), metabolic quotient (Anderson and Domsch, 1985), nitrogen mineralization (Ladd and Jackson, 1982), nitrification (Belser and Mays, 1980) and denitrification (Smith and Tiedje, 1979) potentials, mycorrhizal colonization (Kling and Jakobsen, 1998), or enzyme activity (Dick, 1994; Dick, 1997) are routinely used for describing the condition of a soil and 19 1 GENERAL INTRODUCTION soil its response to environmental and anthropogenic influences. These biological quality analyses are focused on determination of bulk parameters. New technical developments in the recent years, many of them based on molecular tools, allow to gain a more detailed insight into microbial diversity and community structures (Hill er ai, 2000; Nielsen and Winding, 2002; Hill et ai, 2003; Kirk et ai, 2004). Information about microbial diversity and community structures may be very important to understand the relationship between environmental factors and ecosystem functions (Torsvik and Ovreas, 2002; Torsvik et ai, 2002). However, the relation between microbial community structures and soil quality is not well understood yet. The promising new approaches may detect influences of environmental changes at high sensitivity and hold the potential for use as excellent soil monitoring tools. 1.5 Soil biodiversity Soil biodiversity comprises all organisms that live in soil, which can be classified using valid taxonomic techniques. Taxonomic diversity is typically defined by species richness, which is a simple count of species recorded, and by the relative abundance of the species, which reflects the number of individuals of a single species in relation to the total number of individuals of all species detected. Recycling of nutrients, detoxification of noxious chemicals, control of pathogen abundances, regulation of soil microclimate and hydrological processes are largely biologically controlled. These processes are only stably maintained as long as microbial communities are functionally intact, which is highly depending on the types and numbers of organisms present. Therefore, the general ability of a soil community to cope with environmental perturbations has been suggested to be directly linked to soil biodiversity (Kennedy, 1999; Johnsen et ai, 2001; Bardgett, 2002). Stability of ecosystem processes depends on the maintenance of biological diversity (Altieri, 1999). However, although high biodiversity is considered to indicate high soil quality, the relationship between soil biodiversity and soil quality is not understood. A significant reduction in biodiversity may results in a deficit of specific functions (Bell et ai, 2005) and may result in dependence of external supply to maintain system functions. On the other hand, high functional redundancy among microbial populations may provide complete soil functioning also if diversity is affected. 20 1 GENERAL INTRODUCTION 1.5.1 Agricultural influence on soil microbiota the Global factors such as climate or soil formation processes are influencing scale. At the local scale, presence of soil microorganisms at the large agricultural management is supposed to be a key factor in determining soil biodiversity (Garbeva et ai, 2004). Fertilizer and pesticide application, soil tillage and residue management, abundance and distribution in crop rotation, and irrigation are affecting species harbor a reduced agroecosystems. It was recently reported that agricultural soils microbial diversity when compared to forest or pasture soils (Torsvik et ai, 2002). Whereas forest and pasture soils revealed several thousand different genotypes per a few hundred. The recurring gram of soil, arable soils harbored only chronologically of soil management events permanently convert the affected area to new phases conditions, limiting the development of the indigenous community. At which degree agricultural factors affect soil biodiversity is intensively discussed in ecological research and important questions has been raised. Which agricultural factors are microbial responsible for the decreased microbial diversity in arable soils? Is a high store diversity important for soil quality and if yes how could one increase or have to be biodiversity? In soils with low biodiversity, fertilizers and pesticides may applied to compensate for the loss of self-fertilization and self-regulation capabilities. scientific This in turn may reduce soil, water, and food quality. Therefore, strong emphasis has been given to the topic of soil biodiversity and its changes by agricultural factors (Buyer and Kaufman, 1997; Altieri, 1999; Kennedy, 1999; OECD, 2003; Swift et ai, 2004). 1.5.2 Soil microbial diversity at different resolution levels, i.e. Studies on soil microorganisms may be performed strain level, species level, higher groups of organisms, whole microbial communities, the enormous and unknown or ecosystem level (Parkin, 1993). However, diversity in these components of microbial communities complicate detection of changes populations. Whereas higher organisms reflect only a very small part of the phylogenetic biodiversity on earth, the tree of life is dominated by the huge diversity of microorganisms including all prokaryotes (Bacteria and Archaea) and a large part of eukaryotes (Fig. 1.4). 21 1 GENERAL INTRODUCTION Bacteria % *>ÏW/, "°*%fe^ Methanospinttum Leptonema manne Gp.1 low temp Clostridium Bacillus Archaea Cryptomonas Eukarya Figure 1.4. Universal phylogenetic tree based on SSU rRNA gene sequences from all three kingdoms. Higher organisms are indicated by the circle. All other displayed branches belong to microbial organisms. The scale bar corresponds to 0.1 changes per homologous position (adapted from Pace, 1997). 22 1 GENERAL INTRODUCTION Microorganisms reveal an immense phylogenetic variation when compared to the remaining part of life forms. There are many different estimates on how many prokaryotic species are covering the ecosystems. Estimates reach from several ten thousands (Palleroni, 1994), half a million (Tiedje, 1994), or 2-3 millions (Trüper, the 1992) up to 109-1012 (Dykhuizen, 1998) different prokaryotic species. Based on current knowledge one may hypothesize, that approximately 1030 prokaryotic cells consisting of billions of different species are inhabiting the global ecosystems (Dykhuizen, 1998; Whitman et ai, 1998). These are very large numbers when compared to the number of 7683 Bacteria species validly described in the 'Approved List of Bacterial Names' (July 2006, for updates see www.bacterio.net). The heterogeneous environments of soil systems provide the basis for extremely diverse communities. One gram of soil may harbor 10 billions of prokaryotes belonging to several thousands of different species (Torsvik et ai, 2002). This complicates analysis of these communities and makes coverage of their entire diversity by traditional methods commonly applied for other organisms impossible. Therefore techniques have to be developed, which determine microbial characteristics and assess their response to environmental factors. 1.6 Assessing soil microbial community parameters 1.6.1 Soil microbial biomass Definition The soil microbial biomass describes the living component of the soil organic matter pool, and excludes animals and plant roots. Although it comprises less than 5% of the organic matter in soil, the microbial biomass performs 3 key functions (Dalai, 1998). (i) It is a labile source of carbon, nitrogen, phosphorus and sulfur, (ii) it represents the main depot of these elements in soil, and (iii) it is the medium where all nutrient cycling and transformation processes must pass through. Therefore it is considered as an important indicator for soil quality. Soil microbial biomass may reflect the general potential for turn-over of materials in soil. A large biomass is considered to store more nutrients and may be capable to flush more nutrients and energy to the entire soil system (Dalai, 1998; Stenberg et ai, 1998). Efficient plant 23 1 GENERAL INTRODUCTION production therefore strongly relies on high content of soil microbial biomass representing high nutrient cycling and storing capacity. Due to a relatively fast turnover time it may be sensitive to soil disturbances (Dalai, 1998). Factors influencing soil microbial biomass Several factors may significantly affect soil microbial biomass (for review see Dalai, 1998). There is a wide scientific consensus that increased supply of organic substrates leads to a increased quantity of soil microbial biomass. Besides organic substrates, temperature and moisture, and therefore seasonal variations, seem to predominantly affect soil microbial biomass (Wardle and Parkinson, 1990). Furthermore, reduced soil tillage produces an accumulation of plant residues in the upper soil surface layer and leads to higher microbial biomass in non-tilled top layers, but revealing similar total biomass in deeper soil layers. The soil matrix also influences microbial biomass, revealing generally higher biomass with increased soil clay content. Additionally, biomass content may also be related to the size of soil aggregates. Land management was reported to significantly influence microbial biomass, revealing decreased contents with increased cultivation intensity. Several agricultural management factors such as crop rotation and organic or mineral fertilizer applications significantly influence soil microbial biomass contents. Rotations and additives, which increase plant biomass production usually increase soil microbial biomass content. Specific management practices or pollutions may also influence soil pH, leading to a decreased microbial biomass with increased soil acidity. Finally, soil contaminations by heavy metals were reported to strongly decrease soil microbial biomass, while pesticide effects seemed to be highly dependent on the concentrations applied. Techniques to determine soil microbial biomass In the last decades, rapid and reliable assessments of soil microbial biomass were developed, including chloroform fumigation incubation (CFI: Jenkinson and Powlson, 1976), chloroform fumigation extraction (CFE: Brookes et ai, 1985; Vance et ai, 1987), substrate induced respiration (SIR: Anderson and Domsch, 1978), adenosine triphosphate (ATP) analysis (Jenkinson et ai, 1979; Eiland, 1983; Webster er ai, 1984), and phospholipid fatty acids (PLFA: Zelles et ai, 1992). While CFI and CFE measure the entire biomass including dead, non-degraded cells, SIR and ATP are 24 1 GENERAL INTRODUCTION detecting the active part of the biomass. Alternative methods to measure soil and microbial biomass rely on enumeration of individuals by fluorescent staining direct microscopy. Thus, estimation of biomass relies on calculation of cell volumes These and estimation of mean carbon content per cell volume (Bolter et ai, 2002). are not suitable techniques are laborious and time consuming (Jenkinson, 1988), and to determine nutritional values such as carbon content. In this thesis, the prominent entire methods CFE and SIR were applied, which allow to compare between the biomass (living and dead) and the active biomass. CFE and SIR allow for a rapid and reliable estimation of soil microbial biomass, and are routinely applied in soil microbial ecology studies. Chloroform fumigation extraction (CFE) which is With the CFE technique, a soil sample is fumigated with chloroform, leading to the disruption and lysis of the cells. The released cell components are extracted together with other soil carbon using an aqueous K2S04 solution and total extracted carbon content is measured. From this measurement, soil microbial carbon content (Cmic) in the solution is quantitatively determined by subtraction of the organic carbon in carbon content between fraction of a non-fumigated control soil. The difference the fumigated and non-fumigated samples is then converted into the microbial biomass parameter by applying an empirically derived conversion factor (Joergensen, 1996). Therefore, CFE represents a technique to indirectly determine soil microbial biomass. This technique can also be applied to determine the content of microbial nitrogen or phosphorus extracted from soil. Substrate induced respiration (SIR) The SIR method is based on the characteristic that microorganisms respond with an increased respiration, i.e. 02 consumption or C02 production, when an easily degradable carbon source, usually glucose, is applied. For the purpose of biomass of determination, an equilibrated soil sample is mixed with an excess amount substrate to reach saturation. The maximum initial C02 production, or alternatively the 02 consumption, is measured. The maximum respiration rate has been shown to be proportional to the microbial biomass, therefore allowing the application of a conversion factor (Kaiser et ai, 1992). Because microbial metabolic activity is highly dependent on the soil water content, it is important that the soil samples are 25 1 GENERAL INTRODUCTION holds the equilibrated to the same soil moisture content. This equilibration step with the detection of problem of inducing an artificial bias and potentially interferes differences in the original condition of the soil microbial biomass. 1.6.2 Diversity and community structure Cultivation-dependent and -independent approaches been Responses of soil microbial communities to various influences have traditionally studied at the total community level and were based on analyses such as biomass, information about respiration rate, and enzymatic activity. Although yielding important environmental basic soil conditions, these techniques are not suitable to analyze The of effects on distinct parts of the microbial community. development techniques of microbial to analyze presence, distribution, activity, and function specific groups in detail. are important to understand ecosystem processes greater Morphological studies by staining and direct microscopy are limited by the small number of morpho¬ communities et ai, types when compared to the high diversity of microbial (Amann 1995). Techniques such as community level substrate utilization (CLSU) profiling and are (Garland and Mills, 1991) rely on the utilization of specific substrates the that therefore based on cultivation of microorganisms. This includes problem only can be cultivated under standard a very small proportion of soil microorganisms of all conditions (Colwell and Grimes, 2000). There are estimates that only 1% et ai, bacteria present in the soil may be culturable by standard techniques (Torsvik 2002). Optimal growth conditions may strongly vary among different microorganisms, nutritient concentrations, which also e.g. requiring either very low or very high implicate growth competition. Some organisms may only grow as consortia requiring signaling of other organisms (Kaeberlein et ai, 2002). This inability to grow the major count part of microbial species has also been referred to as 'the great plate anomaly' (Staley and Konopka, 1985). Therefore, cultivation-dependent techniques may largely fail to represent entire soil microbial communities. The CLSU technique is based on specific substrate utilizing functions performed by aerobic heterotrophic bacteria (Garland and Mills, 1991). Depending on the community composition, different carbon substrates will be differently utilized. Substrate consumption will be reflected by color development in this substrate for 26 1 GENERAL INTRODUCTION with the example using the Tetrazolium-Formazan system. CLSU analyses have been commercially available Biolog system (Garland and Mills, 1991) applied information on differences of microbial communities in many studies in order to gain O'Donnell et ai, in various soil systems (Grayston et ai, 1998; Gomez et ai, 2000; The is 2001; Larkin, 2003; Fliessbach and Mäder, 2004). Biolog technique assessment restricted to culturable microorganisms and does not provide a precise in soil et ai, 1997; of the functional properties of microbial communities (Garland Preston-Mafham et ai, 2002). It was shown that fast-growing copiotrophic organisms et ai, reducing the abundance of are predominant in Biolog profiles (Smalla 1998), in slow-growing oligotrophs. However, CLSU has been successfully applied and sensitive low comparative soil analyses and has been shown to be a powerful or in soil microbiological cost analytical tool for demonstrating differences changes Gomez et ai, characteristics (Fliessbach and Mäder, 1997; Konopka et ai, 1998; the CLSU was 2000; Widmer et ai, 2001). Due to these advantages, technique to novel molecular techniques applied in the current thesis and compared genetic described in the following section. based methods, Cultivation-independent techniques, in particular nucleic acid may less biased characterization of soil overcome these limitations and may allow for a reaction microbial communities. The introduction of the polymerase chain (PCR) for the specific detection and represented a milestone in molecular biology, allowing and Faloona, investigation of genetic material even at very small quantities (Mullis to microbial (Pace et 1987). Molecular genetic analyses have been applied ecology view on microbial life ai, 1986; Pace, 1997) and have opened a completely new on these (Hugenholtz, 2002; Rappe and Giovannoni, 2003). Based techniques, on their information, which allows for the organisms may be compared based genetic relatedness. Several are classification of groups according to phylogenetic genes research suitable for community studies and their selection depends on the objective. assess microbial with specific Targeting functional genes may allow to groups or denitrification et ai, 2000; functions such as nitrogen fixation, nitrification, (Bothe do not allow for the Zehr and Turner, 2001). However, many functional genes function. Other comparison of diverse microbial groups not exhibiting the same These so markergenes common to a broader range of microorganisms are required. and allow for called house-keeping genes are essential for all organisms comparison 27 1 GENERAL INTRODUCTION such as the ribosomal RNA among diverse groups. Therefore, markers (rRNA) genes of soil microbial are promising targets for the identification community components Classification of (Pace et ai, 1986; Pace, 1997; O'Donnell and Gorres, 1999). of this organisms and their phylogeny may be assessed by sequence comparison gene. Ribosomal rRNA genes for phylogenetic studies see Lafontaine and 2001) The rRNA gene system (for detailed description Tollervey, for allowed to establish a widely accepted classification system microorganisms Rossello-Mora and Amann, (Woese et ai, 1990; Amann ef ai, 1995; Pace, 1997; microbial life 2001) and brought new insights into the diversity of by detecting et The rRNA numerous previously unknown organisms (Hugenholtz ai, 1998). gene several is the most commonly used genetic marker in phylogenetic studies due to advantages: of the (i) The rRNA genes are essential components protein synthesis of apparatus, are present in all organisms, and reveal a high degree functional constancy (Stackebrandt, 2001). Gene mutations during evolution enable its use as a molecular chronometer and provide evolutionary relationships among all living cells (Woese, 1987). which can serve as (ii) The rRNA genes reveal highly conserved regions, essential in 'genetic anchors' in molecular analysis. These regions are Other targeting organism groups along a wide phylogenetic range. regions show high variability and therefore allow for the reconstruction of evolutionary relationships (Stackebrandt, 2001). (iii) Sequence lengths of the small and large subunits contain sufficient information for reliable phylogenetic analysis (Amann et ai, 1995), but is still suitable for rapid analysis (Stackebrandt, 2001). (iv) Sequence databases of rRNA genes, in particular the ones containing of the rRNA are sequences encoding for the small subunit (SSU) gene, all kind of growing fast and build a strong basis for data comparisons among studies. Currently, around 250'000 bacterial SSU rRNA gene sequences are deposited in the RDP-II database (www.rdp.cme.msu.edu, Cole et ai, 2005). 28 1 GENERAL INTRODUCTION (v) Mutations in the conserved core regions are forced towards nucleotide substitution rather than deletion or insertion. This facilitates sequence alignments required to compare among large sets of sequences (Lafontaine and Tollervey, 2001). rRNA low (vi) Lateral gene transfer has not been reported for genes, indicating frequency of DNA exchange at this genome position (Stackebrandt, 2001). Therefore, application of techniques targeting this molecular marker may have the capability to resolve the structure of highly diverse microbial communities (Hughes et ai, 2001). In this thesis, the rRNA gene was used as phylogenetic marker to assess the influence of agricultural management on bacterial diversity and community composition. Sequence analysis in microbial ecology marker such as the Currently, retrieval of sequence information from a representative to assess the rRNA gene may probably reflect the most detailed approach genetic allow for community composition in a sample. The sequence information would phylogenetic affiliation to known organisms and to compare community composition is from among different samples. For this purpose, the marker gene directly amplified soil nucleic acid extracts by PCR with marker-specific primers, followed by molecular cloning and sequencing of the amplification products (Dunbar et ai, 1999; Sambrook and Russell, 2001). Cloning and sequencing approaches for bacterial communities has been successfully applied in several studies (for review see Janssen, 2006) Such an approach may eventually be capable of resolving the entire species diversity in a given sample (Hughes et ai, 2001). However, although information content and resolution power of for this approach is very high, the method is highly laborious and time-consuming diverse communities and has limited automation capabilities. Therefore, the approach is suitable for communities with moderate species diversity and low number of samples. Recent technical developments allow for large-scale sequence analysis (Tyson er ai, 2004; Venter, 2004; Tringe et ai, 2005), but these technical demands have to on are not routinely accessible. Monitoring of soil quality characteristics rely rapidly applicable tools, which currently excludes the routine use of molecular cloning approaches. 29 1 GENERAL INTRODUCTION Genetic profiling approaches microbial Genetic profiling techniques give a relative and simplified image of the community structure present in a sample. They may represent a more practicable structures soil way to routinely assess differences in community among samples. marker These techniques are based on PCR amplification of a specific genetic region, followed by resolution of the amplified genes based on specific sequence characteristics. In principal, the available genetic profiling techniques can be divided that relies on into three groups. A first type of methods was developed conformational changes and melting behavior of amplified sequences, e.g. denaturing and temperature gradient gel electrophoresis (DGGE: Muyzer et ai, 1993; Muyzer and Smalla, 1998; Muyzer, 1999) and single strand conformation polymorphism (SSCP: Schwieger and Tebbe, 1998). A second category is based on length polymorphism of amplified marker genes, e.g. ribosomal intergenic spacer analysis (RISA: Fisher and Triplett, 1999; Ranjard er ai, 2001) or length heterogeneity PCR (LH-PCR: Suzuki et ai, 1998). The third category relies on analysis of restriction endonuclease-derived fragmentation patterns, where the of marker genes are differentiated based on the location specific enzymatic Massol- restriction sites, e.g. PCR restriction fragment length polymorphism (RFLP: Deya et ai, 1995) and terminal RFLP (T-RFLP: Liu et ai, 1997; Osborn et ai, 2000). their own These methods are well developed and widely applied, and all have advantages and limitations (for review see: Hill er ai, 2000; Kirk et ai, 2004). Whereas DGGE, TGGE, and SSCP may differentiate at very low phylogenetic levels, information of the e.g. species, and allow to efficiently accessing phylogenetic operational taxonomic units (DGGE or SSCP bands), these techniques show only a In these moderate resolution power for highly complex communities. addition, of techniques have a low automation capability, which interferes with high throughput samples, and do not allow for comparison among larger batches of samples. Furthermore, DGGE and TGGE are not compatible with capillary electrophoretic systems. RISA represents a rapid, high-throughput and high-resolution technique, as DGGE and with a similar phylogenetic sensitivity and identification capability SSCP, but currently is lacking the extensive sequence database required for comparison of data among studies. Application of capillary electrophoretic systems directly converts banding patterns into digital data and therefore represents an 30 1 GENERAL INTRODUCTION T-RFLP has to optimal analysis for following statistical data analysis. Finally, proven for diverse be a consistent and rapid high-resolution profiling technique highly resolution when to the communities, but may have a lower phylogenetic compared to ensure for other approaches. T-RFLP and RISA were applied in this project (i) comparability of highly diverse soil bacterial communities, (ii) rapid sample processing and automation capability, (iii) high-resolution analysis by capillary data electrophoresis, (iv) comparability among larger data sets, and (v) digital output to perform profound statistical analysis. Terminal restriction fragment length polymorphism (T-RFLP) analysis ribosomal DNA The PCR-RFLP analysis of rRNA genes, also called amplified to differentiate restriction analysis (ARDRA: Massol-Deya et ai, 1995), allows of restriction sites. This sequences based on the location specific enzymatic have different technique relies on the principal that different phylogenetic groups may DNA loci for specific restriction sites due to nucleotide mutations. An amplified target restriction endonuclease, which results in a sequence is digested with a specific an number of fragments with defined length. The fragments are separated on which results in a characteristic for the agarose or Polyacrylamid gel, banding pattern differentiates based on the variation sequences analyzed. This procedure sequences in a of the restriction site loci. The technique allows to process numerous samples and valuable short time without the need for expensive equipment and yields robust information of a information about the similarity of different sequences. However, the when to the RFLP pattern is limited and gives lower distinction power compared have several complete sequence information. Because each sequence may number of restriction sites, the number of fragments dramatically increases with the resolution limit. In addition, sequences present in the sample, often reaching of because signal intensity is directly linked to the number and length fragments sites produced, shorter fragments derived from sequences with many restriction may with less restriction not be detectable in the profile when compared to sequences of soil sites yielding fewer and longer fragments. Therefore, the microbial profiles allow reliable samples are often extremely complex and do not analysis. The development of terminal restriction fragment length polymorphism (T-RFLP, Fig. has the of 1.5) analysis, a modification of the RFLP technique, approached problem the analyzing complex microbial communities (Liu et ai, 1997). For this purpose, 31 1 GENERAL INTRODUCTION labeled marker gene is amplified from the total extracted DNA by using fluorescently PCR primers (one or both). Identical to the RFLP procedure, PCR products are digested with frequently cutting restriction enzymes. The fragmented products are separated on a high-resolution sequencing device with laser-induced detection of fluorescent labels. This allows for the detection of only fluorescently labeled fragments and significantly reduces the amount of fragments in the pattern. This technique produces profiles that are characteristic for the investigated samples. The T-RFLP profiles of different sample may then be compared for presence/absence and/or the relative abundance of each particular fragment and assess relative differences in the community structures (Fig. 1.6). The development of the T-RFLP technique has allowed for the analysis of complex communities and reveals several strong advantages when compared to other community profiling techniques (Marsh, 1999). (i) Similar to DGGE, SSCP and RISA, each fragment represents one phylotype, whereas in the RFLP approach the phylotype is represented by several fragments. (ii) The T-RFLP technology has considerably higher resolution than the commonly used gel electrophoretic systems and may separate the fragments with only one base pair difference. (iii) Quantities of the terminal restriction fragments are detected by laser-induced fluorescence and allows for a more reliable quantitative comparison among different samples. (iv) The output is digital and readily converted into numeric data, which enables subsequent statistic analyses. (v) The technique offers the possibility to compare data with data stored in sequence databases or with data from other studies. (vi) Finally, the method has a high potential for automation and reveals high analytical consistency, which is a prerequisite for high throughput of samples required in monitoring studies. 32 1 GENERAL INTRODUCTION T-RFLP RISA Extraction of nucleic acids Amplification of marker-sequence Target: SSU rRNA gene Target: intergenic spacer region —.-win.iürii IT! I.IIIJ \i)M ^MIHi1!FjlJ3iSwBl ITS213lfffl7M- v y Y V TT? Sequence 1 Sequence 2 Sequence 3 Sequence 1 Sequence 2 Sequence 3 Enzymatic restriction digest of PCR product Digest obligate Digest optional Sequence 1 Sequence 2 Sequence 3 Labeled fragments of different lengths Sequence 1 Sequence 2 Sequence 3 Sequence 1 Sequence 2 Sequence 3 Separation & detection of fragments by capillary electrophoresis ^U-Z^Jl fragment length /TWWW^/ genomic double stranded DNA a terminal fluorescent label ^^^^ amplified double stranded DNA enzymatic restriction site Figure 1.5. Terminal restriction fragment length polymorphism (T-RFLP) analysis and ribosomal intergenic spacer analysis (RISA) used in genetic profiling. Specific marker gene targets are amplified from soil extracted, genomic DNA, and analyzed for specific characteristics of the marker gene, i.e. length of T-RFs (T-RFLP) or the entire marker gene (RISA). Marker gene fragments are analyzed by capillary electrophoresis, yielding characteristic genetic profiles for the community of the sample. 33 1 GENERAL INTRODUCTION relative migration units (rmu) - 2000 3 V) 'c 3 -I—' C CD Ü w CD i_ O _D CD > "4—* CD Figure 1.6. Genetic profile patterns of five different soil samples obtained by capillary electrophoresis of fluorescently labeled fragments. Each peak represents a specific fragment length (relative migration units), whereas the height of the peak (relative fluorescent units) represents the relative abundance of the fragment in each sample. Each unambiguously detectable peak is compared among all samples to detect differences in the abundance of the respective peak (two examples are indicated by black arrows). T-RFLP analysis has been shown to detect changes in complex microbial community structures of different microorganisms and in different habitats and environmental conditions. Microbial community structures were successfully analyzed in marine environments (Moeseneder et ai, 1999; Urakawa et ai, 2000; Braker et ai, 2001; Moeseneder et ai, 2001), saline rich environments (Casamayor et ai, 2002; Ovreas et ai, 2003), rice field soils (Fey and Conrad, 2000; Lüdemann et ai, 2000; Ramakrishnan et ai, 2001; Weber et ai, 2001; Noll et ai, 2005), grassland soils 34 1 GENERAL INTRODUCTION (Kuske et ai, 2002; Brodie et ai, 2003), forest soils (Klamer et ai, 2002; Hackl et ai, 2004; Leckie, 2005), agricultural soils (Blackwood and Paul, 2003), activated sludge (Liu et ai, 1998; Marsh et ai, 1998; Hiraishi et ai, 2000), mine water (Takai et ai, 2001), and in intestines of eukaryotes (Friedrich et ai, 2001; Kaplan et al., 2001; Schmitt-Wagner et ai, 2003). T-RFLP analysis was applied to study effects of metal contamination (Konstantinidis et ai, 2003; Tom-Petersen et ai, 2003; Hartmann et ai, 2005; Mengoni et ai, 2005; Frey et ai, 2006), hydrocarbon pollution (Denaro et ai, 2005), 4-chlorophenol pollution (Jernberg and Jansson, 2002), different crops (Kuske et ai, 2002), transgenic plants (Rasche et ai, 2006), compost amendment (Perez-Piqueres et ai, 2006), dry-rewetting stress (Fierer et ai, 2003; Pesaro et ai, 2004), flooding stress (Graff and Conrad, 2005), or C02 exposure (Klamer et ai, 2002). Although the technique is commonly applied to rRNA genes, it was also successfully applied to functional genes. The genes encoding methyl-coenzyme M reductase a- subunit {mcrA) were targeted to study methanogenesis in rice field soils (Lueders et ai, 2001; Ramakrishnan et ai, 2001) and boreal fen (Galand et ai, 2002). Genes encoding for nitrite reductase (nirS and nirK) (Braker et ai, 2001; Wolsing and Prieme, 2004) or nitrous oxide reductase (nosZ) (Scala and Kerkhof, 2000) were used to study the community structures of organisms involved in the denitrification process. Genes encoding for the active-site polypeptide of ammonia monooxygenase (amoA) was used to study the community structure of ammonia-oxidizing bacteria such as Nitrospira or Nitrosomonas (Horz et ai, 2000). Also genes involved in T-RFLP mercury resistance mechanisms (mer genes) were analyzed with the technique (Bruce, 1997; Bruce and Hughes, 2000). These approaches may allow to obtain information on functional characteristics of microbial communities. In conclusion, T-RFLP analysis proved to be a powerful tool to detect environmentally or anthropogenically induced differences in microbial communities. Ribosomal intergenic spacer analysis Ribosomal intergenic spacer analysis (RISA) allows for the analysis of microbial community structures with a similar principle as the T-RFLP technique, generating digital electrophoretic patterns as output data (Fig. 1.5 and 1.6). This method is based on the fact, that ribosomal internal transcribed spacer (ITS) regions between the small subunit (SSU) and the large subunit (LSU) of the rRNA genes reveal 35 1 GENERAL INTRODUCTION differences in DNA sequence and length heterogeneity (Jensen et ai, 1993; Fisher and Triplett, 1999). A primer pair in the ITS-flanking SSU and LSU rRNA genes are used for amplification of the ITS region. The amplified product contains amplicons of different length and can be separated by common gel electrophoresis, yielding a banding pattern characteristic for the analyzed sample. The ITS region shows a high level of length and sequence polymorphism, which allows for differentiation at the level of genera and species as reported for prokaryotes (Barry et ai, 1991) and fungi (Gardes et ai, 1991). A substantial degree of variation was even found among multiple rRNA loci in the same genome (Jensen et ai, 1993). This method has successfully been applied to assess microbial community structures before and after deforestation in Amazonian soils (Borneman and Triplett, 1997), in nickel contaminated soils (Hery et ai, 2003), in soils with different concentrations of manganese (Marschner et ai, 2003a) or different nitrogen availability (Leckie et ai, 2004), in glacial forefield soils (Sigler et ai, 2002; Sigler and Zeyer, 2002), and in solar ponds (Casamayor et ai, 2002). Similar to the development of the T-RFLP method based on the RFLP approach, the analysis of intergenic spacer regions was further developed to analyze the pattern on a high-resolution sequencing device (Fig. 1.5). The target region is amplified by using fluorescently labeled primers, which allows for detection on a laser-induced sequencer in the same manner as for T-RFLP analysis. Because the amplified fragments already vary in length, restriction digestion prior to electrophoresis is optional but may be used to increase resolution. This method reveals a very high potential for automation and is therefore often referred to as automated ribosomal intergenic spacer analysis (ARISA) (Fisher and Triplett, 1999; Ranjard et ai, 2001). This approach has been commonly applied to analyze microbial community structures in freshwater samples (Fisher and Triplett, 1999; Fisher et ai, 2000), in solid waste leachates (Poly et ai, 2002; Gros et ai, 2003), in lakes of different trophic status (Yannarell et ai, 2003; Yannarell and Triplett, 2004), in differently managed grassland soils (Kennedy et ai, 2005a; Kennedy et ai, 2005b) or during different phases of a composting processes (Schloss et ai, 2003b; Schloss et ai, 2003a), and it has been used to detect effects of heavy metal contamination (Ranjard et al., 2000a; Ranjard et ai, 2000b; Hartmann et ai, 2005), microorganisms in 36 1 GENERAL INTRODUCTION different soil aggregate sizes (Fall et ai, 2004), or in soil with different nutrient amendment (Hewson et ai, 2003). The RISA technique has several advantages and disadvantages when compared to T-RFLP analysis. RISA allows for more rapid processing of samples, because it requires only PCR amplification without subsequent enzymatic restriction (Fig. 1.5). This makes this approach less labor and cost intensive and allows for a higher degree of automation. In addition and due to the high variability of the ITS regions, RISA may have the higher resolution power at species level (Fisher and Triplett, 1999) as compared to 16S rRNA gene T-RFLP analysis, which may approximately resolve at genus level (Dunbar et ai, 2001). However, RISA lacks the extensive sequence databases (Garcia-Martinez et ai, 2001), when compared to the rapidly growing SSU rRNA gene databases of RDP-II (Cole et ai, 2005), ARB (Ludwig et ai, 2004), or GenBank (Benson et ai, 2005). Both T-RFLP and RISA may be affected by the fact, that fragment length and DNA sequence composition influence migration during electrophoresis. This leads to discrepancies between the expected, sequence-based fragment length, and the detected fragment length (Rosenblum et ai, 1997; Kaplan and Kitts, 2003), and in turn will limit phylogenetic inference based on fragment length. In addition, unrelated organisms may have identical ITS or T-RF lengths, impeding differentiation between such organisms (Fisher and Triplett, 1999; Dunbar et ai, 2001). This bias may also be true for other prominent profiling techniques such as DGGE, TGGE, and SSCP (Nicolaisen and Ramsing, 2002; Pesaro et ai, 2003; Zhang et ai, 2004). However, the potential for automation, the high resolution, the consistent analysis under controlled conditions, and the digital output, may more than compensate this limitation. T-RFLP and RISA were used as genetic profiling tools in the thesis to resolve the influence of different long-term agricultural management strategies on soil bacterial community structures. 