Characterization of Bacterial Species in Steinkopf a Communal Farming Area in South Africa: a Closer Look at Pathogenesis

Characterization of bacterial species in Steinkopf a communal farming area in South Africa: A closer look at pathogenesis

By Jodene Foster

Thesis presented in fulfilment of the requirements of the degree Masters of Science at the University of the Western Cape

Supervisors: Dr Adriaan Engelbrecht, Dr Morné Du Plessis, Dr Oriel Moeti Taioe

Faculty of Natural Science

Department of Biodiversity and Conservation Biology

April 2019

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http://etd.uwc.ac.za/ Declaration

By submitting this thesis/dissertation electronically I declare that the entirety of the work contained therein is my own, original work, and that I have not previously in its entirety or in part submitted it for obtaining any qualification

April 2019

Signature:

Date: 02/05/2019

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http://etd.uwc.ac.za/ Abstract

The human population in sub-Saharan Africa has been increasing due to decreases in mortality rates and increases in average human age; in turn increasing poverty and pressure placed on agriculture and agricultural production. However, livestock production in South

Africa, and globally, is declining due to disease and parasite prevalence, lack of feed, poor breeding, marketing management, change in nutrition in both livestock and humans, rapid urbanization, encroachment on wildlife and unfavourable climatic conditions brought about by global change. One unintended consequence has been the emergence and spread of transboundary animal diseases and, more specifically, the resurgence and emergence of zoonotic disease. Zoonotic diseases are sicknesses transmissible from animals to humans, resulting from direct contact or environmental reservoirs. Previous studies have identified small-scale farmers as the group most prevalent to contracting zoonotic diseases, especially those working in a communal dispensation. Therefore, this study focused on the communal farming area of Steinkopf in the semi-arid Namaqualand region of South Africa. Steinkopf is one of the largest Act 9 areas, with communal land tenure and a mixed farming system, sheep and goats, on about 759 ha. Steinkopf is divided into two rainfall regions, the Succulent Karoo

(winter rainfall region) and the Nama Karoo (summer rainfall region). This study aims to identify and characterise the bacterial microbial communities found in the topsoil layer and faecal matter (dung) within the winter and summer rainfall regions of Steinkopf communal rangeland using Next-generation sequencing. Further, the aim is to assess whether pathogenic bacteria are present within the rangeland and what their potential impact on the local farming community might be if present. A high-throughput sequencing technique (Next-generation sequencing) was used to amplify 16S rRNA targeting the V3-V4 hypervariable regions.

The phylotypes produced were 37 phyla, 353 families and 634 genera of which the most abundant bacterial phyla were Planctomycetes, Firmicutes and Bacteroidetes and the most

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http://etd.uwc.ac.za/ abundant genera were Gemmata, Akkermansia and Arthrobacter. Alpha diversity indices showed a variation in species diversity, evenness and richness between soil and dung samples, it shows a higher species richness, evenness and unique OTUs detected in summer soil samples and at natural water holes. Through these analysis soil samples were regarded as superior to dung samples within this particular environment and for this particular study. Natural water holes were identified as a safer option when compared to man-made water holes as there are natural systems in place that combat the spread and growth of harmful bacterial microbes. It was found that seasonality has a great impact on the development and growth of environmental bacterial microbiota and that the current randomness of grazing routes and migrations within the Steinkopf communal rangeland is not a detriment but instead acts as a benefits to environmental and livestock health. Furthermore, a total of three pathogenic bacteria were identified however, they occurred at relatively low abundances. It can thus be concluded that this study thoroughly describes the usefulness of using a high-throughput sequencing technique such as Next-generation sequencing when amplifying a small sample size in order to achieve a large volume of information; and that currently the Steinkopf communal rangeland is not subjected to or at risk of a potential zoonotic threat.

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http://etd.uwc.ac.za/ Acknowledgments

This study would not have been possible without the constant support, encouragement, motivation and advice from my supervisors, Dr Adriaan Engelbrecht, Dr Morne Du Plessis and

Dr Oriel Moeti Taioe. Their constant support during this project was greatly appreciated.

I would also like to send my heartfelt appreciation to the ARC (Agricultural Research Council) at UWC for the guidance in the field and assistance with fieldwork, as well as the community of Steinkopf for accepting the study openheartedly and working with us during the research period, sharing rare knowledge and allowing access into the culture.

Funding for this project, in the form of a study bursary was provided by the NRF (RTF grant) awarded to the Agricultural Research Council. The running cost for this study was provided by the Early Career Research Grant awarded to Dr. Engelbrecht.

Finally, I would like to thank my colleagues, especially Taryn Joy Jacbos and Yameen

Badrodien at UWC in the department of Biodiversity and Conservation Biology, as well as all family and friends who have been supportive during this time. Lastly, a special thank you, to my mother Denise Foster as well as my grandmother Joan Foster, and dear friends Jody

Mathew Taft and Kay-Lynn Esau for their constant motivation, sacrifices, assistance where possible, and guidance. Their constant support and presence was motivating and inspiring.

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http://etd.uwc.ac.za/ Table of Content

Declaration………………………………………………………………………………...... 2

Abstract……………………………………………………………………………………………...………….3

Acknowledgements……………………………………………………………………………………...…...5

Table of Contents……….……………………..……………………………………………………………...6

List of Tables…………………………………………………………………………………………………..8

List of Figures…………………………………………………………………………………………………9

Chapter 1: Introduction……………………………………………………………………………….….11

1 Preamble………………………………………………………………………………………………...….11

1.1 Modernisation of agriculture……………………………………………………………………...…..12

1.2 The livestock sector in South Africa…………………………………………………………………13

1.3 Communal farming and its associated risks and benefits…………………………………..…….16

1.4 Disease and its impact on agriculture………………………………………………………..……….19

1.5 Pathogens in livestock waste…………………………………………………………………………..22

1.6 Pathogens in livestock soil……………………………………………………………………….…….24

1.7 The role of bioinformatics……………………………………………………………………….……..24

1.8 Study sites………………………………………………………………………………………………....26

1.9 Study aim………………………………………………………………………………………….………28

1.9.1 Study questions…………………………………………………………………………………..……29 Chapter 2: Identification of environmental bacterial microbes in the winter rainfall region of Steinkopf communal rangeland, South Africa…………………………………...…….30

2.1 Introduction……………………………………………………………………………………………..30

2.2 Methods and Materials……………………………………………………………………………….31

2.2.1 Study site and collection……………………………………………………………………………..31

2.2.2 DNA extraction…………………………………………………………………………………..……34

2.2.3 Amplification of the V3 and V4 region of 16S rDNA…………………………………………35

2.2.4 Metagenomic data preparation and analysis………………………………………………….….36

2.2.5 Diversity comparison and statistical analysis……………………………………………...... …..36

2.3 Results……………………………………………………………………………………………………….….……37

2.3.1 Classification of bacterial OTUs…………………………………………………………………...37

2.3.2 Alpha diversity…………………………………………………………………………………………39

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http://etd.uwc.ac.za/ 2.3.3 Beta diversity………………………………………………………………………………………...... 40

