Molecular, functional and structural diversity of bacteria in South African Forest soils

(1)

Molecular, Functional and Structural Diversity

of Bacteria in South African Forest Soils

A.E. AMOO

orcid.org/

0000-0001-8060-5015

Thesis accepted in fulfilment of the requirements for the

degree

Doctor of Philosophy in Biology

at the North-West

University

Promoter:

Prof O. O. Babalola

Graduation: April 2019

Student number: 27374564

(2)

DECLARATION

I, the undersigned, Adenike Eunice AMOO, declare that this thesis submitted to the North-West University for the degree of Doctor of Philosophy in Biology in the Faculty of Natural and Agricultural Sciences, School of Environmental and Health Sciences, is my original work with the exception of the citations and that this work has not been submitted at any other University in part or entirety for the award of any degree.

Name: Adenike Eunice AMOO

Signature……… Date………

Supervisor: Prof. Olubukola Oluranti BABALOLA

Signature……… Date………

(3)

DEDICATION

(4)

ACKNOWLEGEMENTS

I am indeed grateful to my supervisor and a worthy role model, Prof. Olubukola Oluranti Babalola. The thorough guidance, constructive criticism and intellectual prowess provided by her made this work come to actualization. Thank you for all the motivation.

I would like to thank the North-West University for postgraduate bursary/scholarship award which made the pursuit of this degree hassle free. I am appreciative of Prof. Helen Drummond for her incessant assistance.

I am obliged to Mr. Philip Hongwane of the South African Forestry Company Limited (SAFCOL), for helping with sample collection.

I thank Dr. B.R. Aremu for helping process my admission and some helpful interactions during my research. Her help added colour to this work. I am indebted to Dr. C.F. Ajilogba, my R tutor; the knowledge gained through her has made this work truly interesting. I thank Dr. M.F. Adegboye for helping review part of my work. I am indeed grateful to Dr. O.S. Aremu and Mr. L. Sizwe of the Chemistry Department for provision of important chemicals during this research. I also thank Mr. Ayansina Ayangbenro for helping with the preparation of some of my chemicals. I am equally thankful to Dr. A.A. Adeniji for the encouragements and all helpful discussions. I also thank Mrs F. Chukwuneme, Dr. B. Ojuederie, Dr. M. Uzoh, Mr. N. Igiehon, Mr. B. Enagbonma, Ms M. Khansti and all members of the Microbial Biotechnology Research Group for making the laboratory conducive during the period of my research.

I am grateful to Prof. P.O. Olutiola, Dr. A.A. Owoseni and Dr. O.E. Atobatele for their kind support in facilitating my coming to South Africa for this degree. I also acknowledge all the members of staff of Biological Sciences Department, Bowen University, Iwo, Nigeria.

(5)

keeping me on my toes and teaching me proper time management. To my brother, Captain T.A. Adeyemo, thank you for the support and listening ears all the way. I am grateful to my parents Sir and Dns. E.A. Adeyemo. Thank you for all the support, prayers and solid foundation. May you reap the fruits of your labour over us. I am grateful to my brother in-law Dr. L. Amoo; your support all the way meant a lot, Sir. To my parents’ in-law Dr. and Mrs. A.A. Amoo, thank you for all the prayers and support. I am grateful to Dr. and Dr. O.S. Aremu; you made South Africa a home not far away from home.

(6)

TABLE OF CONTENTS

DECLARATION ... ii

DEDICATION ... iii

ACKNOWLEGEMENTS ... iv

LIST OF TABLES ...ix

LIST OF FIGURES ...xi

GENERAL ABSTRACT ... xiii

DISSEMINATION OF RESEARCH RESULTS AND LIST OF PUBLICATIONS ... xiv

LIST OF ABBREVIATIONS ... xvi

CHAPTER ONE ... 1

General Introduction ... 1

1.1 Introduction ... 1

CHAPTER TWO... 4

Soil Bacterial Diversity Patterns and Ecosystem Functioning ... 4

Abstract ... 4

2.1 Soil bacterial diversity and ecosystem functioning... 4

2.2 Spatial and temporal patterns of soil microbial diversity ... 6

2.2.1 Spatial heterogeneity of soil microbial diversity... 13

2.2.1.1 Salinity ... 13

2.2.1.2 Soil pH ... 14

2.2.1.3 Climate change ... 14

2.2.2 Temporal heterogeneity of soil microbial diversity ... 15

2.2.2.1 Ecosystem succession ... 15

2.3 Interactions between soil bacterial diversity and ecosystem functioning... 16

2.4 The implications of soil spatial and temporal heterogeneity on bacterial diversity ... 22

2.5 Concluding remarks ... 23

CHAPTER THREE ... 24

Impact of Land-Use Change on Soil Bacterial Communities in Temperate Pine and Indigenous Forests... 24

Abstract ... 24

3.2 Experimental procedures ... 26

3.2.1 Site description and soil sampling ... 26

(7)

3.3 Results ... 29

3.3.1 Effects of differences in land use on soil properties... 29

3.3.2 Composition, diversity and abundance of bacterial communities ... 29

3.3.3 Influence of environmental factors on bacterial communities ... 33

3.4 Discussion ... 36

3.5 Conclusion ... 38

CHAPTER FOUR ... 39

Functional Diversity of Soil Microbial Communities in Temperate Pine and Indigenous Forests ... 39

Abstract ... 39

4.2 Materials and methods ... 41

4.2.1 Study sites ... 41

4.2.2 Soil sampling ... 41

4.2.3 Soil properties analyses ... 42

4.2.4 Multiple substrate-induced respiration and functional diversity ... 42

4.2.5 Statistical analyses ... 43

4.3 Results ... 43

4.3.1 Effects of plantation on soil properties ... 43

4.3.2 Functional diversity and community level physiological profiles ... 44

4.4.1 Effect of land-use type on soil physical and chemical properties ... 51

4.4.2 Microbial properties and ecosystem functioning ... 51

CHAPTER FIVE ... 55

Impact of Land Use on Soil Bacterial Diversity and Consequences for Soil Processes ... 55

Abstract ... 55

5.1. Introduction ... 55

5.2.1 Site description and soil sampling ... 57

5.2.2 Soil properties analyses ... 57

5.2.3 Bacterial 16S rDNA Sequencing and Analysis ... 58

5.2.4 Community respiration and substrate-induced respiration ... 59

5.2.5 Statistical methods ... 60

5.3 Results ... 60

(8)

5.3.3 Composition, diversity and abundance of bacterial communities ... 61

5.3.4 Functional diversity and community level physiological profiles ... 62

5.3.5 Relationship between bacterial communities and soil functions... 62

5.4.1 Effect of land-use type on soil bacterial community ... 73

5.4.2 Drivers of soil functioning in the studied forests ... 73

CHAPTER SIX ... 76

Spatio-temporal Heterogeneity of Soil Bacterial Communities in Temperate Coniferous and Deciduous Forests ... 76

Abstract ... 76

6.2.1 Site description and sample collection ... 78

6.2.2 Soil physicochemical characterization ... 79

6.2.3 Molecular analyses ... 79

6.2.4 Community respiration and substrate-induced respiration ... 80

6.2.5 Data analysis ... 81

6.3 Results ... 82

6.3.1 Bacterial community characterization assessment ... 82

6.3.2 Spatial and temporal turnover: β-diversity in space vs. time ... 82

6.3.3 Soil analyses ... 83

6.3.4 Soil functions ... 83

CHAPTER SEVEN ... 91

Summary and Conclusion ... 91

7.1 Concluding Remarks ... 91

REFERENCES... 93

(9)