1.7 The DOK long-term field experiment of soils Long-term monitoring and therefore investigations in long-term systems may be the only way to measure the magnitude and the direction of anthropogenically and environmentally induced changes in soil properties (Billett, 1996). Experiments that 37 1 GENERAL INTRODUCTION apply the same treatments over a longer period of time allow to detect effects of consistent treatments and to define risk thresholds for the corresponding treatment, in addition, long-term experiments are particularly valuable to detect changes that occur only after a certain period of time and would not be detectable in short-term studies (Powison and Johnston, 1994) Monitoring changes based on different soil parameters and the assessment of their magnitude are fundamental requirements in understanding and defining sustainable agriculture. We used an agricultural long- term experiment to assess the effect of different farming systems and crops on the bacterial community by applying the analyses introduced mentioned above. Figure 1.7. The DOK field experiment in Therwil, Switzerland, established in 1978 The DOK long-term agricultural field experiment in Switzerland (Fig. 1.7) allows to address certain aspects of agricultural sustainability. The experiment was established in 1978 and compares biodynamic, bio-organic and conventional farming systems in a randomized plot design (Alfoldi et ai, 1993a, Alfoldi et ai, 1993b, Alfoldi er ai, 1995a, Alföldi et ai, 1995b, Mäder et ai, 2000; Mäder et ai, 2002, Mäder et al, 2006). Whereas questions initially focused on the feasibility of organic farming, current emphasis is given to farming related effects on soil characteristics and 38 1 GENERAL INTRODUCTION agricultural sustainability. Three different practical farming systems, i.e. biodynamic (BIODYN), bioorganic (BIOORG), and conventional (CONFYM), along with an exclusively minerally fertilized (CONMIN) and an unfertilized (NOFERT) control were compared. The five systems mainly differ in fertilization practice, i.e. differently treated solid and liquid farmyard manure (FYM) and inorganic fertilizers, and in plant protection strategy, i.e. mechanical and/or indirect methods versus combined pesticide applications. Fertilization and plant protection regimes slightly changed over the years to meet the actual regulations of the Swiss government, therefore the actual guidelines are indicated in the text. All systems are based on the same crop rotation, soil tillage, and soil type. 1.7.1 Fertilization The organic systems, i.e. BIODYN and BIOORG, have been maintained according to regulations of Swiss organic farming (Eidg.Volkswirtschaftsdepartement, 1997), whereas the conventional systems were managed according to Swiss guidelines for integrated farming (Eidg.Volkswirtschaftsdepartement, 1998) since 1985 (Table 1.1). Aerobically composted (BIODYN) and slightly aerobically rotted (BIOORG) solid and liquid farmyard manure (FYM) corresponding to 1.2. (1978-1991) and 1.4 (1992- 2005) livestock units were applied to the organic systems per hectare and year. Fertilization intensity of the organic systems depended on crop yield in the respective crop rotation and reflects the intensity typically found on Swiss organic farms. The conventional system (CONFYM) received the same amount of FYM as the organic systems, but was piled on a semi-aerobic stack. In addition, CONFYM plots were additionally supplied with mineral fertilizer up to the recommended level of the plant- specific Swiss standard recommendation. The CONMIN system, mimicking a stockless conventional system, remained unfertilized in the first crop rotation period of the experiment (1978-1985), and was then amended with mineral fertilizers exclusively at the same level as used in system CONFYM. The NOFERT system remained unfertilized since 1978 and served as negative control. Mean annual fertilizer input of total nitrogen (N), phosphorus (P), and potassium (K) between 1978 and 2005 was 35 to 40% lower in the organic systems when compared to system CONFYM (Mäder et ai, 2006). 39 1 GENERAL INTRODUCTION Table 1.1. Fertilizer and plant protection regimes in the five farming systems of the DOK field experiment since 1978. Practices Organic farming Conventional farming Integrated since 1985b Systems3 NOFERT BIODYN BIOORG CONFYM CONMIN0 Fertilization Organic FYM(BIODYN) FYM(BIOORG) FYM(CONFYM) Inorganic - N,P,K N,P,K Plant protection Weed mechanical mechanical mechanical mechanical mechanical control herbicides herbicides Disease indirect indirect indirect fungicides fungicides control Insect plant extracts plant extracts plant extracts insecticides insecticides control bio-control bio-control bio-control Special biodynamic biodynamic - plant growth plant growth treatments preparations9 preparations regulators regulators a Biodynamic (BIODYN), bioorganic (BIOORG), conventional (CONFYM), mineral (CONMIN), unfertilized (NOFERT). b Conventionally managed from 1978 to 1984. In the text still referred to as conventional systems. c CONMIN was unfertilized from 1978 to 1984. d CuS04 was used for plant protection in potato until 1991. e Preparations 500 (horn manure) and 501 (horn silica) were applied (Steiner, 1993). 1.7.2 Plant protection Two different plant protection schemes were applied, an organic type in the systems BIODYN, BIOORG, and NOFERT, and a conventional type in the systems CONFYM and CONMIN. In the organic scheme, plant protection was conducted by mechanical and indirect weed, disease, and insect control. Additionally, appropriate crop density for providing optimal growth conditions as well as application of broad crop rotations including low susceptible cultivars and intercropping systems according to the guidelines of biodynamic and bioorganic farming (Eidg.Volkswirtschaftsdepartement, 1997), were applied to protect the crops. The BIODYN System received supplementary special treatments, e.g. biodynamic preparation 500 (horn manure) and 501 (horn silica), and different compounds of composted herbs according to the 40 1 GENERAL INTRODUCTION theory of Steiner (1974; 1993). Whereas in BIODYN particularly indirect methods has been applied to provide a general "fitness" of the soil, plant protection in BIOORG has often been performed directly by applying compounds such as CuS04, rock powder, or plant-derived insecticides in potato systems. The NOFERT System was treated identical to BIODYN. In the conventional schemes CONFYM and CONMIN, mechanical plant protection was combined with application of pesticides. Herbicides, fungicides, and insecticides were applied according to the guidelines for integrated plant protection and only if thresholds of infections and diseases were exceeded (Eidg.Volkswirtschaftsdepartement, 1998). In addition, plant growth regulators were applied in these conventional systems. 1.7.3 Experimental field, tillage, and crop rotations The experimental field (7° 33' E, 47° 30' N) is located near Basel at 300 meters above sea level with a mean precipitation of 785 mm year"1 and annual mean temperature of 9.5 °C. The field has an extension of 1.4 ha and contains 96 plots with a dimension of 5 x 20 meter each. The plots were arranged as a randomized block with four replicates of each treatment and crop. Between the experimental plots, buffer zone strips of 6 m were planted with grass, which was regularly mulched. The soil is a haplic luvisol on deep deposits of alluvial loess, containing 15% sand, 70% silt, and 15% clay, and had initially a pH of 6.3 and an organic matter content of 1.5%. Soil tillage was similar in all farming and control systems, i.e. plowing to a depth of 15-20 cm in the organic systems and 20-25 cm in the conventional systems. Mechanical plant protection treatments were more often performed in the organic systems when compared to the conventional. The seven-year crop rotation was identical in all systems and was temporally shifted in three parallels to allow for analysis of different crops in the same year. Besides the main crops potato, winter wheat, and grass clover, secondary crops such as white cabbage, winter barley, beetroots, soybean, and maize varied due to the economic request among the four crop rotations since 1978 (Table 1.2). In general, five years of intensive crop production were followed by a two year recovery phase with grass cover. 41 1 GENERAL INTRODUCTION Table 1.2. Crop rotation in the DOK experiment since 1978. 1978 to 1984 1985 to 1991 1992 to 1998 1999 to 2005 1 Potato Potato Potato Potato 2 Winter wheat 1 Winter wheat 1 Winter wheat 1 Winter wheat 1 3 White cabbage Beetroots Beetroots Soybean 4 Winter wheat 2 Winter wheat 2 Winter wheat 2 Corn 5 Winter barley Winter barley Grass clover 1 Winter wheat 2 6 Grass clover 1 Grass clover 1 Grass clover 2 Grass clover 1 7 Grass clover 2 Grass clover 2 Grass clover 3 Grass clover 2 1.7.4 Soil organic carbon, soil acidity, and phosphorus dynamic Soil organic carbon (Corg) is an important soil quality parameter and is often used as an indicator of soil fertility (Mäder et ai, 2002). Corg influences soil structure stability and water holding capacity, and is involved in adsorption of nutrients. Therefore, Corg has an indirect effect on plant growth by influencing various physical, chemical, and biological soil properties. Determination of Corg in the DOK field revealed a heterogeneous distribution among the entire field in the early phase of the experiment in 1980 (Fig. 1.8), reflecting the importance of using randomized replicated experimental designs. The Corg heterogeneity decreased during 18 years of agricultural management and revealed a more uniform distribution of Corg in 1998. Lower input of organic matter in the control systems (CONMIN and NOFERT) decreased Corg contents in the originally Corg-rich areas (Fig. 1.8). In general, all systems tended to a loss of Corg during the first three crop rotations (1978 to 1998), but showed only significance reduction in the unfertilized and the minerally fertilized control systems (Fliessbach et ai, 2006). Soil pH is an important property determining solubility and availability of minerals and nutrients and strongly affects biological activity in soil (Dalai, 2001). Soil pH varied among the different systems in the DOK field experiment (Mäder et ai, 2006). Soil pH decreased over time in the conventional systems, i.e. CONFYM and CONMIN, indicating an acidifying effect of mineral fertilizers. In contrast, soil pH in BIOORG remained constant, whereas BIODYN revealed a moderate increase in pH probably due to the application of aerobically treated and therefore alkaline FYM. 42 1 GENERAL INTRODUCTION 1980 A B mm 95 2 20 ÜI 90 2 09 __ 1 97 _ . . UM MM NLJ 80 1 86 - , — — ^ u ffi^'lti 75 1 74 BrC^DYNx^BlOO IÜ70 1 62 ./ lapilli IHË 65 1 51 20 40 60 80 100 120 140 distance (meter) 20 40 60 80 100 120 140 distance (meter) Figure 1.8. Distribution of soil organic carbon Corg across the DOK field experiment displayed as a contour plot. Location of the 96 field plots and corresponding farming systems are indicated. Corg data are displayed for the year 1980 (first available data set) and 1998 (last available data). Contents of the two different years were determined at the same time point in the year and in the same lab using soils stored from the corresponding year. Corg contents are given as percentage of the maximum content in 1980 (A) and as percentage Corg in soil (B) (source: Oberholzer et al. unpublished). 43 1 GENERAL INTRODUCTION Phosphorus dynamic, determined by nutrient input and removal by harvested crops, was largely affected by the farming systems (Oehl et ai, 2002). Whereas CONFYM revealed a positive phosphorus balance by means of combined application of FYM and mineral fertilizer, all other systems revealed a negative P balance. This has resulted in a remarkable P deficiency over time, which represents a suboptimal basis for P-demanding crops (Mäder et ai, 2006). Although organic P stored in the microbial biomass was larger in the organic systems, this will not compensate for deficiency in P availability determined by the negative P balance. Therefore, particular focus has also to be given on effects of soil P deficiency. 1.7.5 Crop yield Averaged across the total experimental period and all crops, the organic systems revealed 20% less yield when compared to the conventional systems (Fig. 1.9, Mäder et ai, 2002). Strongest differences in yield response between organic and conventional farming were reported for potato (Mäder et ai, 2006), revealing 57% (BIODYN) and 67% (BIOORG) of the harvest in CONFYM. This was ascribed to the lower nitrogen and potassium supply as well as less effective protection against late blight in the organic system. Mean yields of winter wheat in the organic systems accounted for approximately 85% of the yields in CONFYM, being ascribed to the use fungicides and plant growth promoters in the conventional systems. Mean grass clover yields reached 87% of the CONFYM yields and depended on nitrogen fixation ability of clover and low disease pressure in the organic systems. Yields in the system CONMIN reached an overall average 90% of the yields in CONFYM. Because soil microbial communities significantly contribute to crop yields in agricultural systems, differences in the microbial communities between organic and conventional farming were particularly interesting and therefore investigated in the thesis. 44 1 GENERAL INTRODUCTION grass clover yield 160 140 to 120 0} *- 100 fc a» ^r 80 Q (D Ü CD HO Q. 40 potato tuber yield £8 80 *> S. 40 V T winter wheat grain yield t ^ 140 z s i?n - z so 100 Q) »- « o 2 CD HO t'y \ /*\ .£' E co t! tö <=*: 60 Q cp o CD 40 20 ^ 0,N 0> ^ dP cv1 dÎ3 C?> f^ tfe N<£ NeJ> ^ $> $> ^> ^> $> $ ^ Year J K_ J L CRP1 CRP2 CRP3 CRP4 - -A - - - CONFYM CONMIN BIODYN BIOORG NOFERT Figure 1.9. Crop yield in the DOK experiment during four crop rotation periods (CRP1 to between CRP4) 1978 and 2005 for the three main crops grass clover, potato, and winter wheat. Yields are displayed as percentages of total yields in system CONMIN. CONMIN System remained unfertilized during the first crop rotation period (CRP1). 45 1 GENERAL INTRODUCTION 1.8 Objectives and outline of this thesis 1.8.1 Objectives Agricultural management may strongly and persistently influence soil quality and associated factors. Before the impact of agricultural factors on soil functioning and performance can be understood, the factors that change the soil properties have to be characterized. Because microbial soil characteristics are important determinants of soil functions, the project aimed at the investigation of the influence of defined long-term agricultural systems and crops on these characteristics. The project is mainly based on two general research questions: (A) Which agricultural factors affect soil bacterial diversity and community structures at which magnitude? (B) Is microbial diversity linked to agricultural practice and correlating with soil productivity in agricultural systems? Based on these two questions, the DOK long-term agricultural field experiment was used to investigate influences of different management strategies and different crops on diversity and structure of soil bacterial communities, as well as on total community level parameters such as soil microbial biomass or total soil DNA content. Cultivation- dependent and different molecular methods were applied and evaluated for their use in monitoring soil microbial characteristics of agricultural systems. The three main objectives were defined as follows: (1) Investigating effects of long-term management strategies and crops on soil microbial biomass and structure of soil bacterial communities by using cultivation-dependent and -independent techniques. (2) Comparison of community composition and traditional diversity assessments under different management influences by using large-scale gene library analysis. (3) Evaluation of genetic profiling for its use as rapid soil monitoring tool and development of potential indicator diagnostics. 46 1 GENERAL INTRODUCTION 1.8.2 Outline This thesis is composed of a general introduction (chapter 1), three peer-reviewed and published articles (chapters 2 through 4), and a general discussion (chapter 5). A methodological development resulting from this project, but assessed outside the agricultural key question is listed in the appendix. Results published on other topics are quoted in the publication list. Chapter two focuses on the effects of different farming systems and different actual on soil crops bacterial community structures in the DOK field experiment in the year 2000. A cultivation-dependent (CLSU with Biolog GN plates) and a cultivation- independent (T-RFLP) method were used to assess the bacterial community structures and compared to each other for their sensitivity in detecting effects. In addition, effects on other microbiological parameters such as soil microbial biomass, soil DNA contents, and colony forming units were analyzed. describes Chapter three the effects of farming systems and preceding crops on soil bacterial community structures in the DOK field experiment in the year 2003. Two molecular key profiling techniques, i.e. T-RFLP and RISA, were applied and evaluated for their sensitivity and consistency in detecting effects by using profound statistical tools. Chapter four describes a comparison between the genetic profiling approach in applied chapter two and three and a large-scale gene library analysis by cloning and sequencing of the bacterial 16S rRNA gene in three representative systems of the DOK field experiment. The three representative systems reflected main differences in soil bacterial community structures detected by the genetic profiling approaches. Sensitivity of commonly used diversity indices were evaluated by them to the results comparing obtained with community structure analysis. The gene library analysis finally allowed for the identification of treatment-associated indicator taxa in the bacterial community. Chapter five provides a comprehensive discussion of the results achieved including a profound evaluation of the methodology applied in this study. Different aspects of of the responses soil microbial community to agricultural influences are critically assessed. In addition, future research opportunities in detailed analysis of soil microbial community parameters are discussed. 47 1 GENERAL INTRODUCTION * ^Sfejp^ ^j Si haï v-*ar* H Jfjï*ïlt 4i,i ^ ^ F{f* K. / & • rAu ^ ^ 'w«*^ t' "% 48 Chapter 2: Community structures and substrate utilization of bacteria in soils from organic and conventional farming systems of the DOK long-term field experiment Franco Widmer, Frank Rasche, Martin Hartmann, Andreas Fliessbach Published in Applied Soil Ecology 33 (2006), 294-307 © 2005 Elsevier B.V. 2 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS 2 Community structures and substrate utilization of bacteria in soils from organic and conventional farming systems of the DOK long-term field experiment 2.1 Abstract Preservation or improvement of soil quality and productivity is of major importance for sustainable agriculture. Microorganisms strongly influence these soil characteristics as they are involved in nutrient cycling, transformation processes and soil aggregate formation, as well as in plant pathology or plant growth promotion. A profound understanding of structure, dynamics and functions of soil microbial populations represents one key to the understanding and description of soil quality. Therefore, we analyzed long term effects of three farmyard manure (FYM)-based farming systems, i.e. bio-dynamic (BIODYN), bio-organic (BIOORG) and conventional (CONFYM), on microbiological soil characteristics and compared them to long-term effects of minerally fertilized (CONMIN) and unfertilized (NOFERT) control systems. Furthermore, we compared these long-term effects of farming systems to short-term effects of the crops winter wheat and grass-clover ley. The DOK field experiment in Therwil, Switzerland, which was established in 1978, represents in a unique long- term comparison, allowing to approach these questions. Effects on microbiological soil characteristics were assessed with a polyphasic approach by analyzing soil microbial biomass, soil DNA content, colony forming unit (CFU) counts, community level substrate utilization (CLSU) patterns with Biolog EcoPlates, and terminal restriction fragment length polymorphism (T-RFLP) profiles of bacterial 16S rRNA genes. The soil biomass parameters, i.e. microbial biomass, DNA content and CFU, all were strongly influenced by the farming systems, whereas only CFUs were significantly affected by the two crops analyzed. Differences among the FYM-based farming systems BIODYN, BIOORG and CONFYM were only significant for microbial biomass and DNA content. CLSU and T-RFLP profiling, on the other hand, allowed for consistent differentiation of soil bacterial community structure in relation to the influence of farming systems and crops. The analyses revealed that the main and highly significant effect on microbiological soil characteristics was related to FYM 50 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS applications. Less strong but significant effects were caused by the two crops, i.e. winter wheat and grass-clover. Effects of the farming systems BIODYN, BIOORG and CONFYM on soil bacterial community structure were relatively weak and not significant. These results suggest that for successful soil quality management fertilization regime and crop rotation are of major importance and that polyphasic approaches are needed to describe and assess microbiological soil characteristics. 2.2 Introduction In agriculturally managed ecosystems preservation or improvement of soil quality and productivity is of major importance. Soil quality definitions include physical, chemical and biological soil characteristics that are often closely interrelated (Doran and Zeiss, 2000). Soil microbiota play an important role in these soil characteristics since many of them are involved in nutrient cycling, transformation processes and soil aggregate formation, as well as in plant pathology or plant growth promotion (Kennedy, 1999; Buckley and Schmidt, 2001). Understanding structure, dynamics and functions of soil microbial communities represents one key to the understanding of soil fertility and soil quality (Kennedy, 1999; Buckley and Schmidt, 2003). However, due to technical limitations, it is currently difficult to describe dynamics of microbial communities and to assess their role in ecosystem functions (Doran and Zeiss, 2000; Buckley and Schmidt, 2003). Therefore, one of the current challenges is to assess changes or differences in microbial diversity in soils, with respect to community composition and species distribution (Kennedy, 1999; Hill et ai, 2000; Kirk et ai, 2004). If affected populations can be identified, future research then may focus on affiliation of specific populations and their functions in order to assess functional consequences or functional redundancy in soil (Torsvik and Ovreas, 2002). Microbial community structures in agriculturally managed soils need to be reproducibly detectable and the identity and possible functions of detected populations need to be described. Then, agricultural management-dependent influences on soil microbial community structures may be assessed and indicative components be used for biological soil quality diagnosis (Kennedy, 1999). In order to achieve these tasks, effects of different agricultural management systems on 51 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS microbiological soil characteristics in well documented and designed field experiments are necessary. Changes in soil quality may develop slowly and may adjust to a new long-term steady state after a change of management or conversion to a different farming system. Therefore, long-term agricultural field experiments are particularly valuable to detect changes that would not be detectable in short-term studies (Powlson and Johnston, 1994). The DOK long-term field experiment in Therwil, Switzerland (Mäder et ai, 2002) has been established in 1978 and initially focused on the feasibility and agricultural productivity of organic farming systems. In recent years more emphasis has been given to the effects of farming systems on soil quality (Mäder et ai, 2002), which may be reflected in various biological soil characteristics like soil microbial biomass, community structures, functions and activities. Therefore, the approach of assessing these characteristics in the DOK long-term field experiment represents a relevant and attractive approach. Currently, various methods are available for the analysis of microbiological soil characteristics, each with its advantages and disadvantages (Hill et ai, 2000; Kirk et ai, 2004). Determining community level substrate utilization (CLSU) patterns is one approach for the characterization of microbial communities and is based on specific substrate utilizing functions performed by aerobic heterotrophic bacteria. CLSU analyses with the Biolog system (Garland and Mills, 1991) have been applied in many studies in order to gain information on differences of microbial communities in various soil systems (Grayston et ai, 1998; Gomez et ai, 2000; O'Donnell et ai, 2001; Larkin, 2003; Fliessbach and Mäder, 2004). Although the Biolog technique is restricted to culturable microorganisms and does not provide a precise assessment of the functional properties of microbial communities in soil (Preston-Mafham et ai, 2002), it has been successfully applied in comparative soil analyses and has shown to be a powerful and sensitive low cost analytical tool for demonstrating differences or changes in soil microbiological characteristics (Fliessbach and Mäder, 1997; Gomez et ai, 2000; Widmer et ai, 2001). Only a small portion of the whole microbial diversity of ecosystems has been isolated or adequately characterized (Tiedje et ai, 1999; Amann and Ludwig, 2000). Important progress towards cultivation-independent characterization of soil microbial community structures was achieved by direct extraction of microbial community DNA 52 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS from soils (Torsvik et ai, 1990; Bürgmann et ai, 2001) and development of PCR- based, group-specific detection protocols for microbial phyla, generally based on ribosomal RNA genes (rDNA) as phylogenetic markers (Amann and Ludwig, 2000; Hill et ai, 2000; Theron and Cloete, 2000). Genetic profiling of PCR-amplified small subunit (SSU) rDNA, such as restriction fragment length polymorphism (RFLP) analyses (Widmer et ai, 2001), denaturing or temperature gradient gel electrophoresis (DGGE/TGGE: Muyzer, 1999) or single strand conformation polymorphism (SSCP: Schwieger and Tebbe, 1998) have successfully been applied for assessing microbial community structures in various soil systems. Terminal restriction fragment length polymorphism (T-RFLP) analysis (Liu et ai, 1997) or ribosomal intergenic spacer analysis (RISA: Fisher and Triplett, 1999) with capillary electrophoresis enable to characterize highly diverse soil microbial communities and has considerably higher resolution than common gel electrophoretic systems. Depending on the PCR detection primers applied, different microbial groups and populations as well as different marker genes can be detected (Braker et ai, 2001 ; Brodie et ai, 2003; Wolsing and Prieme, 2004). T-RFLP analyses of SSU rDNA can provide resolution at the genus or even at the species level (Dunbar et ai, 2001). The T-RFLP analysis yields numeric data that can be further evaluated with standard statistic analyses and results may be compared with data stored in sequence databases, e.g. the Ribosomal Database Project (RDP-II) or with data from other studies. Organic farming practices are considered ecologically more sustainable than conventional farming practices and to support soil microbial diversity and functions (Mäder et ai, 2002). Therefore, it was our objective to use soils from the DOK long- term field experiment, in order to assess differences in microbiological soil characteristics, and to relate them to effects of long-term biodynamic, bio-organic and conventional farming. In order to rank the magnitude of detected effects, we compared them to long-term effects of minerally and unfertilized controls as well as to short-term effects of two crops. Soils from the DOK field experiment allowed to approach these questions in a unique long-term field experiment with a well designed split-split plot design and three temporally shifted crop rotation parallels. Effects on microbiological soil characteristics were assessed with a polyphasic approach by 53 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS analyzing soil microbial biomass, soil DNA contents, colony forming unit (CFU) counts, CLSU patterns with Biolog EcoPlates and T-RFLP of bacterial SSU rDNA. 2.3 Material and Methods 2.3.1 Experimental system The DOK long-term field experiment was established in 1978 in Therwil, Switzerland, on a haplic luvisol on deep deposits of alluvial loess (Mäder et ai, 2002). The experiment has been designed for evaluation of agronomic and ecological effects of bio-dynamic (BIODYN), bio-organic (BIOORG) and conventional (CONFYM) farming systems. Figure 2.1. Farming system-specific differences in fertilization and plant protection in the DOK long-term field experiment. For details on the experiment refer to Besson and Niggli (1991) and Mäder (2002). Farmyard manure (FYM) application of 1.4 livestock units ha'1 year'1 was performed with aerobically treated composted FYM (C/N = 8), slightly aerobically treated rotted FYM (C/N = 11), anaerobically treated stacked FYM (C/N= 12). CuS04 was used for plant protection in BIOORG potato until 1991. 54 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS The two organic systems have been maintained according to the regulations of the respective organic producer organizations (Eidg.Volkswirtschaftsdepartement, 1997), while the conventional system is managed since 1992 according to the Swiss guidelines for integrated farming (Eidg.Volkswirtschaftsdepartement, 1998). The farming systems mainly differ in fertilization practice and plant protection strategy (Fig. 2.1). The three main systems (BIODYN, BIOORG and CONFYM) are fertilized with system-specific farmyard manure (FYM) corresponding to 1.4 livestock units ha"1, which represents approximately 2 metric tons organic carbon per hectare and year (Mäder et ai, 2002). Fertilization in the CONFYM system was supplemented with mineral fertilizers (N, P and K) according to official recommendations. The system CONMIN mimics a conventional system without livestock and is fertilized with mineral fertilizers only, but was left unfertilized during the first 7 years of the study. NOFERT represents the unfertilized control, which is only treated with bio-dynamic field preparations. The 7-year crop rotation was designed according to scientific and practical needs and has been adjusted in 1999 to (1) potato, (2) winter wheat 1, (3) soybean, (4) maize and (5) winter wheat 2, followed by 2 years of a grass-clover ley. The first five crops represent the arable phase of the crop rotation, whereas the 2 years of permanent grass-clover ley without tillage represent the recovery phase. Crop rotation and soil tillage represent a compromise of the ones typically applied in organic and conventional agricultural practice and were identical in all farming systems. Crop rotation was repeated in three temporally shifted parallels in the field. All farming systems and crops were replicated four times in a split-split plot design. 2.3.2 Soil sampling Soil samples were recovered on March 14th 2000 from the field plots planted with winter wheat 1 and a 2nd year grass-clover ley. Winter wheat represented the arable phase of the crop rotation, whereas grass-clover represented the end of the soil recovery phase after 2 years of permanent plant cover without soil tillage. From each of the four replicate plots of the systems BIODYN, BIOORG, CONFYM, CONMIN and NOFERT, 14 single cores of 3 cm diameter were taken from the plough layer (0-20 cm), pooled, and maintained at 4 °C. In the laboratory soil samples were carefully dried at room temperature to reach approximately 25% water content, which represents 45% of maximum water holding capacity. Soil water content was 55 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS determined gravimetrically by drying soils for 24 h at 105 X^. Soils were then sieved (2 mm) and soil pH was determined in a soil suspension diluted 1:10 (w/v) with CaCI2 (25 mM). Soils were stored for up to 1 week at 4 °C prior to use. 2.3.3 Soil microbial biomass Soil microbial biomass (Cmic) was estimated using the chloroform-fumigation- extraction (CFE) method (Vance et ai, 1987). Soils were equilibrated for 7 days at 20 °C and triplicate sub-samples of 20 g (dry weight equivalent) were fumigated with CHCI3 for 24 h at room temperature. Fumigated and control soil samples were extracted with 80 ml 0.5 M K2S04 (90 min at 300 rpm), filtered (Macherey Nagel 615) and total organic carbon (TOC) was determined by infrared spectrometry after combustion at 850 °C (DIMA-TOC100, Dimatec, Essen, Germany). Cmic was calculated from the difference in extractable carbon of fumigated and unfumigated samples using a /cËC factor of 0.45 (Joergensen, 1996). 