2.4 Discussion…………………………………………………………………………………….…………..48

2.4.1 Sample type………………………………………………………………………………….…………48

2.4.2 Location……………..…………………………………………………………………………………..51

2.4.3 Description……………………………………………………………………………………………..53

2.5 Conclusion……………………………………………………………………………………………….54 Chapter 3: Identification and characterization of bacterial microbes within the Steinkopf communal rangeland: Is bacterial pathogenesis a threat? ………………………………………56

3.1 Introduction……………………………………………………………………………………………..56

3.2 Methods and Materials……………………………………………………………………………….60

3.2.1 Study site and collection……………………………………………………………………………..60

3.2.2 DNA processing……………………………………………………………………………………….60

3.3 Results………………………………………………………………………………………………….…62

3.3.1 Classification of bacterial OTUs…………………………………………………………………...62

3.4 Discussion………………………………………………………………………………………………...68

3.4.1 Identification of bacterial taxa…………………………………………………………………...….68

3.4.2 Pathogenic and unclassified taxa…………………………………………………………………...72

3.5 Conclusion……………………………………………………………………………………………….74

Chapter 4: General conclusion……………………………………………………………………...…..75

References………………………………………………………………………………………………….....78

Appendix 1…………………………………………………………………………………………………....92

Appendix 2…………………………………………………………………………………………………....94

Appendix 3…………………………………………………………………………………………………....95

Appendix 4.…………………………………………………………………………………………………...97

Appendix 5...……………………………………………………………………………………………….…98

Appendix 6...……………………………………………………………………………………….…………99

Appendix 7..……………………………………………………………………………….………………...100

Appendix 8...………………………………………………………………………………….……………..101

Appendix 9...... 102

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http://etd.uwc.ac.za/ List of Tables

Table 1.1 Estimated ruminant livestock numbers in South Africa (2010) (in thousands)

Comm: Commercial; Other: small-scale and communal; (a) milk production is small-scale and communal (“Other”) is not sufficient to meet the definition of dairy cow, therefore cows milked for family needs are included under beef cattle; (b) Merino and other wool sheep comprise 65% of sheep numbers (NWGA, 2011 Pers. Comm.); (c) meat goats exclude 21000 dairy goats

(Smuts, 2011 Pers. Comm.) and 1 million Angora goats (Mohair SA, 2011 Pres. Comm.); (d)

Game number differ vastly among publications and were therefore estimated from total hectare under game, property size, recommended stocking rate, animals hunted and auctions. This approach may result in an underestimate as game species on small properties are often stall- fed or receive supplementary feed. Game species include elephant, hippopotamus, rhinoceros and zebra which are not ruminants (du Toit et al., 2013); (e) commercial cattle include those that are in feedlots at any time (SAFA, 2011 Pres. Comm.)………………………………………..…15

Table 1.2 List of recorded bacterial pathogens present in Ethiopia and South Africa. The table below has been constructed using the list of diseases that were found in Ethiopia during the study done by Pieracci et al. (2016), as well as a list of diseases that have been recorded in

South Africa and appears on the list of controlled and notifiable animal diseases in South Africa

(DAFF, 2016)…………………………………………………………………………………………………21

Table 2.1 Co-ordinates of study sites in Steinkopf communal farming area. Malkop (man-made solar pump), Spring (a natural water hole), Neil (solar-wind water pump) and Halfmens (a dug- well)………………………………………………………………………………………………………...…..34

Table 2.2 Alpha diversity indices per category…………………………………………………...…….39

Table 3.1 Summary of pathogenic bacterial species found in soil and faecal matter sample collected in the Steinkopf communal rangelands……………………………………………………..66

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http://etd.uwc.ac.za/ List of Figures

Figure 1.1 Representation of the Steinkopf communal farming area (yellow) within the

Northern Cape (A) of South Africa (B)………………………………………………………….……….27

Figure 2.1 Map representing the winter rainfall area within the Steinkopf communal farming area. Black spot: Malkop (solar-pump). Blue spot: Spring (natural). Brown spot: Neil (solar- wind pump). Red: Halfmense (dug-well)………………………………………………………………..33

Figure 2.2 Distribution and abundance of bacterial phyla between Dung and Soil samples. Soil has the highest number of phyla (37 phyla) compared to Dung (28 phyla), whereas, Dung has the highest number of unassigned phyla (3.46%) compared to Soil (3.3%)………………...……..38

Figure 2.3 Sample ID Alpha diversity indices, A: Chao 1 diversity index showing species richness; B: PD_whole_tree diversity index showing the phylogeny of detected microbes in ration to their abundance; C: Observed OTUs diversity showing the number unique OTUs produced……………………………………………………………………………………………..………...42

Figure 2.4 Sample Type Alpha diversity indices, A: Chao 1 diversity index showing species richness; B: PD_whole_tree diversity index showing the phylogeny of detected microbes in ration to their abundance; C: Observed OTUs diversity showing the number unique OTUs produced…………………………………………………………………………………………..…………...43

Figure 2.5 Location Alpha diversity indices, A: Chao 1 diversity index showing species richness; B: PD_whole_tree diversity index showing the phylogeny of detected microbes in ration to their abundance; C: Observed OTUs diversity showing the number unique OTUs produced……………………………………………………………………..………………………………...44

Figure 2.6 Description Alpha diversity indices, A: Chao 1 diversity index showing species richness; B: PD_whole_tree diversity index showing the phylogeny of detected microbes in ration to their abundance; C: Observed OTUs diversity showing the number unique OTUs produced………………………………………………………………………..……………………………...45

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http://etd.uwc.ac.za/ Figure 2.7 Weighted UniFrac distance matrices where A is observed beta diversity of

Description, B is observed beta diversity of Sample Type and C is observed beta diversity of

st rd Location. The bottom and top of each box represents the 1 and 3 quartiles whereas the whiskers represent the values outside the upper and lower quartiles. The plus signs below the whiskers represent statistically significant observations……………………………….…….……...46

Figure 2.8 Unweighted UniFrac distance matrices where A is observed beta diversity for

Description, B is for Sample Type and C is for Location. The bottom and top of each box

st rd represents the 1 and 3 quartiles whereas the whiskers represents the values outside the upper and lower quartiles. The plus signs above and below the whiskers represent statistically significant observations……………………………………………….……………………………………47

Figure 3.1 Representation of Steinkopf communal rangelands. Dark black line illustrating the division between winter and summer rainfall regions. Purple dots representing winter rainfall areas sampling sites. Orange dots representing summer rainfall areas sampling sites…...……...61

Figure 3.2 Relative abundance of the four most prevalent bacterial phyla derived from 16S rRNA next-generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of Steinkopf communal rangeland……………………………...…………..63

Figure 3.3 Relative abundance of the three most prevalent bacterial families derived from 16S rRNA next-generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of Steinkopf communal rangeland……………………..…………….……..65

Figure 3.4 Relative abundance of the four most prevalent bacterial genera derived from 16S rRNA next-generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of Steinkopf communal rangeland……………………...…………………..67

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http://etd.uwc.ac.za/ CHAPTER 1

Introduction

1. Preamble

The human population in sub-Saharan Africa has been rapidly increasing since 1990 due to decreases in the rate of mortality and increases in average human age (Maxwell, 1999;

Drechsel et al., 2001). This increase added additional pressure on the agricultural industry to relieve food insecurity. In many developing countries including those found south of the Sahara in Africa, agriculture is used to provide a form of employment and economic growth beyond its rural setting (Drechsel et al., 2001; Meissner et al., 2013). Therefore, agricultural communities provide and assist not only themselves but also their countries at large by alleviating food insecurity and poverty (Baliscan, 1993; Turpie et al., 2008).