NOTE: Each chapter represents an individual entity intended for publication in separate

journals therefore repetition between each chapter is unavoidable

LIST OF TABLES

Table 2.1: Seasonal and geographic distribution patterns of soil microbes in different soil

types………. 6

Table 2.2: Microbial diversity and implications for ecosystem functioning in different soil

types………. 10

Table 2.3: Functional variations of soil bacteria over space and time………. 17

Table 3.1: Mean values of particle size distribution of the forest soils………. 30

Table 3.2: Mean values of the physical and chemical properties of the forest soil samples... 31

Table 3.3: OTU number, Chao1 and ACE indices………. 35

Table 4.1. Mean values of soil organic carbon, nitrate concentrations (NO3-), total carbon, total

nitrogen, pH, phosphorus, calcium, magnesium, potassium and sodium contents across the different forests plantations……… 44

Table 4.2: Analysis of similarities (ANOSIM) for the community-level physiological profiles

(CLPPs) of the sites………. 45

Table 4.3: Similarity percentages (SIMPER) analysis results of the community-level

physiological profiles (CLPPs) of the sites………. 47

(10)

Table 5.2: Analysis of similarities (ANOSIM) for the community-level physiological profiles

(CLPPs) of the sites……… 69

Table 5.3: Similarity percentages (SIMPER) analysis results of the community-level

physiological profiles (CLPPs) of the sites………. 70

Table 6.1 Paired comparisons between sampling periods for phylogenetic diversity (PD)… 82

Table 6.2 Paired comparisons between sampling periods for α-diversity (SR)………… 83

Table 6.3 Paired comparisons between sampling periods for Shannon index (H’) ……. 83

Table 6.4 Seasonal dynamics of soil chemical composition in temperate coniferous and

deciduous forests………. 84

Table 6.5 Correlations among soil physicochemical parameters of samples collected from

temperate coniferous and deciduous forests………. 85

Table 6.6 Influences of season and site on bacterial community computed on both relative

(11)

LIST OF FIGURES

Figure 2.1: Forest plot showing the effect of soil bacterial loss on carbon cycling produced by

meta-analysis………. 16

Figure 3.1: Distribution of the sampling sites in Tweefontein and Witklip forests………. 26

Figure 3.2: Taxonomic classification of the bacterial reads at phylum level……… 32

Figure 3.3: Principal coordinates analysis (PCoA) plot of bacterial community composition at

OTU level……… 33

Figure 3.4: Canonical correspondence analysis (CCA) of relative abundances at the phylum

level and major physicochemical parameters in the soil samples………. 34

Figure 3.5: Effect of land-use type on OTU richness………. 35

Figure 4.1: Effects of land-use change on the mean soil respiration rates. Error bars indicate

standard error………. 46

Figure 4.2: Canonical Correspondence Analysis (CCA) ordination plot of the soil properties

and physiological abilities of the microbial communities. The carbon substrates are; CS1: L -alanine, CS2: Cellobiose, CS3: D-mannose, CS4: D-galactose, CS5: L-lysine monohydrochloride, CS6: D-glucose, CS7: D-maltose, CS8: D-fructose, CS9: malic acid and CS10: oxalic acid………... 48

level and major physicochemical parameters in the soil samples………. 63

Figure 5.2: Principal coordinates analyses (PCoA) of Bray-Curtis distances of bacterial

community composition……… 64

(12)

Figure 5.4: Effects of land-use change on OTU richness (A), Simpson’s index of diversity (B)

and Buzas and Gibson's index of evenness (C)………. 66

Figure 5.5: Effects of land-use type on the mean soil respiration rates. Error bars indicate

standard error………. 67

Figure 5.6: Canonical Correspondence Analysis (CCA) ordination plot of the soil bacterial

communities and soil respiration. The carbon substrates are; CS1: L-alanine, CS2: Cellobiose, CS3: D-mannose, CS4: D-galactose, CS5: L-lysine monohydrochloride, CS6: D-glucose, CS7: D-maltose, CS8: D-fructose, CS9: malic acid and CS10: oxalic acid………. 68

Figure 6.1: Rarefaction curves indicative of the correlation between the number of sequences

and the number of operational taxonomic units (OTUs), assigned at 97% sequence similarity. Time 1 (winter), green; Time 2 (summer), red……… 82

Figure 6.2: Effects of seasonality and site on the mean soil respiration rates. Error bars indicate

(13)

GENERAL ABSTRACT

Forest soils store substantial quantities of carbon and mediate vital stages of the global carbon cycle. Their capability to act as carbon sinks makes them important terrestrial biomes. Soil microbes, specifically bacteria and fungi, thrive well in these soils because of the accumulation of carbon. Soil biodiversity advances ecosystem functioning thus delivering numerous ecosystem services. A good perception of the relationship between biodiversity and ecosystem functioning and their response to environmental variation can maximize the contribution of soil microbes to ecosystem services. The impact of land use in temperate forest ecosystems on the composition of soil bacterial communities and the consequences on soil functioning was examined using high-throughput sequencing. Soil samples were collected from two sites namely the Tweefontein plantation and the Witklip plantation. Tweefontein is in Graskop while Witklip is sited at Witrivier. The Tweefontein forest plantation is made up of the Tweefontein commercial forest (TC) and the Tweefontein indigenous forest (TI) while the Witklip forest plantation consists of the Witklip commercial forest (WC) and the Witklip indigenous forest (WI). The dominating tree species in the two commercial forests is Pinus patula while Acacia

xanthophloea and Celtis africana dominate the indigenous forests. Using the MicroResp™

method, we used multiple substrate-induced respiration and community-level physiological profiles to investigate the microbial activity and functional diversity of the microbial communities in these forest soils. The consequence of seasonality and spatial heterogeneity on soil bacterial community composition and functioning was also investigated. The different land use types had effect on bacterial communities and subsequently soil functioning. Spatial heterogeneity in the distribution of the microbial communities and significant relationships between the microbes and soil characteristics were observed. Moisture was seen to have notable impact on the bacterial community composition in the various land use types. Community composition and functional diversity exhibited high spatio-temporal variability.

(14)

Spatial horizontal patterns at the local scale which shape microbial community structure and functioning were observed across sites. Notwithstanding the spatial differences, dominating tree species in each forest had important influences on the activity, structure and function of soil microbial communities. A link exists in the reduction of soil bacterial diversity and a declination in ecosystem functioning. An understanding of this can improve ecosystem services thus enhancing environmental sustainability and food security.

DISSEMINATION OF RESEARCH RESULTS AND LIST OF PUBLICATIONS

A. Presentation (Conference proceeding) at the 10th Symposium of the International Society of Root Research (ISRR10), Ma'ale HaHamisha, Israel, 8-12 July 2018. B. Presentation (Conference proceeding) at the European Geosciences Union General

Assembly, Vienna, Austria, 23–28 April 2017.

Chapter 2: Soil Bacterial Diversity Patterns and Ecosystem Functioning.

This chapter has been formatted for publication in Soil Biology and Biochemistry.