2.3.4 Community level substrate utilization (CLSU) Community level substrate utilization (CLSU) analysis was performed with sieved soil pre-incubated for 6 days at 20 °C. Three replicate sub-samples of 10 g (dry weight equivalent) were suspended (30 min at 300 rpm) in 90 ml sterile saline solution (0.8% NaCI). Soil suspensions were allowed to settle for 10 min before the supernatant was diluted 10-fold to obtain a final dilution of 10"2 (Fliessbach and Mäder, 1997). Each well of a Biolog-EcoPlate (Biolog Inc., Hayward, CA, USA) was filled with 125 ml of the final dilution (Garland and Mills, 1991). Inoculation density was determined by counts of colony forming units on a glucose minimal medium (Pochon and Tardieux, 1962). From each soil sample three replicate plates, with three replicate substrate sets were used (n = 9). Including the four replicate field plots, a total of 36 replicate data sets were prepared for each treatment. Plates were incubated at 20 °C and optical density at 600 nm (OD600) was read periodically in a microplate reader (MRX, Dynex Technologies, Inc., Chantilly, USA) at 12 predetermined time points between 24 and 96 h of incubation. Individual absorbance values of the 31 single substrates were corrected by subtraction of the blank control value (raw difference, RD). Negative RD-values were set to zero. To minimize effects of different inoculum 56 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS densities, data were normalized by dividing the RD values by their respective average well colour development (AWCD) values. 2.3.5 Soil DNA extraction Soil DNA extraction was performed on sieved soils using a slightly modified bead beating protocol developed by (Bürgmann et ai, 2001). Approximately 0.5 g fresh soil and 0.75 g silica beads (diameter 0.10-0.11 mm, Braun Biotech International GmbH, Melsungen, Germany) were suspended in 1.3 ml extraction buffer (0.2% hexadecyltrimethylammonium-bromide (CTAB), 1 mM dithiotreitol (DTT), 0.2 M sodium phosphate, 0.1 M sodium chloride, 50 mM EDTA, pH 8.0) and processed for 40 s using a FP 120 bead beater (Savant Instruments, Inc., Holbrook, NY, USA) at setting 5.5. Samples were centrifuged (14,000 x g, room temperature, 5 min) and supernatants were transferred to fresh tubes. Each sample was extracted two additional times with 1 ml extraction buffer and all three corresponding extracts were pooled in the same tube, yielding a total extract of approximately 3 ml. Following extraction with 3 ml chloroform, nucleic acids were precipitated with 3 ml precipitation solution (20% polyethylenglycol 6000, 2.5 M NaCI) for 1 h at 37 °C followed by centrifugation (14,000 x g, room temperature 15 min). Pellets were washed with 800 ml 70% ethanol, air dried and resuspended in 1 ml TE (10 mM Tris-HCI, 1 mM EDTA, pH 8.0) per gram extracted soil (dry weight equivalent). DNA was stored at -20 °C until further processing. 2.3.6 Quantification of DNA Quantification of DNA yield was performed with PicoGreen (Molecular Probes, Eugene, OR, USA) according to (Bürgmann et ai, 2001). Two microlitres of PicoGreen, 2 ml DNA-extract, and 396 ml TE-buffer were mixed and maintained at room temperature until fluorometric quantification at 480 nm excitation and 520 nm emission (Luminescence Spectrometer LS50 B, Perkin-Elmer, Rotkreuz, Switzerland). Bacteriophage I DNA (Promega, Madison, Wl, USA) was used as DNA concentration standard. DNA-yield was presented as mg extracted DNA g"1 soil (dry weight equivalent). 57 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS 2.3.7 PCR-amplification of bacterial SSU rRNA genes PCR-amplification of bacterial SSU rDNA was performed by using primers 27F (5'- AGAGTTTGATCMTGGCTCAG-3') and 1378R (5'-CGGTGTGTACAAGGCCCGGGA ACG-3') (Heuer et ai, 1997). Primer 27F was 5'-labeled with carboxyfluorescein (FAM6, Microsynth, Balgach, Switzerland). Reaction mixes of 50 ml contained 3 ng soil DNA, 1 x PCR reaction buffer (Qiagen, Hilden, Germany), 1.5 mM MgCI2, 0.2 mM of each primer, 0.2 mM of each desoxynucleoside triphosphate, 1.2 mg ml'1 BSA (Sigma, Buchs, Switzerland) and 1 U HotStar DNA polymerase (Qiagen). PCR conditions were: initial denaturation for 15 min at 95 °C followed by 37 cycles consisting of denaturation for 45 s at 94 °C, primer annealing for 30 s at 48 °C and polymerization for 2 min at 72 °C followed by a final extension for 5 min at 72 °C. Quality of PCR products was inspected by electrophoresis of 5 ml PCR product in 1% (w/v) agarose gels (Life Technologies, Paisley, Scotland) containing ethidium bromide (0.5 mg ml'1). 2.3.8 Terminal restriction fragment length polymorphism (T-RFLP) analysis For terminal restriction fragment length polymorphism analysis PCR products (45 ml) were precipitated with 45 ml isopropanol for 1 h at -20 °C and centrifuged at 10,000 x g for 15 min at room temperature, followed by a wash-step with 100 ml ethanol (70%). Air-dried pellets were re-suspended in 20 ml restriction-mix (2 U restriction enzyme Mspl in 1 x reaction buffer B, Promega Corporation, Madison, Wl, USA) and digested overnight at 37 °C. Quality of digests was inspected by gel electrophoresis of 7 ml digests in 3.0% (w/v) MetaPhor® gels (FMC, BioProducts, Rockland, ME, USA) containing ethidium bromide (0.5 mg ml"1). One microliter of Mspl-digested PCR products was mixed with 12 ml HiDi formamide (Applied Biosystems, Foster City, CA, USA) and 0.2 ml internal size standard (2500 TAMRA Size Standard, Applied Biosystems), followed by denaturation at 92 °C for 2 min. T-RFLPs were analyzed by capillary electrophoresis on an automated sequencer (ABI 310 Genetic Analyzer, Applied Biosystems) equipped with a 47 cm capillary and POP-4 polymer (Applied Biosystems) and the GeneScan software v3.1 (Applied Biosystems). The baseline threshold for signal detection was set to 50 fluorescence intensity units. Electropherograms obtained were transformed into numeric data of individual peak heights using the Genotyper 3.6 NT software (Applied Biosystems). Manual peak 58 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS calling was performed for peaks whose heights could unambiguously be quantified in all samples. The values of all scored peaks were compiled in a data matrix. To minimize effects of different PCR product quantities, data were normalized by dividing the value of each T-RF by the average value of all T-RFs from the corresponding sample. 2.3.9 Descriptive and discriminative statistical analyses Various descriptive and discriminative statistical analyses were applied for comparison and evaluation of data. Soil microbial biomass parameters, i.e. Cmic, CFU and soil DNA content, were correlated by applying the Pearson Product-Moment correlation coefficient (JMP software, SAS Institute Inc., Cary, NC, USA). Soil parameters were tested for significant differences in relation to all factors, i.e. farming systems and crops, or in relation to single factors, i.e. farming system or crop, as well as for interaction between farming system and crop using two-way analysis of variance (ANOVA) and post hoc Tukey tests (JMP software). Explorative statistical analyses of mean-transformed CLSU- and T-RFLP-data were based on cluster analysis with Euclidean distances and Ward clustering (Statistica Version 6.1, StatSoft Inc., Tulsa, OK, USA) according to Blackwood et ai (2003). Significant correlations of CLSU or T-RFLP fingerprint data with effects of farming systems or crops were determined by applying Monte Carlo permutation testing with a linear model (CANOCO for Windows 4.5, Microcomputer Power, Ithaca, NY, USA) according to ter Braak and Smilauer (2002). Permutation tests were conducted on all canonical axes with 1000 permutations. If only subsets of the data were analyzed the Bonferroni correction of significance levels was applied (Shaffer, 1995). Influence of farming systems and crops on total variance of the CLSU and T-RFLP data sets were determined by partitioning the variance (CANOCO for Windows 4.5) using redundancy analysis of a linear model according to Borcard et ai (1992). Two-way ANOVA (JMP software) was used to determine significant effects of farming systems and crops on each T-RF or CLSU value, as well as interaction between farming system and crop. 59 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS 2.4 Results 2.4.1 Soil microbial biomass (Cmic) and colony forming units Farming systems receiving farmyard manure, i.e. BIODYN, BIOORG and CONFYM, showed significantly higher (average increase 37%, p < 0.001) soil microbial biomass (Cmic) contents, when compared to systems without FYM application, i.e. CONMIN and NOFERT (Tables 2.1 and 2.2). Table 2.1. Soil biomass parameters determined for two crops planted in the five farming systems of the DOK long-term field experiment. Plots planted with winter wheat or grass-clover, which represent two positions in the crop rotation, were analyzed. Data represent mean values and standard deviations of four field replications. Crop Farming system3 Soil Cmic content Soil DNA content CFUb (H9 S"1 soi dry wt) (ug g"1 soil dry wt) (x 106g"1 soil dry wt) Winter wheat NOFERT 211 ±21 18 ±3 4.30 ±0.51 CONMIN 196 + 35 21 ±3 7.20 ± 0.59 BIODYN 395 ± 35 33 ±4 7.30 ± 0.49 BIOORG 290 ± 29 22 + 5 5.20 ±0.71 CONFYM 304 ±17 23 ±3 6.58 ± 0.44 Grass-clover NOFERT 243 ± 49 20 ±6 2.83 ± 0.39 CONMIN 211 ±26 18 + 3 5.13 ±0.79 BIODYN 369 ± 55 33 ±9 3.82 ±0.16 BIOORG 390 ± 57 32 + 7 3.14±0.18 CONFYM 308 ± 21 26 ±4 4.47 ±0.13 NOFERT, no fertilization; CONMIN, conventional exclusively with mineral fertilizer; BIODYN, bio¬ dynamic; BIOORG, bio-organic; CONFYM, conventional with farmyard manure. b CFU, colony forming units. These differences were also significant (p < 0.01) when analyzed separately for winter wheat and grass-clover. CmiC-content of soils from CONFYM averaged at 15% lower values when compared to organic systems BIODYN and BIOORG, but differences were not significant (p > 0.05). Differences between CONMIN and NOFERT as well as between BIODYN and BIOORG were also not significant (p > 0.05). Soil Cmic-content varied slightly between winter wheat and grass-clover, but 60 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS revealed no significant differences (p > 0.05). Two-way ANOVA showed significant (p < 0.001) changes of microbial biomass among the farming systems, but no significant (p >. 0.05) changes were found in relation to the crops (Table 2.2). Interaction between farming systems and crops was also not significant (p > 0.05). Colony forming units of bacteria did neither show significant (p £ 0.05) changes in relation to FYM application, nor within the FYM treated systems. CFU values were 58% (p < 0.001) higher in the winter wheat plots when compared to grass-clover plots (Table 2.1). Two-way ANOVA revealed significant (p < 0.001) changes of CFU-counts in relation to farming system and crops, but no interaction between these factors were found (Table 2.2). There was no significant correlation (p > 0.05) between Cmic and CFU values. 2.4.2 Total soil DNA content Farming systems receiving FYM revealed higher soil DNA contents (average increase 32%, p < 0.01) when compared to systems without FYM application (Tables 2.1 and 2.2). Table 2.2. Significance of effects of farming system-specific factors of the DOK long- term field experiment on soil biomass parameters determined with two-way ANOVA. Two different models were used for the calculation of significant influences of either all five farming systems individually or of farmyard manure application. Significance levels3 Cmie DNA CFU All five farming systems Farming system *** Crop *** Farming system x crop FYMb application FYM treatment Crop FYM treatment x crop - a ("*) p < 0.001 ; (**) p < 0.01 ; (*) p < 0.05; (-) p > 0.05. b FYM, farmyard manure. 61 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS These differences were only significant (p < 0.05) in grass-clover plots, but not in winter wheat plots. BIODYN showed at the average 18 and 26% higher DNA contents when compared to BIOORG and CONFYM, but differences were not significant (p £ 0.05). Differences between CONMIN and NOFERT were also not significant (p > 0.05). DNA contents between winter wheat and grass-clover plots revealed no significant (p > 0.05) differences. Two-way ANOVA showed slightly not significant (p = 0.052) changes of DNA content in relation to farming systems, whereas correlation to crop was clearly not significant (p > 0.05, Table 2.2). Interaction between farming systems and crops was also not significant (p > 0.05). However, linear correlation of soil DNA contents to soil microbial biomass was highly significant (r = 0.75, p < 0.01). 2.4.3 Community level substrate utilization As determined by counts of bacterial CFU on a glucose minimal medium, 7644 ± 1365 CFU were inoculated to each well of the Biolog EcoPlates for the winter wheat soils and 4846 ± 923 CFU for the grass-clover soils. Twelve time points were predetermined for CLSU data collection from the 10 different sample types. Substrate utilization of all 31 substrates was monitored at these time points revealing clear differences among CLSU-fingerprints from the different farming systems and crops (data not shown). Since average well colour development (AWCD) in the Biolog EcoPlates was more rapid for winter wheat soil samples than for grass-clover soil samples, six pairwise sets of winter wheat and grass-clover data were selected, which displayed most similar AWCD values (Table 2.3). Analysis of this time course revealed that CLSU patterns developed towards a clear differentiation of certain treatment groups. Cluster analysis of average values from data set six (Table 2.3 and Fig. 2.2) exemplified and supported results obtained with Monte Carlo permutation testing performed on the CLSU data sets from all four replicate samples (Table 2.3). Average CLSU values of the 31 substrates most clearly separated NOFERT from all other treatments, i.e. BIODYN, BIOORG, CONFYM and CONMIN (Fig. 2.2, clusters I and II). This separation was highly significant for all six data sets (Table 2.3). On the second level of branching the dendrogram a strong influence of the two crops was evident (Fig. 2.2, clusters IIa and Mb). 62 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS Table 2.3. Time course of data determined with Biolog EcoPlates. Average well colour development (AWCD) values were determined at twelve predetermined time points during plate incubation and six sets with most similar AWCD values were composed. Significant effects related to overall fertilization, mineral or organic fertilizer types and crops were determined. The percentage of total variance explained by the whole model was determined for each of the six data sets. Set Winter wheat Grass-clover Significance levels3 Varb Timec (h) AWCD Timec (h) AWCD Fertilization0 Fertilizer type6 Crops' (%) *** 1 44 0.30 48 0.27 - - 43.7 ** * 2 48 0.41 52 0.35 - 41.1 *** * 3 52 0.51 60 0.52 - 39.2 4 56 0.60 64 0.60 *** * ** 38.1 5 60 0.68 68 0.68 *" * ** 36.6 6 64 0.76 72 0.74 * ^ 36.6 a > (-) p 0.05; (*) p < 0.05; (**) p < 0.01 ; (***) p < 0.001 as determined by Monte Carlo permutation testing. b Percent variance related to the seven factors WW, GC, NOFERT, CONMIN, BIODYN, BIOORG and CONFYM as determined by partitioning the variance based on redundancy analysis. Incubation time of Biolog EcoPlates. NOFERT vs. CONMIN, BIODYN, BIOORG and CONFYM. CONMIN vs. BIODYN, BIOORG and CONFYM. Winter wheat vs. grass-clover. Monte Carlo permutation testing revealed that significance levels of crop effects increased from data sets two to six (Table 2.3). Cluster analysis also revealed that CONMIN (Fig. 2.2, branches Mai and lla2) associated with the FYM treated winter wheat samples but had a tendency to separate from BIODYN, BIOORG and CONFYM (Fig. 2.2, cluster lla3). Monte Carlo permutation testing revealed significant differences between CONMIN and FYM-treated plots in data sets four to six (Table 2.3). Differences among the FYM-treatments, i.e. BIODYN, BIOORG and CONFYM, were not significant for both crops (Fig. 2.2, clusters lib and lla3). The fraction of variance in the CLSU data related to the five farming systems and two crops decreased over the time course from 43.7% in data set 1 to 36.6% in data set six (Table 2.3). Crop and farming system effects accounted for 8.6 and 27.9% of the variance, respectively, while a 'FYM-application'-effect accounted for 2.7% and the 'no FYM-application'-effect accounted for 11.5% of the variance. 63 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS CLS>U GC_NOFERT I WW_NOFERT GC_CONMIN lla1 *** WW_CONMIN "a2 IIa WW_BIODYN WW_BIOORG lla3 ## WW_CONFYM II GC_BIODYN GC_CONFYM lib GC BIOORG Euclidean Distance Figure 2.2. Cluster analysis of CLSU data derived from the DOK long-term field experiment based on mean values of four replicates of the five farming systems and two crops. The Ward dendrogram was determined based on Euclidean distances calculated from all 31 blank- and AWCD-corrected Biolog EcoPlate substrate utilization values. Monte Carlo permutation testing on all four field replications was used to determine significant branching in the dendrogram. GC, grass-clover; WW, winter wheat; BIODYN, bio-dynamic; BIOORG, bio-organic; CONFYM, conventional; CONMIN, mineral fertilizer; NOFERT, unfertilized; ***p < 0.001; **p < 0.01. Labels on specific branches refer to information specified in the text. Two-way ANOVA of individual substrate utilization intensities revealed significant effects (p < 0.05) of the two crops in 13 of the 31 substrates and also 13 of the 31 substrates displayed a system effect (Table 2.4). Eight of 31 substrates showed significant effects for both, crop and system, whereas cross effects of the two factors were significant for two substrates (Table 2.4). Thirteen substrates did not show any significant changes related to farming system or crop. In general, the two crops had 64 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS significant effects on the CLSU patterns, while for the five systems significant effects were exclusively related to differences between the systems with FYM and those that did not receive FYM. No statistically significant differences were found between BIODYN, BIOORG and CONFYM, even when excluding CONMIN and NOFERT from the calculation (data not shown). 2.4.4 Terminal restriction fragment length polymorphisms analyses Further analysis of bacterial community structures was performed by RFLP analysis of PCR-amplified bacterial SSU rDNA. Visual inspection by agarose gel electrophoresis of RFLP patterns obtained from pooled samples containing equal amounts of DNA from all four field replicates revealed no noticeable differences among the different farming systems and between winter wheat and grass-clover (data not shown). T-RFLP analysis based on 32 terminal restriction fragments (T- that could RFs) unambiguously be identified and quantified among all 40 samples, allowed for statistically supported distinction of genetic profiles. Cluster analysis of the average values of the 32 T-RFs most clearly separated field plot soils fertilized with FYM from those not receiving FYM (Fig. 2.3, clusters I and II). the Furthermore, dendrogram revealed a strong influence of the two crops in the FYM treated plots, which resulted in crop-dependent sub-clustering (Fig. 2.3, clusters IIa and lib). On the other hand, the CONMIN and the NOFERT treatments dominated effects of the two crops (Fig. 2.3, clusters la and lb). Monte Carlo permutation testing performed on the T-RF data set with all 40 samples revealed that the separation of clusters I and II and of clusters Ma and lib were highly significant with p < 0.001 and p < 0.01, respectively. Separation of all other clusters was not significant (p £ 0.05) (Fig. 2.3). The five farming systems and the two crops accounted for 43% of the variance in the T-RFLP data set. Crop and farming system effects explained 7.4 and 35.6% of the variance, respectively, while a 'FYM application'- effect accounted for 3.6% and the 'no FYM-application' accounted for 6.4% of the variance. Two-way ANOVA of individual T-RF values revealed significant effects (p < 0.05) of the two crops for 8 of the 32 T-RFs while 22 T-RFs displayed system effects. Seven of the 32 T-RFs showed significant effects for both, crop and system, whereas cross effects of the two factors were significant for 6 T-RFs (Table 2.5). Nine of the 32 T-RFs revealed no 65 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS significant changes related to farming system or crop. No statistically significant differences were found between BIODYN, BIOORG and CONFYM, even when excluding CONMIN and NOFERT from the calculation (data not shown). T-RFLP WW_NOFERT la GC_NOFERT 1 WW„CONMIN lb GC_CONMIN WW_BIODYN ±±± WW^BIOORG IIa WW^CONFYM *U *1* ff il GC_BIODYN GC BIOORG IIb GC_CONFYM 2 3 Euclidean Distance Figure 2.3. Cluster analysis of T-RFLP data derived from the DOK long-term field experiment based on mean values of four replicates of the five farming systems and two crops. The Ward dendrogram was determined based on Euclidean distances calculated from all 32 normalized T-RF peak height values. Monte Carlo permutation testing on all four field replications was used to determine significant branching in the dendrogram. GC, grassclover; WW, winter wheat; BIODYN, bio-dynamic; BIOORG, bio¬ organic; CONFYM, conventional; CONMIN, mineral fertilizer; NOFERT, unfertilized; ***p < 0.001; **p < 0.01. Labeling of specific branches refers to information specified in the text. 66 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS Table 2.4. Significances of correlations between the 31 CLSU values of Biolog EcoPlates and the two factors farming system and crop as well as interactions between the two factors. Substrates3 Significance levels Crops0 Systems Interaction6 b-Methyl-D-Glucoside D-Galactonic Acid g-Lactone L-Arginine Pyrovic Acid Methyl Ester D-Xylose D-Galacturonic Acid L-Asparigine Tween 40 l-Erythritol 22-Hydroxy Benzoic Acid L-Phenylalanine Tween 80 D-Mannitol 4-Hydroxy Benzoic Acid L-Serine a-Cyclo-dextrin N-Acetyl-D-Glucosamine g-Hydroxy-butyric Acid L-Threonine Glycogen D-Glucosaminic Acid Itaconic Acid Glycyl-L-Glutamic Acid D-Cellubiose Glucose-1-Phosphate a-Keto Butyric Acid Phenylethylamine a-D-Lactose D,L-a-Glycerol Phosphate D-Malic Acid Putrescine a All 31 specific substrates on a Biolog EcoPlate. b > (-) p 0.05; O p < 0.05; (**) p < 0.01 ; (***) p < 0.001 (as determined by ANOVA). c Winter wheat and grass-clover. d BIODYN, BIOORG, CONFYM, CONMIN and NOFERT. e Interaction between crops and systems. 67 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS Table 2.5. Significances of correlations between the 32 terminal restriction fragment values and (T-RF) peak height the two factors farming system and crop as well as interactions between the two factors. T-RF sizes3 Significance levels [rmu] Crops0 Systemsd Interaction - 62 - 65 * * ** 74 - 82 ** ** * *** 85 - 91 ** ** - * 92 - 94 *** * 125 *** - * 126 - 127 * * - - 136 - 138 ** + #** 142 - - 147 * * ** 149 - 155 *** - * 158 - 159 - - - 165 - 168 * ** - 197 ** *** - 265 * *** - * *** 277 - 280 ft** - 290 *** - 295 *** - 437 ** - 450 - - 494 - - 513 - - 539 * - a Sizes in relative migration units (rmu) of all 32 terminal restriction fragments (T-RF) scored. b > (-) p 0.05; O p < 0.05; (**) p < 0.01 ; (***) p < 0.001 (as determined by ANOVA). c Winter wheat and grass-clover. d BIODYN, BIOORG, CONFYM, CONMIN and NOFERT. e Interaction between crops and systems. 68 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS 2.5 Discussion In agricultural soils microbial diversity may be decreased as compared to natural soils, which in turn might lead to reduced or less robust soil functionality (Torsvik et ai, 2002). Currently, knowledge about the relation of soil functionality and soil microbial diversity is scarce as both characteristics are difficult to assess. However, it is important to know, whether agricultural management practices have an impact on soil microbial characteristics, and which of the agricultural management factors, like fertilization or choice of crops, induce strongest effects on soil microbial communities. The DOK field experiment was established in 1978 and has been operated according to guidelines for conventional and organic farming (Mäder et ai, 2002). It was designed as a split-split plot with three temporally shifted crop rotation parallels and four field replications. Therefore, effects of farming systems and crops on microbial soil characteristics could be investigated in this well designed field experiment. The polyphasic approach, based on soil biomass parameters and microbial community profiling techniques, allowed to successfully assessing these effects. 2.5.1 Soil biomass parameters The five farming systems of the DOK experiment significantly affected soil microbial biomass Cmic (Table 2.2) while the influence on soil DNA content was only nearly significant (p = 0.052). In general, the FYM treated soils in the farming systems BIODYN, BIOORG and CONFYM, revealed significantly higher values for microbial biomass and DNA when compared to those not fertilized with FYM, i.e. CONMIN and NOFERT. Among the systems with FYM treatment, BIODYN tended to higher Cmic values for both crops, when compared to the conventional system. These results are supported by other studies (Bossio et ai, 1998; Carpenter-Boggs et ai, 2000a; Peacock et ai, 2001), indicating that the DOK system is a stable and representative system for assessing agricultural management effects on biological soil characteristics. Assessment of crop effects on soil Cmic and DNA content revealed less pronounced differences, which were not significant (Table 2.2), and not consistent between the two crops. In contrast, the CFU values revealed significant correlations with farming systems and crops. In various studies moderate effects of plants on biological soil characteristics were reported (Bardgett et ai, 1999; Johnson et ai, 2003; Kennedy et ai, 2004). The magnitude of effects strongly depended on 69 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS plant species, composition of plant populations, as well as on whether samples were derived from rhizosphere or bulk soil. One distinct effect of crops in our study was a higher content of Cm,c and DNA in soils from grass-clover plots of the BIOORG system, when compared to the corresponding winter wheat plots. This observation, however, may mainly be attributed to the system-specific fertilization regime of BIOORG, where a larger quantity of slurry was applied to the grass-clover plots than in the other farming systems. Overall, strongest effects on soil biomass parameters were induced by application of FYM, which may be explained by the addition of nutrients as well as microbial biomass contained in FYM. Crop effects were only significant for the culturable fraction of soil bacteria detected with plate counts. These significant effects of agricultural management factors indicated distinct effects on microbiological soil characteristics. If soil microbial community structures are resolved based on SSU rDNA gene analyses it is important that soil DNA represents total soil microbial biomass. Non-representative DNA extracts may lead to biased description of soil microbial communities (Miller et ai, 1999; Roose-Amsaleg et ai, 2001). A strong correlation between soil Cm.c and soil DNA contents, ascertained the representativeness of soil DNA extracts (Marstorp et ai, 2000; Bundt et ai, 2001 ; Blagodatskaya et ai, 2003; Hartmann et ai, 2005). The high correlation between soil and DNA = Cmic soil contents presented here, i.e. r 0.75 (p < 0.001), is supporting this concept and the quality of the DNA extraction and quantification protocol used (Bürgmann et ai, 2001). 2.5.2 Soil bacterial community structures The influence of the farming systems explained 28% of the total CLSU data variability while 42% of the Biolog EcoPlate substrates revealed significant changes due to the farming system with no crop interaction (Table 2.4). T-RFLP analyses revealed that 36% of total variance was specifically related to the farming systems, which also influenced 56% significantly of all detected soil bacterial T-RFs with no crop interaction (Table 2.5). These statistical analyses of CLSU and T-RFLP data indicated a good comparability of the two approaches applied even though they revealed a slightly better differentiation for the T-RFLP data set (Tables 2.4 and 2.5). Comparing the cluster analyses of the CLSU and the T-RFLP data revealed one clear difference between the two approaches. Cluster analysis of T-RFLP data 70 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS significantly separated the two farming systems without FYM amendment, i.e. CONMIN and NOFERT, from the FYM-based farming systems (Fig. 2.3), while the CLSU-derived dendrogram significantly (p < 0.001) separated the unfertilized controls, i.e. NOFERT, from the other four farming systems and clustered the CONMIN samples to FYM amended winter wheat samples (Fig. 2.2). However, as substantiated by time course and Monte Carlo permutation analyses, also the CLSU analysis tended to separate CONMIN from FYM treated systems by revealing significant (p < 0.05) separation between CONMIN and FYM treated soils in the last three time points of the time course (Table 2.3). Possibly, NOFERT was separated so significantly from the other farming systems because of the decrease in soil pH or because crop yield was strongly reduced due to the lack of nutrients after 22 years without fertilizer input. These specific soil conditions may well be represented in microbial community structure. Enhanced effects induced by reduced crop or root biomass are likely and will have to be studied in greater detail in the future. For T- RFLP analysis of CONMIN this explanation may also apply since the corresponding soils tended to lower soil Cmic contents. In CLSU analysis all CONMIN samples clustered with the FYM amended winter wheat plot samples, which represented the arable in phase the crop rotation. Even though CONMIN produces normal crop these soils yields, tended to lower biomass contents, and therefore may have displayed similar substrate utilization patterns as the soils from winter wheat plots in the arable phase of the crop rotation. In an earlier study on soils from winter wheat plots from the DOK field experiment, principal component analysis of CLSU profiles detected seasonal variations, while differences between the farming systems were only observed in spring but variation among field replications was relatively high (Fliessbach and Mäder, 1997). O'Donnell et ai (2001) also reported only small differences of CLSU data among FYM and minerally fertilized soils. Possibly, the relatively low variability among a high number of replicate Biolog microplates, the reduced number of substrates in Biolog EcoPlates as compared to the GN-plates used and the earlier, time course analysis applied in the present study may explain the higher resolution and significances found. Denaturing gradient gel electrophoreses (DGGE)-based comparison of organic and inorganic fertilizer types for their long-term effects on bacterial community structures revealed strong differences between organic and inorganic fertilization as determined with redundancy discriminate analysis (Marschner et ai, 2003b). T-RFLP analysis 71 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS targeting the functional genes nirK and nirS, which are specific for denitrifying bacteria, revealed clear differentiation of soils receiving no fertilizers, mineral fertilizers or cattle manure (Wolsing and Prieme, 2004). Significant differences in phospholipid fatty acid (PLFA) profiles of bacteria and fungi were found in soils from organic or conventional farming of the sustainable agricultural farming system (SAFS) project (Bossio et ai, 1998; Lundquist et ai, 1999). Peacock étal. (2001) detected significant differences in bacterial PLFA profiles among FYM amended, minerally fertilized and control soils. Carpenter-Boggs et ai (2000a) have compared unfertilized, minerally fertilized, and compost-amended soils based on bacterial fatty acid methyl ester (FAME) profiles, which revealed strong differences between compost-amendment and mineral or no fertilizer application, but could not distinguish between minerally and unfertilized soils as well as between bio-organically and bio- dynamically treated soils. Overall, these reports and the data presented in the present study indicate that input of fertilizer and FYM play an important role by representing a general enrichment in organic substrates, and therefore promoting growth of microbial communities (De Fede et ai, 2001; Grayston et ai, 2001). Crop effects of winter wheat and grass-clover allowed to explain 8.6% of CLSU data and were variability, supported by 39% of the CLSU values that significantly (p < 0.05) changed due to the two crops without interaction with farming systems (Table 2.4). Only 7.4% of total variability in the T-RFLP data set was explained by effects related to the two crops. Twenty two percent of the bacterial T-RFs were significantly (p < 0.05) changing in relation to crop effects without showing interaction with the farming systems (Table 2.5). Among the FYM treated soils, crop effects significantly (p < 0.01) dominated changes in CLSU (Fig. 2.2 and Table 2.3) and T-RFLP (Fig. 2.3) profiles revealing consistency between the two analytical methods and validity of the results obtained. These findings were supported by results from others showing a significant influence of plant species on soil microbial community structures determined by CLSU (Grayston et ai, 1998; Larkin and Groves, 2003) and soil DNA analyses (Buckley and Schmidt, 2001 ; Smalla et ai, 2001 ; Marschner et ai, 2004). Several studies that soil showed, bacterial community structures depend on crop or other plant species (Grayston et ai, 1998; Bardgett et ai, 1999; Smalla et ai, 2001; Larkin, 2003). Kuske étal. (2002), for example, were able to clearly distinguish three different grass species based on rhizosphere bacterial community structures by using a T-RFLP approach on SSU rDNA. In our study, plant effects occurred on a lower 72 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS level as compared to relatively strong effects induced by fertilizers. Kennedy et ai (2004) reported that fertilizer amendments, such as combination of lime and nitrogen influenced soil bacterial T-RFLP profiles significantly stronger than different grassland species. Effects of different plant species on bacterial community composition in agriculturally improved and unimproved grassland soils based on PLFA analyses clearly showed that effects of plant species also depended on the management of the soil (Innés et ai, 2004). They found that effects on PLFA profiles of all plant species investigated depended on whether they were growing in improved or unimproved soils. This supports our findings, that crops dominantly influenced bacterial community composition in the FYM treated soils, but not in minerally fertilized and unfertilized soils. In agricultural practice, however, it may be difficult to separate plant-derived effects from those of fertilizers, since each crop has its specific management requirements, which may lead to strong interactions of the two factors. Overall, soil bacterial community profiling based on CLSU with Biolog EcoPlates and SSU rRNA gene T-RFLP analysis yielded largely consistent results. 2.6 Conclusions The polyphasic approach to evaluate and compare factors affecting microbiological soil characteristics in the DOK long-term field experiment allowed to differentiate the impact of specific farming systems and selected crops. Soil biological parameters like microbial biomass and DNA content were strongly influenced by the farming systems, whereas winter wheat and grass-clover, representing two positions in the crop did affect rotation, not these parameters to a significant extent. Differences among the FYM-based farming systems BIODYN, BIOORG and CONFYM were not significant. CLSU and T-RFLP analyses revealed that the main impact of FYM application was followed by intermediate effects of the two crops winter wheat and grass-clover. Smallest and insignificant effects on soil bacterial communities were detected among the three farming systems BIODYN, BIOORG and CONFYM. Most of the specific substrate utilization values and T-RF abundances were affected by the specific treatments in the DOK soils, indicating extended shifts in the soil bacterial communities. These results suggest that for successful soil quality management, input of FYM and crop rotation are of major importance. 73 2 COMMUNITY STRUCTURES AND SUBSTRATE UTILIZATION OF BACTERIA IN THE DOK SOILS 2.7 Acknowledgments Paul Mäder, Roland Kölliker and David Dubois are acknowledged for valuable contributions to this manuscript. We are grateful to Vit Fejfar for his assistance with laboratory analyses. Special thanks go to the field crew of the DOK-experiment and to the farmers and representatives of the Swiss farming organizations involved. This study was financially supported by the Swiss Federal Office for Agriculture and by a grant of the Swiss National Science Foundation. 