However, livestock production is declining due to disease and parasite prevalence, lack of feed, poor breeding, marketing management and unfavourable climatic conditions brought about by global change (Musemwe et al., 2008; Meissner et al., 2013). There are also further concerns about industrial technologies used in livestock production, animal welfare, zoonosis, the impact of livestock products on human health, loss of natural ecosystems and biodiversity as well as the contribution of livestock production to the greenhouse effect (Meissner et al.,

2013). According to King (2011) and Grace et al. (2017), zoonotic diseases in particular are closely associated with impoverished communities, due to lack of hygiene, crowded spaces, malnutrition, and lack of education where they disproportionately affect these often-neglected populations. Additionally, when considering the manner in which communal farming rangelands are managed and the close contact herders and livestock have, it was decided that communal rangelands are a preferred study site to investigate the exchange of bacterial species between wildlife, livestock and humans.

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http://etd.uwc.ac.za/ Thus, this study focuses on identifying and characterizing the bacterial microbial community found in the topsoil layer and faecal matter within the Steinkopf communal rangeland using Next-generation sequencing. This study further assesses whether pathogenic bacteria are present within the rangeland and what their potential impact on the local farming community might be if present. Possible finding will identify if potential pathogenic bacterial reservoirs occur within this communal rangeland and could be used to prevent future health risk cases. One could also use the finding to determine if the current methods used by herders in the communal rangeland are detrimental to the environment, livestock, and themselves as herders or if the current methods of livestock farming are suitable for the rangeland. If future studies are done in which comparisons between communal and commercial farming is investigated then studies such as this one could form a good guideline and structure when delving into communal farming.

1.1 Modernisation of agriculture

With the drastic increase in pressure on agriculture, subsistence farming has no longer become the norm. Instead, agriculture has become one of the greatest forms of economic income in the shape of commercial farming. The modernisation of agriculture not only altered the process of agricultural production but also affects the decisions of farmers in a way where profit became the most important objectives at the expense of a healthy and safe environment

(Hill 1994; Hendricks and James, 2005; Hazell et al., 2010; Smalley, 2013).

Agricultural modernisation is characterized by standardization, mass production and specialization (Hinrichs and Welsh, 2003; Hendricks and James, 2005). This process became most apparent in the 1950’s especially with the production of broilers and the vertical integration of companies and capital spent on these broilers. Industrial size processing facilities

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http://etd.uwc.ac.za/ focused on mass production and global marketing networks requiring a huge amount of capital

(Hinrichs and Welsh, 2003; Hendricks and James, 2005). This led to the development of large, well-financed organisations and partnerships injecting a growing concentration of capital in food and agricultural systems in order to gain control thereof. Those responsible for food production (farmers and farm workers) have become increasingly deskilled in the process and lost more and more decision making ability (Hendricks and James, 2005).

As such, farmers’ decision-making abilities have been impacted in two ways. Firstly, the economic environment creates constraints; limiting the options the farmer has available.

Secondly, as a result of the constrained decision making abilities, farmers face new ethical challenges (Hendricks and James, 2005). For example, a study done by Hendricks and James

(2005) found that poultry production contracts restrict farmers from using alternative production processes and techniques that are environmentally friendly.

One key implication of constrained choice is the loss of indigenous farming knowledge.

Hendricks and James (2005) argues that knowledge is lost from the production line, to the preparation of food, to the taste of food and management of biodiversity. This type of production has shifted agriculture from a biological system, which follows biological processes, to an industrial manufacturing system ignoring the natural or biological limits.

1.2 The livestock sector in South Africa

South Africa’s livestock sector contributes substantially to food security, clothing, and provides social as well as economic attributes to the country. However, livestock productivity is declining due to disease and parasite prevalence, lack of feed and poor breeding and marketing management (Musemwe et al., 2008; Meissner et al., 2013). Seventy percent of

South Africa’s land can be used to house livestock and game, with majority of this land

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http://etd.uwc.ac.za/ occurring in the eastern higher rainfall regions. According to Meissner et al. (2013) the gross value of livestock between the years 1995-2010 has increased by 185%. The main reason behind the increase is the high demand for meat products. On a weight basis, livestock food products contribute 27% of a local consumers shopping basket (Meissner et al., 2013).

However, concerned citizens have raised issues with regard to industrial technologies used in livestock production, animal welfare, zoonosis, the impact of livestock products on human health, loss of natural systems and biodiversity and more recently livestock productions impact on the greenhouse effect (Meissner et al., 2013). Scientists do predict that several of these issues will have drastic implications on the livestock sector in the following 20-30 years

(Kauffman and Kruger, 1984; Thornton, 2010; de Vries and de Boer, 2010). Recent international trends do show that these debates have prompted international coordinating and administrative bodies to start strategising proactively in order to meet the agricultural demand in a sustainable manner (Capper et al., 2009; O’Leary, 2013).

Livestock farming is the backbone of the South African socio-economy and provides the subsistence of most non-metropolitan towns and rural communities (Meissner et al., 2013).

Livestock are produced throughout South Africa with a variation in livestock numbers, breeds and species as well as according to the types of grazing, environment and production systems

(being either commercial, small-scale or communal). According to estimates produced in 2010, the Eastern Cape boasts the highest number of cattle, sheep and goats whilst KwaZulu-Natal is second in beef cattle and the Northern Cape second in sheep (Table 1.1, extracted from

Meissner et al., (2013)). Proportionally, the small-scale and communal sectors own 41% beef cattle, 12% sheep and 67% goats (DAFF, 2010a). Between the years 1995-2005 the gross value of livestock products was estimated at 42%, but in the years thereafter it increased to 47%

(Meissner et al., 2013). The global demand for livestock products have increased and are expected to continue increasing mainly due to the upsurge in human population, shifts in

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http://etd.uwc.ac.za/ consumption of livestock products and the affluence of developing countries (Scollan et al.,

2010; Meissner, 2012).

In South Africa, livestock farming occurs on 70% of the land and uses about 80% of the resources (DAFF, 2006; WWF, 2010). Livestock production has an extensive impact on land degradation, water depletion, pollution and at times biodiversity, depending on management strategies (Meissner et al., 2013). Global concern within the livestock sector is currently focused on the decrease in biodiversity in the form of genetics, due to inappropriate cross-breeding and inter-breeding (DAFF, 2007). Genetics has become a greater concern because of its importance in food security and sustainable livelihoods and livestock animal diversity. This is key to future generations developing breeds adapted to the future climatic conditions (DAFF, 2007; Meissner et al., 2013).