Authors: Adenike Eunice Amoo, Bernard R. Glick and Olubukola Oluranti Babalola.

Candidate’s contributions: managed the literature searches, analysed data and wrote the first draft of the manuscript.

Chapter 3: Bacterial Diversity and Community Structure in Temperate Pine and Indigenous

Forest Soils.

This chapter has been formatted for publication in Applied Soil Ecology.

Authors: Adenike Eunice Amoo and Olubukola Oluranti Babalola.

Candidate’s contributions: performed the research, analysed data, contributed new methods/models and wrote the first draft of the manuscript.

Chapter 4: Functional Diversity of Soil Microbial Communities in Temperate Pine and

Indigenous Forests.

This chapter is undergoing the second review process in Microbial Ecology.

(15)

Chapter 6: Spatio-temporal Heterogeneity of Soil Bacterial Communities in Temperate

Coniferous and Deciduous Forests.

This chapter is under consideration for publication in The ISME Journal.

(16)

LIST OF ABBREVIATIONS

βsne………. Speciesnestedness

βsim………Species spatial turnover

βspace………Spatial variability

βtime………. Temporal variability

ACE………. Abundance-based coverage estimator ANOSIM Analysis of similarities

CCA Canonical Correspondence Analysis CLPPs………. Community-level physiological profiles H’………...Shannon index

MSIR………Multiple substrate-induced respiration OTUs………...Operational Taxonomic Units PCoA………Principal coordinates analysis PD………Faith’s phylogenetic diversity PerMANOVA………Permutational Multivariate Analysis of Variance SIMPER………. Similarity percentages SR………. Species richness TC……… Tweefontein commercial forest TI………. Tweefontein indigenous forest USA……… United States of America WC………. Witklip commercial forest WI………. Witklip indigenous forest

(17)

CHAPTER ONE

General Introduction 1.1 Introduction

Soil communities are an essential part of ecosystems that possess the capability to improve ecosystem services. The various goods and services delivered by ecosystems contribute unequivocally to the wellbeing of mankind (Crossman et al., 2013). Ecosystem services are divided into four main groups namely supporting, regulating, provisioning and cultural services. About one billion bacterial cells belonging to several taxa, 230 million fungi, protists, mites, nematodes, enchytraeids and earthworms can be found in 1 g of soil (Bardgett and van der Putten, 2014; Bender et al., 2016). Diverse microorganisms are associated with soils thriving below the ground in the rhizosphere and above in the phyllosphere. Soil microorganisms are the greatest contributors to the diversity of terrestrial ecosystems and are the major controllers of almost all global biogeochemical cycles. They are essential in the maintenance of plant health through their nutrient cycling roles and relationships with other organisms. Plant diversity are part of the above-ground diversity which are influenced by soil biodiversity. The contributions of soil microbes to the cycling of nutrients influences several ecological properties of forests including the growth of trees, productivity, soil carbon and emission of greenhouse gases (Knief, 2014; Amoo and Babalola, 2017). The central significance of soils in the delivery of ecosystem services like the mitigation of climate and food production depends on the cycling of nutrients and carbon sequestration by soil microorganisms (de Vries et al., 2013).

Land use which is described as the series of operations implemented with the intention of procuring goods and services is controlled by the biophysical limits and potentials enforced by the ecosystems in which they occur. In South Africa, 79.4% is agricultural land, forests cover

(18)

Forest soils stock significant quantities of carbon and mediate crucial phases of the global carbon cycle. Their ability to act as carbon sinks makes them significant terrestrial biomes. Soil microbes, especially bacteria and fungi thrive well in these soils because of the accrual of carbon. These organisms facilitate majority of biogeochemical processes in the soil and eventually regulate the accessibility of nutrients and the fate of carbon in forest soils. This makes the activities of microbes in forest soils extremely significant and ranks them into the centre of contemporary ecological research (Baldrian, 2017a; Baldrian, 2017b).

Microbial processes in forest soils are regulated by the dynamics of environmental variables. Soil bacterial community composition and functional diversity are linked to the resource patterns of soils and changes in nutrients associated with seasonality (Colombo et al., 2016; Buscardo et al., 2018). The composition of microbial communities exhibits spatial and temporal heterogeneity because of variations in soil chemistry and seasonality respectively. Spatial horizontal patterns have been noticed at local scales by various studies (Buscardo et al., 2018; el Zahar Haichar et al., 2008; Hättenschwiler et al., 2011).

Although several studies have investigated biodiversity in forest soils bacterial communities, the understanding of the dynamics of forest soil microorganisms is still limited. Knowledge of the association between belowground diversity and the functioning of the ecosystem is incomplete. The impact of land-use change and land management on soil microbial diversity and ecosystem functioning is an area of research that also needs more exploration.

The specific objectives of this study were to:

1. Investigate the physicochemical properties of soils in different land-use types and how this affects belowground diversity and ecosystem functioning.

(19)

3. Measure the functional diversity of soil bacterial communities by estimating substrate-induced respiration.

4. Determine the influence of seasonal and site related variations on the composition of soil bacterial communities and consequently their functioning.

(20)

CHAPTER TWO

Soil Bacterial Diversity Patterns and Ecosystem Functioning Abstract

Soil biodiversity promotes the functioning of the ecosystem thereby contributing to the provision of various ecosystem services. Understanding the link between biodiversity and ecosystem functioning and their reaction to environmental heterogeneity can maximize the contribution of soil microbes to ecosystem services. Here, the general relationships between soil biodiversity and ecosystem functioning are discussed, in particular the spatial and temporal patterns of biodiversity and how these factors affect the relationship between biodiversity and ecosystem functioning. The implications of this heterogeneity have been compiled and a meta -analysis to evaluate the impact of loss of soil bacterial diversity on ecosystem functioning was conducted. The findings of this analysis support studies that link a loss in soil bacterial diversity to a declination in the functioning of the ecosystem. We expect an understanding of this to improve key ecosystem services thereby improving environmental sustainability and food security.

Keywords: Environmental, microbial diversity, soil structure, spatial heterogeneity, temporal

heterogeneity

2.1 Soil bacterial diversity and ecosystem functioning

Soil communities are exceptionally complicated and distinct; they constitute one of the most biologically diverse ecosystems on Earth. It has been estimated that a gram of soil contains millions of microorganisms (Bardgett and van der Putten, 2014; Babalola et al., 2009). Biodiversity has great significance in the conservation of balanced and productive ecosystems. In fact, it has been suggested that biodiversity and ecosystem services are inseparably linked.

(21)

endangered by depletion of biodiversity. The functional stability of the soil ecosystem is controlled by microbial biodiversity (de Groot et al., 2014; Tardy et al., 2014); losses in microbial diversity tend to diminish the measure of multiple ecosystem functions (Delgado-Baquerizo et al., 2016).