74 Chapter 3: Ranking the magnitude of crop and farming system effects on soil microbial biomass and genetic structure of bacterial communities Martin Hartmann, Andreas Fliessbach, Hans-Rudolf Oberholzer, Franco Widmer Published in FEMS Microbiology Ecology 57 (2006), 378-388 © 2006 Federation of European Microbiological Societies, Blackwell Publishing Ltd. 3 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 3 Ranking the magnitude of crop and farming system effects on soil microbial biomass and genetic structure of bacterial communities 3.1 Abstract Biological soil characteristics such as microbial biomass, community structures, activities, and functions may provide important information on environmental and anthropogenic influences on agricultural soils. Diagnostic tools and detailed statistical approaches need to be developed for a reliable evaluation of these parameters, in order to allow classification and quantification of the magnitude of such effects. The DOK long-term agricultural field experiment was initiated in 1978 in Switzerland for the evaluation of organic and conventional farming practices. It includes three representative Swiss farming systems with biodynamic, bio-organic and conventional fertilization and plant protection schemes along with minerally fertilized and unfertilized controls. Effects on microbial soil characteristics induced by the long-term management at two different stages in the crop rotation, i.e. winter wheat after potato or corn, were investigated by analyzing soil bacterial community structures using analysis of PCR-amplified rRNA genes by terminal restriction fragment length polymorphism and ribosomal intergenic spacer analysis. Application of farmyard manure consistently revealed the strongest influence on bacterial community structures and biomass contents. Effects of management and plant protection occurred regimes on an intermediate level, while the two stages in the crop rotation had a marginal influence that was not significant. 3.2 Introduction Sustainable land use for the preservation and improvement of soil fertility is important for agriculturally managed ecosystems (Tilman, 1999b; Doran and Zeiss, 2000). Factors influencing soil characteristics have mostly been determined based on chemical or physical parameters (Gerhardt, 1997; Eck and Stewart, 1998; Castillo 76 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS and Joergensen, 2001 ; Izquierdo et ai, 2003). However, transformation processes and nutrient cycles mediated by soil biota also influence soil quality, and therefore, biological soil characteristics such as microbial biomass, community structure, activity and function may also reflect the influences of different environmental or anthropogenic factors (Kennedy and Smith, 1995). Soil microbiota play an important role in nutrient turnover (Dighton, 1995; Kennedy, 1999). Therefore, it has been suggested that it is essential to analyze the effects of different factors on the bacterial community structures in agricultural soils (Kennedy and Smith, 1995; Pankhurst et ai, 1996; Kennedy and Gewin, 1997; Kennedy, 1999). Because cultivation- dependent techniques access only a small proportion of the microbial community (Colwell and Grimes, 2000), molecular tools may allow for a more detailed assessment of changes in microbial community structures in soil, and thus may be of assistance for the determination and monitoring of soil quality (Hill et ai, 2000; Kirk et ai, 2004). PCR-based techniques such as denaturing gradient gel electrophoresis (DGGE), single strand conformation polymorphism (SSCP), terminal restriction fragment length polymorphism (T-RFLP), and ribosomal intergenic spacer analysis (RISA) have been frequently applied in analyses of soil microbial community structures (for a review see Kirk et ai, 2004). Microbial diversity in agricultural soils was found to be low when compared to pristine soils (Torsvik et ai, 2002), but the specific factors responsible for these differences are not well understood. Many studies have shown that organic farming, which avoids the use of synthetic fertilizers and pesticides, may lead to increased soil biodiversity and biological activity in soils when compared to conventional farming (Mäder, 1995; Bossio et ai, 1998; Carpenter-Boggs et ai, 2000a). Biodynamic farming, as a specific form of organic farming (Steiner, 1993), has also been reported to sustain better soil quality than conventional farming practices (Reganold et ai, 1993). In addition to different farming systems, factors such as plant species (Grayston et ai, 1998; Smalla et ai, 2001; Marschner et ai, 2004), soil type (Girvan et ai, 2003) and tillage (Lupwayi et ai, 1998) may also influence biological soil characteristics. When investigating system-specific differences in agricultural soils it is important to consider changes that become apparent only after a long period of time (Temple et ai, 1994; Poulton, 1996). In a long-term experiment at Rothamsted, UK, started in 77 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 1848, it has been demonstrated that effects on yield and ecological parameters can become apparent only after 40 years (Powlson and Johnston, 1994). However, at that time, tools for determining specific biological soil characteristics such as community structures were not available. The DOK long-term field experiment in Switzerland was established in 1978 and enables the investigation of effects of fertilization and plant protection strategies in agricultural systems on soil characteristics and crop yield (Mäder et ai, 2002). The DOK experiment includes biodynamic (BIODYN), bio-organic (BIOORG) and (FYM) based conventional farm yard manure (CONFYM) farming systems along with mineral (CONMIN) and unfertilized (NOFERT) controls and a 7-year crop rotation that is running temporally shifted in three parallels. The effects of farming systems and crops in a defined rotation were thoroughly investigated assessing chemical, physical and biological soil characteristics as well as yield and energy balance (Alfoldi et ai, 1995a; Mäder et ai, 2000; Mäder et ai, 2002). Mean crop yields were 20% lower in the organic systems, i.e. BIODYN and BIOORG, when compared to the conventional CONFYM system, but reduced input of NPK fertilizer (34-51%), pesticides (97%), and energy (20-56%) revealed ecological advantages of organic farming (Mäder et ai, 2002). In addition, increases in microbial activity, soil aggregate stability, microbial and earthworm biomass, root colonization by mycorrhiza, and number of arthropods represent some characteristics reported for the organic farming systems when compared to CONFYM (Mäder et ai, 2002). In this study, microbial soil characteristics were determined for a detailed assessment and ranking of effects of the different factors in the DOK system. Soil microbial biomass was determined in order to allow for the comparison of data to previous studies, and to validate the quality of soil DNA extraction. Extracted soil DNA was used to resolve bacterial community structures with two independent PCR-based ribosomal RNA gene Operon profiling techniques, i.e. T-RFLP and RISA. Both techniques are compatible with semi-automated application and detailed statistical analyses, allowing one to test the robustness of the results obtained (Hartmann et ai, 2005). In addition, T-RFLP and RISA target different genetic regions and therefore differ in their phylogenetic resolution, which has been reported to be approximately at the genus level for T-RFLP and at the species level for RISA (Fisher and Triplett, 1999; Dunbar et ai, 2001). 78 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 3.3 Material and Methods 3.3.1 Experimental system and soil sampling Analyses were performed on soil samples from the DOK long-term agricultural field experiment in Switzerland, which was started in 1978. The experiment is situated 300m above sea level in a topographically leveled area on alluvial loess (haplic luvisol) that has been agriculturally cultivated for decades (Mäder et ai, 2000). The mean precipitation in the area is 785mm year'1 and the annual mean temperature is 9.5 °C. For a detailed description of the experimental concept and characteristics, including data on energy efficiency, system productivity and different physical, chemical, and biological soil characteristics see previous publications (Alfoldi et ai, 1995a; Mäder et ai, 2000; Mäder et ai, 2002). Five different treatment types, i.e. BIODYN, BIOORG, CONFYM, CONMIN, and NOFERT (Table 3.1), were sampled from the split-split block field design with four replicates. Table 3.1. Agricultural management regimes in the DOK long-term field experiment Treatment Unfertilized Biodynamic Bio-organic Conventional Mineral (NOFERT) (BIODYN) (BIOORG) (CONFYM) (CONMIN) Fertilization - Farm yard manure FYM(Biodyn) FYM(Bioorg) FYM(Confym) - (FYM)a and slurry and slurry and slurry - Mineral fertilizer - - mineral mineral (N.P.K) (N,P,K) Plant protection Weed control mechanical mechanical mechanical mechanical & mechanical & herbicides herbicides Disease control rock powder rock powder rock powder chemical chemical Insect control plant extracts plant extracts plant extracts chemical chemical & bio-control & bio-control & bio-control Special treatments biodynamic biodynamic Cuc plant growth plant growth preparations'3 preparations regulators regulators a Farm yard manure (FYM) application of 1.4 livestock units ha'1 year'1 (~2000 kg organic carbon ha"1 was year"1) performed with aerobically composted FYM(B|0DYn) (C/N = 8), slightly aerobically rotted = FYM(bioorg) (C/N 11); anaerobically rotted FYM(confym> (C/N = 12). For details on the experiment refer to previous publications (Mäder et ai, 2000; Mäder étal., 2002). b Preparations 500 (horn manure) and 501 (horn silica) were applied (Steiner, 1993). c CuS04 was used for plant protection in BIOORG potato until 1991. 79 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS Plots of 5 x 20 m have been arranged on an area of 1.4 ha. BIOORG has been maintained according to standard organic farming practice in Switzerland (Eidg.Volkswirtschaftsdepartement, 1997), while for BIODYN guidelines for biodynamic farming have been applied (Kirchmann, 1994). The conventional system CONFYM has been maintained according to Swiss standard recommendations (Eidg.Volkswirtschaftsdepartement, 1998). The same 7-year crop rotation has been used in all systems, i.e. potato, winter wheat 1, soy bean, corn and winter wheat 2, followed by 2 years of grass clover, and has been temporally shifted in three parallels. Therefore, the effects of different combinations of preceding and actual crop could be investigated. Soil preparation was identical for all systems. Bulk soils were sampled in March 2003 before the first fertilizer application after winter. Samples of all four field replicates from all five treatments and from two different positions in the crop rotation, i.e. winter wheat 1 after potato (P-WW1) and winter wheat 2 after corn (C-WW2), were obtained. From each plot, 16 soil cores each with a diameter of 2.5 cm were taken to a depth of 20 cm, pooled and transported to the laboratory. Plant debris was removed and soils were sieved and immediately subjected to microbial biomass determination and DNA extraction. 3.3.2 Soil microbial biomass To obtain a validated determination of soil microbial biomass, two techniques were applied. Chloroform fumigation extraction (CFE) was performed according to Vance et ai (1987). Total soluble organic carbon of 20 g (dry weight equivalent) chloroform fumigated and control soil was extracted with 80 ml of 0.5 M K2S04 and analyzed for extracted carbon by infrared spectrometry (DimaTOC, Dimatec, Essen, Germany). Soil microbial biomass Cmic-CFE (mg kg"1 soil) was determined using the conversion factor 0.45 (Martens, 1995). Substrate-induced respiration (SIR) was performed according to Anderson & Domsch (1978). To 50 g (dry weight equivalent) of pre-equilibrated soil samples, 150 mg glucose was added, mixed, and the initial C02 production response was measured with an infrared gas analyzer (IRGA) according to Heinemeyer et ai (1989). Soil microbial biomass Cmic-SIR (mg kg"1 soil) was calculated from the initial respiration rates using the conversion factor 30 (Kaiser et ai, 1992). 80 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 3.3.3 Extraction and quantification of DNA from soil Nucleic acids were extracted from 0.5 g fresh soil according to the protocol of Bürgmann et ai (2001) using a FastPrep bead beater (FP 120, Savant Instruments Inc., Holbrook, NY). Extracted DNA was quantified with PicoGreen® (Molecular Probes, Eugene, OR) on a luminescence spectrometer (Perkin Elmer, LS 30, Wellesley, MA) (Hartmann et ai, 2005). In addition to the four field replicates, a pooled DNA sample of the four corresponding replicates was prepared. 3.3.4 Genetic profiling of soil bacterial populations T-RFLP and RISA were performed as described by Hartmann et ai (2005). PCR for T-RFLP analysis was performed with bacteria-specific small subunit ribosomal RNA (SSU rRNA) gene primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3', FAM-labeled) and 1378R (5'-CGGTGTGTACAAGGCCCGGGAACG-3') (Heuer et ai, 1997) on 10 soil ng DNA, corresponding to an average of 200 mg dry weight soil, in a total volume of 50 ml. PCR products were digested with Mspl (Promega, Madison, Wl). Restriction products were purified with Microcon YM-30 filter columns (Millipore, Billerica, MA). The T-RF product sizes and quantities were analyzed on an ABI Prism 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA) equipped with 36 cm capillaries filled with POP-4. Peak calling was performed using Genotyper v3.7 NT (Applied Biosystems) and raw data were standardized using z-transformation (Excel, Microsoft, Redmond, WA). PCR for RISA was performed with bacteria-specific primers bRISAfor (5'-TGCGGCTGGATCCCCTCCTT-3', HEX-labeled) and bRISArev (5'-CCGGGTTTCCCC-ATTCGG-3') (Normand et ai, 1996) on 30 ng DNA, corresponding to 600 mg dry weight soil, in a total volume of 25 ml. The PCR products were purified with Microcon YM-100 filter columns (Millipore). Analysis of RISA products was as described for T-RFLP. 3.3.5 Statistical analyses The consistency of the microbial biomass determined with CFE and SIR as well as DNA content was evaluated with Pearson Product-Moment Correlation Coefficients (r). The two-sided t-test was used to determine treatment specific differences. Explorative statistical analyses of T-RFLP and RISA data were performed with Ward 81 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS cluster analysis based on Euclidean distances with Statistica version 6.1 (StatSoft Inc., Tulsa, OK). For cluster analysis, the mean of replicates and the corresponding pool samples were used in order to reduce dendrogram complexity. The distance matrices used for dendrogram construction were compared using the Mantel test (Mantel, 1967) with NTSYS-pc 2.1 (Rohlf, 2000). The main factors explaining differences among genetic profiles as well as differences observed in Ward dendrograms were quantified by applying Monte Carlo permutation tests using CANOCO for Windows 4.5 (Microcomputer Power, Ithaca, NY) according to ter Braak and Smilauer (2002), followed by Bonferroni correction (Bland and Altman, 1995). Significance values were assigned to nodes in dendrograms. Partitioning of variance based on the different treatment factors (Borcard et ai, 1992) and ordination of samples relative to treatment factors were performed using redundancy analysis (RDA) with CANOCO (Hartmann et ai, 2005). The percentage of fragments with significantly different abundance in each farming system and/or in each position in the crop rotation was determined by one way analysis of variance (ANOVA) with Statistica (StatSoft Inc). Potential indicator fragment categories affected by a specific treatment factor, without interaction with other factors, were identified using two-factorial ANOVA. 3.4 Results 3.4.1 Biomass and DNA content Average soil DNA content was highest in BIODYN at both stages in the crop rotation, i.e. winter wheat after potato (P-WW1) and winter wheat after corn (C-WW2) (Fig. 3.1). The lowest DNA contents were detected in NOFERT. Differences were only significant (p < 0.05) between BIODYN and the three systems NOFERT, CONMIN and CONFYM at the P-WW1 crop rotation stage. The soil microbial carbon (Cmic) biomass determined with chloroform fumigation extraction (CFE) revealed highest average values in BIODYN plots and lowest average values in CONMIN plots for both stages in the crop rotation. Cmic biomass in FYM-treated systems, i.e. BIODYN, BIOORG and CONFYM, was significantly (p < 0.05) higher than in NOFERT and CONMIN, except for CONFYM at the C-WW2 crop rotation stage. 82 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 100 NOFERT CONMIN BIODYN BIOORG CONFYM Farming System Figure 3.1. Microbial biomass parameters determined in soils of the DOK long term field experiment for two stages in the crop rotation, i.e. winter wheat 1 after potato (P-WW1; open bars) and winter wheat 2 after corn (C-WW2; hatched bars). Microbial biomass was determined with soil DNA extraction (a), chloroform fumigation extraction (CFE; b) and substrate induced respiration (SIR; c). Data are presented as mean values and standard deviations of four independent field replicates. Systems marked with different lower case (P-WW1) or upper case (C-WW2) letters are significantly different (t-test; p < 0.05). BIODYN: biodynamic; BIOORG: bio-organic; CONFYM: conventional; NOFERT: unfertilized; CONMIN: minerally fertilized (see Table 3.1). 83 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS In addition, significant (p < 0.05) differences were also observed among plots receiving FYM, i.e. between BIODYN/BIOORG and CONFYM at the P-WW1 crop rotation stage. The soil Cmic biomass determined with SIR revealed highest average values in BIODYN plots and lowest values in NOFERT plots in both stages in the crop rotation. The FYM-treated plots, i.e. BIODYN, BIOORG and CONFYM, contained significantly (p < 0.05) higher amounts of CmiC compared to NOFERT and CONMIN plots. Significantly (p < 0.05) higher biomass was also found in BIODYN as compared to CONFYM in the C-WW2 crop rotation stage. The correlation between soil DNA contents and biomass was r = 0.69 (p < 0.001) for SIR and r = 0.64 (p < 0.001) for CFE. The correlation between CFE- and SIR-biomass was r = 0.84 (p< 0.001). 3.4.2 Soil bacterial community structures In bacterial genetic community profiles, determined with T-RFLP and RISA targeting the rRNA gene operon, 79 T-RFs with sizes of 61 to 488 relative migration units (rmu) and 73 RISA fragments of 288 to 998 rmu were unambiguously scored across all samples. A cluster analysis of pools and of mathematical averages of replicates of both profiling data sets revealed identical dendrogram topologies with distinct groups for each farming and control system (Fig, 3.2). Arithmetic averages of the four replicates clustered consistently with the corresponding pools of DNA extracts. The correlation of distance matrices used for dendrogram construction revealed coefficients between 0.87 and 0.93 (p < 0.001). Systems receiving FYM were differentiated at the highest branching level (p < 0.001) from the control systems with both methods and at both stages in the crop rotation (Fig. 3.2; branches I vs. II). Among soils receiving FYM, BIODYN was significantly (p<0.01) separated on the second branching level from systems BIOORG and CONFYM (Fig. 3.2; branches IIa vs. Mb). BIOORG and CONFYM formed distinct groups at the lowest branching node (Fig. 3.2; branches llbi vs. Ilb2), but differences were only significant (p < 0.05) for RISA profiles at the P-WW1 crop rotation position. CONMIN separated consistently from NOFERT (Fig. 3.2; branches la vs. lb), but differences were statistically significant only for T-RFLP profiles of the P-WW1 crop rotation stage. 84 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS P-WW1 C-WW2 NOFERT (m) la _J NOFERT (p) I CONMIN (m) ! -1 CONMIN (p) lb *** BIODYN (m) 1 Ha h- BIODYN (p) TU. BIOORG (m) .^ II II BIOORG (p) CONFYM (m) lib CONFYM (p) lib. 0.93 0.89 [7] 0.87 0.92 la NOFERT (m) NOFERT (p) CONMIN C (m) < lb CONMIN (p) lb 00 Ha BIODYN (m) IIa rr BIODYN (p) 3 lib, BIOORG (m) -.Hb, II II BIOORG (p) CONFYM (m) lib —| IIb lib CONFYM (p) —TgjT 35 30 25 20 15 10 5 5 10 15 20 25 30 Euclidean Distance Euclidean Distance Figure 3.2. Cluster analysis based on Euclidean distances of bacterial terminal restriction fragment length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA) data for two stages in the crop rotation of the DOK long-term field experiment, i.e. winter wheat 1 after potato (P-WW1) and winter wheat 2 after corn (C-WW2). Asterisks at nodes indicate significant branchings as determined with Monte Carlo permutation testing performed on the four independent replicates (*** p < 0.001; ** * < 0.01; < 0.05). Labels on specific branches refer to information specified in the text. Euclidean distance matrix correlation was determined with Mantel test statistics and are indicated for comparison of the two stages in the crop rotation (P-WW1 and C-WW2) within the same profiling methods (horizontal arrows) and for comparison of different profiling methods within the same crop rotation (vertical arrows). BIODYN: biodynamic; BIOORG: bio-organic; CONFYM: conventional; NOFERT: unfertilized; CONMIN: minerally fertilized (see Table 3.1); (m): arithmetic mean of all four field replicates; (p): pooled DNA sample of all four field replicates. 85 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS Farming systems and controls significantly (p < 0.001; Table 3.2) influenced the soil bacterial community structures. However, the major effect was attributed to the application of FYM, i.e. NOFERT/CONMIN vs. BIODYN/BIOORG/CONFYM (p < 0.001; Table 3.2). Highly significant differences (p = 0.002) were also observed related to the organic fertilization scheme, i.e. BIODYN, BIOORG and CONFYM. The influence of the stage in the crop rotation on the genetic profile data was marginally insignificant (T-RFLP p = 0.055; RISA p = 0.054). Table 3.2. Significance of effects of agricultural management factors in the DOK experiment on bacterial community structures as determined with Monte Carlo permutation testing of terminal restriction fragment length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA) data Treatment T-RFLP RISA Farming system and controls3 0.001 0.001 FYM application15 0.001 0.001 Organic fertilization schemec 0.002 0.002 Stage in the crop rotation0 0.055 0.054 a BIODYN, BIOORG, CONFYM, NOFERT, and CONMIN (see Table 3.1 ). b BIODYN, BIOORG, and CONFYM vs. CONMIN and NOFERT. c BIODYN, BIOORG, and CONFYM. d Different stages in the crop rotation, i.e. winter wheat after potato (P-WW1) and winter wheat after corn (C-WW2). Ordination of data by constrained redundancy analysis also revealed a strong effect of FYM application on the bacterial community profiles (Fig. 3.3). Samples from NOFERT and CONMIN separated from BIODYN, BIOORG, and CONFYM on the first ordination axis (RDA 1), explaining 25.3% (T-RFLP) or 18.1% (RISA) of the variance. NOFERT vs. CONMIN and BIODYN vs. BIOORG and CONFYM separated on the second ordination axis (RDA 2) explaining 6.3% (T-RFLP) or 6.8% (RISA) of the variance. The stage in the crop rotation had minor influence on the distribution of the genetic profiling data. 86 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 0.8 • • •* NOFERT 00 o Ö BIODYN O V--J P-WW1 II o ^""~--\ CO CO BIOORG \± aa7 \^\_ eg C-WWZ < / 'q, qCONMIN Q CONFYM * A P a P 0.8 , H -1.0 RDA 1 (25.3%; r = 0.90) 1.3 RDA1 (18.1%; r = 0.88) Figure 3.3. Constrained ordination determined by redundancy analysis of soil bacterial terminal restriction fragment length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA) profiles for two stages in the crop rotation of the DOK long- term field experiment, i.e. winter wheat 1 after potato (P-WW1) and winter wheat 2 after corn (C-WW2). Data points are based on T-RFs (a) or RISA fragments (b) of each of the four field replicates and the corresponding pool (indicated by asterisks). First (RDA 1) and second (RDA 2) ordination axes are displayed with explained variance in parentheses. Overall correlations between analyzed factors (farming system and stage in the crop rotation) and dependent variables (T-RFs and RISA fragments) on the first two ordination axes were indicated by the corresponding r value. Vector directions indicate maximum variation due to the corresponding factor, while vector lengths indicate strength of the correlation. P-WW1: winter wheat 1 after potato (closed symbols); C-WW2: winter wheat 2 after corn (open symbols). BIODYN: biodynamic (f/O); BIOORG: bio-organic (k/W); CONFYM: conventional (Â.//S); NOFERT: unfertilized (• /O); CONMIN: minerally fertilized ( /D) (see Table 3.1). 87 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS Partitioning of total variance in the data sets based on the redundancy analysis on all canonical axes revealed that 39.6% (T-RFLP) or 34.7% (RISA) of the variance was explained by the treatment factors, i.e. the farming system and crop rotation (Table 3.3). The influence of farming systems and controls was approximately eight times higher than that of the stage in the crop rotation. FYM application accounted for 30.9% and 26.1% of the variance, respectively. BIODYN, NOFERT and CONMIN contributed more strongly to the total variance than BIOORG and CONFYM. Table 3.3. Percentage of variance in terminal restriction fragment length polymorphism (T-RFLP) and ribosomal intergenic spacer analysis (RISA) data sets explained by treatment factors of the DOK experiment as determined with redundancy analysis. T-RFLP RISA All measured factors3 39.6 34.7 Farming systems and controls'5 35.7 30.6 FYM application0 30.9 26.1 Stage in the crop rotationd 3.9 4.1 NOFERT 14.1 9.0 CONMIN 8.9 8.2 BIODYN 12.8 11.2 BIOORG 4.4 5.3 CONFYM 4.4 4.5 a Influence of all treatment factors investigated, i.e. stage in the crop rotation (P-WW1 and C-WW2) and farming or control systems (BIODYN, BIOORG, CONFYM, NOFERT, and CONMIN; see Table 3.1). b BIODYN, BIOORG, CONFYM, CONMIN, and NOFERT. c BIODYN, BIOORG, and CONFYM. d P-WW1 and C-WW2. T-RFs and RISA fragments, which significantly discriminated the different genetic profiles, were identified using ANOVA (Table 3.4). This analysis revealed that 81% of the fragments were significantly (p < 0.05) influenced by the treatment factors. Quantitative differences in abundances were found for 44% (T-RFLP) and 60% (RISA), while qualitative differences were detected for 37% (T-RFLP) and 21% (RISA) of the fragments. Seventy-six percent or 78% of all fragments revealed significantly altered abundance among all treatments, i.e. BIODYN, BIOORG, CONFYM, CONMIN and NOFERT, while 25% or 28% altered between the two 88 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS stages in the crop rotation. Most of the fragments with significantly different abundances among the farming systems were attributed to FYM application. Table 3.4. Number and percentage of terminal restriction fragments (T-RF) and ribosomal intergenic spacer analysis (RISA) fragments significantly (p < 0.05) influenced by treatment factors of the DOK experiment as determined with ANOVA. N(% 1 N (%) Total number detected3 79 ( 100) 73 (100) Fragments differing in at least one factor13 64 (81) 59 (81) Quantitative differences'5 35 (44) 44 (60) Qualitative differences0 29 (37) 15 (21) Fragments differing between0: stage in the crop rotation 22 (28) 18 (25) all farming systems and controls 60 (76) 57 (78) FYM and no FYM application 49 (62) 44 (60) NOFERT and CONMIN 17 (22) 11 (15) NOFERT and BIODYN 46 (58) 41 (56) NOFERT and BIOORG 42 (53) 33 (45) NOFERT and CONFYM 42 (53) 28 (38) CONMIN and BIODYN 47 (60) 35 (48) CONMIN and BIOORG 40 (51) 27 (37) CONMIN and CONFYM 39 (49) 27 (37) BIODYN and BIOORG 23 (29) 26 (36) BIODYN and CONFYM 35 (44) 39 (53) BIOORG and CONFYM 9 (11) 16 (22) Potential farming and control system indicator6 36 (46) 38 (52) Potential FYM application indicator6 30 (38) 36 (49) Potential preceding crop indicator6 3 (4) 2 (3) a Percentages are indicated in parentheses. b Significantly (p < 0.05) different intensities in at least one treatment. c Present or absent T-RF or RISA fragment in at least one treatment. d Differences regarding stage in the crop rotation (P-WW1 and C-WW2) and farming or control systems (BIODYN, BIOORG, CONFYM, NOFERT, and CONMIN; see Table 3.1). e Significant (p < 0.05) differences without interaction of farming system and crop rotation. Using pairwise comparisons of the treatments, 37-60% of all fragments were significantly different in abundance between the plots that did receive FYM and plots that did not receive FYM, whereas 11 to 53% altered within the three farming systems and 15 or 22% between the controls. Forty-six percent or 52% of all 89 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS fragments significantly altered only based on the farming system without revealing a farming system x crop rotation position interaction and were designated potential farming system indicators. Three percent or 4% of the fragments were identified as potential indicators for the stages in the crop rotation. Thirty eight percent and 49% of the fragments were identified as potential FYM application indicators. Percentages of altering fragments between T-RFLP and RISA as determined with ANOVA (Table 3.4) showed high correlation of r = 0.94 (p < 0.001). 3.5 Discussion Data on physical, chemical and biological soil characteristics of the present and previous studies from the DOK field experiment revealed reproducible and representative differences induced by different agricultural management and control systems (Alfoldi et ai, 1995a; Mäder et ai, 2002). Biomass and DNA contents were correlating with previous biomass determinations in the DOK field experiment (Mäder et ai, 2002; Widmer et ai, 2006b) and confirmed the high comparability of these indices as reported before (Marstorp et ai, 2000; Bundt et ai, 2001; Blagodatskaya et ai, 2003; Hofman and Dusek, 2003; Hartmann et ai, 2005; Widmer et ai, 2006b). The extraction of high quality DNA is essential for performing unbiased genetic profiling, and correlating DNA content with established biomass estimates represents one means to validate the quality and representativity of extracted DNA. The relatively large variation among the replicates of DNA measurements, which reduced the statistical discrimination power, may be mainly explained by extraction from fresh soil as well as by the small sample sizes of 0.5 g soil. However, the highly reproducible genetic profiles among replicate samples and the profiling techniques revealed a high representativity of these DNA extracts, which represents a prerequisite for the detailed analysis of effects due to agricultural management factors (Widmer et ai, 2006b) or other influences (Hartmann et ai, 2005). Statistical analysis of data from genetic profiling revealed (i) identical dendrogram topologies and highly similar distance matrices (Fig. 3.2), (ii) similar significant differences by permutation testing (Table 3.2, Fig. 3.2), (iii) similar sample distribution by redundancy analysis (Fig. 3.3), and (iv) a high correlation of changes detected by ANOVA (Table 3.4). In addition, the genetic profiles of pooled samples, representing 90 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS the experimental averages of replicates, clustered consistently with the corresponding arithmetic averages of the four field replicates (Fig. 3.2). Altogether, the high consistency provided strong support that T-RFLP and RISA are reliable tools for studying effects on soil bacterial community structures (Hartmann et ai, 2005). Agriculturally managed soils tend to show decreased microbial diversity when compared to pristine soils (Torsvik et ai, 2002). However, few direct comparisons reported the extent to which factors such as fertilization and crop species induce differences in microbial community structures (Kennedy et ai, 2004; Marschner et ai, 2004; Widmer et ai, 2006b). Cluster analysis and hierarchical ranking of treatment effects on soil bacterial community structures with discriminative statistics represented a helpful approach to classify the extents of different environmental or agricultural impacts (Hartmann et ai, 2005). In addition, fragments indicating treatment-specific effects such as the farming system or crop species in the genetic profiles can be statistically identified (Table 3.4, Hartmann et ai, 2005). In the future, this may allow for the phylogenetic identification of organisms associated with specific treatments and the development of treatment specific indicator diagnostics, which are prerequisites for environmental monitoring (Widmer et ai, 2006a). The genetic profiles of bacterial communities in the soils of the DOK field revealed major differences between soils receiving FYM, i.e. BIODYN, BIOORG and CONFYM, and soils receiving no (NOFERT) or mineral fertilization (CONMIN). Cluster analysis and permutation testing (Fig. 3.2, Table 3.2), canonical ordination (Fig. 3.3) and ANOVA (Table 3.4) revealed that the effects on microbial communities induced by FYM application were the most pronounced. Most of the detected ribotypes, i.e. 62% of T-RFs and 60% of RISA fragments, indicated highly significant differences between FYM and non-FYM treated plots. This may be explained by the addition of the readily available organic matter and nutrients contained in FYM, by the introduction of bacterial populations via FYM application, or by secondary effects of the FYM application such as altered soil characteristics or plant growth (Bossio et ai, 1998). Strong effects on microbial communities induced by addition of organic matter via FYM application were found with DNA-based approaches (Parham et ai, 2003; Sun et ai, 2004; Widmer et ai, 2006b), by analyzing fatty acid profiles (Carpenter-Boggs et ai, 2000a; Peacock et ai, 2001) or using community level substrate utilization (CLSU) analysis (Widmer et ai, 2006b). In contrast, in a field 91 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS experiment started in 1984, Suzuki et ai (2005) suggested that effects on microbial communities, as determined by T-RFLP and fatty acid methyl ester (FAME) profiles, may be dominated by mineral fertilizer application due to changes in chemical soil parameters such as soil pH or exchangeable calcium. In the present study, mineral fertilization induced consistent differences in microbial communities when compared to unfertilized plots (Fig. 3.2, Table 3.4), but these differences were clearly smaller when compared to FYM-related effects. This finding is supported by the results of other studies comparing FYM and minerally fertilized systems (Carpenter-Boggs et ai, 2000a; Parham et ai, 2003; Sun et ai, 2004; Widmer et ai, 2006b). The causes for these different findings are not known, but different conditions regarding systems, soil types or climate may be important. Furthermore, comparisons among different studies may be difficult, because terms such as 'biodynamic' or 'conventional' farming in current agriculture are subject to different definitions regarding fertilization, plant protection and soil cultivation. Beside the changes in the community structures, the number of microorganisms also increased in FYM-related systems, as indicated by higher microbial biomass, i.e. CFE-Cmic, SIR-Cmic and DNA content (Fig. 3.1). An increase in biomass parameters through the stimulation of microbial growth by providing organic substrates was previously observed in the DOK field experiment (Mäder et ai, 2002; Widmer et ai, 2006b) and in other systems (Goyal et ai, 1993; Bossio et ai, 1998; Carpenter-Boggs et ai, 2000a; Peacock et ai, 2001; Parham et ai, 2003). Previous results from CLSU analyses in the DOK field experiment revealed smaller differences of bacterial profiles between BIODYN and BIOORG than between any other analyzed system (Mäder et ai, 2002). In contrast, T-RFLP and RISA profiles in the present study revealed smallest differences between BIOORG and CONFYM, whereas BIODYN was significantly different from these systems (Fig. 3.2, Table 3.4). Differences between organic and conventional farming systems have been reported in an other long-term system, managed for 8 to 9 years before sampling (Bossio et ai, 1998; Lundquist et ai, 1999). Differences between bioorganically and biodynamically treated soils have not been observed in a short-term experiment with a duration of one growing season (Carpenter-Boggs et ai, 2000a). This suggested that the differences between the systems may develop over a long period of time and not be detectable in may short-term experiments. Long-term factors such as 92 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS increased soil pH, humus content and organic carbon, in combination with short-term factors such as different amounts of N, P, K and Mg, may have lead to the development of distinct microbial communities in BIODYN (Alfoldi et ai, 1995a). The special character of the biodynamic system was also observed in the microbial biomass parameters, which revealed the highest average amounts in BIODYN, followed by BIOORG, and the lowest amounts in CONFYM (Fig. 3.1). The lower content of microbial biomass in the conventional system was in agreement with former studies (Bossio et ai, 1998; Castillo and Joergensen, 2001; Mäder et ai, 2002). Therefore, more detailed investigations of factors and affected soil microorganisms in the BIODYN soils of the DOK field experiment are required. The influence of crops on soil microbial communities has been reported to be complex and to depend on crop type as well as on specific soil characteristics, sampling time and sample type (Grayston et ai, 1998; Smalla et ai, 2001; Marschner et ai, 2004). Widmer et al. (2006b) have reported significant effects of grass-clover and winter wheat on soil microbial community structures by using T- RFLP analysis in the DOK field experiment. These crop effects were smaller than the effects of FYM application, but stronger than those of BIODYN, BIOORG and CONFYM. Other studies have come to various conclusions by finding either dominant effects of fertilizer when compared to crops (Kennedy et ai, 2004), or opposite results (Bardgett et ai, 1999). In the present study, the effects of the different stages in the crop rotation, i.e. winter wheat following potato and winter wheat following corn, which simulates a preceding crop effect on the soil microbial community structures, were smaller than any farming system related effects and marginally insignificant (Fig. 3.2). Although the effects of preceding crops on the bacterial community structures have been reported before (Lupwayi et ai, 1998), they appeared to be smaller when compared to fertilization and plant protection. This was also confirmed by the dominant influence of the farming systems on data variance when compared to the preceding crop (Table 3.3). The crop effects described by Widmer et ai (2006b) ranked between the effects of FYM application and those of different FYM types and thus were clearly more pronounced than the effects of preceding crops reported in the present study. 