Table 1.1 Estimated ruminant livestock numbers in South Africa (2010) (in thousands).

(b) (c) Beef cattle Sheep Meat goats (a) (d) Province Dairy cattle Game (e) Comm. Other Comm. Other Comm. Other

Western Cape 219 232 323 2 380 336 62 152 34 Northern Cape 603 208 13 5 361 758 144 355 671 Eastern Cape 1 531 1 272 348 6 410 906 643 1 588 341 KwaZulu-Natal 1 409 1 116 268 676 95 227 561 117 Free State 1 232 911 198 4 271 604 67 165 158 Mpumalanga 868 603 60 1 534 217 25 61 273 Limpopo 650 433 12 226 31 349 861 1 109 Gauteng 321 245 44 91 13 11 27 90 North West 1 035 713 102 62 86 202 498 198 Total 7 868 5 733 1 368 21 561 3 046 1 730 4 268 2 991 National Total 13 601 1 368 24 607 5998 2 991

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http://etd.uwc.ac.za/ Comm: Commercial; Other: small-scale and communal; (a) milk production is small-scale and communal

(“Other”) is not sufficient to meet the definition of dairy cow, therefore cows milked for family needs are included under beef cattle; (b) Merino and other wool sheep comprise 65% of sheep numbers (NWGA, 2011 Pres. Comm.);

2011 Pres. Comm.); (d) Game number differ vastly among publications and were therefore estimated from total hectare under game, property size, recommended stocking rate, animals hunted and auctions. This approach may result in an underestimate as game species on small properties are often stall-fed or receive supplementary feed.

Game species include elephant, hippopotamus, rhinoceros and zebra, which are not ruminants (du Toit et al.,

2013); (e) commercial cattle include those that are in feedlots at any time (SAFA, 2011 Pres. Comm.).

South African livestock farmers have shown their concern towards this genetic awareness campaign by boasting a wide variety of breeds (including Merino, Dorper, Döhne

Merino sheep and Nguni cattle) and composites (Musemwe et al., 2008; RPO, 2010). South

African farmers also contribute to the maintenance of endangered and rare wildlife species with an increase in game farms by 5% in 2006 according to the National Agricultural Marketing

Council (NAMC, 2006).

South Africa’s livestock sector is a major employer, employing approximately 245 000 workers. However, employment within the commercial farming sector has decreased mainly due to unfavourable economic conditions, increases in minimum wages, conversion of large grazing areas to wildlife protected areas and eco-tourism (Meissner et al., 2013). Some towns in the non-metropolitan area only came into existence due to commercial and small-scale farming activities. Majority of these town economies are dependent on the money spent by commercial and small-scale farmers (DAFF, 2006; WWF, 2010).

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http://etd.uwc.ac.za/ 1.3 Communal farming and its associated risks and benefits

Livestock is housed throughout South Africa, in both rural and urban communities, and globally across developing countries. It is estimated that two-thirds of resource-poor households keep some type of livestock (LIDD, 1999; Randolph et al., 2007; FAO, 2009;

DAFF, 2010c). Similarly, in South Africa, rural and urban communities keep livestock in communal or tribal lands (Meissner et al., 2013). Livestock keeping by rural communities defines the daily challenges and constraints these communities face (Randolph et al., 2007).

Communal farming is defined as the agricultural practice in which many farmers use the same grazing lands, called the commonage, to graze and water their livestock (May and

Lahiff, 2007). Water points are often man-made and operates by essentially extracting water using solar-pumps and wind-pumps that are maintained by the municipality for the use of these livestock farmers and their herds. However, one would suspect a clash of heads when thinking of a group of livestock owners with high numbers of livestock all using the same grazing areas.

Yet these conflicts are kept to a minimum by following a list of immemorial informal rules.

In South Africa, the commonage areas are managed based on long-term sustainable practices through understanding the rangeland, care for livestock and respect for fellow herders as well as a sophisticated indigenous knowledge system developed over centuries. Rules that guide all livestock practices within these areas are not drawn up in a book of constitutions or applied by law enforcement, but are rather a common understanding between livestock owners and herders (Allsopp et al., 2007; Ntombela, 2017). One such rule states that all herders have equal access to water points unless such water point falls within a particular herder’s stock- post. At which point an arrangement will be made between such herders in order to gain access to the water point (Allsopp et al., 2007).

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http://etd.uwc.ac.za/ One solution is also to dig holes into the ground (called dug-wells) to access underground water providing all herders using the commonage has access to these dug wells; which is maintained through cooperative action by herders (Allsopp et al., 2007; Ntombela,

2017). Whilst wind- and solar-pumps are established by local municipal leaders, and is used by all herders, yet is later no longer maintained by local authorities it ends up being neglected and dilapidated (Allsopp et al., 2007).

Another unspoken rule states that livestock keepers try to keep as many livestock as possible, without increasing animal numbers beyond that which they cannot manage, while simultaneously respecting fellow herders who have to share the same grazing lands. Thus, communal farming areas in South Africa are not monopolised by individuals who abuse the rangeland for their own benefit (Allsopp et al., 2007).

Animal safety is another important aspect which requires consideration. In the

Steinkopf region of the Northern Cape, many components have been put in place to ensure livestock safety and health (Allsopp et al., 2007; Salomon et al. 2013; Ntombela, 2017). In order to avoid extreme conditions such as extremely high or low temperatures, 85% of herders move their livestock between winter and summer rainfall areas (Ntombela, 2017). To prevent overgrazing of the field herders choose different grazing routes daily, but these routes are based on the proximity to a water point, the types of foraging available on the route and the safety of livestock from predators (Samuels, 2013; Ntombela, 2017).

When analysing these communal farming areas without any understanding of the level of complexity in which they operate one could easily mistake these commonages as an unproductive farming system, which they are not (Cousins, 1999). However, the diversity of these rural households include land-based strategies for arable farming, husbandry and consumption and trade in natural resources (Shackleton et al., 2001). In South Africa, a

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http://etd.uwc.ac.za/ minority (30%) of households own cattle while a majority (60%) own sheep and goats

(Shackleton et al., 2001; Peacock, 2005). These livestock, provide resources in the form of milk, occasional meat products, generating of cash through sales (of skins, meat products or as transportation, in order to pay bills, school fees, and see to medical needs), meeting social obligations (status level), providing draught power, forming part of the households pluriactivity

(multiple job-holding) and religious beliefs (Shackleton et al., 2001; Davie et al., 2006;

Longley et al., 2006; Anseeuw and Laurent, 2007; Siegmund-Schultze et al., 2012).