The consideration of multiple ecosystem functions reveals the importance of the impact of biodiversity on ecosystem functioning. When studying the soil ecosystem, concentrating on specific functions or taxonomic groups typically results in an underestimation of the role of biodiversity in the functioning of the ecosystem. The interaction amidst functions is ignored when the contribution of biodiversity is deduced from single positive ecosystem functions. Biodiversity has more distinctive influences with the examination of more functions because functions are correlated with each other. Considering multiple ecosystem functions together produces possibly stronger impact of diversity. Also when multiple functions are examined, the complementary nature of the functioning of species is observed (Lefcheck et al., 2015; Byrnes et al., 2014). Biodiversity can influence many ecosystem functions at the same time. This concept is known as ecosystem multifunctionality, which is boosted by soil microbial diversity. Augmenting multifunctionality analyses with the influence of species richness on specific functions provides a good comprehension of the mechanism of multifunctionality (Byrnes et al., 2014; Wagg et al., 2014).

Structural and chemical heterogeneity in the soil influence microbial diversity patterns and cause extreme alterations to the functioning of the ecosystem. The cognizance of spatial and temporal variations makes it easy to have an idea of the pattern of response of microorganisms to environmental changes. Over temporal and spatial scales, biodiversity may be extremely significant in the maintenance of the resilience and stability of ecosystems. A good understanding of the factors controlling microbial diversity in the soil ecosystem is therefore

(22)

needed to make a practical understanding available to decision-makers (Raffaelli and White, 2013; Vos et al., 2013; Lohbeck et al., 2016).

Here, we describe diversity patterns due to structural and chemical heterogeneity in soil and examine how this affects ecosystem functioning. First, the relationship between soil biodiversity and ecosystem functioning is highlighted. Then the effects and implications of spatial and temporal factors on this relationship are described.

2.2 Spatial and temporal patterns of soil microbial diversity

The structure and activity of soil biodiversity are dependent on the consistency of biotic and abiotic components of the soil. Alterations to this order can trigger changes in soil biodiversity (Brevik et al., 2015). Spatial and temporal disparities in soil abiotic characteristics and climatic conditions cause geographic variances in the correlation between biodiversity and ecosystem functioning (Jing et al., 2015). Table 2.1 shows the patterns of distribution of soil microbes across different soil types. It is believed that the spatial and temporal heterogeneity of the soil ecosystem is pivotal for preserving biodiversity. Variances in the distribution and activities of soil microbes can also be used in the determination of soil health (Martirosyan et al., 2013; Chen et al., 2016b). In Table 2.2, the consequences of biodiversity for ecosystem functioning in different soil types are addressed.

(23)

Table 2.1: Seasonal and geographic distribution patterns of soil microbes in different soil types

Soil order Soil type Soil

pH

Vegetation type Associated microorganisms Season Climate type Reference(s)

Anhyorthels and anhyturbels Open mineral soils NA NA Pseudonocardia sp., Nocardioides sp., Geodermatophilus obscurus, Franki alni, Sporichthya polymorpha, Modestobacter versicolor, Streptomyces tubercidicus

Summer Ice cap

climate (Van Horn et al., 2013; Babalola et al., 2009) Entisols Alluvial 6.9 – 8.0 Salicornia fruticosa, Suaeda fruticosa, Juncus subulatus, Juncus bufonius, Phragmites australis, Aster squamatus, Polypogon Proteobacteria, Actinobacteria, Acidobacteria, Verrucomicrobia, Firmicutes, Bacteroidetes, Summer Semiarid Mediterranean (Canfora et al., 2014)

(24)

monspeliensis, Hainardia cylindrica

Chloroflexi, Chlorobi, Gemmatomonadates

Inceptisols Frigid Aeric

Endoaquepts

4.1 - 5.5

Mosses, grasses, rushes,

Phyllodoce empetriformis, Micranthes tolmiei, Luetkea pectinata, Anaphalis margaritacea, Tsuga mertensiana, Lupinus latifolius Betaproteobacteria, Acidobacteria, Bacteroidetes, Verrucomicrobia Summer Humid subtropical (Castle et al., 2016; Lab, 2017; Alexander et al., 1993) Spodosols Humicryods 8.1 - 8.4 Mosses, grasses, Epilobium spp., Populus spp.,

Salix spp, Picea sitchensis

Acidobacteria Summer Perhumid maritime climate

Cambisols Calcocambisol 6.28

–

Natural lawns Azotobacter sp.,

Ammonifiers, Free-living Spring Oceanic climate (Hamidovic et al., 2013) Summer

(25)

Winter

Luvisols Terra rossa 5.51

– 6.73 Spring Summer Autumn Winter

Luvisols Ferric luvisol 5.7 Cauliflower, spinach, bush

bean, cabbage, maize,

onion, watermelon, potato, turnip Acinetobacter baumannii, Aeromonas hydrophila, Agrobacterium radiobacter, Bacillus cereus, Burkholderia cepacia, Escherichia vulneris, Ewingella americana, Proteus penneri, Pseudomonas fluorescence NA Semi-arid savanna climate (Babalola and Akindolire, 2011; Maraka, 1987) Cryosols Leptic cryosols 4.60 - 5.60

Salix polaris, Luzula arcuata subsp. confusa,

Alphaproteobacteria, Cyanobacteria,

Summer High arctic (Cooper et al.,

(26)

Alopecurus magellanicus, Dryas octopetala, Bistorta vivipara Proteobacteria, Actinobacteria Schostag et al., 2015; Halbach, 2016) Proteobacteria, Actinobacteria, Acidobacteria Winter Cambisols Mountain yellow brown soil

5.07 Abies fargesii, Syringa

reflexa Acidobacteria, Actinobacteria, Armatimonadetes, Bacteroidetes, Chlamydiae, Chloroflexi, Crenarchaeota, Firmicutes, Gemmatimonadetes, Planctomycetes, Proteobacteria, Verrucomicrobia Summer Northern subtropical monsoon zone (Cong et al., 2015; Zhang et al., 2014a) Cambisols Mountain yellow brown soil 5.36 Acer maximowiczii, Schisandra incarnate Cambisols Mountain yellow brown soil 5.27 Carpinus viminea, Viburnum erosum

(27)

Table 2.2: Microbial diversity and implications for ecosystem functioning in different soil types

Soil type Microbial diversity Effect(s) on ecosystem functioning Reference(s)

Hypersalic Fluvisol

Archaeal and bacterial communities

The richness and diversity of archaea significantly improved with the increasing gradient of soil salinity while the bacterial community demonstrated a declining trend

(Canfora et al., 2015)

Lithosol Bacterial and fungal

communities

The diversity and composition of soil microbial communities were controlled by the rotation phase of cereal-fallow

(Castro et al., 2016)

Local clay soil Arbuscular mycorrhizal

fungi

Alteration of the effects of competition in plants (Abbott et al., 2015;

Vogelsang and Bever,

2009) Mountain yellow brown soil Proteobacteria, Acidobacteria, Actinobacteria,

Environmental heterogeneity can cause changes in the composition and functions of microbial communities which are associated with forests succession

(Zhang et al., 2014b; Cong et al., 2015)

(28)

Planctomycetes and

Verrucomicrobia

Brown pedocals, castanozems, chernozems, cold calcic soils, dark felty soils, felty soils, frigid

calcic soils, frigid frozen soils, grey–brown desert soils, grey-cinnamon soils and Actinomycetales, Rhizobiales, Bacillales, Rhodospirillales, Nitrososphaerales, Halobacteriales, Diversisporales

The influence of biodiversity on ecosystem

multifunctionality can be controlled by climate change

(29)

2.2.1 Spatial heterogeneity of soil microbial diversity

The manipulation of land for different purposes often leads to changes in soil biodiversity. Land management and use influence the structural and chemical complexities in soils and thereby influence the dispersal of soil microbes (Abegaz et al., 2016; Mendes et al., 2015). The distribution of microorganisms in the soil appears to be more or less random because of soil abiotic factors that exhibit inherent spatial variability. The state of localized environments determines the relationship between biodiversity and ecosystem functioning. This relationship is essential in the evaluation of the functioning of soil ecosystems on regional scales (Grossiord et al., 2014; Chen et al., 2016b). Abiotic components of the soil elicit responses from microorganisms which in turn affect their functions (Stone et al., 2014). Spatial heterogeneity occurs in the chemical properties of soil thereby controlling how the bacterial communities are structured spatially. Spatial heterogeneity is a fundamental characteristic of soils and has notable influences on ecosystem functioning and soil fertility (Bardgett and van der Putten, 2014; Pajares et al., 2016). The quantity and functioning of microorganisms in the soil thus show variations along environmental gradients (Martirosyan et al., 2013).