93 3 RANKING THE MAGNITUDE OF CROP AND FARMING SYSTEM EFFECTS 3.6 Conclusions The stability and consistency of the experimental system and the methodological approach demonstrated the importance of well designed long-term field experiments and robust monitoring techniques. The application of T-RFLP and RISA in combination with statistical analysis allowed to hierarchically rank the effects of defined agricultural management factors on soil bacterial community structures. Data from the present as well as a previous study by Widmer et ai (2006b) revealed that the application of farm yard manure to the soils had the most significant influence on bacterial community structures and biomass parameters. The influence of the crops occurred on the second hierarchical level followed by effects driven by biodynamic, and bioorganic conventional management systems. The preceding crops in the crop rotation had only a minor influence on community structures and soil biomass. The identification of treatment-associated taxa detected by statistical tools may be an important subsequent step in order to provide powerful indicators and diagnostic tools. 3.7 Acknowledgments The DOK field experiment is a long-term project funded by the Swiss Federal Office for Agriculture (BLW). The continuous high quality management of the field experiment by the field teams of Agroscope FAL Reckenholz and FiBL, as well as support given by farmers, is greatly acknowledged. We wish to acknowledge Roland Kölliker for providing important assistance in statistical analysis and for helpful comments on this manuscript. We are grateful to Manuel Pesaro and David Dubois for critical discussions and comments on this manuscript. The project was supported by funding from the Swiss National Science Foundation (SNF). 94 Chapter 4: Community structure analyses are more sensitive to differences in soil bacterial communities than anonymous diversity indices Martin Hartmann and Franco Widmer Applied and Environmental Microbiology, 72 (2006), 7804-7812 © 2006 American Society for Microbiology 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 4 Community structure analyses are more sensitive to differences in soil bacterial communities than anonymous diversity indices 4.1 Abstract in the and Changes diversity structure of soil microbial communities may offer a key to understanding the impact of environmental factors on soil quality in agriculturally managed systems. Twenty-five years of biodynamic, bio-organic, or conventional management in the DOK long-term experiment in Switzerland significantly altered soil bacterial community structures, as assessed by terminal restriction fragment length polymorphism (T-RFLP) analysis. To evaluate these results, the relation between bacterial diversity and bacterial community structures and their discrimination potential were investigated by sequence and T-RFLP analyses of 1,904 bacterial 16S rRNA clones derived from gene the DOK soils. Standard anonymous diversity indices such as Shannon, Chaol, and ACE or rarefaction analysis did not allow detection of management dependent influences on the soil bacterial community. Bacterial community structures determined by sequence and T-RFLP analyses of the three libraries gene substantiated changes previously observed by soil bacterial community level T-RFLP profiling. This supported the value of high-throughput monitoring tools such as T-RFLP analysis for assessment of differences in soil microbial communities. The gene library approach also allowed identification of potential management- specific indicator taxa, which were derived from nine different bacterial phyla. These results demonstrate the clearly advantages of community structure analyses over those based on anonymous diversity indices when analyzing complex soil microbial communities. 4.2 Introduction Soil microorganisms play an important role in maintaining soil quality in agriculturally managed systems and may be highly responsive to environmental influences (Elliott 96 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES and Lynch, 1994; Kennedy and Smith, 1995). Microbial soil characteristics may indicate changes in resource availability, soil structure, or pollution and represent one important key to understanding impacts of environmental and anthropogenic factors (Pankhurst et ai, 1996; Tiedje et ai, 1999; DeLong and Pace, 2001). Soil microbial diversity may represent the ability of a soil to cope with perturbations (Kennedy, 1999; Johnsen et ai, 2001; Bardgett, 2002) and has been proposed as an indicator for soil quality (OECD, 2001). Therefore, analyses of soil microbial diversity and community structures appear to be essential when monitoring environmental influences on soil quality. Arable soils have been reported to harbor a few hundred species per gram of soil, while pasture or forest soils may harbor more-complex communities consisting of several thousand species (Torsvik et ai, 2002). Disturbances through agricultural treatments such as soil tillage, fertilization, and plant protection may favor certain species, resulting in reduced complexities of these communities (Torsvik et ai, 2002). Agricultural treatments have been reported to influence soil microbial community structures (Rousseaux et ai, 2003; Marschner et ai, 2004; Sun et ai, 2004; Hartmann et ai, 2006; Widmer et ai, 2006b) and to decrease soil bacterial diversity (Lupwayi et ai, 1998; Torsvik et ai, 2002). The DOK long-term field experiment in Switzerland was designed to investigate the effects of agricultural management factors on soil and plant parameters (Mäder et ai, 2002). In a previous study in the DOK experiment, soil bacterial community structures from three different agricultural management systems treated with farmyard manure (FYM), i.e. biodynamic (BIODYN), bio-organic (BIOORG), and conventional (CONFYM), along with an unfertilized (NOFERT) and a minerally fertilized (CONMIN) control were compared using terminal restriction fragment length polymorphism (T-RFLP) analysis and ribosomal intergenic spacer analysis (RISA) (Hartmann et ai, 2006). Increase of soil organic matter due to the application of FYM had a major influence on the bacterial community structures at the DOK site, as observed in previous studies of the same system (Widmer et ai, 2006b) and other systems (Bossio et ai, 1998; Carpenter-Boggs et ai, 2000a; Peacock et ai, 2001 ; Sun et ai, 2004). Crop effects were significant (Widmer et ai, 2006b), whereas the effects of preceding crops were statistically not significant (Hartmann et ai, 2006). This is consistent with reports on cropdependent effects in other studies (Grayston et ai, 1998; Lupwayi et ai, 1998; 97 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES Marschner et ai, 2004). Some differences among the FYM-treated systems, i.e. biodynamic, bioorganic, and conventional, were significant, but their magnitudes were lower than the effects caused by the actual crops (Widmer et ai, 2006b). However, bacterial diversity and its comparison with the detected community structures remained unresolved. Because of consistent treatment effects on soil bacterial community structures (Hartmann et ai, 2006; Widmer et ai, 2006b), the DOK field experiment offered a model system to compare bacterial diversity to the underlying community structures. PCR-based genetic profiling techniques such as T-RFLP are promising techniques to assess differences in microbial communities. While they reveal analytic consistency, have highthroughput capability, and provide data compatible to standard statistical evaluation (Marsh, 1999; Osborn et ai, 2000; Kirk et ai, 2004; Hartmann et ai, 2005), they appear limited for assessing microbial diversity. Recent technical developments for efficient screening of microbial communities by cloning and sequencing of ribosomal or functional genes (Widmer et ai, 1999; Venter, 2004; Janssen, 2006) combined with suitable statistical analyses (Staley, 1997; Bohannan and Hughes, 2003) can provide more-detailed information on the composition and diversity of microbial communities. Comparison of community level genetic profiling data with results deduced from gene library analyses allows evaluation of the expressiveness of genetic- profiling approaches (Graff and Conrad, 2005; Noll et ai, Even 2005). though community profiling and sequence analysis in agricultural soils have been reported (McCaig et ai, 2001 ; Sessitsch et ai, 2001 ; Smit et ai, 2001 ; Sun et ai, 2004), their sensitivities in detecting environmental effects remained unassessed and there has been no direct comparison of these two approaches. The five different DOK systems significantly influenced soil bacterial community structures as determined by T-RFLP analysis of soil nucleic acid extracts (Hartmann et ai, 2006; Widmer et ai, 2006b). Analysis of terminal restriction fragments (T-RFs) four for each among independent replicates system revealed three major groups, represented by the conventional, biodynamic, and unfertilized systems. In the present study, bacterial diversity was determined for these three representative and systems by sequence T-RFLP analyses of 16S rRNA gene libraries. Results were compared to those previously obtained by soil community level T-RFLP profiling (Hartmann et ai, 2006). The relationship between bacterial diversity and community 98 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES structures in the gene libraries was assessed, with emphasis on the discriminatory potential of these parameters and the detection of potential system-specific indicator taxa. 4.3 Material and Methods 4.3.1 Agricultural management systems The DOK long-term experiment in Switzerland was established in 1978 and includes BIODYN, BIOORG, and CONFYM management, as well as CONMIN and NOFERT controls, which mainly differ in fertilizer application and plant protection strategies (Kirchmann, 1994; Mäder et ai, 2002; Hartmann et ai, 2006; Widmer et ai, 2006b). BIOORG and BIODYN are two organic farming systems receiving systemspecific FYM and mechanical or alternative plant protection treatments. CONFYM received system-specific FYM and additional mineral fertilizer, as well as common chemical plant protection treatments. CONMIN received exclusively mineral fertilizer and the same plant protection applied in CONFYM, whereas NOFERT was not fertilized and plant protection was as in BIODYN. BIODYN, CONFYM, and NOFERT, which revealed representative differences in community level T-RFLP profiling (Hartmann et ai, 2006), were selected for a detailed cloning and sequencing approach in the present study. 4.3.2 Amplification and cloning of bacterial 16S rRNA gene fragments Soil DNA extracts of four independent field replicates were prepared by a bead beating procedure (Hartmann et ai, 2006), adjusted to a concentration of 10 ng DNA pi"1 in Tris-EDTA, and pooled. Four independent PCRs with primers 27F and 1378R (Heuer et ai, 1997) were performed with 20 ng template DNA for each pool in a total volume of 50 pi containing 1x PCR buffer (Qiagen, Hilden, Germany), 2 mM MgCI2, 0.2 uM of each primer, 0.4 mM deoxynucleoside triphosphate, 0.6 mg ml'1 bovine serum albumin, and 2 U HotStar Taq polymerase (Qiagen). PCR amplification was performed with initial hot-start denaturation for 15 min at 95 °C followed by 30 cycles with denaturation for 45 s at 94 °C, annealing for 45 s at 48 °C, and extension for 2 min at 72 °C, with final extension for 5 min at 72 °C. Three microliters of each of the 99 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES four replicate PCR products was cloned by using the pGEM-T Easy vector system cloning kit and Escherichia coli JM109 (Promega, Madison, Wl). 4.3.3 Gene library screening Vector inserts were amplified by colony PCR with M13for (5'-TGTAAAACGACGGCC AGT-3', Promega) and M13rev (5'-CAGGAAACAGCTATGACC-3\ Promega) vector primers in a total volume of 20 pi containing 1x PCR buffer, 2.5 mM MgCI2, 0.2 uM of each 0.4 mM primer, deoxynucleoside triphosphate, 0.6 mg ml"1 bovine serum albumin, and 1 U HotStar Taq polymerase (Qiagen). Initial hot-start denaturation for 15 min at 95 °C was followed by 30 cycles with denaturation for 45 s at 94 °C, for annealing 60 s at 60 °C and extension for 2 min at 72 °C, with a final extension for 5 min at 72 °C. PCR products were purified with Montage PCRp96 plates (Millipore, Billerica, MA) and examined on agarose gels. 4.3.4 T-RFLP analysis Target genes were amplified from 1 pi M13 colony PCR product in 20-pl reaction volume with 27F primers (6-carboxyfluorescein labeled) and 1378R and the same conditions as described above, but with only 12 amplification cycles. Twenty microliters of PCR products was mixed with 40 pi concentration conversion buffer for 4 mM Mspl (CCBmspi; Tris-HCI [pH 3], 50 mM NaCI, and 8 mM MgCI2) (Hartmann et ai, 2005) and 5 pi restriction enzyme mixture containing 2 U restriction endonuclease in Mspl (Promega) 1x supplied restriction enzyme buffer and incubated overnight at 37 °C. Two microliters of digested PCR products was analyzed along with 0.2 pi MapMarker 1000 X-rhodamine (Bio-Ventures, Murfreesboro, TN) and 12 pi HiDi formamide (Applied Biosystems, Foster City, CA) on an ABI Prism 3100 genetic analyzer (Applied Biosystems) with 36-cm capillaries filled with POP-4 polymer. T-RFLP were profiles analyzed using Genotyper v3.7 NT (Applied Biosystems) with a signal threshold of 50 relative fluorescence units. 4.3.5 Sequence analysis Partial sequences of cloned target genes were determined with primer UNI-516-rev (5'-TACCGCGGC[G/T]GCTGGCA-3\ position 532 to 516 on the E. coli sequence 100 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES with GenBank accession number J01695; modified from the sequence described by Giovannoni et ai (1988) and corresponding vector primer T7 (5'-TAATACG ACTCACTATAGGG-3'; Promega) or SP6 (5'-ATTTAGGTGACACTATAG-3'; Promega) on M13 colony PCR products using the BigDye sequence terminator kit, v1.1 (Applied Biosystems). Sequences were analyzed on an ABI Prism 3100 genetic analyzer. Sequences were assembled using ContigExpress of the Vector NTI 9.0 software (Invitrogen, Carlsbad, CA), trimmed at the 5' and 3' ends up to the primer sites, i.e. 27F and UNI-516-rev, and visually examined to detect base-calling errors. Sequence similarity searches for phylogenetic inference were performed with SEQUENCE_MATCH of the Ribosomal Database Project RDP-II v.9.26 (Cole et ai, 2005) based on all entries with a size larger than 1,200 bp. Sequences with ambiguous affiliation were analyzed for chimeric nature with CHIMERACHECK 2.7 of RDP-II v.8.1. Sequences were defined as chimera only when consisting of fragments derived from different phyla. 4.3.6 Diversity analyses Sequences of all three gene libraries (BIODYN, CONFYM, and NOFERT) were pooled and aligned using the ClustalW routine in Vector NTI 9.0 (Chenna et ai, 2003). Constant grouping of sequences from the same phyla was tested by neighbor- joining cluster analysis based on Jukes and Cantor distance calculation using TreeCon 1.3 (Van de Peer and De Wächter, 1994). Sequences were then split into phylum-specific groups, followed by refined alignment of these groups. Diversity analyses were performed using six different definitions of operational taxonomic units (OTUs). Three OTU definitions were based on different percent sequence identity levels (PSIL), i.e. 97% (PSIL97-OTU), 90% (PSIL90-OTU), and 80% (PSIL80-OTU), using FastGroup v1.2 (Seguritan and Rohwer, 2001). The other three OTU definitions were based on T-RFLP analysis, i.e. DNA sequence-defined in silico T-RF of each clone (T-RFseq-OTU), the experimentally determined T-RF of each clone (T-RFexp-OTU), and the T-RFs obtained from the soil bacterial community profiles (T-RFcom-OTU), which have been previously published (Hartmann et ai, 2006). Gene library T-RFLP-based OTUs were restricted to T-RF sizes between 50 and 500 bp in order to allow for comparison to previously reported community level profiling data (Hartmann et ai, 2006). In silico Mspl T-RFLP analysis was performed using 101 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES Expression 1.1 (Genamics, Hamilton, New Zealand). Community profiles of T-RFseq-OTU and T-RFexp-OTU abundances for comparison to the community level T-RFLP profile were plotted by using line plots with SigmaPlot 8.02 (SYSTAT Software, Chicago, IL). Observed OTU richness (S0bs), i.e. number of different OTUs per gene library or soil community profile, was determined for each of the six OTU definitions. Rarefaction analysis, displaying the number of OTUs detected versus the number of clones analyzed, was performed using the Analytic Rarefaction 1.3 calculator (Holland, 2001) and displayed using SigmaPlot 8.02 (SYSTAT Software). Maximal OTU richness was estimated using models of Michaelis-Menten (SMM), Chaol (Schaoi), and ACE (Sace) (Raaijmakers, 1987; Hughes et ai, 2001). OTU diversity and distribution in the gene libraries were calculated using Shannon diversity (H) and evenness (E) indices (Shannon and Weaver, 1963). Calculations of Pearson product-moment correlations and Ward clustering of genetic profiles based on Euclidean distances were performed using Statistica version 6.1 (StatSoft Inc., Tulsa, OK). 4.3.7 Nucleotide sequence accession numbers The 1,904 partial 16S rRNA gene sequences reported here have been deposited in GenBank with accession numbers DQ827724 to DQ829627. 4.4 Results A total of 712 clones from BIODYN, 816 clones from CONFYM, and 720 clones from NOFERT were recovered. Only intact sequences revealing (i) both primer sites, i.e. 27F and UNI-516-rev, which defined the stretch for phylogenetic analyses, and (ii) unambiguously detectable T-RF peaks (T-RFexp), were included in the analysis, i.e. 600 BIODYN clones (84%), 691 CONFYM clones (85%), and 613 NOFERT clones (85%). 4.4.1 Phylogenetic affiliation Sequences were assigned to 11 different phyla, among which the phyla Actinobacteria (35 to 39%), Proteobacteria (26 to 33%), and Acidobacteria (11 to 102 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 13%) were most abundant (Table 4.1). Five to 6% of all sequences could not be unambiguously affiliated at the phylum level and were assigned as unclassified. For these sequences no indication for a chimeric nature at the phylum level was found (data not shown). Distributions of the phylogenetic groups were very similar among the three gene libraries (Table 4.1), revealing an overall correlation (r) of 0.99 (p < 0.001) for each of the three pairwise comparisons. Differences in abundance for each phylum among the gene libraries ranged from 1 to 4%, with an average difference of 1.2% ± 1.0%. Table 4.1. Phylogenetic categorization and relative abundance of soil bacterial 16S rRNA gene sequences in the gene libraries of three farming systems in the DOK field experiment. No. of sequences (%) for: Phylogenetic Group BIODYN CONFYM NOFERT Gram-Positives 254 (42) 279 (40) 245 (40) Actinobacteria 231 (39) 240 (35) 229 (37) Firmicutes 23 (4) 39 (6) 16 (3) Proteobacteria 154 (26) 226 (33) 193 (31) Alphaproteobacteria 96 (16) 107 (15) 92 (15) Betaproteobacteria 13 (2) 33 (5) 29 (5) Gammaproteobacteria 10 (2) 26 (4) 17 (3) Delta/epsilonproteobacteria 35 (6) 60 (9) 55 (9) Acidobacteria 76 (13) 79 (11) 69 (11) Verrucomicrobia 34 (6) 18 (3) 24 (4) Bacteroidetes 19 (3) 18 (3) 13 (2) Gemmatimonadetes 10 (2) 9 (1) 15 (2) Chloroflexi 9 (2) 10 (1) 10 (2) Nitrospira 3 (1) 8 (1) 3 (1) Planctomycetes 4 0) 2 (0) 5 (1) Cyanobacteria 0 (0) 2 (0) 6 (1) Unclassified3 37 (6) 40 (6) 30 (5) Total 600 (100) 691 (100) 613 (100) Sequence affiliations were not unambiguously determinable at the phylum level. 103 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 4.4.2 Richness and relative abundance of operational taxonomic units Classification of OTUs at a PSIL of 97 revealed an average Sobs of 385 + 25 different OTUs in the three gene libraries, representing 61% ± 1% of the clones The sampled (Table 4.2). relative abundances of PSIL97-OTUs in each phylum were very similar for the farming systems (Fig. 4.1; filled bars), showing identical overall correlations (r = 0.98; p < 0.001) for each of the three pairwise comparisons. Maximal differences in relative abundance of PSIL97-OTUs among the three gene libraries were in the ranges of 3% (Actinobacteria), 2% (Alpha-, Gamma-, Delta/epsilonproteobacteria, Acidobacteria, Verrucomicrobia), and ^ 1% (Bacteroidetes, Betaproteobacteria, Chloroflexi, Cyanobacteria, Firmicutes, Gemmatimonadetes, Nitrospira, and Planctomycetes). Table 4.2. Richness and diversity estimations of OTUs of different definitions derived from soil bacterial 16S rRNA gene libraries from three farming systems in the DOK field experiment. Farming No. of OTU Coverage SDbs (%) Smm Schaol Sace H E system3 clones3 definition0 ±SDC Sequence analysis BIODYN 600 (100) PSIL97 369 (62) 1068 971 1031 36 ± 2 5.7 0.96 CONFYM 691 (100) PSIL97 413(60) 1259 115 1332 33 ± 2 5.7 0.95 NOFERT 613 (100) PSIL97 372(61) 1080 953 1093 36 ± 3 5.7 0.96 BIODYN 600 (100) PSIL90 140(23) 256 348 320 46 ± 8 3.7 0.76 CONFYM 691 (100) PSIL90 153(22) 288 496 367 42 ±11 3.7 0.74 NOFERT 613 (100) PSIL90 118(19) 184 204 199 60 ± 3 3.6 0.76 T-RFLP analysis BIODYN 560 (93) T-RFseq 142(24) 214 218 233 64 ± 3 4.3 0.87 CONFYM 652 (94) T-RFseq 141 (20) 199 186 196 73 ± 3 4.3 0.87 NOFERT 574 94) T-RFseq 135(22) 195 185 187 71 ± 2 4.3 0.87 BIODYN 560 (93) T-RFexp 172(29) 276 254 292 63 ± 4 4.6 0.90 CONFYM 649 (94) T-RFexp 169(24) 248 266 245 67 ± 3 4.6 0.89 NOFERT 570 (93) T-RFexp 153(25) 229 215 225 69 ± 2 4.5 0.89 Number of clones used for calculation. T-RFLP analysis revealed that 93 to 94% of the clone T-RF sequences produced sizes between 50 and 500 bp. Only these T-RFs were used in these calculations. OTUs are based on PSIL (sequence analysis), i.e. 97 or 90%, or T-RF length (T-RFLP analysis), i.e. based on in silico restriction (T-RFseq) or experimentally retrieved (T-RFexp). of / Percentage coverage: Sobs mean (Smm, Schaoi, Sace) * 100. 104 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES In silico T-RFLP analysis of cloned sequences revealed 93 to 94% of all T-RFs in the range between 50 and 500 bp (Table 4.2). An S0bs of 139 ± 4 different T-RF sizes (T-RFSeq-OTUs) corresponding to 22% ± 2% of the clones analyzed was detected across the three agricultural treatment types (Table 4.2). The relative abundances of T-RFseq-OTUs in each phylum (Fig. 4.1; narrow white bars) were consistently lower, i.e. between 0 and 12% compared to PSIL97-OTU abundance, and were very similar among the three gene libraries, with pairwise overall correlations between 0.94 and 0.97 (p< 0.001). Figure 4.1. Relative abundance of OTUs detected in the three soil bacterial 16S rRNA gene libraries obtained from three farming systems of the DOK field experiment. Numbers of OTUs for each bacterial phylum and gene library are displayed as percentages of the corresponding gene library size, i.e. BIODYN (100% - 600 sequences), CONFYM (100% ss 691 sequences), and NOFERT (100% = 613 sequences). Three different OTU definitions, i.e. PSIL97-OTU (filled bars), PSIL90-OTU (small white framed bars), and T-RFseq-OTU (narrow white bars), were applied. 105 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES For comparison of data based on sequence identities and data based on T-RFLP analysis at similar resolutions, the PSIL yielding similar observed OTU richness as the T-RFseq-OTU was determined. Sobs at a PSIL of 90 revealed 137 ± 18 different OTUs representing 21% ± 2% of the clones analyzed (Table 4.2). Again, no pronounced differences were observed in the relative abundances of PSIL90-OTUs in each phylum (Fig. 4.1; small white framed bars), revealing pairwise overall correlations between 0.86 and 0.94 (p < 0.001). The richness of scorable T-RFs determined by soil community level T-RFLP analyses on four field replicates for each system revealed S0bs values of 76 ± 2 (BIODYN), 76 ± 1 (CONFYM), and 76 ± 1 (NOFERT) (Hartmann et ai, 2006). 4.4.3 Estimated complexity of the gene libraries Determination of rarefaction curves, maximal-richness estimations, and diversity indices was performed with different OTU definitions, i.e. PSIL97-OTU, PSIL90-OTU, PSIL80-OTU, and T-RFseq-OTU (Fig. 4.2; Table 4.2). Differences among the rarefaction curves for the three gene libraries were small for all OTU definitions, showing a small deviation only for NOFERT at PSIL90. Rarefaction curves at PSIL90 and of in silico T-RFLP analysis (T-RFseq) were similar. Maximum PSIL97-OTU richness was estimated with three different models, i.e. Smm, Schaoi, and Sace, and revealed 1,023 ± 49 for BIODYN, 1,181 ±68 for CONFYM, and 1,042 ± 77 for NOFERT (Table 4.2). Based on these estimations, 33 to 36% of the diversities were covered by the applied sampling survey. H (Table 4.2) was 5.7 for all three gene libraries, whereas E varied between 0.95 and 0.96. High similarity among the gene libraries was also observed at the other OTU definitions (Fig. 4.2; Table 4.2). 106 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 0 100 200 300 400 500 600 700 Number of Sequences Figure 4.2. Rarefaction analysis of soil bacterial 16S rRNA gene libraries from three agricultural farming systems of the DOK field experiment, displaying number of OTUs detected versus number of sequences analyzed. Aligned sequences were grouped using FastGroup v1.2 software at different OTU definitions, i.e. PSIL97-OTU, PSIL90- OTU, and PSIL80-OTU. In addition, an OTU definition based on unique in silico T-RFseq sizes is displayed. Rarefaction curves are displayed for each OTU definition. 4.4.4 Comparison of in silico and experimental T-RF sizes Community profiles compiled from experimental (T-RFexp; Fig. 4.3a) and in silico T- RFLP analysis (Fig. 4.3b) revealed similar patterns and distributions of predominant peaks. These T-RFLP profiles, derived from individual clones of the gene libraries, were similar to patterns obtained from soil bacterial community level T-RFLP profiling (Fig. 4.3c) (Hartmann et ai, 2006). Averaged size shifting between T-RFseq size (bp) and T-RFexp size (relative migration unites [rmu]) was 1.6 ± 1.5 units. In total, 47% of all T-RFseqs revealed a size specific for one particular phylum, while the remaining T-RFseqs derived from two phyla (30%), three phyla (13%), four phyla (7%), five phyla (2%), or six phyla (1%). 107 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 50-1 a) 40- 30- 20- 10- œ jtijrV ^a . — n. Li. _ -jjj JULiV^-AX /A_| ÛC 50 100 150 200 250 300 350 400 450 500 H T-RF size (rmu) 50 100 150 200 250 300 350 400 450 500 T-RF size (rmu) Figure 4.3. T-RFLP profiles of bacterial 16S rRNA genes from DNA of CONFYM in the DOK field experiment, (a) Experimental T-RFLP profile compiled from T-RFexp of each clone In the corresponding gene library yielding T-RFexp between 50 and 500 rmu. (b) In silico T-RFLP profile compiled from sequence-predicted T-RFseq of each clone in the corresponding gene library yielding T-RFseq between 50 and 500 bp (c) Bacterial community level T-RFLP profile from soil DNA extracts in a range from 50 to 500 rmu as obtained in a previous study (Hartmann et al., 2006). 4.4.5 Community structures represented in the gene libraries For this analysis, sequences assigned to "unclassified" were integrated into the closest phylogenetic group as determined by cluster analysis (data not shown). A total of 899 different PSIL97-OTUS, i.e. 369 for BIODYN, 413 for CONFYM, and 372 for NOFERT, and a total of 272 different PSIL90-OTUs, i.e. 140 for BIODYN, 153 for CONFYM, and 118 for NOFERT, were found (Table 4.2). Differences in the relative OTU abundance in the profiles generated at PSIL of 97 and 90 were assessed by cluster analysis and revealed clear differentiation of NOFERT from BIODYN and CONFYM, while profiles from BIODYN and CONFYM were more similar (Fig. 4.4a and b). Identical dendrogram topologies were obtained when the "unclassified" 108 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES sequences were omitted from the analysis, revealing 817 OTUs at PSIL97 and 223 OTUs at PSIL90 (data not shown). The topology was identical to the one obtained from soil bacterial community level T-RFLP profiling performed on pooled DNA extracts of four independent field replicates per farming system (Fig. 4.4c) (Hartmann et ai, 2006). a) BIODYN CONFYM NOFERT 6.3 6.4 6.5 6.6 6.7 PSIL 90 BIODYN CONFYM NOFERT i i i 5.8 6.2 6.6 7.0 7,4 ') community T-RFLP BIODYN CONFYM NOFERT i i i 7 9 11 13 15 Euclidean Distance Figure 4.4. Differences of bacterial 16S rRNA gene-based community structures between the three farming systems in the DOK experiment as determined by Ward clustering of Euclidean distances. Cluster dendrograms are based on OTUs derived from 16S rRNA gene libraries and from soil bacterial community level T-RFLP profiles. OTUs were defined at a PSIL of 97 with 899 OTUs (a) and at a PSIL of 90 with 272 OTUs (b). (c) Cluster analysis of soil bacterial community level T-RFLP profiles of pooled DNA extracts from four field replicates and based on intensities of 79 scorable T-RF sizes among four field replicates (community T-RFLP) (Hartmann et al., 2006). 4.4.6 Potential treatment associated indicator taxa OTUs which revealed treatment specific differences and represent potential indicators (PI) were detected by applying the following definition. The potential 109 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES indicator PSIL90-OTUs had to (i) include more than five sequences and (ii) reveal an increase or decrease in relative abundance of at least 50% for one particular farming system when compared to the other two. By applying this definition, 27 PSIL90-OTUs containing between 6 and 75 sequences per OTU were determined (Table 4.3). Table 4.3. OTUs revealing potential treatment-specific indicator characteristics. PI No. of No. of Taxonomic rank LCLb LCL identity0 Indicator OTU seq. T-RFsa systemd PI01 27 8 Actinobacteria Class Actinobacteria BIODYN T PI02 9 3 Actinobacteria Class Actinobacteria BIODYN î PI03 24 3 Verrucomicrobia Order Verrucomicrobiales BIODYN î PI04 6 2 Verrucomicrobia Order Verrucomicrobiales BIODYN î PI05 12 5 Bacteroidetes Genus Chitinophaga BIODYN î PI06 75 24 Betaproteobacteria Class Betaproteobacteria BIODYN i PI07 10 4 Deltaproteobacteria Order Myxococcales BIODYN I PI08 7 3 Deltaproteobacteria Genus Geobacter BIODYN I PI09 36 7 Acidobacteria Genus Acidobacterium BIODYN I PI10 6 1 Chloroflexi Bacteria uncult. bacterium BIODYN 1 PI11 6 4 Gammaproteobacteria Genus Pseudomonas CONFYM T PI12 18 3 Deltaproteobacteria Order Myxococcales CONFYM î PI13 14 1 Nitrospira Genus Nitrospira CONFYM î PI14 7 3 Actinobacteria Family Microbacteriaceae CONFYM 1 PI15 8 0 Alphaproteobacteria Genus Rhodoplanes CONFYM | PI16 7 1 Actinobacteria Class Actinobacteria NOFERT T PI17 9 4 Alphaproteobacteria Family Acetobacteraceae NOFERT î PI18 6 6 Gammaproteobacteria Family Xanthomonadaceae NOFERT T PI19 8 5 Deltaproteobacteria Class Deltaproteobacteria NOFERT T PI20 9 5 Deltaproteobacteria Family Cystobacteraceae NOFERT î PI21 7 4 Acidobacteria Genus Acidobacterium NOFERT T PI22 21 4 Acidobacteria Genus Acidobacterium NOFERT î PI23 8 4 Gemmatimonadetes Genus Gemmatimonas NOFERT T PI24 7 5 Firmicutes Family Clostridiaceae NOFERT i PI25 7 3 Deltaproteobacteria Order Myxococcales NOFERT | PI26 7 2 Acidobacteria Bacteria uncult. Holophaga NOFERT | PI27 6 1 Chloroflexi Bacteria uncult. Chloroflexi NOFERT | Number of different T-RF sizes between 50 and 500 bp determined by in silico analysis. Least common level (LCL) on which phylogenetic affiliation was unambiguous. of Phylogenetic group the corresponding OTU based on the least common level. Farming systems revealing specifically increased (f) or decreased (J,) abundance of the corresponding OTU. 110 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES These 27 PI consisted of members from nine different phyla. No potential indicators were found in the groups Planctomycetes and Cyanobacteria. A decrease in NOFERT, i.e. PI24 to PI27, also indicated an increase in BIODYN and CONFYM, which were both treated with FYM and may therefore reflect indicator characteristics for FYM application. Analogously, a decrease in CONFYM, i.e. PI14 and PI15, also reflected an increase in BIODYN and NOFERT and therefore may have indicator characteristics for the biodynamic plant protection treatment. The least common levels of phylogenetic identity of these 27 potential indicator PSIL90-OTUs were very different, i.e. reaching from the domain level to the genus level (Table 4.3). Similarly, in silico Mspl restriction of the sequences from each corresponding potential indicator PSIL90-OTU consisted of different numbers of T-RFseq sizes, i.e. between 1 and 25 different T-RFseq sizes between 50 and 500 bp for one particular OTU (Table 4.3). 