1.4 Disease and its impact on agriculture

The World Health Organization (WHO) defines zoonotic diseases, as sicknesses transmissible from animals to humans, resulting from direct contact with animal products or environmental components which serve as reservoirs for zoonotic disease causing agents

(WHO, 2009). Three-quarters of emerging pathogens and at least two-thirds of human pathogens are zoonotic in origin (Dazak et al., 2001; Taylor et al., 2001; Gummow, 2003,

WHO, 2006). Karesh et al. (2012) highlighted the impact of these zoonotic diseases on human health and livelihood, as it is globally implicated in one billion cases of morbidity and at least one million cases of mortality annually. It has long been regarded as a threat to humans but only recently have the impacts been quantified (Gummow, 2003; Bengis et al., 2004; DAFF,

2016). According to King (2011) and Grace et al. (2017), zoonotic diseases are closely associated with impoverished communities where they disproportionately affect these often- neglected populations.

Moreover, King (2011) has highlighted the occupational risk experienced by small- scale livestock farmers who are at greater risk of contracting diseases such as tuberculosis, anthrax and brucellosis as farmers are often in contact with livestock and the environments

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http://etd.uwc.ac.za/ they occupy. In any given society the particular disease system, the frequency of transmission of zoonosis as well as its impact on socio-economic circumstances reflects the nature of the local human-animal relationships as well as climatic conditions (Gummow, 2003).

A zoonotic disease has the ability to impact society in three main ways. Firstly, the health of the animal is threatened which results in a reduction in the quality of the animal which, unless treated, ultimately dies. Secondly, the livelihoods of those who depend on the livestock as a major source of income or for cultural purposes are threatened. Finally, the zoonotic disease could cause illness and death in people resulting in further socio-economic strain on governments particularly in third world countries such as South Africa (Pieracci et al., 2016).

The impact of these diseases are amplified in underdeveloped nations as outbreaks have the potential to reduce both human health and economic activities by limiting agricultural growth and development (King, 2011). The only African country that has been thoroughly studied, and where many zoonotic diseases have been identified, is Ethiopia (Pieracci et al.,

2016). Similarly to South Africa, Ethiopia is a country that has an economy driven by agriculture as its main source of food (Pieracci et al., 2016), justified by its large human population (second largest population in Africa), and its large livestock population (largest on the continent) (Pieracci et al., 2016). This creates an opportunity for direct contact between humans and both domestic and wild animals.

A lack of knowledge on how to properly slaughter, clean meat, house and treat infected livestock has created uncertainty allowing a spike in zoonotic disease (Karesh et al., 2012).

The Global Health Securities Agenda (GHSA) is an initiative created by the US government to increase livestock production within Ethiopia and minimizing the occurrence of zoonotic diseases (Pieracci et al., 2016). The table below (Table 1.2) has been constructed using the list of diseases that were found in Ethiopia as well as a list of diseases that have been recorded in

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http://etd.uwc.ac.za/ South Africa and appears on the list of controlled and notifiable animal diseases (DAFF, 2016;

Pieracci et al., 2016).

The rate at which emerging zoonotic diseases have increased over the past century has been investigated and studies implicate wildlife as the main vector source, followed by livestock, domestic pets and eventually humans. Studies have also shown that waste material, such as dung and soil, are mediums in which these diseases can be transferred (Kruse et al.,

2004; Morand et al., 2014).

1.5 Pathogens in livestock waste

Most livestock are housed, for at least part of the year, normally winter, to protect them from environmental conditions such as wind chills. This leads to an accumulation of faecal matter within buildings and sheds used for livestock housing. In the UK, approximately 200 million tons of livestock faecal matter accumulates during this housing period, of which cattle and sheep (Mawdsley et al., 1995) contribute 90%. Half of this waste is then used as fertilizers and distributed across grazing fields and croplands. Although they act as good fertilisers, these wastes are threats to the environment as they are filled with microorganisms, have biochemical oxygen demands and have the ability to release nitrates and phosphates into the environment.

With the current increase, demand on agriculture to alleviate food insecurities or just to satisfy the general demand for agricultural produce, only recently have the concerns about potential faecal pathogens been expressed (Mawdsley et al., 1995; Machethe, 2004).

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Table 1.2 List of recorded bacterial pathogens present in Ethiopia and South Africa.

Ethiopia South Africa

Bacillus anthracis Bacillus anthracis

Bartonella Brucella

Borrelai burgdorferi Burkholderia mallei

Brucella Contagious haemopoeitic necrosis

Campylobacter Contagious pancreatic necrosis

Coxiella burnetti Chlamydia psittaci

Escherichia coli Dermatophilus congolensis

Francisella tularensis Haemorrhagic septicaemia

Leptospirosis Mycobacterium avium (subsp. paratuberculosis)

Listeria Salmonella enteriditis

Mycobacterium bovis Salmonella gallinarum

Orientia tsutsugamushi Salmonella pullorum

Rickettsia Streptococcus equi equi

Salmonella sp. Swine erysipelas

Staphylococcus aureus Theileria parva lawrencei

Streptococcus suis

The table above has been constructed using the list of diseases that were found in Ethiopia during the study done by Pieracci et al. (2016), as well as a list of diseases that have been recorded in South Africa and appears on the list of controlled and notifiable animal diseases in South Africa (South African Department of Agriculture, 2016).

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Many pathogens are excreted through the faeces of both infected animals and healthy carrier animals. These pathogens include viruses, bacteria and protozoa. Whilst some parasites are of limited to no concern, others can survive in faecal matter for extended periods. The length of time these parasites persist however is determined by three main factors: temperature, moisture and type of waste (Wilkes et al., 2011).

Studies have shown that pathogens such as E. coli and Salmonella sp. are able to survive in faecal matter for up to four to six months if found in the correct environmental conditions

(Mawdsley et al., 1995). If the waste is in slurry form or moist then bacteria will be able to survive. However, once the faecal matter is exposed to sunlight for an extended period, the faecal matter dries out and kills off bacterial species.

Some parasitic bacteria are able to survive if enclosed by a cyst or forms an oocyst until a favourable host is found. This cyst formation is only able to occur if temperatures within the faecal matter are optimal, mainly below 23 ̊C (Mawdsley et al., 1995; Venglovsky et al., 2009).

Types of waste also play a major role in the transportation and survival of the pathogen. It has been found that organic compounds are able to compete with bacteria, viruses and protozoans for space in faecal matter. In the UK, because livestock is housed on straw, the majority of its waste occurs in the form of slurry, which makes for easy transport of microorganisms

(Mawdsley et al., 1995; Venglovsky et al., 2009). Studies show that despite the competition, microorganisms survive better in faecal matter such as slurry instead of dry pellets (Mawdsley et al., 1995; Venglovsky et al., 2009; Wilkes et al., 2011).

In South Africa, communal livestock are not housed through winter or any time of the year. Instead, livestock are cared for according to knowledge passed down from older herder generations to younger herder generations (Ntombela, 2017). In the Northern Cape communal

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http://etd.uwc.ac.za/ grazing lands, livestock are housed in an enclosure called a kraal (livestock pen). The kraal keeps all livestock in a particular area at night, which makes it easier for the herder to care for them and to protect them from predators. In the Eastern Cape, livestock are allowed to roam free in these communal farming areas and interact freely with wildlife, other livestock herds and human beings. These communal farming conditions create opportunities for pathogens to establish and transferred.