The impacts of several important environmental gradients are addressed in the sections below.

2.2.1.1 Salinity

In saline soils, excessive concentrations of salts exist, and the distribution of water is not uniform. Salinity has a great influence on the biodiversity and structure of soil ecosystems. Microorganisms in such severe environments have some approaches in common and acclimatize in numerous ways to preserve their population. Halotolerant and halophilic microorganisms are often found in such environments. Highly saline soils have been shown to be disadvantageous to the activities of soil microorganisms. Salinity causes spatial heterogeneity in the activities of soil microbes (Canfora et al., 2015; Chen et al., 2016b).

(30)

The spatial variability of soil can be more advantageous to certain groups of microorganisms than others. This affects ecosystem functioning in different ways. The rate of soil respiration decreases with increasing salinity. Thus, the heterogeneity of archaea in saline soils is greater than that of bacterial communities. Overall, the spatial variability of soil has been suggested to be related to the spatial heterogeneity of the microorganisms (Canfora et al., 2015; Asghar et al., 2012).

2.2.1.2 Soil pH

The structure and function of soil microbial communities is driven by soil pH. Soi l microbes react differently to changes in soil pH (Alori et al., 2017). Bacteria thrive well under neutral or moderately alkaline environments; pH has no notable effect on the activities of fungi while the relationship between pH and archaeal abundance is unclear. The effects of soil pH on biodiversity patterns and activities of microbes has been shown to be much greater than the effects of land use on biodiversity and microbe activity (Gleeson et al., 2016; Tripathi et al., 2013).

2.2.1.3 Climate change

Differences in climate on geographical levels as well as climatic alterations have influences on the effects of soil biodiversity and the functioning of the ecosystem. In this regard, climatic changes introduce species and cause species to be extinct from ecosystems (Jing et al., 2015; Bowker et al., 2013).

Considering the “stress-gradient” hypothesis (Bertness and Callaway, 1994), biodiversity is believed to thrive optimally under conditions of finite resources and is expected to decline in environments with infinite resources. Trends of climatic change have been forecast for different zones around the world. In fact, severe environmental conditions are anticipated in most regions of the world. These changes could lead to a decrease in water availability in the soil

(31)

ecosystem thereby influencing the relationship between biodiversity and ecosystem functioning (Grossiord et al., 2014; He et al., 2013).

2.2.2 Temporal heterogeneity of soil microbial diversity

Soil microbial communities may also change following seasonal variations. Temporal variations have been shown to modify the composition of soil communities and bacterial diversity. Thus, different phyla or subphyla often react differently to these temporal changes (Pasternak et al., 2013a).

2.2.2.1 Ecosystem succession

The activities of soil microbes control the evolution of soil which occurs over time. Microbial communities are gradually replaced because of significant disruptions to the ecosystem that alters ecosystem development. Soil microbial communities can undergo primary or secondary succession. Connections exist between the dissimilarities in the structure of microbial communities and variations in the relative abundance of distinct bacterial phyla. Differences in community composition through various phases of succession have been linked to changes in the relative abundance of certain bacterial phyla (Castle et al., 2016; Lopez-Lozano et al., 2013).

Succession causes changes in the properties of the soil ecosystem such as soil pH, total nitrogen, total phosphorus and organic carbon. These factors shape microbial communities through succession consequently affecting their functioning. Shifts in soil carbon, for instance, is caused by plant communities and this favours heterogeneous microbial communities (Castle et al., 2016; Lopez-Lozano et al., 2013). The structure of soil microbial communities is driven by the concatenation of the activities of enzymes involved in nutrient cycling. In addition, the accessibility of substrates influences microbial community successions (Ditterich et al., 2016; Turner et al., 2014; Kodisang et al., 2013).

(32)

2.3 Interactions between soil bacterial diversity and ecosystem functioning

The knowledge of connections between ecosystem functions and composite soil microbial communities are still vague (Prosser et al., 2007; Martiny et al., 2015). Differences in the structure of microbial communities might have control over ecosystem function rates. Microbial community shifts which could result from competition for supplies in the soil may bring about alterations in the community’s functions like decomposition (Kaiser et al., 2014). Spatial and temporal variations in soil microbes are induced by numerous factors and these differences invariably affect the relationship between microbial communities and ecosystem functioning (Graham et al., 2016).

A meta-analysis was conducted to evaluate the effect of bacterial diversity on soil carbon cycling (Figure 2.1). Data on studies that explored the impacts of soil bacterial diversity on the carbon cycle were collected from published data. A search was conducted using the ISI Web

of Science [v.5.25.1] on the 25th_{October, 2017. Every imaginable combination of soil carbon}

search term (soil carbon, decomposition, respiration, etc), bacteria and *diversity were used revealing 102 different references. Studies included in these analyses presented data on the influence of bacterial diversity on soil carbon cycling. Review papers were excluded from our analyses. For the meta-analysis, 6 types of data were included. Data were extracted from tables and text where possible. In other cases, the data were extracted from graphs using GetData Graph Digitizer version 2.26.0.20. Results obtained from the analysis emphasize the importance of bacterial diversity in the functioning of the soil ecosystem.

Table 2.3 shows the interactions between soil biodiversity and function. The variations of these interactions over space and time are described with reference to soil carbon sequestration and nitrogen cycling.

(33)

(34)

Table 2.3: Functional variations of soil bacteria over space and time Ecosystem service Specific ecosystem function

Season Location Soil

pH

Bacterial diversity Interaction between bacterial

diversity and ecosystem

functioning Reference(s) Regulating services Carbon sequestration Summer USA 6.50 – 8.40 NA

The portion of microbial

biomass carbon in soil organic carbon increased as years progressed. The carbon dioxide flux obtained was mainly from respiration of microbes and roots. (Li et al., 2017) Winter USA 6.00 – 8.00 NA

Carbon storage capacity of the soils increased with increasing pH. The roles of microbes in breaking down and recreating

(Yao and

(35)

organic matter contributes to equilibrium in the ecosystem.