4.5 Discussion DNA sequence and T-RFLP analyses of 1,904 16S rRNA gene amplicons cloned from three differently managed agricultural soils, i.e. BIODYN, CONFYM, and NOFERT, revealed highly similar diversities but strongly different compositions of the communities at defined phylogenetic levels. Although microbial diversity has been suggested as important indicator of soil quality and ecosystem stability (Stenberg, 1999; OECD, 2001), the bacterial diversity based on common definitions was insensitive for detecting the agricultural influences in the present study. In contrast, differences in soil bacterial community structures detected by genetic profiling and sequence analysis were consistent and allowed identification of specific potential treatmentassociated indicator taxa. Most of the sequences obtained from the gene libraries could be assigned to known bacterial phyla (Table 4.1) which are commonly reported for arable soils (Hugenholtz et ai, 1998; Dunbar et ai, 2002; Janssen, 2006) and originated to a major part (83% ± 1.3%) from Actinobacteria, Proteobacteria, Acidobacteria, and Verrucomicrobia. These four phyla were reported to be predominant and ubiquitous in soils (Hugenholtz et ai, 1998; Janssen, 2006), indicating that bacterial communities contained in the gene libraries were representative. Affiliation uncertainties at the phylum level for 5 to 7% of the sequences may be ascribed to several factors such m 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES as (i) insufficient coverage by databases (Janssen, 2006) and novel candidate phyla (Hugenholtz et ai, 1998; Dunbar et ai, 2002), (ii) short query sequences (Wang et ai, 2004), or (iii) various PCR artifacts such as chimeras (von Wintzingerode et ai, 1997; Qiu et ai, 2001). Constant grouping of sequences according to their phylogenetic inference at phylum level by cluster analysis (data not shown) and the assumption that PCR biases occur stochastically and at a constant extent (Zhou et ai, 2002) still legitimate comparative studies. Consistency in detecting differences in soil bacterial community structures among replicates by genetic profiling as previously shown in the same experimental system supports this assumption (Hartmann et ai, 2006; Widmer et ai, 2006b). Similar distributions and abundances of the different phyla were found across all three agricultural treatments, i.e. BIODYN, CONFYM, and NOFERT (Fig. 4.1, Table 4.1). The large phenotypic diversity within and potentially high redundancy among some bacterial phyla may explain why certain environmental factors do not change total abundance at this phylogenetic level. It has recently been suggested (Janssen, 2006) that environmental factors may influence the community at lower phylogenetic levels within major groups rather than changing the abundance at the phylum level. Reports of the effects of fertilization, crop rotation, and pesticide application on soil microbial diversity as measured by parameters including richness, relative abundance, and distribution vary among different studies (McCaig et ai, 1999; Buckley and Schmidt, 2001 ; Johnsen et ai, 2001 ; Kent and Triplett, 2002; van Elsas et ai, 2002; Sun et ai, 2004), and the sensitivities of these parameters in responding to environmental influences are largely unknown. Diversity estimation by defining OTUs at different levels of sequence identities of cloned rRNA gene amplicons is one common approach in soil microbial ecology (Woese, 1987; Stackebrandt and Goebel, 1994; Gevers et ai, 2005; Schloss and Handelsman, 2005). Observed richness, estimated maximum richness, Shannon diversity indices, and rarefaction analysis, representing richness and relative abundance of OTUs, at PSIL of 97, 90, and 80 and at the T-RF level indicated similar soil bacterial diversities among the farming systems (Fig. 4.2, Table 4.2), although differences could not be tested statistically since the gene libraries were not replicated. The marginally higher richness in CONFYM may be explained by the larger number of clones screened in this system (Kemp and Aller, 2004), but estimates also depend on the mathematical 112 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES model used and (Bohannan Hughes, 2003). Furthermore, 600 to 700 sequences per library only partially covered soil bacterial species diversity (Curtis and Sloan, 2004), i.e. one-third in the approximately present study at PSIL97 (Table 4.2), and may represent mainly dominant genotypes, while less-abundant species remain Based insufficiently represented. on extrapolation of rarefaction curves, approximately 500,000 clones would have to be screened in order to completely cover the in diversity the analyzed libraries (data not shown). This large sampling effort demonstrates the need for fast and high-throughput monitoring tools such as T-RFLP It has been analysis. suggested that dominant species may contribute most to ecosystem processes (Bardgett, 2002; Dunbar et ai, 2002). Based on this assumption, a description of abundant species, as achieved by genetic profiling, would be suitable for environmental-effect studies, although whether dominant species truly reflect functional keystone species has been critically discussed (Bengtsson, 1998). in Changes microbial community structures may not necessarily lead to altered diversities, because changes of some taxonomic groups may be compensated by changes of others. It has been suggested that, for instance, species richness may exhibit less in variability response to environmental factors than species composition (Ernest and Brown, 2001). Similar diversities, despite large changes in species composition, were observed in communities of mammals, birds, and plants (Brown et ai, and here it 2001), was shown that this may also be the case for microbial communities in the DOK system. In addition, standard diversity parameters do not include taxonomic of the identity different groups but rather treat each OTU as an For anonymous entity. example, soils differing in management regimen and plant species composition showed no effect on bacterial diversity as determined by richness and Shannon species diversity of 16S rRNA gene libraries but tended to moderate in changes diversity of subgroups such as Alphaproteobacteria (McCaig et ai, 1999). This the supports suggestion that changes occur at lower phylogenetic levels rather than at major groups (Janssen, 2006) and underscores the importance of assessing the underlying community compositions rather than just the anonymous In the diversity. present study, the applied agricultural factors altered the soil bacterial community structures but not soil bacterial diversity. 113 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES The FYM-treated systems BIODYN and CONFYM separated from the NOFERT system at PSIL97 and -90 (Fig. 4.4a and b). FYM-based agricultural management providing organic substrates and readily available nutrients may promote organisms with higher growth rates and faster adaptability (Torsvik et ai, 2002) and may therefore lead to altered community composition when compared to unfertilized soils. In addition, organisms introduced by FYM application or secondary effects of the FYM application such as altered soil moisture, temperature, pH, and aggregate size or altered plant growth may also influence community composition (Bossio et ai, 1998; Mäder et ai, 2002; Suzuki et ai, 2005). These differences were supported by reproducible differences in soil bacterial community structures in the DOK field by using community level T-RFLP, RISA, and substrate utilization profiling (Fig. 4.4c) (Hartmann et ai, 2006; Widmer et ai, 2006b). Similarity of predominant taxonomic units among soil community level profiles and in silico and experimental gene library T-RFLP profiles was observed (Fig. 4.3). Resolution power of T-RFLP analysis was determined to be at a PSIL of 90 (Fig. 4.2, Table 4.2) and was therefore below the species level, which is commonly defined at a PSIL of 97 (Stackebrandt and Goebel, 1994; Gevers et ai, 2005). Cluster analysis at PSIL97, at PSIL90, and at community level T-RFLP yielded identical dendrogram topologies, supporting the suitability of the T-RFLP technique. Furthermore, community level genetic profiling by T-RFLP and RISA (Hartmann et ai, 2006) as well as gene library analysis in the present study revealed very similar results, indicating validity of the data obtained. However, detection of differences in community composition seemed to depend on the applied phylogenetic level, revealing differences at lower levels such as PSIL97 and -90 (Fig. 4.4), but not at higher levels such as phyla (Fig. 4.1), which may be explained by the high phenotypic variation and therefore high redundancy within higher phylogenetic levels (Janssen, 2006). The T-RFLP approach was feasible to differentiate agricultural influences at certain phylogenetic levels but, in combination with previous reports, revealed also some limitations, (i) Discrepancies between in silico and experimental T-RF sizes, i.e. 1.6 ± 1.5 units in this study, may reduce specific inference of particular T-RFs in complex profiles (Rosenblum et ai, 1997; Kaplan and Kitts, 2003). Migration discrepancies for different T-RF sequences with the same length led to differences between in silico and experimental T-RFLP profiles (Fig. 4.3). (ii) Closely related sequences yielded 114 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES very different T-RF sizes, and sequences from different phyla yielded similar T-RF sizes (Manachini et ai, 2000; Zhou et ai, 2002; Schmitt-Wagner et ai, 2003; Graff and Conrad, 2005). Fifty-three percent of all T-RF sizes occurred in more than one phylum and impeded phylogenetic inference based on T-RF sizes even at this high phylogenetic level, (iii) Formation of false T-RFs by formation of heteroduplexes and chimeric molecules, incomplete digestion, or nondigested single-stranded DNA has also been reported to bias the profiles (Qiu et ai, 2001 ; Egert and Friedrich, 2003; Kanagawa, 2003; Graff and Conrad, 2005). The potential to affiliate phylogenetic information from gene libraries to complex community level T-RFLP profiles is therefore controversially discussed in the literature (Hackl et ai, 2004; Graff and Conrad, 2005; Noll et ai, 2005). The relatively large sampling survey in the present study indicated that phylogenetic inference based on soil community T-RFLP profiles with one restriction enzyme is strongly limited in complex communities. Based on gene library data at PSIL90, 27 taxa from 9 different phyla could be defined, which revealed differences of at least 50% relative abundance between the gene libraries and may represent potential treatment-specific indicator taxa (Table 4.3). Genera such as Pseudomonas (P111) and Chitinophaga (PI05), for example, have already been reported to be abundant in soils (Janssen, 2006). Besides taxa that revealed increases in one particular system, i.e. NOFERT, BIODYN, or CONFYM, potential indicators were also found for FYM application (decrease in NOFERT) or for biodynamic management (decrease in CONFYM). The treatment specificity of these taxa has to be confirmed by specific primer design and quantitative PCR on soil DNA extracts (Pesaro and Widmer, 2006; Widmer et ai, 2006a). Furthermore, these taxa have to be evaluated in other systems with different environmental factors such as soil type or climate conditions to substantiate their indicator function for the treatments considered here. This will represent a further step towards treatment-associated indicator diagnostics, which are prerequisites for environmental monitoring. Data presented here suggest that differences in agricultural management are reflected in soil bacterial community structures, but not in standard bacterial diversity estimations, and further demonstrated the applicability of the T-RFLP approach in environmental effect studies. 115 4 SENSITIVITY OF COMMUNITY STRUCTURE ANALYSES 4.6 Acknowledgements The DOK field experiment is a long-term project funded by the Swiss Federal Office for Agriculture (BLW). The project was supported by funding from the Swiss National Science Foundation (SNF). The continuous high quality management of the field experiment by the field teams of Agroscope Reckenholz-Tänikon and FiBL as well as support by farmers is greatly acknowledged. We thank Yvonne Häfele for technical assistance with cloning, sequencing and community profiling. We are grateful to Jürg Enkerli, Roland Kölliker and Manuel Pesaro for critical discussions and helpful comments on this manuscript. 116 Chapter 5: General Discussion 5 GENERAL DISCUSSION 5 General Discussion 5.1 Potentials and limitations in assessing microbial communities New molecular methods to analyze microbial communities as applied in the present thesis may be able to overcome many problems, which remained unsolved by traditional approaches such as cultivation. Detection of novel unculturable organisms, phylogenetic inference, construction of extensive databases, fast sample processing, high-throughput capabilities, and enhanced data processing and evaluation have opened up new perspectives in microbial ecology research. Nevertheless, the methods themselves exhibit several limitations, which have to be carefully considered when interpreting the results. In this section, potentials and advantages as well as current biases and limitations of the molecular approaches are critically discussed. 5.1.1 Spatial and temporal heterogeneity of microbial soil characteristics Spatial and temporal variability among many different physical, chemical, and biological soil parameters are known to influence the abundance and distribution of soil microbial species (Ettema and Wardle, 2002). Factors such as climate, topography, soil type, vegetation, and anthropogenic impacts generate various gradients in the landscape. Important parameters such as temperature, moisture, soil porosity, air permeability, nutrient concentrations and availability, are of heterogeneous characteristic and spatially and temporally variable (Gibson, 1986; Cain et ai, 1999; Gomez-Plaza et ai, 2000; Poulsen et ai, 2001; Zebarth et ai, 2002; Reichstein et ai, 2003; Schreiner, 2005). Therefore it has to be taken into account that microbial diversity and community structures described in a soil sample potentially reflect a snapshot of the present soil conditions. Spatial heterogeneity of soil may take place at large scales, i.e. across landscape gradients, and at smaller scales, i.e. within soil aggregates. These levels imply the importance of the sampling strategy for an experimental field in order to obtain representativity of the sample analyzed. In addition, temporal variability is primarily 118 5 GENERAL DISCUSSION induced by climatic factors and plant growth. Variability in soil ecosystems may be particularly pronounced in agricultural systems. Agricultural soils may display higher spatial and temporal dynamics when compared to grassland, forest, or arid ecosystems, because these areas undergo continuous disturbances due to cropping and soil management (Felske and Akkermans, 1998; Mummey and Stahl, 2003). The phenomenon of spatial and temporal dynamic of microbial communities in agricultural soils were already reported (Bossio et ai, 1998; Lukow et ai, 2000; Buckley and Schmidt, 2003; Wolsing and Prieme, 2004). In the present thesis, consistent results of biomass parameters and community structures among replicates indicated reliable coverage of spatial heterogeneity at the large scale (chapter 2 and 3). At smaller scales, sample size used for genetic analysis appeared to be appropriate, because microbial carbon biomass estimations determined from larger sample sizes were highly correlated to DNA contents retrieved from small sample sizes (chapter 3). All these results indicate appropriate consideration of the spatial heterogeneity at the different scales. Temporal variation was not assessed in this project as samples of the same time point were compared. Consistent results of community structure analysis between both years, i.e. 2000 (chapter 2) and 2003 (chapter 3), indicated a reliable detection of the main effects in this agricultural system. This also indicated that effects detected after approximately 25 years of continuous management not just represent an erratic snapshot. However, beside the long-term effects in this agricultural system, crops and fertilization are also expected to induce short-term effects and may vary among different sampling points during the year. 5.1.2 Potential and advantages of molecular analyses The black box of microbial diversity The basic problem of describing microbial communities in different environments is caused by the huge diversity of currently unknown species and has been described as the black box in microbial ecology (Tiedje et ai, 1999). Culture-dependent approaches do not allow to explore this black box, because only approximately 1% of all microorganisms can be cultivated under common conditions (Colwell and Grimes, 2000; Torsvik et ai, 2002). Molecular genetic methods have allowed to overcome 119 5 GENERAL DISCUSSION this limitation and led to a widely accepted (O'Donnell et ai, 1994; Rossello-Mora and Amann, 2001 ; Cohan, 2002; Stackebrandt et ai, 2002; Kämpfer and Rossella- Mora, 2004; Gevers et ai, 2005; Hey, 2006) although not universally valid concept of microbial taxonomy (Bergey's Manual of Systematic Bacteriology). In particular the analysis of ribosomal RNA genes allowed for the development of a concept to describe bacterial taxonomy (Pace, 1997). Genetic tools led to the discovery of a large diversity of previously unseen microbial species (Pace, 1996; Pace, 1997; Hugenholtz et ai, 1998). There are estimates, that up to 109 different prokaryotic species are living on planet earth (Dykhuizen, 1998), but only about 8'000 bacterial species are validly described to date (2006; www.bacterio.net). Information gained by molecular methods is immense. For example, almost 250'000 entries of partial bacterial 16S rRNA gene sequences are deposited in the RDP-II database (Cole et ai, 2005), impressively demonstrating the new insights into the black box of microbial diversity gained by molecular tools. The independency from cultivation and the genetic information retrieved reflect the main advantages of molecular approaches. Reproducibility, consistency and sensitivity molecular genetic data Analytical consistency and reproducibility are prerequisites for reliable analysis of soil microbial communities. Genetic profiling data have to be evaluated based on (i) their reproducibility or robustness, i.e. repeatability of results from the same sample or among sample replicates, and (ii) their consistency, i.e. comparability of results with different approaches. Both genetic methods applied in this thesis, i.e. T-RFLP and RISA, revealed very small variation among replicates and consistent results (chapter 3, Fig. 3.2). These results demonstrated reproducibility of genetic profiling among replicates as recently suggested for T-RFLP and RISA (Fisher and Triplett, 1999; Osborn et ai, 2000; Dunbar et ai, 2001; Ranjard et ai, 2001; Ikeda et ai, 2004). Several factors along the analysis chain ensure the reproducibility, e.g. sampling, DNA extraction, or PCR. The fluorescence-based capillary electrophoretic system is a key factor providing high reproducibility and standardization capability (Hewson and Fuhrman, 2006), and represents a main advantage of T-RFLP and RISA. The lack of reproducibility among different analyses was reported for common gel electrophoresis profiling methods such as DGGE (Fromin et ai, 2002). In addition to reproducibility, this thesis also demonstrated the consistency of results among two different approaches, i.e. T-RFLP and RISA. Both approaches revealed 120 5 GENERAL DISCUSSION identical results in detecting effects of different agricultural factors on the soil bacterial community structures (chapter 3, Fig. 3.2), although they are not based on the same methodology. Recently, we have also demonstrated the consistency of these tools by analyzing bacterial communities in heavy metal contaminated soils (Hartmann et ai, 2005). This study in combination with results from this thesis demonstrated for the first time the high consistency between T-RFLP and RISA data by direct statistical comparison. A third important factor for reliable analysis of microbial community structures is provided by the sensitivity of the method applied. Sensitivity is based on resolution and detection limits as well as on the sensitivity of the analyzed parameter to the environmental effects. First, high detection sensitivity of the fluorescence-based capillary electrophoretic system is a major advantage of this approach (Marsh, 1999). In addition, it has recently been reported that T-RFLP for example allowed for the detection of populations comprising 0.1 to 1% of the total community (Dunbar et ai, 2000). The sensitivity of the methodology is a prerequisite to detect environmental effects in the profiles. Second, the sensitivity of an approach is also depending on the parameter analyzed. Microbial communities are suggested to respond sensitive to environmental changes (Kennedy and Papendick, 1995; Pankhurst et ai, 1995). In the present thesis, the bacterial community structure responded sensitive to the applied agricultural factors, allowing for the discrimination of soil bacterial communities among different agricultural management strategies and crops with multiple approaches including T-RFLP, RISA, and gene library sequencing. Analytical efficiency Tools suitable for environmental monitoring of soil microbial characteristics require fast analysis and high-throughput capability to handle the complexity of these communities. These are major advantages of genetic profiling techniques over other comparable tools. Profiling techniques allow for a description of the microbial community structures in one sample by one single analysis without analyzing each taxon individually (Forney et ai, 2004; Hewson and Fuhrman, 2006). Relative comparison of changes or similarities of community composition among different samples can be rapidly processed (Marsh, 2005). The development of novel capillary electrophoresis based methods such as T-RFLP or RISA enhanced the high- throughput capability (Marsh, 1999; Trotha et ai, 2002; Ikeda et ai, 2004). These 121 5 GENERAL DISCUSSION two characteristics of genetic profiling tools allowed to efficiently monitor agricultural effect on soil microbial communities in this thesis. 5.1.3 Biases and limitations of molecular analyses Genetic profiling approaches for the analysis of microbial communities in environmental samples may be limited by several biases. The analysis chain from sampling to final data processing reveals different steps potentially biasing the view on the microbial community. Roughly the chain can be classified in six categories, i.e. (i) sampling strategy, (ii) nucleic acids extraction, (iii) PCR amplification, (iv) enzymatic restriction (only for some techniques), (v) separation of operational taxonomic units (OTUs), and (vi) data processing and statistics. In this section, the potential limitations of the molecular approach and their implications on the analysis of microbial communities are critically discussed. Sampling strategy and sample size The heterogeneity of the soil matrix (see chapter 5.1.1) influences representativity and robustness in community analysis (Ranjard and Richaume, 2001 ; Franklin and Mills, 2003). Therefore, the impact of heterogeneity at larger and smaller scales on sampling strategy and sample size has to be considered. Several factors are responsible for heterogeneous distribution of soil microorganisms. First, soil type is a key determinant of microbial distribution. Soil aggregate size and distribution depend on soil type and vary between sandy and clayey soils. Fine texture with a homogenous network of micropores in clayey soils may provide lower spatial heterogeneity and more uniform distribution of microorganisms at a microscale (Dexter, 1988; Foster, 1988). Second, different community types, e.g. bacteria or fungi, reveal different distribution in soil. Fungi are located within or between macro- aggregates and therefore show more spatial heterogeneity, whereas bacteria are rather located within micro-aggregates and reveal a more homogenous distribution (Foster, 1988; Ranjard and Richaume, 2001; Ranjard et ai, 2003). In addition, fungi are approximately 100 times less abundant in soil than bacteria, which in turn can lead to a higher heterogeneity (Foster, 1988). Therefore, bacterial communities as targeted in this thesis appeared to be less sensitive to the sampling strategy and sampling size (Ranjard et ai, 2003), whereas fungi are more heterogeneously 122 5 GENERAL DISCUSSION distributed (Horner-Devine et ai, 2004) and significantly influenced by the sampling scheme (Ranjard et ai, 2003; Schwarzenbach et ai, 2007). Third, nutritional resources and other factors that influence microbial growth such as plant roots are unequally distributed in the soil, revealing so called "hotspot" microhabitats for microbial communities. In conclusion, insufficient sampling may result in inconsistent representation of the microbial community and may favor the detection of dominant organisms (Grundmann and Gourbiere, 1999). Sample size may be crucial for the representativity of microbial community profiling. Commercial kits to extract soil nucleic acids are often based on sample sizes smaller than one gram of soil to allow for a rapid and efficient recovery of DNA (Ranjard et ai, 2003). Although sample sizes of 0.5 g as applied in this thesis may bias representation of the community (Ellingsoe and Johnsen, 2002; Ranjard et ai, 2003), it was suggested that threshold for significant variation in bacterial communities may lay around 250 mg of soil (Ranjard et ai, 2003). The high consistency in community structure analysis among replicates in this thesis supported this suggestion. Sampling strategy and size may therefore influence representativity of the sample and has to be considered and tested for data consistency of replicates (Ranjard et ai, 2003). High microbial diversity and strong heterogeneity of soil parameters require suitable and robust sampling strategies. In the present thesis, 14 or 16 soil cores were taken per plot (5 x 20 m) and subsequently pooled. The pooling approach is suggested to compensate for the field heterogeneity of the sampled area (Schwarzenbach et ai, 2007). The robustness and consistency of the biomass and genetic profiling results, revealing small variation among the four independent field replicates (see chapters 2 and 3), indicated the representativity of the samples analyzed. DNA extraction Quantitative extraction of high quality nucleic acids and appropriate purification are prerequisites for reliable genetic analysis (Frostegard et ai, 1999; Miller et ai, 1999; Martin-Laurent et ai, 2001; Robe et ai, 2003). Efficient extraction and minimal degradation of nucleic acids are prerequisites for representative characterization of the microbial community in a soil sample. Extraction methods are either based on cell separation from soil matrix with subsequent lysis of the cells and nucleic acids 123 5 GENERAL DISCUSSION extraction from separated cells, or by direct in situ lysis of cells in the sample with subsequent extraction and purification of nucleic acids from soil extract (Robe et ai, 2003). Analytical efficiency of direct in situ lysis of cells used for genetic approaches represents a major advantage over cell extraction tools. Critical steps such as dislodgment of the cells from soil compartments or particle surfaces without disruption of sensitive cells are not required for direct lysis extraction. The in situ lysis step determines efficiency and quality of extracted nucleic acids. Harsh mechanical lysis will result in sufficient extraction efficiency but induce degradation (shearing) of nucleic acids, whereas mild lysis will protect nucleic acids from strong fragmentation but will not be capable to extract DNA from robust cells such as spores or gram- positive bacteria. The balance between these two forces will determine the optimal extraction process (Bürgmann et ai, 2001). The extraction protocol used in this thesis was developed by Bürgmann et al. (2001) with focus on efficient and quantitative extraction of DNA. Combination of physical cell disruption using beat beating and chemical lysis by a detergent allowed for efficient recovery of nucleic acids. Optimization of beat beating time and speed, extraction volume and temperature, type and amount of beads, number of extraction cycles, and the selection of chemical reagents to stabilize extracted nucleic acids allowed for a robust and quantitative extraction of high quality nucleic acids from soil (Bürgmann et ai, 2001). This protocol allowed for a minimally biased genetic analysis of the bacterial communities in this thesis and previous studies (Pesaro et ai, 2004; Hartmann et ai, 2005), as indicated by high correlations between DNA content and microbial carbon biomass as well as reproducibility and consistency of differences in bacterial community structures. The extracted DNA was suitable to target single genes such as the 16S rRNA gene, but DNA shearing will limit analysis of fragments larger than 7 to 18 kb (Bürgmann et ai, 2001). Furthermore, suitable purification procedures provided by the protocol minimized the inhibition of subsequent PCR amplification by inhibitory substances such as humic acids. PCR amplification bias and rRNA gene copy numbers Marker gene amplification by PCR may represent the main source of biases in genetic analysis of microbial community structures (for review see: von Wintzingerode et ai, 1997; Kanagawa, 2003). PCR artifacts may be separated in two categories, i.e. (i) artifacts related to primer specificity and amplification efficiency, 124 5 GENERAL DISCUSSION and (ii) artifacts related to artificially produced PCR products. PCR primers that do not adequately match target sequences or amplify non-target sequences are major problems of the first category (Kwok and Higuchi, 1989; Devereux and Wilkinson, 2004; Sefers et ai, 2005) and therefore high quality primer design and optimization of PCR conditions are essential (Ishii and Fukui, 2001). Other factors of the first category may influence the markergene composition in a sample, i.e. relative template concentrations (Chandler et ai, 1997), amplification inhibition (Miller et ai, 1999), template re-annealing at increasing cycle numbers (Mathieu-Daudé et ai, 1996; Suzuki and Giovannoni, 1996; Kurata et ai, 2004), and preferential amplification (Reysenbach et ai, 1992; Wagner et ai, 1994; Weissensteiner and Lanchbury, 1996; Polz and Cavanaugh, 1998; Kopsidas et ai, 2000). Prominent biases of the second category are the formation of chimeric sequences (Kopczynski et ai, 1994; Wang and Wang, 1997; Qiu et ai, 2001; Hugenholtz and Huber, 2003; Kanagawa, 2003) and heteroduplexes (Judo et ai, 1998; Qiu et ai, 2001 ; Thompson et al., 2002; Kanagawa, 2003), and sequence alterations by base insertion/deletion or misincorporation (Cariello et ai, 1991; Eckert and Kunkel, 1991). Artifacts of the first category are systematic and affect different samples in the same way, which legitimates relative comparisons among PCR products. Artifacts of the second category are stochastic and may be significantly reduced by optimization of PCR protocols, including adaptation of cycling conditions, concentrations of salts, dNTPs, primers and polymerases, as well as neutralization of inhibitory substances such as humic acids in the template, e.g. by BSA. In addition, pooling of PCR products may dilute the stochastically produced proportions of the PCR product and reduce these biases. Due to its stochastic nature, PCR biases of category two may occur to a similar extent in samples analyzed under the same conditions and may affect a minor part of the PCR product (Zhou et ai, 2002). This may legitimate relative comparison among different samples. However, the consistent detection of agricultural effects among replicates and between different analyses, i.e. T-RFLP, RISA, or cloning and sequencing, in this thesis indicated that PCR artifacts did not affect the detection of dominant effects. There are also non-PCR induced biases, which may cause a shift between community composition and observed markergene composition in a sample. The rRNA Operon copy number may strongly vary among different microbial species 125 5 GENERAL DISCUSSION (Fogel et ai, 1999; Tourova, 2003). The rRNA operon copy number was reported to reflect ecological strategies dealing with resource availability (Klappenbach et ai, 2000), showing general correlation between growth rate and copy number (Fogel et ai, 1999), but may also reflect the ability to quickly adapt to new environmental conditions. Bacterial species deposited in the rrndb database (www.rrndb.cme.msu.edu) harbor between 1 and 15 rRNA gene copies with an average of 4.1 copies per species (Klappenbach et ai, 2001). Differences in rRNA gene copy numbers among different species clearly demonstrates that relative abundances of markergenes and related OTUs may not correspond to the relative abundance of the corresponding species in the sample (Farrelly et ai, 1995; Crosby and Criddle, 2003). Careful interpretation of relative abundances in genetic profiles such as rRNA gene based T-RFLP, RISA, or DGGE is therefore required. Sequence polymorphisms within multiple rRNA opérons by macro-heterogeneity among the same strains and micro-heterogeneity within the same organism was also reported to influence phylogenetic analyses (Clayton et ai, 1995; Istock et ai, 1996; Acinas et ai, 2004). This may influence genetic profiling and potentially leading to multiple T- RF or RISA peaks and DGGE bands for one organism (Crosby and Criddle, 2003). In conclusion, all these biases may cause shifts in the markergene composition when compared to the original sample. Dominant OTUs may not reflect dominant microbial species in the soil sample. Therefore it may be important to treat each OTU with the same weight by applying statistical standardization as performed in this thesis. Restriction digestion Digestion of PCR products by restriction endonucleases is a commonly applied analytical step for example used to produce RFLP and T-RFLP genetic profiles. This step may also exhibit potential biases in microbial community analysis. Sequence anomalies by deletion or misincorporation at the restriction sites (Cariello et ai, 1991; Eckert and Kunkel, 1991) and the formation of heteroduplexes that affect the restriction sites (Qiu et ai, 2001 ; Thompson et ai, 2002) may affect digestibility by restriction enzymes. In addition, it has recently been shown that incomplete DNA synthesis during PCR may result in partially single stranded DNA that cannot be digested by double-stranded DNA targeting (type II) restriction enzymes (Egert and Friedrich, 2003; Egert and Friedrich, 2005). This artifact was reported to influence T- RFLP, but may also affect non-restriction based methods such as DGGE and SSCP 126 5 GENERAL DISCUSSION (Jensen and Straus, 1993; Simpson et ai, 1999). Unspecific cleavage by restriction enzymes known as star activity, as well as incomplete or even inhibited digestion may also significantly influence restriction digestion of DNA (Pingoud et ai, 1993). Most of these artifacts can be avoided or at least substantially reduced by adaptation of enzyme and salt concentration or digestion time and temperature. Optimized digestion protocols and establishment of appropriate restriction controls may substantially minimize the influence of restriction biases in genetic analyses. Residual polymerase activity Another bias significantly affecting restriction-based genetic profiling was identified in the present thesis (Hartmann et ai, 2007: see appendix). Residual PCR polymerase activity during digestion of PCR products induced a stepwise fill-in at the 5'-overhang restriction sites of the digested fragments. This artifact resulted in multiple fragments of increasing lengths originating from the same sequence. In addition, residual Taq polymerase added an A-overhang to blunt end sites resulting in artificial polymorphism. The different artificially produced fragments could be observed by analyzing single clones and clone mixtures. This bias significantly influenced the apparent diversity of soil DNA T-RFLP profiles. Suitable purification techniques that eliminated the residual polymerase activity prior to digestion and the establishment of analytical controls will help to avoid this bias in restriction-based high-resolution genetic profiling approaches. Separation of operational taxonomic units (OTUs) Complexity of PCR amplified OTUs is assessed either by electrophoretic analyses (genetic profiling) or by cloning and sequencing (gene library). Most of the genetic profiling approaches rely on electrophoretic analyses. The electrophoretic system, i.e. different gel separation techniques or capillary electrophoretic systems, determines the resolution and precision in analyzing the genetic markers. However, co-migration of different gene fragments may induce the apparent abundance of several genetic markers at the same migration position, e.g. several different sequences in one DGGE band or one T-RF peak (Gafan and Spratt, 2005). In addition, DNA sequence, e.g. purine content, may induce variation in the migration behavior and lead to a different migration behavior than theoretically expected (Kaplan and Kitts, 2003). Ambiguities of OTUs limit the comparison to information 127 5 GENERAL DISCUSSION stored in sequence databases such as RDP-II (Cole et ai, 2005) or GenBank (Benson et ai, 2005) and among different laboratories. This thesis demonstrated that information given by single ribotypes such as T-RFs is often ambiguous (see chapter 4), revealing same T-RFs for very different phylogenetic groups. OTU composition of an amplified markergene may also be analyzed by cloning and further downstream analyses, e.g. sequencing or RFLP analysis. This approach may also introduce biases in community analysis (von Wintzingerode et ai, 1997). Sequence length and structure influence the cloning efficiency, which potentially affects markergene composition in the resulting gene library (Reysenbach et ai, 1992). The nature and occurrence of cloning biases is poorly understood and will highly depend on the cloning system used (Rainey et ai, 1994) and the type of gene cloned. Prediction of this bias is therefore very difficult. Short and homologous sequences as cloned in this thesis, i.e. approximately 1500 bp of the 16S rRNA gene, may have minor influence on cloning preferences when compared to cloning of large genomic and heterologous fragments. It is important to be aware of the fact, that phylogenetic analyses applied in chapter 4 reflect the markergene composition in the gene library and not in the soil bacterial community. However, results from genetic profiling (chapter 2 and 3) and from cloning and sequencing approach (chapter 4) were highly consistent, indicating that cloning may not severely bias diversity analyses. Data evaluation and statistics Evaluation of genetic profiling data by careful scoring of OTUs and suitable statistical tools represent the last step in the analysis chain. Careful data processing is absolutely fundamental to strengthen the conclusion drawn from genetic profiles (Kitts, 2001; Blackwood et ai, 2003; Hartmann et ai, 2005). Setting signal thresholds to exclude background noise and scoring of unambiguously detectable OTUs are prerequisites for a robust interpretation of data. The need for manual scoring of OTUs may represent one of the main limitations towards a completely automated analysis by genetic profiling. Furthermore, combination of descriptive and discriminative statistical tools is required to comprehensively describe differences and similarities among genetic profiles. Whereas descriptive statistics such as cluster or principal component analysis allow for the description of relative similarities among communities, discriminative tools such as permutation or Mantel testing are 128 5 GENERAL DISCUSSION necessary to statistically validate similarities observed. Profound statistical evaluation is absolutely required to interpret effects on microbial communities. 