1.6 Pathogens in soil

Depending on the soil type and the condition the soil is in, the route of pathogens through the soil will vary. In areas with impermeable soil, high levels of contamination can be found on the surface and sub-surface areas. Whereas in areas with permeable soil, high levels of contamination would occur in land drains and ground water because the pathogens would be able to move through the porous soil profile (Mawdsley et al., 1995).

Literature has shown that irrespective of soil type, nature has many measures in place to make all soil types pathogen suppressive. Pathogen suppressive soils have been known for over 100 years. This suppressiveness is mediated by many biotic or abiotic factors. Soils may be pathogen suppressive in which plant pathogens cannot survive, or disease suppressive in which the presence of pathogens in the soil does not result in disease (Larkin et al., 1996;

Löbmann et al., 2016; Mwendwa, 2018).

In South Africa, a hierarchical system is used to classify soils. This system has identified 73 different soil forms based on the top soil layer in a particular area (Benhin, 2006).

Soil profiles are important to determine if it is sufficiently moist, warm and porous to accommodate bacterial species (Löbmann et al., 2016). For this study, however, soil types were not distinguished or analysed but random soil samples were collected at water points.

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http://etd.uwc.ac.za/ 1.7 The role of bioinformatics

The spike in agricultural production has opened a need to study the interactions between humans and their livestock as well as the interactions of livestock with wildlife.

However, South Africa’s biosystematics research is challenged, as there is minimal funding, many gaps in the knowledge, and a lack of interest in the field (DAFF, 2016). Research in the field of bacteria, especially zoonosis in South Africa, has not been studied in detail, justifying why this research is crucial.

Conventional analytical methods, such as culturing and BLAST polymerase chain reactions (PCR), used to characterize and classify soil microbes are laborious, time consuming and some bacterial cultures cannot be cultured using current methods (Pontes et al., 2007;

Boughner and Signh, 2016). In the case of uncharacterized samples, such as soil, we find that culture based techniques are limited in the sense that the process is both resource and time consuming, especially in instances where the bacteria being isolated exhibits slow growth or fastidious tendencies (Hasmas et al., 2014). In addition to this, the culture based approach is constrained by the heterogeneous nature of soil which comprises of numerous microhabitats each with their own suitability for bacterial growth (Kirk et al., 2004). Consequently, this gives rise to uneven aggregation of bacterial cultures and may lead to under reporting of bacterial diversity (Wall and Virginia, 1999).

Matters are further complicated by the difficulties associated with culturing soil bacteria. Studies by Borneman et al., (1996) and Giller et al., (1997) have suggested that up to

90% of bacteria observed using microscopy are incapable of being cultured using standard culture techniques. Taking this into consideration, the accuracy of the technique is brought into question as there is a possibility that the 1% of culturable bacteria may not be a true reflection of the entire bacterial community (Rondon et al., 1999).

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http://etd.uwc.ac.za/ Biosystematics research provides a platform to fill this knowledge gap since recent advances in sequencing technologies, especially next-generation sequencing (NGS), affordability of sequencing and high-throughput sequencing information can help in environmental and ecosystem studies (Wu et al., 2016). Next-generation sequencing allows for a cost-effective manner in which to run hundreds of thousands of sequences during a single instrumental run enabling one to characterise both abundance within a community and rare community members. The ability to generate detailed profiles on the bacterial composition in faecal matter and soils highlights the benefit of this approach (Shanks et al., 2011).

In the case of bacterial investigations, NGS targets the 16S rRNA gene, which has since become the most common genetic marker in bacterial investigations (Patel, 2001; Janda and

Abbott, 2007). Three features underpin the utility of this gene: Firstly, it is ubiquitous in all bacteria; secondly, its function appears to have remain unchanged and finally, the size of the gene (1500 bp) lends itself well to bioinformatics applications (Patel, 2001; Janda and Abbott,

2007). In order to answer the questions posed by this study, the universal primer base on the

V3-V4 region of prokaryotic 16S rRNA were selected, due to its universal use and recognition.

A study performed by Takashi et al., (2014), in which these specific primers were made, showed that these primers pick up 98% bacterial DNA and 94.6% archaea DNA. They also proved that it picks up rare bacterial species and more bacterial species than the bacterial universal primer (Takashi et al., 2014; Zhang et al., 2017).

For this project NGS sequencing on the Illumina MiSeq genome analyser was the respective NGS system chosen. The Illumina MiSeq system has established itself as the dominant sequencing platform in the industry (Caporaso et al., 2011; Caporaso et al., 2012;

Degnan and Ochman, 2012; Wu et al., 2015). Illumina’s dominance is underpinned by its cost efficiency, large throughputs, and a relatively lower error return rate when compared to other platforms (Caporaso et al., 2012; Wu et al., 2015).

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http://etd.uwc.ac.za/ 1.8 Study site

This study focused on the communal farming area of Steinkopf in the semi-arid

Namaqualand region in South Africa (Figure 1.1). The Steinkopf communal rangeland is one

of the oldest communal rangeland in South Africa as it was established in 1819 (Allsopp et al.,

2007; Ntombela, 2017).

Figure 1.1 Representation of the Steinkopf communal farming area Northern Cape (A) of South Africa (B), Red spot representing NamaKhoi Municipality.

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http://etd.uwc.ac.za/ Steinkopf is one of six former “Coloured Reserve Areas” now known as Act 9 areas.

The rest being Richtersveld, Komaggas, Leliefontein, Concordia and Pella (Oakley, 2006; May and Lahiff, 2007). Until recently, Steinkopf was the largest of the Act 9 areas, with communal land tenure and a mixed farming (sheep and goats) system on about 50 000 ha. These rangelands are managed by an intricate web of bodies composed of the environment, socio- economics, and political enterprises and, of utmost importance, a pool of indigenous herding knowledge (Allsopp et al., 2007; Samuels, 2013; Ntombela, 2017).

This landscape is characterised by the western coastal plain or Sandveld (Sandy region) with rocky outcrops (Inselbergs) of the eastern interior region and then the western mountainous Hardeveld region. Steinkopf is a semi-desert region with winter rainfall in the western regions, Succulent Karoo (70% of the total surface area) and summer rainfall in the east, Nama Karoo. Bushmanland (summer rainfall region in east) has the lowest rainfall

(25mm-50mm annually) and is characterised by mainly grasslands while the western mountainous region has the highest rainfall (151mm-175mm annually) and is characterised by dwarf succulent and large non-succulent shrubs. Summers are hot, with an average daytime temperature of 35 °C, while winters are cold, with an average of 7 °C and at night temperatures sometimes drop below zero. In Steinkopf animals are not restricted by fences in the large landscapes allowing for constant contact between livestock, feral horses and wildlife; and thus is prone to soil erosion because it has suffered from severe overgrazing in the past. Drought is a common phenomenon which occurs every 7-10 years in the region (Mackellar et al., 2007).