- China 4.60 – 6.00 Azospirillum lipoferum, Rhodopseudomonas palustris, Bradyrhizobium japonicum, Ralstonia eutropha

The activity of ribulose 1,5- bisphosphate

carboxylase/oxygenase

(RubisCO) and bacterial

abundance of RubisCO gene were positively correlated. This

significantly affected the

synthesis rate of soil organic carbon. (Yuan et al., 2012) Supporting services Nitrogen cycling Summer Australia 7.77 – 7.83 Pseudomonas putida, Pseudomonas aeruginosa, Bacillus cereus, Nitrosospira

There was reduced nitrogen cycling functional ability as the

soils became wetter. A

noticeable shift from organic nitrogen cycling to autotrophic

(Phillips et

(36)

multiformis,

Nitrobacter vulgaris

mineral nitrogen cycling

occurred. Rainy/dry season Burkina Faso 4.09 – 5.51 NA

The availability of water

influenced the communities of functional microbes found in the rhizosphere. When water

availability decreased, the

abundance of functional genes also decreased. (Hai et al., 2009) Summer Switzerland NA Paenibacillus azotofixans, Streptomyces coelicolor, Pseudomonas fluorescens,

Nitrogen mineralization was the key driver for nitrogen in primary soils. Older soils were typified by high abundance of nitrogen fixing microbes.

(Brankatschk et al., 2011)

(37)

irakense, Pseudomonas stutzeri, Pseudomonas fluorescens, Nitrosomonas sp. Summer China 7.30 – 7.88 Pseudomonas, Paracoccus, Sulfuritalea, Achromobacter, Bradyrhizobium, Nitrosospira, Nitrososphaera,

Increase in nitrogen cycling functional genes was observed with successional time.

(Zeng et al., 2016)

(38)

2.4 The implications of soil spatial and temporal heterogeneity on bacterial diversity

Spatial and temporal variations in climatic conditions and the accessibility of resources have large effects on soil biodiversity and consequently ecosystem functioning. Land-use changes also have significant effects on soil biodiversity. For example, some studies in the Amazon forest have shown a drop in the levels of alpha diversity with changes in land management (Mendes et al., 2015). The microbial communities in this forest displayed additional unevenness compared to other sites because of higher temporal beta diversity. Alpha diversity is also known as local diversity and it describes variation at the most minute scale of examination. A reduction in beta diversity has been suggested to signify biotic integration. Excessive beta diversity indicates the variability of a site over time compared to other sites. The way local communities in an area or a region vary in abundance or composition of species is described as beta diversity. Abundance instead of diversity of microbes in an ecosystem has been proposed to preserve functional equilibrium (Forrester, 2014; Mendes et al., 2015; Nemergut et al., 2013; Maaß et al., 2014).

The species of plants utilised in cropping also influences soil microbial community structure alongside land management. Species richness implicitly influences soil microbial communities. Spatial variation which is demonstrated by soil microbes modifies competition in plants. The interaction of soil microbes boosts the activities of plant species that are reactive to microbial mutualists. Other plants are, however, unaffected by the activities of such microbes in the soil. Since the response of plants to the activities of soil microbial mutualists differ, alpha diversity of the mutualists plays a key role in the determination of plant-plant interaction. These mutualists also benefit nutrients from the pla nts. When the most reactive plants stimulate the growth of mutualists, a beneficial feedback dynamic is produced. This consequently gives rise to other secure states. External disruptions also give rise to spatial

(39)

heterogeneity and this in turn diminishes alpha diversity of these soil microbial mutualists (Abbott et al., 2015; Babalola, 2014).

Soil microbial communities manifest exceptional variability across spatial scales. In this regard, unanticipated heterogeneity in the relative abundance of taxa is sometimes displayed between soil samples taken only a few centimetres apart. This heterogeneity could be due to parameters in the ecosystem that randomly display variation across spatial scales. Bacterial communities have been insinuated to be dependent on small-scale dispersal restriction. At scales pertinent to biological methods, diversity in microbial communities certainly drives the spatial structures in bacterial communities (O'Brien et al., 2016; Landesman et al., 2014). Soil factors which are variable control the community structure of microbes at large spatial scales while plant communities have effects on microbial diversity at smaller scales (Pajares et al., 2016).

2.5 Concluding remarks

Soil is a treasure which contributes directly and indirectly to almost all processes on Earth through the activities of the various organisms it harbours. Correct functioning of the ecosystem is aided by the preservation and strengthening of the soil ecosystem. Pivotal steps in crucial biogeochemical cycles are influenced by microorganisms. Biodiversity of the soil ecosystem structures how the ecosystem responds to changes in the environment. To describe how soil communities are structured, the spatial and temporal patterns of the soil biodiversity must be elaborated. Clearly, biodiversity controls multiple ecosystem processes (Bardgett and van der Putten, 2014).

Evaluation of spatial and temporal variations in the soil ecosystem provides an understanding of the factors controlling the distribution of microbes. The patterns of microbial community structure can be linked to variability in biogeochemical processes. Soil microbial diversity has explicit implications for soil fertility and various ecosystem processes.

(40)

CHAPTER THREE

Impact of Land-Use Change on Soil Bacterial Communities in Temperate Pine and Indigenous Forests

Abstract

Soil microbial communities are an important part of ecosystems that possess the capability to improve ecosystem services however, several aspects of the ecology of forest soils bacterial communities are still unknown. Here, we investigated the impact of land-use change on soil bacterial communities and the soil characteristics. High-throughput sequencing was used to ascertain the bacterial diversity and canonical correspondence analysis was used to determine relationships between the bacterial communities and environmental variables. Our results show spatial heterogeneity in the distribution of the microbial communities and significant relationships between the microbes and soil characteristics (axis 1 of the CCA plot explained 49.72% of the total variance while axis 2 described 83.98%). The bacterial communities of the indigenous forests were separated from the communities of the managed forests along the PCoA axis 1 which elucidated 43.62% of the absolute dissimilarity. Total carbon had a chief effect on the composition of the bacterial communities. Proteobacteria, Verrucomicrobia and Acidobacteria the three most abundant phyla were negatively correlated with Total C. A knowledge of this is essential as it has direct consequences for the functioning of the soil ecosystem.

3.1 Introduction

The soil possesses the vastest quantity of biodiversity on Earth and majority of terrestrial ecosystem functions take place in it. Forests are ecosystems that are extremely productive and display great spatial heterogeneity because of their multi-layered vegetation. Trees are the

(41)

biomes. Forests frequently act as carbon sinks and are thus principal terrestrial biomes. The composition of soil microbial communities plays pivotal roles in the productivity of forests and in soil functioning (Chodak et al., 2016; Baldrian et al., 2012; Baldrian, 2017a). Microbial activities, mainly bacteria and fungi, in forest soils are extremely important because they facilitate majority of the biogeochemical processes and eventually regulate the accessibility of mineral nutrients in the soils (Baldrian, 2017b).

Conventionally, the variation of tree types has been believed to influence belowground activities. The composition of bacterial communities has been shown to have slight association with plant species present in a locality (Barberán et al., 2015; Kivlin and Hawkes, 2016a). The land management practices in natural and managed forests are different. The modification of the physical, biological and chemical properties of soils by land management practices are significant and this can consequently have intense consequences on numerous ecosystem functions. In temperate forests, there is excessive spatial variability of microbial biomass and soil chemistry. Through soil chemistry and vegetation composition, the community composition of microorganisms in forest soils are determined (Purahong et al., 2014; Colombo et al., 2016; Baldrian, 2017a). Variations in environmental conditions cause changes in microbial communities which are obvious in the relative abundance of taxa in the community (Allison et al., 2013).