5.2 Impact of agricultural factors on soil bacterial communities Bacteria are key-organisms in soil ecosystem processes and changes in their community composition may have significant influence on soil functioning (Altieri, 1999; Kennedy, 1999; Madigan et ai, 2003). Bacterial communities will have strong influence on soil functions in agricultural systems and contribute to crop yield and crop quality. It is hypothesized that soil management, e.g. fertilizer and pesticide application, tillage, and crop rotation, as well as soil type and crop rotation are key determinants of soil microbial communities (Garbeva et ai, 2004). This thesis aimed at assessing the relative changes in the bacterial communities under different long- term agricultural management and at defining the magnitude of impact of these agricultural factors (see chapter 1, Research Question A). 5.2.1 Changes in bacterial community structures Agricultural factors applied in the DOK long-term field experiment significantly and consistently influenced the composition of soil bacteria (see chapter 2 and 3, and Introduction objective 1). Solid and liquid farmyard manure - the primary factor The major influence arose from application of FYM to the soils. This was no surprise, because the addition of readily available organic matter, the introduction of non- indigenous organisms, and altered secondary parameters such as soil pH, aggregate size and stability, or moisture may have significantly changed growth conditions in these environments. Based on the theory of r- and K-selection (Pianka, 1970), soils with high nutritional status may select for bacteria with high growth rates (r- strategists), whereas low-input systems may select for bacteria with low growth rates but higher substrate competition capability (K-strategists) (Torsvik and Ovreas, 2002). Therefore, the composition of bacterial communities may represent the nutritional status of a soil. Soils receiving FYM may be dominated by copiotrophic bacteria, species preferring environments rich in carbon, whereas the control plots 129 5 GENERAL DISCUSSION receiving no FYM may harbor more oligotrophic bacteria, species living in soils with lower carbon concentrations (Koch, 2001). However, it is not known if the presence of copiotrophic species will result in a high soil quality with regards to better supply of plants with nutrients. Agricultural crops At the second level, crops appeared to be an important factor in controlling soil bacterial composition. Significant differences of bacterial composition were observed between plots planted with winter wheat or grass clover. This effect may be even more pronounced when exclusively rhizospheric soil was analyzed. Plant-microbe interactions by plant exudates (ethylene, sugars, amino acids, organic acids, vitamins, polysaccharides, and enzymes) and microbial compounds are creating a unique environment that co-determines the microbial community living in the plant- surrounding soil (Grayston et ai, 1998; Wieland et ai, 2001; Garbeva et ai, 2004; Marschner et ai, 2004; Pesaro and Widmer, 2006). The crop effect in the DOK soils was prominent, but revealed a clear short-term characteristic. Crop effects were already detectable in early spring, but almost vanished one season after as concluded from the insignificant preceding crop effects observed. In conclusion, the project demonstrated the appearance of crop specific soil bacterial community compositions and their flexibility in the investigated agricultural systems. These crop effects on the bacterial community structures may vary among different crops and have to be validated in the same and other agricultural systems. Organic and conventional farming Among the three investigated farming systems, i.e. biodynamic, bio-organic, and conventional, significant differences were only observed between biodynamic and the other two managements (see chapter 3). Based on previous reports, remarkable influence was expected to come from pesticide application in the conventional systems (for review see: Johnsen et ai, 2001), but such effects were not detected in the DOK soils. Pesticide effects may largely depend on the chemicals applied and on the community investigated (Levanon, 1993). In the DOK field experiment, fungal communities may respond more sensitive to pesticide application than bacteria, because fungicides were used for disease control in the conventional systems (Bj0rnlund et ai, 2000). However, effects of fungicides on soil bacterial and fungal 130 5 GENERAL DISCUSSION communities are varying and not well understood (Johnsen et ai, 2001). In addition to the pesticide effect, also the exclusive application of mineral fertilizers in the conventional systems did not significantly affect the bacterial community structure when compared to the organic systems. This fertilizer effect was only visible when comparing the unfertilized and the exclusively mineral fertilized controls. The significant difference of the biodynamic system was surprising, because previous studies reported highly similar soil microbial biomass, enzyme activities, soil respiration, metabolic quotient, and microbial fatty acid profiles (Carpenter-Boggs et ai, 2000a) as well as crop yield, crop quality and soil fertility (Carpenter-Boggs et ai, 2000b) between biodynamic and organic management. It is currently unknown what led to the development of a distinct bacterial community in the BIODYN soils. Most likely, the aerobic treatment of the BIODYN-specific FYM may have led to the production of manure with a higher pH, which consequently may have induced a higher soil pH in the corresponding DOK plots (Mäder et ai, 2006). Higher pH may influence soil parameters such as nutrient availability or aggregate size and may have selected a distinct bacterial community. Alternatively, constantly higher organic carbon contents of these soils may also have led to this unique community and was potentially reflected in the higher biomass in the biodynamic plots. Whether distinct bacterial communities reflect higher soil quality as suggested for biodynamic systems (Reganold, 1995; Carpenter-Boggs et ai, 2000a) is currently unknown. Detailed effects of biodynamic management are not well understood and certainly require further investigation. Data from this thesis allowed to rank the magnitude of effects derived from different agricultural factors according to their influence on soil bacterial communities. It was possible to demonstrate, which treatments are key factors in determining the composition of bacteria and which appear to be of minor importance. This ranking provided new possibilities in environmental monitoring of effects on soils. Interpretations regarding the beneficial or detrimental influence of changing bacterial community structures on soil quality are not possible yet. 5.2.2 Abundances of bacteria in soils Relative abundances of bacterial phyla detected in the DOK soils by cloning and sequencing of 16S rRNA genes were in agreement with reports from other studies. 131 5 GENERAL DISCUSSION Recently, Janssen (2006) summarized the results of twenty-one 16S rRNA gene libraries constructed from different soil ecosystems including agricultural, woodland, forest, grassland, pasture, and moorland soil. Overall distribution of predominant bacterial phyla were similar (r = 0.72) to the one observed in the current project (Fig. 5.1). 45 Hartmann and Widmer 2006 0-77 o> CO ,— 40 in Janssen 2006 CO to CD 35 c 'Tim r = 0.72 (p <0.01) o o 30 i4__ o 25 te CD 20 CO 4—' y^A H** « '' |||ff| 15 ^B o 0) o T— Iw OJ 10 r, Ô oo CD CD i <9 <=> 0_ CO CO 1 CO - HI QQ "Ell __ C\J O 5 ci ^H ',//> HO 1X1H T 0 05 en w e/> w T! co CO 05 CO ro co CO 03 >< CD •c CD CD CD Q. CD *^ _CD CD CD CD CD M— "53 S O O o cteri. cteri t5 Ü Ü T3 o O 2 >. C/) ro Co ro ro ro F/fifure 5.7. Mean relative abundance (%) of bacterial phyla based on 16S rRNA gene clones in the three DOK libraries (chapter 4; Hartmann and Widmer 2006) and in 21 reviewed libraries from the literature (Janssen 2006). Number at the top of each bar gives the abundance (%) range between the three DOK gene libraries (Hartmann and Widmer 2006) or the 21 reviewed gene libraries (Janssen 2006). The 21 libraries included a total of 2920 clones ranging from 90 to 258 clones per library and contained only sequences longer than 300 bp. Correlation of the averaged relative distribution between the two data sets is indicated. Analysis with alternative tools such as oligonucleotide in vitro hybridization, fluorescent in situ hybridization (FISH), or quantitative PCR are difficult to compare, because these studies did not target all the phyla that were observed in the gene libraries (Janssen, 2006). However, relative bacterial abundance also indicated 132 5 GENERAL DISCUSSION strong variation among the different studies, ranging for example from 0 to 34% for Actinobacteria or 10 to 77% for Proteobacteria. This large variation can mainly be explained by insufficient coverage of diversity in the gene libraries in most of the studies, which have analyzed between 90 and 258 clones (157 ± 136). These low numbers are certainly insufficient to cover the high diversity of soil bacterial communities. In addition, the selection of primers may also significantly favor the detection of some groups (Reysenbach et ai, 1992; Wagner et ai, 1994; Polz and Cavanaugh, 1998). In this thesis it could be shown that with 600 to 700 clones one third of the estimated diversity may be covered and that the estimated diversity may even further increase with increasing numbers of clones analyzed. In general, bacterial abundances reported from the gene libraries are similar to the number of 16S rRNA gene sequence entries in the RDP-II database (Cole et ai, 2005) and vice versa (Fig 5.2). The RDP-II database with around 100'000 long sequence (>1200bp) or a total of 250'000 sequences (including <1200bp) showed a similar overall distribution to the gene libraries (r = 0.74), but only when excluding the highly varying gram positive bacteria (Actinobacteria, Firmicutes). The large variations of these two phyla may have several reasons. Firmicutes reveal particularly high representation in the database, but were lower in the soil gene libraries. This difference may be explained by the fact that many pathogens derive from Firmicutes and therefore RDP entries of this phylum may mostly originate from clinical studies, whereas their abundance in soil is rather low. Another explanation may be that gram-positive cells are more difficult to lyse and therefore are underrepresented in gene libraries due to insufficient DNA extractability. The higher abundance of detected Actinobacteria in the present thesis when compared to the reviewed studies (Janssen, 2006) and the RDP entries may be explained similarly. One may hypothesize that potentially higher DNA extraction efficiency of robust cells by the optimized protocol (Bürgmann et ai, 2001) may have led to an increased detection of gram positives, and therefore Actinobacteria, in the present thesis. In addition, this increased abundance may also be explained by a large increase in sequence entries in the databases in the recent past (e.g. from around 2000 in RDP release 8 to around 24'400 in RDP release 9), which may have led to an increased identification of members from this phyla. Also important to mention is the remarkably low abundance of relatives from the phyla Acidobacteria, Verrucomicrobia, 133 5 GENERAL DISCUSSION Gemmatimonadetes, Chloroflexi, and Planctomycetes (Fig. 5.2). This lack of representation may indicate low culturability of these bacteria (Rappe and Giovannoni, 2003), which consequently influences reliable affiliation of environmental clones to these phyla. The poor coverage of the databases becomes obvious with the finding that 79 to 89% of all 16S rRNA genes included in the review by Janssen (2006) could not be affiliated to known genera. This problem also occurred in the current study, where relatively few sequences could be unambiguously affiliated at the genus level. This indicated that the increase in sequence entries during the last few years did not help to gain a better resolution at lower phylogenetic levels. Most bacterial groups may still be highly underrepresented in the current databases. 40 r Hartmann and Widmer 2006 CO CD 35 RDP entries (all entries) O c | I RDP entries (isolates) CD 30 ^ cr r = 0.43 (p > 0.05) - all phyla CD 25 r = 0.73 < - CO (p 0.01) excluding gram+ o 20 CD O) 15 CO -i—* 10 CD Ü }_ CD b Q_ co co co w in m o .j_ CD CD CD Q. CD •c .Q '8 CD CD M— O CD CD M— % o "w tj Q o Ü CJ T3 o O CO w co ro CO CO CO co co 'o o en _Q _D n I) b £ £Z E _cg o O o o o CD o .c CJ O o o o CD CD 0) CD CD CD T3 O c o o F tï Ü ^ co o to c 1 < m co Û. CD I I E ro > E to CD O Figure 5.2. Relative abundances of sequence entries (percentages to total entries) in the RDP-II database (www.rdp.cme.msu.edu, October 2006) for the bacterial phyla detected in the gene libraries (Fig. 5.1). Sequences were displayed either for uncultured and isolates (hatched bars) or for isolates exclusively (empty bars) and only sequences t 1200 bp were included. Correlations between the averaged abundance in the gene libraries (Hartmann and Widmer 2006) and the RDP entries (including uncultured and isolates) are indicated for all phyla and for data sets excluding Actinobacteria and Firmicutes. 134 5 GENERAL DISCUSSION Based on the current (chapter 4) and previous results (Janssen, 2006), soil bacterial communities appear to be dominated by few phyla, predominantly Proteobacteria, Actinobacteria, Acidobacteria, Verrucomicrobia, Bacteroidetes, Firmicutes, Gemmatimonadetes, Chloroflexi, Nitrospira, and Planctomycetes. Although at least 52 bacterial phyla are expected based on DNA sequence information (Rappe and Giovannoni, 2003), these ten phyla represent over 90% of all 16S rRNA gene sequences in this thesis and in the reviewed studies. Many of the less abundant phyla have few or no culturable representatives and are virtually unstudied. 5.3 Soil bacterial diversity as soil quality indicator 5.3.1 Biodiversity and functional redundancy in microbial communities The role of functional redundancy Maintenance or loss of biodiversity and its impact on ecosystem functioning is one of the main issues in environmental stability research (Walker, 1992; Symstad et ai, 1998; Tilman, 1999a; Fonseca and Ganade, 2001; Hector et ai, 2001; Hunt and Wall, 2002; Naeem, 2002b; Naeem, 2002a; Rosenfeld, 2002; Symstad et ai, 2003; Coleman and Whitman, 2005; Fitter et ai, 2005; Hooper et ai, 2005). Due to the important role of microbial communities in soil functioning, microbial diversity has become an important topic in agricultural research (Hawksworth, 1991 ; Kennedy and Smith, 1995). Biodiversity and ecosystem functioning are directly related to the concept of functional redundancy, i.e. the ability of a number of different species to perform the same function in an ecosystem. This concept suggests that a diverse and redundant community may better sustain environmental perturbations and maintain soil functioning (Ekschmitt and Griffiths, 1998; Kennedy, 1999; Johnsen et ai, 2001 ; OECD, 2003). This led to the suggestion that microbial diversity may be an indicator for soil quality. Many concepts of biodiversity were developed for the above-ground compartment and include plants and animals. However, their validity for soil microbial communities is poorly understood. On the one hand, significant changes of community structures or loss of biodiversity may not result in altered processes. On the other hand, 135 5 GENERAL DISCUSSION bacteria are responsible for a high diversity of processes and reveal a high degree of functional specialization. Therefore soil functions may be sensitive to changes in bacterial diversity (Kennedy, 1999). However, only little is known about functional loss in soil groups among soil microorganisms and the impact of species ecosystems. Predictions of optimal diversity levels for optimal soil functioning are currently impossible. Therefore, linking differences in soil microbial community composition to changes in functional processes is one of the current and future challenges in microbial ecology (Torsvik and Ovreas, 2002). when tested It was reported that many bacterial species are functionally redundant on selected individual substrates (Yin et ai, 2000). The relation between community structure and functioning may for example be assessed by artificial induction of diversity loss or community change by a defined stress such as chloroform fumigation or biocide application. Many studies applied such approaches, but no or only minor effects of altered microbial communities on functional processes in soil were reported (Andren et ai, 1995; Brookes, 1995; Giller et ai, 1998; Griffiths et ai, 2000; Griffiths et ai, 2001a; Griffiths et ai, 2001b; Chander et ai, 2002). Recently, the effect of increasing species richness on functioning was measured by assessing the respiration rate of artificially designed bacterial communities in 1374 microcosms at the same condition (Bell et ai, 2005). Respiration rate significantly increased with increasing species richness, i.e. from monocultures to 72 different species, but increments became smaller at higher community richness, indicating the occurrence of a plateau. This observation implies that there may be a minimal diversity threshold for the same functional performance. Extrapolation to natural communities of higher diversity and non-culturable organisms is, however, very difficult. All these results indicate the occurrence of functional redundancy among soil microorganisms. However, it is important to consider that different organisms may perform the same function, but each under different environmental conditions. Loss of some functional groups may only under specific conditions be functionally detectable. In conclusion, little is understood about functional redundancy of soil microbial communities. Changes in community composition may occur before changes in soil functioning become detectable and the changes may serve as early indicators to detect important environmental or anthropogenic impacts. The microbial 136 5 GENERAL DISCUSSION to elucidate their groups that specifically respond to a specific stress may be targeted functional significance and the reason for their indicator function. Bacterial diversity in the DOK experiment Based on the proposed importance of microbial diversity, one general research question in this thesis focused on the feasibility for using soil bacterial diversity as quality parameter in agricultural soils (chapter 1, Research Question B). Bacterial diversity was estimated in the biodynamic, conventional, and unfertilized systems by large-scale sequencing of 16S rRNA genes (chapter 4). Bacterial diversity revealed no relation to the management systems. Although the three systems significantly varied in fertilizer and pesticide input and showed significantly different bacterial community structures, diversity estimations were very similar. As a general hypothesis, it might be assumed that low-input management may favor K-strategists and that the avoidance of pesticides may preserve diversity. These two factors might lead to a more diverse community. However, this hypothesis was not confirmed as bacterial diversity appeared to be insensitive to management differences. The question, whether organic farming represents a more ecological way of agricultural management than conventional farming is controversially discussed in the literature (Reganold, 1995; Condron et ai, 2000; Edwards-Jones and Howells, 2001; Rigby and Caceres, 2001; Mäder et ai, 2002). A recent review (Hole et ai, 2005) demonstrated that the effect of organic farming on biodiversity is depending on the organisms investigated. Many studies cited in this review found evidence that organic farming generally increased richness and abundance for example of plant species, earthworms, spiders, beetles, mites, ants, birds and some mammals. Among microbial parameters, community level measures such as microbial biomass revealed a trend towards higher contents in organic farming, which was confirmed in the present project. In contrast, effects of farming systems on abundance and distribution of bacteria and fungi are little understood (Hole et ai, 2005). In conclusion, the different nutritional states and the presence/absence of pesticides changed the composition of soil bacterial communities, but not their overall diversity. The relationship between low-input management and biodiversity may be valid for low-diversity communities of higher organisms, but may fail as a concept for highly diverse and complex microbial communities, where detection of diversity is very difficult and laborious. 137 5 GENERAL DISCUSSION In addition, microbial diversity did not correspond with differences in crop yield in the DOK system, which is a general indicator of soil fertility. Although biodynamic, conventional, and unfertilized management resulted in significantly different yields, bacterial diversity was very similar in these DOK systems. This was particularly surprising, because yield in the unfertilized systems was low when compared to the manure-based systems, demonstrating the strong effect of nutrient deficiency. In conclusion, the anonymous bacterial diversity estimations assessed in this thesis appeared not to reflect the strong differences among agricultural management regimes and crop yields, whereas the bacterial community structures significantly changed in relation to these factors. The limitations of these commonly used diversity indices are therefore discussed in the following section. 5.3.2 Limitations of common diversity estimations At the genetic level, taxonomic diversity can be defined as the number of different bacterial types (richness) and their relative abundance (evenness) in a community. High diversity is therefore indicated by communities with a high richness and an even distribution (Garbeva et ai, 2004). In this thesis, bacterial diversity estimation with common approaches including richness and evenness did not reflect the different agricultural farming systems and their performances (chapter 4). Therefore, commonly defined anonymous diversity measurements appear not to be suitable in describing soil quality. This failure may be based on two aspects. First, common diversity estimations treat each taxonomic unit as an anonymous entity, simply counting how many different taxons at which abundance are present in a sample. Taxonomic identity is lost, reducing the information content drastically. Second, it has been proposed that each ecosystem may possess a native diversity that is not changing in response to moderate environmental influences (Brown et ai, 2001 ; Ernest and Brown, 2001). The loss of a specific taxon from a niche may be compensated by another taxon. Therefore, although significant changes in bacterial community composition may be observed, no changes in their diversity may occur. Therefore, it may be more important to determine "who is present", than to simply count "how many are present". This requires to include the identity of the taxonomic units as given by community structure analysis (Garbeva et ai, 2004). Nevertheless, the sensitivity of diversity estimations in detecting effects may be different for other 138 5 GENERAL DISCUSSION soil communities such as fungi or nematodes and also for other soil systems, and may vary for different strength of the environmental effectors. 5.3.3 Novel identity-based similarity estimations Novel models to estimate the similarity of multiple communities include the identity of each OTU. Recently, two abundance-based methods were developed that calculate pairwise similarities between communities by calculating the number of shared OTUs between two data sets (Chao et ai, 2005). The Chao-Jaccard and the Chao- Sorensen abundance-based similarity indices include OTU identity and relative abundance, but have the disadvantage to allow only for pairwise comparisons. Other similarity indices such as Morisita-Horn or Bray-Curtis address similar abundance- based estimations of shared OTUs (Magurran, 2004). These models may better reflect differences in bacterial community structures observed in the DOK field experiment. For this purpose, these four similarity indices were determined based on the data sets derived from gene library analysis (see chapter 4) of the three farming systems BIODYN, CONFYM, and NOFERT (Table 5.1). Highest similarities were detected between BIODYN and CONFYM, reflecting the similarity observed in the genetic profiling approach. Lowest similarity was detected between BIODYN and NOFERT, whereas CONFYM and NOFERT revealed an intermediate similarity. In contrast, similarities between CONFYM and NOFERT were equal to similarities between BIODYN and CONFYM when using Morisita-Horn and Bray-Curtis indices. These trends reflect the observation from the genetic profiling approach, but are under representing the compositional differences of the gene libraries. For example, BIODYN and CONFYM showed distinct community composition at a PSIL of 90 (see chapter 4, Fig. 4.4), but the Chao-based indices assigned them as 100% similar although they shared only 170 of 272 OTUs (Table 5.1). These similarity indices appeared to better represent differences in community structures, but failed to adequately represent the magnitude of these differences. Furthermore, similarity estimations are based on pairwise comparisons, which give no information about the absolute complexity of an individual community. These indices give not an estimation about the diversity of individual communities and therefore do not replace traditional diversity indices. It remains questionable whether the complexity of information given in microbial community structures can be 139 5 GENERAL DISCUSSION adequately represented in single indices without loss of important information (Kennedy and Smith, 1995). Data gained from this thesis demonstrated the importance and the need for detailed analysis of microbial community structures by genetic profiling approaches. Table 5.1. Abundance-based diversity similarity estimations of operational taxonomic units (OTUs) derived from soil bacterial 16S rRNA gene libraries from three farming systems in the DOK field experiment. Pairwise co Tiparison Similarity Indices (%) — Shared Chao- Chao- Morisita- Bray- 1 2 OTUsb System System Jaccardc S0rensenc Hornd Curtisd e PSIL 9T 899 BIODYN CONFYM 293 52 69 62 35 BIODYN NOFERT 177 34 50 54 30 CONFYM NOFERT 342 44 61 62 35 e PSIL 903 272 BIODYN CONFYM 170 100 100 97 70 BIODYN NOFERT 81 77 87 95 66 CONFYM NOFERT 97 85 92 97 71 a Percent sequence identity levels (PSIL) of defined OTUs, i.e. 97 or 90% sequence identity b Estimated number of species shared between two gene libraries according to Chen ef al. (1995) c Estimated similarity of species distribution between two libraries according to Chao ef al. (2005) d Estimated similarity of species distribution between two libraries according to Magurran (2004) e Total number of species among all three gene libraries at the corresponding PSIL 5.4 Perspectives and Conclusions Molecular microbial ecology is rapidly progressing field and therefore other tools and into concepts may be available or developed, which may allow for further insights effects of agriculture on soil microbial community structures and diversity. ' 5.4.1 The lull-cycle molecular approach The molecular genetic approach applied in the present thesis provided a wide range of information on the soil bacterial community (Fig. 5.3). First of all, quantification of nucleic acids content represented an alternative straight-forward method to measure 140 5 GENERAL DISCUSSION a parameter related to the soil microbial biomass. Second, genetic profiling directly on soil nucleic acid extracts allowed for determination of relative differences in bacterial community composition. Furthermore, molecular cloning of amplified marker and their genes gave detailed insights into the identity of bacterial organisms in the diversity. However, up to this step the occurrence of specific OTUs corresponding experimental system has not been verified. Although consistency of if the replicated data may exclude stochastic artifacts, it has not been proven The differences are induced by environmental conditions or methodological biases. 'full-cycle' molecular approach allows for verification of detected markers in the original sample or intermediate steps by a feedback loop (Fig. 5.3). Specific probe/primer design based on a marker of interest may allow to verify its presence and abundance for example by fluorescent in situ hybridization (FISH: Amann et ai, 2001) or quantitative PCR (Heid et ai, 1996) directly on the environmental sample. The 'full-cycle' approach represents a strong tool for indicator diagnostics in microbial ecology. First, potential indicators significantly responding to a specific treatment can be identified from the genetic information, i.e. from genetic profiles or gene libraries. Second, downstream probe design for such an indicator allows for specific verification of its presence in the environment and its specific reaction to the treatment. Recently, we were able to demonstrate the 'full-cycle' verification approach using T-RFLP analysis in heavy metal contaminated soils (Widmer er ai, 2006a). Briefly, a potential indicator T-RF, which significantly increased in the genetic profiles of heavy metal amended soils was identified. The potential indicator T-RF was cloned and sequenced by a novel adapter-ligation and re-amplification procedure, which allowed to retrieve phylogenetic information of the corresponding T-RF. An unclassified member of Cyanobacteria was identified and specific primers could be designed for this taxon. Direct amplification from soil DNA extracts demonstrated the high abundance of this sequence in the heavy metal amended soils, whereas almost no signal could be detected in the non-amended control soils, verifying the relatedness of the taxon to the heavy metal treatment in this system. Several potential indicators were detected by molecular cloning and sequencing in the DOK field experiment (see chapter 4, Table 4.3). Twenty-seven taxa from nine different phyla were significantly correlated to a specific farming system, i.e. BIODYN, CONFYM, and NOFERT. The 'full-cycle' approach opens up the possibility to track 141 5 GENERAL DISCUSSION in the DOK soils and to the presence and abundance of these potential indicator taxa verify or falsify their relatedness to the agricultural treatment also in other systems. Recently, a novel soil Pseudomonadaceae cluster specifically associated with winter wheat in the DOK field was detected and verified by the 'full-cycle' approach (Pesaro and Widmer, 2006). This verification procedure holds great potential for use in soil quality indicator diagnostics. Environmental Sample extraction in situ detection quantificationn DNA Content Community " Nucleic Acid ("Biomass") PCR genetic Proft//n0i Specific Amplified Community Primer Si Probe Target Gene i Structures L ^ cloning comparative analysis design sequencing i j Community ' phylogenetic inference Diversity Confirmation Characterization Feedback Loop Figure 5.3. The 'full-cycle' molecular approach to characterize and verify distribution and relative abundance of soil microbial communities in ecosystems by a cultivation- independent molecular approach. Detection of rRNA genes allow for characterization of community structures and diversity as well as phylogenetic inference of single taxonomic units via databases. Downstream probe design allows for specific detection of the taxonomic units at different levels in the process and enables direct confirmation of results obtained in a feedback loop (modified from Hugenholtz, 2002). 142 5 GENERAL DISCUSSION 5.4.2 Alternative gene families House-keeping genes The rRNA gene approach allowed to study the impact of agricultural factors on a wide range of soil bacteria. However, also other 'house-keeping' genes, which are present in all bacteria, may be targeted for genetic profiling. Protein-encoding genes such as reck (DNA repair & recombination) or gytB (unwinding of supercoiled DNA) may have advantages over the common rRNA approach (Felis et ai, 2001; Watanabe et ai, 2001). First, these genes may reveal a higher level of sequence variation, which implies a better resolution of closely related strains. Resolution may be moderately limited in approaches targeting the coding regions of the ribosomal Operon, i.e. SSU or LSU rRNA genes. However, the intergenic regions of the rRNA gene operon (ITS sequences) as targeted by RISA profiling also reveal high variability and may be suitable to resolve between closely related strains. Second, in silico DNA to protein translation of recA or gyrB genes may allow for a more accurate phylogenetic analysis of distantly related strains. However, it has been reported that phylogenetic analysis of the family Geobacteraceae base on 6 different genes, i.e. 16S rRNA gene, recA, gyrB, fusA, n/'/D, and rpoB, revealed similar results and may rather provide complementary information of phylogenetic differences (Holmes et ai, 2004). Finally, analyses based on the SSU rRNA genes allow for comparison of the results to a broad range of phylogenetic data stored in public databases. No other gene has more entries in databases, implying a large advantage to use this target as genetic marker in community analysis. Functional genes A more detailed insight into changes of bacterial communities may be achieved by targeting specific functional groups. Nutrient cycling processes such as the nitrogen turnover are important forces to gain high crop yield in agricultural soils. Bacteria involved in the processes of the nitrogen cycle such as nitrogen fixation (diazotrophs), ammonia oxidation (AOB), or nitrite oxidation (NOB) are of primary importance in agricultural systems. Targeting genes encoding for components involved in these processes, i.e. n/7H (nitrogenase reductase), amoA (ammonia monooxygenase), or nitK (nitrite reductase), allow to generate genetic profiles for these functional groups (Widmer et ai, 1999; Braker et ai, 2000; Horz et ai, 2000; 143 5 GENERAL DISCUSSION Hamelin et ai, 2002; Ibekwe et ai, 2002; Prieme et ai, 2002; Rosch et ai, 2002; Bürgmann et ai, 2004). These approaches provide insights into changes of community components that perform a specific function in the soil ecosystem. Targeting functional genes will probably give more information on soil processes than achieved with phylogenetic markers, but knowledge and databases of functional genes are currently still limited. However, functional genomics will definitely gain importance in microbial ecology. 