1.9 Study aim

Firstly, to identify and characterize the bacterial microbial community found in the soil body and faecal matter (dung) within the Steinkopf communal rangeland in South Africa, using

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http://etd.uwc.ac.za/ Next-generation sequencing. Secondly, to assess whether pathogenic bacteria are present within the rangeland and what their potential impact on the local farming community might be if present.

1.9.1 Study questions

1. What is the composition and density of bacterial species within faecal matter and soils

in communal farming areas in South Africa?

2. How much variability are within these bacterial species found in Steinkopf?

3. Which of these bacterial species found in Steinkopf are pathogenic and what are their

likely implications for livestock and people?

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http://etd.uwc.ac.za/ Chapter 2

Identification of environmental bacterial microbes in the winter

rainfall region of Steinkopf communal rangeland, South Africa

2.1 Introduction

Estimates of bacterial microbiota are needed for various studies especially within agricultural crop growing, animal husbandry as well as veterinarian science. However, when analysing bacterial communities one needs to ensure that you have encompassed a good proportion of the community and not just a mere estimate.

During this study, in the Steinkopf communal rangeland, three key components were unfolded, which influenced every aspect of livestock husbandry within these rangelands. These components were sample type, location and seasonality. In this study the term location points to the type of waterhole that was sampled, seasonality refers to winter or summer and sample type refers either dung or soil. Sample type is an important aspect when considering investigating bacterial microbes within a particular area. If the wrong sample type was selected a complete reading of the environmental microbes and the movement of these microbes between wildlife, livestock and humans would not be sufficiently investigated. Therefore, in this study two sample types (soil and dung) were selected and compared to one another in order to determine which sample type would be a sufficient reader of environmental bacterial microbes.

The effect of dung patches on pasture ecosystems and nutrient cycling has been well studied but very little is known about the effect of dung patches on the soil microbial biomass.

Microbial communities in soil participate in diverse ecological interactions between organisms and in biogeochemical processes of nutrient mobilization, decomposition, and gas fluxes

(discussed in Chapter 1 section 1.6 and 1.7) (Su et al., 2012; Soliman et al., 2017). Hence, the

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http://etd.uwc.ac.za/ current study is focused at the soil and dung around congregation points where potentially harmful bacteria could occur.

As such, the aims of this aspect of the study were:

1) To identify the microbial communities found in the soil and faecal matter, in the winter

rainfall area, by applying Next-generation sequencing.

2) To ascertain the diversity of bacteria at water points (man-made versus natural).

3) To determine the effect of seasonality (summer versus winter) on the bacterial

composition in the winter rainfall area within Steinkopf communal rangelands.

2.2 Methods and materials

2.2.1 Study site and collection

Steinkopf is a semi-arid desert characterized by granite hills (koppies) separated by sandy plains. The rainfall is around 100-200 mm per year but in individual years can be as high as 400mm. Summers are hot with an average maximum daytime temperature of 35°C while winters are cold, with an average minimum temperature of 7°C and at night temperatures drop below zero. Steinkopf is prone to soil erosion because of heavy overgrazing which depleted the vegetation cover.

Within the Ikosis region of the winter rainfall area of Steinkopf there are nine water holes of which two are naturally occurring and seven are man-made (either wind or solar pumps). Five of these nine waterholes are non-functional leaving a remainder of three operating waterholes, which were used in the study along with one broken solar-wind pump waterhole; located 2km, in either direction, from the operating holes used in the study.

Therefore, four study sites in the Steinkopf communal rangeland were selected (Figure

2.1). These study sites included Malkop (man-made solar pump), Spring (natural water-hole),

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http://etd.uwc.ac.za/ Niel (non-functional solar-wind pump) and Halfmens (dug-well); GPS coordinates of the sampled sites are provided in Table 2.1.

In the study location refers to the specific water point chosen. The four water points used in this study, are the most used water points in the winter rainfall region of Steinkopf.

Malkop being a solar-pump that is found at the periphery of the winter rainfall region and Neil a broken down solar-wind-pump located 2 Km from Malkop further into the winter rainfall region. Whereas Spring and Halfmens are both natural water points. Spring being a natural occurring water body that carries water all year round, and Halfmens a dug-well dug by herders on one of the koppies in the area and is regarded as the last reliable water point in the winter rainfall region. Location is regarded as an important component because the state of the waterhole determines if herders allow their livestock to use that particular waterhole or not.

Man-made water holes could be characterized by a long water trough canal which has a wind or solar pump on one end, pushing water up from the ground and draining it along the trough which livestock then congregate around to drink water. Hence, soil samples were collected from the upper most soil layer directly surrounding the water trough and fresh dung samples were collected along the same canal during the period of occupation by livestock. At natural occurring water holes, soil and dung samples were collect around the circular water body in the same manner. At each site, two seasonal samples were collected totaling eight samples per site (two winter soil samples and two winter dung samples as well as two summer soil samples and two summer dung samples) (Appendix 1).

The third important component is seasonality. Seasonality is important because the

Steinkopf rangeland receives rainfall in both winter and summer. However, these rains occur in different areas, thus a winter and summer rainfall area. Due to this rainfall regime, 85% of herders move their livestock between the winter and summer rainfall regions depending on which area of the rangeland receives rain. For this aspect of the study only the winter rainfall

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http://etd.uwc.ac.za/ area will be investigated. The samples compared were samples collected in both winter (the

raining season) and summer (the dry season) at the above-mentioned water holes.

Figure 2.1 Map representing the winter rainfall area within the Steinkopf communal farming area. Black spot: Malkop

(solar-pump). Blue spot: Spring (natural). Brown spot: Neil (solar-wind pump). Red: Halfmense (dug-well).

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Table 2.1 Co-ordinates of study sites in Steinkopf communal farming area. Malkop (man-made solar pump),

Spring (a natural water hole), Neil (solar-wind water pump) and Halfmens (a dug-well).

Site Name Latitude Longitude

Malkop 29°12'34,49 17°39'56,24

Spring 29°10'36,86 17°37'49,04

Neil 29°12'38,45 17°39'34,68

Halfmens 29°8'40,68 17°38'17,99

At this point it is important to highlight that dung samples are obtained from individual

animals and soil samples are obtained from the broader environment that animal occurs in. For

the purpose of this section in the study, bacterial microbe comparisons will only be done on

both the phyla and genera level, however details about the phyla and genera and its impact on

animal husbandry in this rangeland will only be delved into further in the study.

2.2.2 DNA extraction

Total community genomic DNA was extracted using a Macherey-Nagel soil extraction

kit following the manufacturer’s instructions (Gmbh & Co. KG). The extracted DNA was

stored at -20°C for later use for PCR (polymerase chain reaction) at 55°C for 34 cycles.

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http://etd.uwc.ac.za/ Extracted samples were PCR’d in 3 replicates and replicates were combined before secondary pcr (library preparation) was done.

2.2.3 Amplification of the V3 and V4 region of 16S rDNA

Amplification of the hypervariable regions V3 and V4 was conducted according to the

Illumina 16S metagenomics sequencing library preparation guide (Illumina, 2013). Primers used were 341F forward primer (TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG

CCT ACG GGN GGC WGC AG) and 805R reverse primer (GTC TCG TGG GCT CGG AGA

TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C) with added Illumina adapter overhang sequences, namely, Forward overhang: TCG GCA GCG TCA GAT GTG

TAT AAG AGA CAG and Reverse overhang: GTC TCG TGG GCT CGG AGA TGT GTA

TAA GAG ACA G, respectively.