Soil microorganisms which encompass the main portion of the entire living soil biomass are the major drivers of biogeochemical cycles and control approximately 90% of soil activities (Fierer et al., 2012; Babalola and Glick, 2012; Alori et al., 2017). The role of soil microbes in ecosystem functioning can be affected by the same factors that affect their community composition (Kivlin and Hawkes, 2016a). An understanding of the response of soil microbes to land-use change would therefore enable predictions of their influence on the soil ecosystem functioning.

(42)

We investigated the influence of land-use change in forest ecosystems on bacterial diversity. We also elucidated the relationships between microbial communities and their environments. We hypothesized that the different vegetation types and the environmental conditions of the sampling sites would affect the diversity of the bacterial communities inhabiting the soils. We also proposed that the indigenous forests which are natural would have more diverse bacterial communities.

3.2 Experimental procedures

3.2.1 Site description and soil sampling

Soil samples were collected from four sites: the Tweefontein indigenous forest (TI), the adjacent Tweefontein commercial forest (TC), the Witklip indigenous forest (WI) and the adjacent Witklip commercial forest (WC) (Figure 3.1). Tweefontein plantation is situated in Graskop and is typified by biodiversity. Lush grasslands on the pinnacle of the western mountain range to extensive areas of indigenous forest comprising the kranses fashioned by quartzite bands. Diverse and spectacular waterfalls also characterize this plantation. Sampling was done in the commercial and indigenous forest plantations which are illustrative of temperate climatic conditions. The dominating tree species in the commercial plantation is the

Pinus patula (which are coniferous trees) while Acacia xanthophloea and Celtis africana

(deciduous trees) dominate the indigenous forest. The commercial plantation covers an area of 5965.84 ha while the indigenous forest covers 10484.09 ha. The mean annual precipitation of the area is 1012 mm and a mean annual temperature of 16.2°C. In the Witklip plantation, the commercial plantation covers 5616.62 ha while the indigenous forest covers 4693.44 ha. Pinus

patula also inhabit the commercial forest. The two commercial forests are currently on second

rotation (one rotation = 30 years) and practice sustainable forest management. These plantations have been FSC certified for the past 20 years.

(43)

Soil samples were collected from the two indigenous and commercial forests in July 2016. Soil cores (2 cm in diameter and 10 cm in depth) were collected within multiple tree rows (ten soil cores, about 6,000 km apart) at various points within the four sampling sites. These cores were then pooled together and homogenized into a composite sample per site. Using a 2 mm mesh, the samples were sieved and stored in plastic bags in the dark at 4°C until analyses.

3.2.2 Analyses of soil properties

Soil properties were measured between 2 weeks of sampling. The soil pH was measured by mixing 2 g of fresh soil in 10 ml deionised water using a Jenway 3520 pH -meter (Cole-Parmer Instruments, Staffordshire, UK). Total carbon and nitrogen were measured using the dry combustion method as described by Santi et al. (2006). Moisture content was determined by oven-drying the soil samples for 24 h at 105°C. The weight of samples was obtained before and after drying and the differences in weights was recorded (Colombo et al., 2016). Soil nitrate was determined by KCl extraction method. Organic carbon in the soil was determined by the Walkley Black method (Walkley and Black, 1934). For the purpose of statistical analysis, duplicate readings were taken for all the measured soil properties.

3.2.3 Determination of relative bacterial diversity and taxonomic richness

Total genomic DNA was extracted less than 48 hours after sampling from 0.25 g of soil using the PowerSoil® DNA isolation kit (MoBio Laboratory, CA, USA) according to the manufacturer’s instructions. The examination of the bacterial community was carried out using 16S amplicon sequencing. The 16S rRNA gene variable region V4 was sequenced using an Illumina MiSeq sequencer by the Next Generation Sequencing Service at Molecular Research LP (MR DNA, Shallowater, TX, USA). Employing the PCR primers 515F and 806R, paired-ends reads of 312 bp were obtained (Caporaso et al., 2012). Data analysis was carried out using the MR DNA analysis pipeline. The paired ends were joined, barcodes, sequences <150 bp and sequences with ambiguous base calls were removed. The Sequences were then denoised and

(44)

screened for the presence of chimeras. Analogous sequences were binned into operational taxonomic units (OTUs). OTUs were defined by clustering at 3% divergence (97% similarity). Final OTUs were taxonomically classified using BLASTn against a curated database derived from RDPII and NCBI (Garcia-Mazcorro et al., 2017). Rarefaction, Chao1 and ACE were used to estimate species richness. Taxonomic richness was expressed as OTU number.

Figure 3.1: Distribution of the sampling sites in Tweefontein and Witklip forests

3.2.4 Statistical analyses

The circos software (http://circos.ca/) was used to plot a graph of the relative abundance of bacterial communities at phylum level. The bacterial community composition (abundance data of relative OTU) was analysed using Principal Coordinates Analysis (PCoA) (Gower, 1966) based on a Bray-Curtis distance matrix using CANOCO 5 (Microcomputer Power, Ithacha,

(45)

evaluate the significance of land-use types (Anderson, 2001) using the software R 3.3.3 (R Core Development Team 2017). The correlations between physicochemical parameters were determined by one-way ANOVA with Tukey’s HSD test using the SPSS package (v25.0). P < 0.05 were considered statistically significant. CANOCO 5 was also used to carry out the Canonical Correspondence Analysis (CCA) which was used to evaluate the likely connections between microbial communities and the measured physicochemical parameters.

3.3 Results

3.3.1 Effects of differences in land use on soil properties

The physicochemical properties of the soils were affected by differences in land use and forest

management practices (Table 3.2). Organic carbon (F3,4 = 104.570, P = 0.000), soil nitrate

(NO3-) (F3,4 = 56900.188, P = 0.000), total carbon (F3,4 = 792.180, P = 0.000), pH (F3,4 = 63.115,

P = 0.001), phosphorus (F3,4 = 751.937, P = 0.000), soil calcium (F3,4 = 9840.714, P = 0.000),

magnesium (F3,4 = 2412.648, P = 0.000), potassium (F3,4 = 6457.109, P = 0.000) and sodium

(F3,4 = 78.443, P = 0.001) were significantly influenced by land-use practices. However, total

nitrogen was not found to be significantly different across the four sites (F3,4 = 2.434, P =

0.205).

The mineral soils in TC had the lowest share of the sand fraction (23.50%) and the highest share of silt (50.80%). Site WI had the highest clay fraction (25.05%) (Table 3.1). The sand

particles (F3,4 = 67.251, P = 0.001), silt (F3,4 = 135.737, P = 0.000) and clay fractions (F3,4 =

102.436, P = 0.000) were significantly different across the forest soils.

3.3.2 Composition, diversity and abundance of bacterial communities

As shown by the PCoA of the sequencing data (Figure 3.3), the composition of bacterial communities was affected by land-use change (conversion of land from indigenous to managed plantations). WI and TI samples were disconnected from the other samples along PC1 which

(46)

explained 43.62% of the total variation. Sample WC had a dissimilar microbial assemblage compared to the other land-use type on axis 2 which elucidated 83.81% of the variation. Significant dissimilarity in the bacterial communities was shown by PerMANOVA (P = 0.001). In the indigenous forest soil samples, variation in community composition at the phylum level as revealed by relative abundance was primarily driven by the dominance of Proteobacteria at the detriment of Acidobacteria and Verrucomicrobia (Figure 3.2).