5.4.3 Active groups Phylogenetic analysis of soil microbial communities by the rRNA gene approaches applied in the current project detects active but also dormant organisms. The active part of the microbial community will be of primary importance to soil functioning. Therefore, methods targeting only active bacteria may help to link community structure information to the processes performed and several methods are available to assess the active part of a soil microbial community. In this section, two prominent approaches are briefly discussed. Molecular analysis of RNA markers Metabolically active cells are reported to contain larger amounts of ribosomes than dormant cells (Nomura et ai, 1984). It was reported that rRNA contents correlate with growth rates and intracellular rRNA levels rapidly decrease after growth has declined (Kerkhof and Kemp, 1999). Therefore, description of the community composition based on transcribed genes by assessing their rRNA may offer one way to target active communities. Thus analogous to the rRNA gene approach, reverse transcribed rRNA profiling may represent a straight forward tool to assess bacteria in a metabolically active state (Felske et ai, 2000; Duineveld et ai, 2001; Gremion et ai, 2003; Pesaro et ai, 2004). However, it has been reported that rDNA to rRNA transcription may not be an unequivocal indicator of active communities. Common to all house-keeping genes, ribosomal RNA may be present at a certain level even in resting and dormant cells. For example, it has been reported bacteria may maintain high cellular rRNA content even under starving or inhibitory conditions (Flärdh et ai, 1992; Fukui et ai, 1996; Schmid et ai, 2001). Additionally, the rRNA turnover in a microbial system was reported to be relatively slow and may limit the exclusive 144 5 GENERAL DISCUSSION detection of active cells by targeting this molecule (Pesaro et ai, 2004). Similarly to the rRNA approach, mRNA analysis of protein-encoding genes (see paragraph 5.4.2) may target active functional groups. Assessing a specific function and not a house¬ keeping process may enhance the probability to detect active parts of the microbial community. The turnover rate of mRNA may also be important to know in this case. In conclusion, although RNA profiling may describe the metabolically more active component in a community, differences in DNA to RNA ratios between active and non-active bacteria as well as the RNA turnover are not well understood and careful interpretations are required. Stable isotope probing Stable isotope probing (SIP: Radajewski et ai, 2000) of nucleic acids is one of the most promising approaches to assess active microbial populations (Boschker and Middelburg, 2002; Manefield et ai, 2002; Wellington et ai, 2003; Dumont and Murrell, 2005). The method relies on the incorporation of stable isotopes into molecules of microorganisms specifically growing on a substrate labeled with this isotope (e.g. 13C). The stable isotope will be incorporated into the cellular components such as nucleic or fatty acids, which will allow selective recovery of these isotope-enriched components and enables downstream analysis of the microbial community grown on the labeled substrate (Fig. 5.4). Common molecular tools can then be applied to this fraction of cell components. The SIP technique is compatible for example with analysis of DNA (Radajewski et ai, 2000), RNA (Manefield et ai, 2002) or PLFA (Treonis et ai, 2004). Ribosomal markers can be targeted as well as functional genes. In addition, SIP is not only restricted to analysis using carbon isotopes but can also be applied to other stable isotopes such as 15N (Cadisch et ai, 2005). The application range of SIP is constantly growing. SIP was for example already applied to study microorganisms using acetate (denitrifiers: Ginige et ai, 2005), methane (methanotrophs: Hutchens et ai, 2004; Lin et ai, 2004; McDonald et ai, 2005), methanol (Ginige et ai, 2004; Lueders et ai, 2004), methyl bromide or chloride (Miller et ai, 2004; Borodina et ai, 2005), phenol (DeRito et ai, 2005), benzene (Kasai et ai, 2006), or naphthalene and phenantrene (Singleton et ai, 2005). SIP can also be applied to study plant-microbe interactions such as tracking the path of a substrate from the plant to the microbe for example by incubation of the plant with labeled 13C02 (Griffiths et ai, 2004). 145 5 GENERAL DISCUSSION Incubation of soil with 13C-labeled substrate in situ in vitro I I MtamimMaOÉÊhm* Extraction of nucleic acids Phospholipid fatty acid & isopycnic centrifugation analysis (PLFA) "C substrate ( light DNA/RNA) "C substrate ('heavy' DNA/RNA) Cloning & sequencing Genetic Profiling Microarray diagnostic (Phylogenetic analysis) (e.g T-RFLP, RISA, DGGE) v. _____ y __JlL_J_a __JU__L_ aj^L __, /__\>_, A Figure 5.4. Stable isotope probing of nucleic acids from soil microbial communities. The environmental sample is incubated with 13C-labeled substrate (in situ or in vitro) and the heavy isotope is incorporated in cellular components (e.g. DNA/RNA or PLFA) of actively growing microorganisms. These components are extracted, purified, and separated on a density gradient by isopycnic centrifugation. Nucleic acids with incorporated 13C can be used for molecular analyses, i.e. phylogenetic analysis, genetic profiling, or microarray analysis, of the microbial community actively growing on the labeled substrate. 146 5 GENERAL DISCUSSION Besides the enormous potential for a wide range of applications, SIP reveals also some limitations. First, the choice of substrate is often difficult and strongly influences the research question. Second, for sufficient isotope incorporation efficiency and to compensate for isotope dilution (particularly in situ) strong labeling of the substrate is required. Third, high substrate concentrations and long incubation time may induce an enrichment bias by cross-feeding of non-target organisms on primary substrate consumers. Fourth, incomplete separation by the gradient centrifugation may results in a background of light isotope organisms and may therefore bias the analysis of the active microbial population. However, despite all these potential limitations, this novel technique is promising to assess many research questions with regards to metabolically active parts of a microbial community. 5.4.4 Molecular large-scale approaches Application of molecular microbial ecology in routine soil quality assessments requires high-throughput tools to gain large amounts of information in an adequate period of time. Compilation of information on multiple genes may provide important insights into ecosystem functions of microbial communities and their relative changes in relation to various effectors. Two novel approaches are promising to expand the still limited knowledge on microbial communities in soil. Metagenomic analysis and application of DNA microarrays in microbial analysis are discussed in this section on future perspectives. Metagenomics Metagenomic analysis, the habitat based investigation of mixed microbial communities at the genome level (Handelsman et ai, 1998), may provide information of microbial communities based on more than just single genetic markers. Metagenomic libraries may lead to the identification of novel genes and gene products and will potentially unravel diversity and functions of soil microorganisms (Rondon et ai, 2000; Handelsman, 2004; Riesenfeld et ai, 2004; Daniel, 2005). One prominent application of metagenomic analysis was the detection of a rhodopsin gene cluster in a marine y-Proteobacterium, which was the first indication that this group is capable of performing a form of phototrophy previously reported to be exclusive to Archaea (Beja et ai, 2000). This discovery demonstrated the power of 147 5 GENERAL DISCUSSION metagenomics in enhancing the knowledge about functions of environmental microbial communities. So far, large metagenomic libraries of prokaryotes have been generated for a marine system (Venter, 2004), an agricultural soil (Tringe et ai, 2005), or a natural acidophilic biofilm (Tyson et ai, 2004). It will be interesting to see what the future development of metagenomic approaches will contribute to our understanding of soil microbial ecology. Microarray diagnostics DNA microarrays (Wallace, 1997) allow for the selective detection of defined gene segments in environmental samples and have become one tool of choice to screen complex gene pools as for example those of environmental microbial communities. Many reviews have recently been published (Li and Liu, 2003; Zhou, 2003; Liu and Zhu, 2005; Gentry et ai, 2006; Sessitsch et ai, 2006), demonstrating the potential of this method for microbial ecology research. This technique allows for simultaneous detection of abundance and expression of thousands of genes. Microarray analysis can be applied to investigate community composition and diversity of microbial communities based on group-specific rRNA gene probes (Wilson et ai, 2002; Cook et ai, 2004) or to detect the abundance of functional genes in a gene pool (Wu et ai, 2001; Cho and Tiedje, 2002; Taroncher-Oldenburg et ai, 2003; Bodrossy et ai, 2006). Recently, a high-density DNA microarray has been developed that allows to screen microbial communities for the presence of 62'358 different SSU rRNA genes on one chip (DeSantis et ai, 2005). This array has recently even been improved to SOO'000 probes (Brodie et ai, 2006). DNA chips are developed towards rapid high- throughput applications and are therefore very suitable to investigate highly complex soil microbial communities. In particular, it will also facilitate screening of large metagenomic libraries described in the paragraph before (Sebat et ai, 2003). Whereas these chips appear suitable to detect presence or absence of gene segments in a complex gene pool, the quantitative detection of relative abundances are currently limited and have to be further optimized. Statistical handling of these huge amounts of data will also become a primary issue in microarray diagnostics (Svrakic et ai, 2003). DNA microarrays represent a tool providing the requirements for routine molecular analysis and will certainly be an important tool for future microbial ecology research. 148 5 GENERAL DISCUSSION 5.4.5 Final conclusions Data obtained during the course of this thesis revealed several important findings that may help to increase the understanding of bacterial soil characteristics in agricultural systems. 1) Agricultural management, i.e. fertilization and plant protection regimes, as well as crops significantly changed the bacterial community structures and the agricultural effects could be ranked according to their magnitudes. 2) Whereas bacterial community structures revealed clear differences in relation to the agricultural factors, common bacterial diversity estimations failed to detect these effects. This demonstrated the value of analyzing bacterial community structures as compare to common anonymous diversity indices. 3) The molecular genetic methods applied, i.e. T-RFLP, RISA, and detailed DNA sequence analyses, revealed high robustness and consistency in detecting effects of agricultural factors. The outcomes were consistent no matter which method was applied or which time point was analyzed. Therefore, these tools proved highly suitable for monitoring effects on soil bacterial communities. 4) The long-term experimental system allowed for the detection of agricultural management effects that may not become apparent in short-term studies. Long- term experiments may represent the only way to assess sustainability of such systems. 5) The genetic approach allowed for the detection and verification of potential treatment-specific indicator groups in soil bacterial communities. The 'full-cycle' approach of molecular ecology will help to validate these indicators and thereby to increase our capability in monitoring microbial soil characteristics of soil quality. 6) Changes in bacterial community structure cannot yet be linked to detrimental or beneficial effects on soil quality. Future investigations may help to link these structural differences with differences in soil functions and may therefore provide more information on soil quality. 149 5 GENERAL DISCUSSION ^ ? jr~\ £ !" ^ \, jf \K «^ ? Wi, J b i 150 Appendix Residual polymerase activity-induced bias in terminal restriction fragment length polymorphism analysis Martin Hartmann, Jürg Enkerli, Franco Widmer Published in Environmental Microbiology 9 (2007), 555-559 © 2007 Society for Applied Microbiology, Blackwell Publishing Ltd. APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS Appendix: Residual polymerase activity-induced bias in terminal restriction fragment length polymorphism analysis Residual activity of polymerase chain reaction DNA polymerases in restriction analyses strongly affected genetic profiling based on terminal restriction fragment length polymorphisms. Artificial fragment sizes produced as a result of 5-overhang restriction site fill-in and addition of a terminal A may bias genetic profiling and genotyping of microbial communities. Efficient removal of polymerases retained original fragment sizes and significantly reduced this profiling bias in soil bacterial communities. Terminal restriction fragment length polymorphism (T-RFLP) analysis (Liu et ai, in 1997) has become a commonly used genetic profiling approach to assess changes microbial community structures in different environments (Moeseneder et ai, 1999; Tiedje et ai, 1999; Dunbar et ai, 2000; Lukow et ai, 2000; Buckley and Schmidt, 2001; Pesaro et ai, 2004; Hartmann et ai, 2006; Widmer et ai, 2006b). Although the T-RFLP approach has proven to be highly consistent and reproducible (Osborn et ai, 2000; Hartmann et ai, 2005), the method may still suffer from several biases, most of them characteristic for all polymerase chain reaction (PCR)-based techniques (von Wintzingerode et ai, 1997; Kanagawa, 2003). Artefacts are known to originate from nucleotide chimeric sequences (Wang and Wang, 1997; Qiu et ai, 2001), misincorporation (Cariello et ai, 1991; Qiu et ai, 2001), heteroduplexes (Judo et ai, 1998; Thompson et ai, 2002), or partially single-stranded DNA (Egert and Friedrich, 2005). These artificially generated sequences may strongly influence genetic profiles, leading to overestimation of diversities and limitations when associating T-RFLP data with DNA sequence-derived information. Here, a DNA polymerase-induced bias and a solution to prevent it are described. The three different sequences used in the study, i.e. D101, D311 and D321, were obtained from a bacterial SSU rRNA gene library constructed from agricultural soils and deposited in GenBank under accession numbers DQ827816, DQ828000 and DQ828010. The three sequences were examined individually and in combination by 152 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS T-RFLP analysis using two different types of DNA polymerases, i.e. the Taq-type Hotstar enzyme (Qiagen, Hilden, Germany) and the Pfu-type Phusion enzyme (Finnzymes, Espoo, Finland). Target sequences were PCR amplified using bacteria- specific primers 27F (6-FAM-labeled) and 1378R (Heuer et ai, 1997) and 0.3 ng Plasmid DNA with conditions according to manufacturer's recommendations and Hartmann and colleagues (Hartmann et ai, 2006). Restriction digests for T-RFLP analyses were performed with and without purification with the MinElute PCR purification kit (Qiagen). Ten microlitre of purified or non-purified PCR products was digested according to manufacture's recommendations (Promega, Madison, Wl) in 20 ml volumes employing three different restriction endonucleases (6 U per reaction) representing the three different types of restriction, i.e. Mspl (CACGG, 5'-overhang), Haelll (GGACC, blunt end), and Cfol (GCGAC, 3'-overhang). T-RF sizes were analysed with an ABI Prism 3130x1 Genetic Analyser and Genotyper v3.7 NT software (Applied Biosystems, Foster City, CA) as described by Hartmann and colleagues (Hartmann et ai, 2006). Terminal restriction fragment length polymorphism profiles of non-purified Taq- polymerase-PCR products digested with Mspl revealed up to four different T-RFs within a range of four bases (Fig. A.1a). These four T-RF sizes could be explained with different stages of fill-in and extending the recessed 3'-terminus by the Taq polymerase, i.e. the original T-RF (Fig. A.1: ®), the two T-RFs resulting from fill-in of the 5'-overhang (Fig. A.1: 1 base ©, or 2 bases ®) and the T-RF resulting from a terminal A extension (Fig. A.1: ©). This phenomenon is well known in cloning approaches, where residual polymerase activity can fill in 5'-overhangs and as a consequence reduces cloning efficiency (Bennett and Molenaar, 1994). Alternatively, this very same phenomenon is used to produce A-termini for universal TA cloning (Zhou and Gomez-Sanchez, 2000). In the present example (Fig. A.1 a), residual polymerase in the restriction digest was sufficient to completely fill in and add an A- overhang, leading to a predominant T-RF size which was three bases larger than expected. 153 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS clone D311 clone D101 clone D321 a) Taq-type Mspl (CACGG) 4000 2000 5'-overhang ffi©®@ ^ i i i i 5'' 3'-A A-3' 5' I ' I ' I i I i I ' I i I ' I i I ' I ' ' I ' I i I ' I i I i I ' I ' I ' I ' I ' I ' I ' I ' I i I i I i I i T i 1'rr (16118 120122124 126126 130132 131 162 164166 168170 172174 176176180 180 182181 186188 190192 191196 198 b) Pfu-type 4000 3000 '2000 3000 3 Mspl (CACGG) 2000 2000 Q. 1000 5'-overhang 1000 gl 1000 ©@® ©@® ©CD© \ I I I IÏ22Ë , |122 9| 1000 5'* 3 3000 2000 3000 3' 5' 2000 1000 2000 1000 -1000 3 1 A. Q. I I ' I I ' I ' I ' I I t I ' i I I 'I I I I I I I I I I I I I I I I ' I I I I I I I I ' I ' I H~ 11611812012212412612S 130132134 162164166 168170172171176178 180 180182184166188190192 194196198 c) Taq-type 6000 1236 4l -6000 |197 3| 6000 Haelll 13 -4000 4000 (GGACC) 4000 >, 9- -2000 2000 2000 Ü blunt end c c o d) Taq-type o [5641 1365 7| M 84 Ol 8000 I r8000 A 6000 II -6000 Cfol 3 -4000 (GCGAC) 4000 M -1000 -2000 11 -2000 2000 3'-overhang C i J1 o n © ® © r-Tüän , ? 1365 9l r6000 5' 3' 0) 4000 1 -6000 '—1—' 6000 A-3' 5' 4000 A 1000 2000 I |l -2000 \\ 2000 Q. 1 11 11 1 1 1 1 1 1 1 11 1 1 1 1 11 | 1 | 1 | 1 | 1 | 1 | 1 | I | I | I | I | I | I | I | TT 1 1 '11 1 M 1 1 1 46 48 50 52 54 56 58 60 62 64 358360362 364 366 368370372374 376 176178180182181 186188190192 194 e) Taq-type "P 1124 91 |186 1||1B7 4||188 5| -J: IÏ23 -1500 Mspl (CACGG) -1000 |122 7(1 5'-overhang -50O <= ®©@® ©as® l i i i \ 800 5' 3'-A -600 A-3 5' 100 200 _A_ I I I I I I I I I I I 1 I I I ~1 120 125 130 135 110 115 150 155 160 165 170 175 1B0 185 190 195 T-RF size (rmu) Figure A. 1. Figure legend on the next page. 154 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS Figure A.1. Terminal restriction fragment length polymorphism profiles of three different SSU rRNA gene sequences (D101, D311 and D321) generated with different PCR DNA polymerases, restriction enzymes, and DNA purification. Sequences were amplified with two different polymerase types, i.e. Taq-type (a, c, d, e) and Pfu-type (b), and digested with three different types of restriction enzymes, i.e. Mspl (a and b), Haelll (c) and Cfol (d). Restriction enzymes, sites, and fragment ends (5'-overhang, blunt end, 3'-overhang) are indicated at the left margin. Numbers in circles indicate different fill-in stages referred to in the text. The three target sequences were analysed individually (a-d) and by mixing prior to PCR (e). The upper panel of each box shows T-RFLP profiles of the non-purified PCR products, which revealed polymerase activity- induced artificial peaks. The lower panel of each box shows T-RFLP profiles of the purified PCR products with efficient removal of the polymerases. The x-axis represents the T-RF size given as relative migration units (rmu) and the y-axis represents the T-RF frequency given as relative fluorescent units (rfu) in the profiles. All assigned peaks revealed intensities higher than the threshold at 50 rfu. In order to confirm this explanation, a series of tests were performed. The Pfu-type polymerase, which does not add A-termini, resulted in shifting the T-RF by two bases (Fig. A.1b). Production of blunt end fragments with restriction endonuclease Haelll resulted in two different T-RF peaks for the Taq-type enzyme (Fig. A.1c). Because no fill-in can take place, only the A-termini are added to the fragment, resulting in the original T-RF and an additional one, which is one base larger. Production of 3'- overhangs with Cfol, prevented fill-in and A-tailing, and yielded only the original T-RF (Fig. A.1d). In all these experiments, 2 units Taq-type or 1 unit Pfu-type polymerase per reaction as recommended by the manufacturers, retained sufficient activity during restriction digests to induce these strong T-RF sizing artefacts. All these experiments were consistent with the original explanation that polymerase activity was the cause of this artefact. Although purification of PCR products prior to T-RFLP analysis is commonly used (Liu et ai, 1997; Osborn er ai, 2000), this step is not always reported in published protocols (for example see: Scala and Kerkhof, 2000; Fuhrman et ai, 2002; Jernberg and Jansson, 2002; Conn and Franco, 2004; Hackl et ai, 2004; Hartmann etat., 2005; Stralis-Pavese et ai, 2006). Practically important is that the efficiency of the polymerase removal is not assessed and may strongly depend on the purification procedure and polymerase type used. The here presented results clearly demonstrate that quantitative removal or inactivation of the polymerase after 155 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS the PCR step is required to avoid the bias induced by residual activity during restriction digestion. DNA purification (MinElute kit) efficiently removed both DNA polymerases, resulting in original T-RF sizes after digestion (Fig. A.1, purified). Alternatively, chloroform or proteinase K treatments were unable to eliminate the polymerase activity (data not shown), and heat inactivation is not feasible for heatstable polymerases. The importance of avoiding these artefacts became apparent when analysing mixtures of the three target sequences, which resulted in strong overestimation of T- RF numbers in the profile (Fig. A.1e). Extrapolation of this scenario to a highly complex soil microbial community, which may contain several hundreds of different target sequences, can result in a highly biased profile. To demonstrate this phenomenon on environmental samples, we analysed a DNA extract from an agricultural soil using bacterial T-RFLP analysis with the same primers and the restriction enzyme Mspl with and without purification (MinElute kit). Fill-in of restriction sites and production of artificial T-RF peaks in the nonpurified samples was also in this experiment clearly detectable (Fig. A.2). The overall profile showed a higher complexity in the non-purified sample, resulting in an increase of the overall scorable T-RF richness from 48 (purified) to 65 (non-purified) between 50 and 500 relative migration units (Fig. A.2a). This indicated the presence of 35% of artificially induced peaks. Two enlarged regions in these profiles demonstrated the different stages of restriction site fill-in by residual polymerase activity (Fig. A.2b: ®©CD©) and revealed an increase in T-RF richness from 8 (purified) to 15 (non-purified) scorable peaks in these two regions (asterisks in Fig. A.2b). Community diversity analysis based on T-RFLP can therefore significantly be influenced by the here presented bias. Beside the quantitative removal of the polymerase, another possibility to eliminate this artefact may be to add additional polymerase during the restriction digest to completely fill in 5'-overhangs and add A-tails to all T-RFs. This might result in only one peak per sequence and retention of the original diversity. However, such an approach may not be quantitative for all sequences. A more effective way would be to use restriction enzymes producing 3'-overhangs, which allows to completely avoid the problem of restriction site fill-in and addition of a terminal A. However, this will substantially limit the array of available enzymes for community profiling. 156 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS a) T-RF size rmu 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 11 i i 1 i i 1 i 1 1 ' ' 1 é é 11 i i i . I. i 11 1 1 I 1 i 1 i il. ih i 1 i 1 i 1 i il. 111 i i 1 i i 800 ~ 600 •t 400 "^ 200 ° © ©® ® (D(DcD® ©@dXD 62.6 1000 1500 800 1000 600 400 500 200 i—i—i—i—i 1 r -1 1 1 I I I I I I I I I I I I I I I I I I I I I I I 60 61 62 63 64 65 66 67 68 69 144 146 148 150 152 154 156 158 160 162 164 166 Figure A.2. Soil bacterial community level T-RFLP profiles based on PCR amplification of soil DNA extracts with Taq-type polymerase and digestion with restriction enzyme Mspl prior to and after purification, (a) Overall profiles between 50 and 500 relative migration units (rmu) and (b) two enlarged sections for detailed analysis, are displayed. T-RF frequency is given as relative fluorescent units (rfu). Asterisks indicate scorable peaks with signal intensities above a threshold of 50 rfu. T-RFs representing different fill-in stages are labelled with fragment size (rmu) and with circled number referred to in the text and Figure 1. Polymerase chain reaction-based biases such as formation of chimeric sequences, heteroduplexes, single-stranded DNA, and point or insertion/deletion mutation at restriction sites have been reported (Qiu et ai, 2001; Kanagawa, 2003; Egert and Friedrich, 2005) and may produce artificial peaks in T-RFLP profiles. These biases may explain additional minor peaks in the profile of multiple sequences (see Fig. A.1e). In this study we identified an additional possible artefact occurring during T- RFLP analysis, which is induced by residual polymerase activity during restriction analyses. Beside adjustment of PCR conditions, e.g. template concentration, polymerase type, elongation time, cycle number (Qiu et ai, 2001), and PCR 157 APPENDIX RESIDUAL POLYMERASE ACTIVITY INDUCED BIAS IN T-RFLP ANALYSIS downstream reconditioning such as removal of single stranded DNA (Egert and Friedrich, 2005), the quantitative elimination of the DNA polymerase appears to be an additional prerequisite for representative T-RFLP analysis. Comparative studies based on T-RFLP profiles may not be strongly affected by this polymerase-induced bias, because it may be similar in different samples analysed in parallel. However, diversity estimations and comparison with DNA sequence-derived in silico T-RF sizes could be limited, in particular because different sequences seemed to reveal a different affinity for this bias (see Fig. A.1). In addition, occurrence of peak shoulders, as often observed in highly complex community profiles and the consequential difficulties in analyzing such profiles may be increased by this artefact. Therefore, optimal methods to efficiently eliminate or inhibit different types of polymerases without affecting the PCR products, e.g. specific inhibition of the polymerase by antibodies, have to be further evaluated. High resolution genetic profiling with minimized biases will allow to more reliably analyse community structures and are prerequisites for reliable detection of differences in genetic community structures and diversities. Labelled single sequences with known restriction sites may be included as control in T-RFLP analysis (Kitts, 2001). This approach would help to assess the efficiency in polymerase removal and to avoid associated biases in T-RFLP analysis of environmental samples. 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[email protected] Telephone +41-44-377-73-81 Date of Birth: 02-16-1977 Marital status: married Nationality: Swiss Education and Diplomas 2007 PhD in Sciences, ETH Zurich, Switzerland 2001 MS degree in natural sciences, ETH Zurich, Switzerland 1996 Matura Type B, SAMD Davos, Switzerland Professional Career 2003-2006 PhD studies at the Department of Molecular Ecology, Agroscope Research Station ART, Zurich Switzerland PhD Thesis: Genetic characterization of soil bacterial communities in the DOK long-term field experiment: Influences of management strategies and crops 2004 Visiting scientist at the Federal Biological Research Centre for Agriculture and Forestry, Institute for Plant Virology, Microbiology and Biosafety, Braunschweig, Germany 2002-2003 Post-Graduate practicum Department of Molecular Ecology, Agroscope Research Station ART, Zurich Switzerland 2002 Teaching in chemistry at the evangelic school of Schiers 1996-2001 Studies at the Department of Biology, ETH Zurich, Switzerland; major subject Neurosciences Diploma Thesis: Influence of music on physiological, psychological and biochemical parameters in humans 1989-1996 Secondary school, SAMD Davos, Switzerland 1982-1989 Elementary school, Davos, Switzerland 194 PUBLICATIONS AND PRESENTATIONS Publications Peer-reviewed Journals Hartmann M, Enkerli J, Widmer F (2007). Residual polymerase activity induced bias in terminal restriction fragment length polymorphism analysis. Environmental Microbiology 9: 555-559. Hartmann M, Widmer F (2006). Community structure analyses are more sensitive to differences in soil bacterial communities than anonymous diversity indices. Applied and Environmental Microbiology 72: 7804-7812. Hartmann M, Fliessbach A, Oberholzer H-R, Widmer F (2006). Ranking the magnitude of crop and farming system effects on soil microbial biomass and genetic structure of bacterial communities. FEMS Microbiology Ecology 57: 378- 388. Hartmann M, Frey B, Kölliker R, Widmer F (2005). Semi-automated genetic analyses of soil microbial communities: comparison of T-RFLP and RISA based on descriptive and discriminative statistical approaches. Journal of Microbiological Methods 61:349-360. Lazzaro A, Hartmann M, Blaser P, Widmer F, Schulin R, Frey B (2006). Bacterial community structure and activity in different Cd-treated forest soils. FEMS Microbiology Ecology 58: 278-292. Widmer F, Rasche F, Hartmann M, Fliessbach A (2006). Community structures and substrate utilization of bacteria in soils from organic and conventional farming systems of the DOK long-term field experiment. Applied Soil Ecology 33: 294-307. Widmer F, Hartmann M, Frey B, Kölliker R (2006). A novel strategy to extract specific phylogenetic sequence information from community T-RFLP. Journal of Microbiological Methods 66: 512-520. Other Publications Hartmann M, Fliessbach A, Kölliker R, Enkerli J, Dubois D, Widmer F (2006). Anbausysteme beeinflussen die Bodenbakterien. Agrarforschung 13: 494-499. Oberholzer H-R, Wysser M, Hartmann M, Widmer F (2006). Regeneration of impaired biological soil properties caused by physical soil impact. In: Chapter IV: Soil Biological Quality and Health (Horn R, Fleige H, Peth S, Peng X, Eds.), pp. 140-148. Catena Verlag, Reiskirchen, Germany. Hartmann M, Widmer F (2005). Der Boden lebt! - Der Vielfalt von Mikroorganismen auf der Spur. Hotspot 11:18. 195 PUBLICATIONS AND PRESENTATIONS Published Abstracts and Presentations Oral presentations Hartmann M, Widmer F (2006). Is soil bacterial diversity reflecting changes in agricultural productivity. Second Swiss Microbial Ecology Meeting, September 28- 29, Bellinzona, Switzerland. Hartmann M, Kölliker, R, Widmer F (2006). Populationsstrukturen von Boden- Bakterien in verschiedenen Anbausystemen. Tagung der Schweizerischen Gesellschaft für Pflanzenbauwissenschaften (SGPW), March 24, Bern, Switzerland. Hartmann M, Widmer F (2004). Soil bacterial community structures in the DOK agricultural experiment. 1st Annual Symposium of the PhD Program in Agroecology (ASPPA), November 25, Zurich, Switzerland. Hartmann M, Widmer F (2004). Comparison of soil bacterial community structures in different agricultural management systems as determined with T-RFLP and RISA. First Swiss Microbial Ecology Meeting, September 23-24, Neuenburg, Switzerland. Hartmann M, Frey B, Kölliker R, Widmer F (2004). Semi-automated genetic analyses of soil microbial communities: a comparison of T-RFLP and RISA. Annual Meeting of the VAAM. March 28-31, Braunschweig, Germany. Poster presentations Hartmann M, Widmer F (2006). Is soil bacterial diversity reflecting changes in agricultural productivity? 11th International Symposium on Microbial Ecology (ISME 11), August 20-25, Vienna, Austria. Hartmann M, Frossard E, Widmer F (2005). Fertilization reduces soil microbial diversity in agricultural systems. 2nd Annual Symposium of the PhD Program in Sustainable Agriculture (ASPSA), November 4, Zurich, Switzerland. Hartmann M, Kölliker R, Frey B, Widmer F (2005). Identification of treatment-specific soil microbial taxa using a novel strategy to extract phylogenetic sequence information from community T-RFLP. BAGECO 8, June 26-29, Lyon, France. Hartmann M, Widmer F (2004). Comparison of effects of different crops and different agricultural management practices on soil microbial community structures. ZOEK PhD conference, October 15-16, Davos, Switzerland. Hartmann M, Widmer F (2004). Comparison of effects of different crops and different agricultural management practices on soil microbial community structures. Rhizosphere Conference, September 12-17, Munich, Germany. Hartmann M, Kölliker R, Widmer F (2004). Comparison of microbial community structures in soils under different agricultural management as determined by T- RFLP and RISA. 10th International Symposium on Microbial Ecology (ISME 10). August 22-27, Cancun, Mexico. Hartmann M, Frey B, Kölliker R, Widmer F (2004). Semi-automated genetic analyses of soil microbial communities: a comparison of T-RFLP and RISA. Annual Assembly of the Swiss Society for Microbiology (SSM), March 11-12, Lugano, Switzerland. 196 ACKNOWLEDGMENTS Acknowledgments I am very grateful to Franco Widmer for patiently introducing me to the deepest secrets of molecular microbial ecology and for sharing his enthusiasm for this research topic. His perfect support and convincing ideas trained me to think I differently. appreciate all his effort in correcting and improving my manuscripts, talks and posters, and all the opportunities to attend international conferences. Thanks for all the personal discussions that made me feel like a friend. I would like to thank Prof. Emmanuel Frossard for taking the responsibility for this PhD thesis. He delved into this research topic although our research fields were often not close to each I very other. also want to thank the people from his group for their kindness at all the Eschikon meetings. I want to particularly acknowledge Prof. Alex Widmer and Prof. Jakob Pernthaler for assessing this thesis. I want to thank Roland Kölliker and Jürg Enkerli for very helpful suggestions and discussions, although my topic dealt with boring prokaryotes and not interesting eukaryotes. They both contributed substantially to this thesis. I would express my gratitude to all the present and former members of the Molecular Ecology group for the warm working atmosphere. I really enjoyed the time including all the debates about many "meaning of life"-questions. The groups surrounding Andreas Fliessbach (FiBL) and Hansruedi Oberholzer (ART) are acknowledged for substantial contributions to this project, particularly by providing results on biomass determinations. I want to thank all the people involved in creating and maintaining the valuable DOK field experiment. In a time where sustainability is one of the global key questions, agricultural long-term experiments are doubtlessly indispensable. I want to thank the Swiss National Science Foundation (SNF) for funding this interesting project and the Swiss Federal Office of Agriculture (FOAG) for support of the DOK experiment. And this out to dearest wife goes my Claudia and my beloved family. Without your endless this would not support have been possible. I know, with your trust in me, I will never walk alone. Without your love this would all be meaningless. I love you forever. 197