OTU richness ranged from 19 (TC, WC and WI) to 21 (TI) as shown by the rarefaction curve (Figure 3.5). A related trend was also observed in the other diversity indices examined (Table 3.3).

(47)

Table 3.1: Mean values of particle size distribution of the forest soils

Sample Sand (2.0–0.05 mm) (%) Silt (0.05–0.002 mm) (%) Clay (<0.002 mm) (%)

TC 23.50 (1.40) 50.80 (1.60) 19.00 (0.10)

TI 59.85 (3.25) 35.20 (2.10) 9.00 (0.60)

WC 55.15 (1.95) 17.45 (0.75) 23.45 (1.05)

WI 55.10 (0.60) 16.95 (0.45) 25.05 (0.75)

(48)

Table 3.2: Mean values of the physical and chemical properties of the forest soil samples Sample Organic C (%) NO3- (mg/kg) Total C (%) Total N (%) pH P (mg/kg) Ca (mg/kg) Mg (mg/kg) K (mg/kg) Na (mg/kg) TC 3.21 (0.06) 63.38 (0.16) 3.97 (0.05) 0.01 (0.00) 4.28 (0.01) 3.16 (0.00) 15.85 (0.05) 52.35 (0.15) 54.00 (0.10) 11.60 (0.30) TI 4.08 (0.07) 65.13 (0.01) 4.58 (0.02) 0.02 (0.01) 4.78 (0.11) 2.33 (0.02) 234.50 (1.50) 95.60 (0.10) 70.30 (0.10) 15.35 (0.15) WC 2.97 (0.05) 22.01 (0.19) 3.04 (0.00) 0.01 (0.00) 5.31 (0.03) 3.21 (0.08) 320.50 (1.50) 117.00 (1.00) 69.95 (0.35) 16.60 (0.30) WI 2.67 (0.07) 5.91 (0.03) 2.91 (0.03) 0.001 (0.00) 5.10 (0.01) 5.03 (0.01) 164.50 (1.50) 97.65 (0.45) 92.20 (0.10) 15.50 (0.20)

(P < 0.05). Values in parenthesis represent ±one standard error (n = 2).

(49)

Figure 3.2: Taxonomic classification of the bacterial reads at phylum level

3.3.3 Influence of environmental factors on bacterial communities

The impact of the measured environmental factors on bacteria communities was correlated using the CCA. Axis 1 of the CCA described 49.72% of the total variation and was positively

TC - Tweefontein commercial TI - Tweefontein indigenous WC - Witklip commercial WI - Witklip indigenous

(50)

were negatively correlated to the first axis. The influence of Na, Ca, Mg and P on the microbial communities was weaker than those of K, pH, Org. C, MC and NO3 as shown by the lengths of the vectors. Total C had the greatest influence on the microbial communities. Proteobacteria, Verrucomicrobia and Acidobacteria the three most abundant phyla were negatively correlated with Total C. Axis 2 of the CCA plot explained 83.98% of the total variation and was negatively correlated with Total C (Figure 3.3).

Figure 3.3: Principal coordinates analysis (PCoA) plot of bacterial community composition at

-1.0

1.5 -1.

0

1.

5

TC TI WC WI

PC1 - Percent variation explained 43.62%

P C 2 P e rc e n t v a ri a ti o n e x p la in e d 8 3 .8 1 %

(51)

level and major physicochemical parameters in the soil samples

-1.0

1.0 -1.

0

1.

0

Org. C N-NO3 Total C Total N pH P Ca Mg K Na MC Fibrobac Elusimic Gemmatim CandSacc Actinobc Armatimn Planctom Chlamydi Deinococ Tenerict Nitrospr Firmicut Bacteroi Cyanobac Acidobac Ignavibc Fusobact Spirocha Chlorofl Proteobc Verrucom TC TI WC WI C C A A x is 2 ( 8 3 .9 8 % ) CCA Axis 1 (49.72%)

(52)

Figure 3.5: Effect of land-use type on OTU richness

Table 3.3: OTU number, Chao1 and ACE indices

Sample Richness Estimator

Sobs SChao1 SACE

TC 19.00 19.00 19.52

TI 21.00 21.50 23.17

WC 19.00 19.00 19.45

WI 19.00 19.00 19.00

Sobs, Observed richness

Chao1 Estimate of OTU richness, (values with 95% confidence interval)

SACE, Abundance-based coverage estimator

3.4 Discussion

(53)

Bacterial communities from both natural forests (TI and WI) were distinct from the communities from the managed forests along the major axis (Figure 3.3). Proteobacteria accounted for the highest abundances and was the most dominant phylum across the four sites. Proteobacteria have been reported to be dominant at the expense of other phyla in various environments including natural woodlands, forest plantations (Colombo et al., 2016) and mine tailings. Members of the phylum Proteobacteria have been described to possess versatile metabolic capabilities (Xiao et al., 2016). The abundance of the phyla Verrucomicrobia, Acidobacteria, actinobacteria and Chloroflexi was also relatively high as have been reported by other studies of forests soils (Colombo et al., 2016; Faoro et al., 2010). Bacterial communities in TI and WI seemed very similar suggesting that a change in the land-use has a strong impact on the bacterial communities. TI which had the highest OTU richness value was also observed to have the highest value of organic carbon. In the Tweefontein plantation, the managed forest had higher bacterial diversity while the natural forest had higher bacterial diversity in the case of the Witklip plantation. Various studies have reported change in bacterial diversity in response to land use type (Colombo et al., 2016).

The CCA explained the relationship between microbial communities and the physicochemical parameters. All the parameters considered were vital in determining the microbial communities. Considering the length of the vector of pH, it was a strong determinant in the shaping of the microbial communities. The pH of the soils ranged from 4.28±0.01 to 5.31±0.03 and was significantly different across the sites. The structure and function of soil microbes have been reported to be driven by soil pH. The reaction of microbes to soil pH changes differs. Soil pH has great effects on the biodiversity patterns and activities of microbes in contrast to land use (Gleeson et al., 2016; Tripathi et al., 2013).

Organic carbon also had a strong influence on the community composition of soil bacteria. Changes in microbial biomass have been linked to variations in soil carbon and nitrogen

(54)

concentrations. Environmental variables are key factors in the structure and function of bacterial communities in soils. Shifts in bacterial communities in response to environmental properties have been published (Peralta et al., 2013; Arroyo et al., 2015). Our results also confirmed that nutrients affected the bacterial communities. Studies that examined the influence of edaphic factors on soil bacterial communities reported that these communities were shaped by the accessibility of nutrients like carbon and nitrogen (Rasche et al., 2011; Vries et al., 2012).

3.5 Conclusion

The examination of belowground communities in temperate forests presented evidence that environmental variables are crucial regulators of soil bacterial diversity, abundance and community composition. This has explicit consequences for the functioning of the soil ecosystem. These results provide further evidence that the patterns of distribution of soil bacteria and their structure undergo spatial heterogeneity. The characterization of abiotic properties of soils provides perception on the factors that influence the diversity of soil bacterial communities and how these communities are altered. Additional evidence from manipulative and extensive experiments are essential.