The constructed libraries were purified with Agencourt AMPure XP beads (Beckman

Coulter Genomics, California, USA) according to the manufacturer’s protocol and quantified using a Qubit fluorometer (Qubit 3.0, Thermo Fisher Scientific, USA). Library quality control was performed with agarose gel electrophoresis to determine the quality and average distribution size. Libraries were normalized to 4 nM and the 24 samples pooled libraries were denatured with NaOH, diluted to a final concentration of 2 pM and spiked with a 5 % PhiX

(Illumina, 2013) control. The library was heat denatured for 2 min at 96 °C prior to loading.

Sequencing was performed using Illumina MiSeq System with libraries prepared using MiSeq

Reagent Kit V3 (2 X 300 bp). De-multiplexing and initial analysis of reads were performed on the MiSeq system with the MiSeq reporter software (Illumina, San Diego, CA, USA).

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http://etd.uwc.ac.za/ 2.2.4 Metagenomic data preparation and analysis

Raw reads in FASTQ format were retrieved from MiSeq Illumina sequencer inspected with FastQC V0.11.3. Then, paired-end reads FASTQ files were joined using PandaSeq with a maximum length of 460 bp (Masella et al., 2012). Using QIIME (V1.7.0), the resulting

FASTQ files were concatenated into a single FASTA file. In order to assign OTUs (Operational taxonomic units) to the sequences, a closed-reference OTU picking strategy was used in QIIME

(V1.7.0) (Caporaso et al., 2010a). The sequences were clustered at 97 % identity and aligned by PYNAST to the Greengenes V13_8 reference database (Caporaso et al., 2010b; McDonald et al., 2012). The generated OTUs were clustered into phylotypes by using taxonomic information of order and family to assign taxonomy to each representative set. This helps assign

OTUs to named organism based on the Greengenes reference database (Caporaso et al., 2010a;

McDonald et al., 2012).

2.2.5 Diversity comparison and statistical analysis

Alpha diversity estimates for observed OTUs were conducted by executing the alpha_rarefaction.py in QIIME (Caporaso et al., 2010a). The metrics calculated were: Whole tree, Chao 1, Shannon, Simpson and Observed species. Rarefaction curves were created for

Whole tree, Chao 1 and Observed species (Caporaso et al., 2010a). Beta diversity was calculated to determine the differences in the bacterial communities between and within different sample sites using box plots analysis on QIIME whereby, two-sampled t-test with 999

Monte Carlo permutations was conducted. Weighted and Unweighted UniFrac distances were calculated by executing beta_diversity_through_plots.py in QIIME (Caporaso et al., 2010a).

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http://etd.uwc.ac.za/ 2.3 Results

2.3.1 Classification of bacterial OTUs

To ascertain bacterial communities in Steinkopf communal rangeland, a total of 7 295

220 sequences were analyzed. The minimum number of sequences was 1500 and the maximum

754 140 with a mean of 260 054.4 and standard deviation of 17 490.2, respectively. The phylotypes produced 37 phyla, with 353 families and 634 genera. Samples J21 and J25 have been excluded from all analysis due to low sequence reads.

The most abundant bacterial phyla within soil samples were Planctomycetes (34%),

Firmicutes (15.4%) Bacteroidetes (11.1%) and Chloroflexi (8.5%). The least abundant phyla within all soil samples were classified as other (0.001%), PO8 and NKB19 both with a representation of (0.0013%) (Figure 2.2).

The most abundant bacterial phyla in the dung samples were Firmicutes (28.3%),

Bacteriodetes (18.7%), Verrucomicrobia (13.7%) and Actinobacteria (12.6%). The least abundant phyla within the dung samples were BRC1 (0.003%), FBP (0.004%) and

Armatimonadetes (0.012%). The total percentage representation of Proteobacteria in soil and dung samples were 4.5% and 2.7% respectively with total unclassified phyla at 3.3% and 3.5% respectively (Figure 2.2). Distribution of phyla between Sample Type, Location and

Description can be found in Appendix 2.

The abundant bacterial genera in soil samples were Gemmata (9.1%), Akkermansia

(7.1%) as well as classes Phycispaerae and Clostridia with 11.5% and 7.4% respectively; however these members were not classified to genus level. The abundant bacterial genera found in the dung samples were Akkermansia (13.5%), Arthrobacter (7.5%) and classes Clostridia

(13.75) and Bacteroidia (9.4%); however, these members were not classified to genus level.

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120

100

40 Abundance (%) Abundance

0 Dung Soil Sample Type

Unassigned Euryarchaeota Parvarchaeota Other p__ Acidobacteria Actinobacteria Armatimonadetes BRC1 Bacteroidetes Chlorobi Chloroflexi Cyanobacteria Elusimicrobia FBP Fibrobacteres Firmicutes Fusobacteria Gemmatimonadetes Lentisphaerae NKB19 Nitrospirae OD1 OP8 Planctomycetes Proteobacteria SC4 SR1 Spirochaetes Synergistetes TM7 Tenericutes Verrucomicrobia WPS-2 WS2 WS4 Thermi

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http://etd.uwc.ac.za/ Rare bacterial genera (bacterial genera that are only found in a specific sample were

identified and can be found in Appendix 3. Further analysis also revealed a list of 57 potential

pathogenic bacterial genera, a few of which are: Campylobacter, Dietzia, Shewanella,

Straphylococcus, Mycobacterium, Treponema, Pseudomonas and Helicobacter. The entire list

can be found in Appendix 4 along with the distribution of these potential pathogenic genera in

Appendix 5.

2.3.2 Alpha Diversity

Observations from Shannon index, Simpson index, Chao 1, PD whole tree and

Observed OTUs showed that there was a variation in species diversity, evenness and richness

between dung and soil microbes from samples collected in the winter rainfall area of Steinkopf

communal rangeland (Table 2.2).

Table 2.2 Alpha diversity indices per category.

Observed Shannon Simpson Categories PD_whole_tree Chao1 OTU's Index Index Description 0,593 0,29 0,487 0,893 0,91 Sample Type 0,603 0,769 0,646 0,124 0,039 Location Neil vs Spring 1 1 1 1 1 Malkop vs Spring 0,396 1 1 1 1 Halfmens vs Malkop 1 1 1 1 1 Halfmens vs Spring 1 1 1 1 1 Halfmens vs Neil 1 1 1 1 1 Malkop vs Neil 1 1 1 1 1

Rarefaction curves for individual samples (Sample ID) (Figure 2.3 A-C) from Chao1,

PD_whole_tree and observed OTUs indices, indicate that ample sampling depth was achieved

however, additional OTUs could be discovered with more sequencing. From the observations

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http://etd.uwc.ac.za/ made in Chao1 diversity index sample J06 has the highest microbial species richness and the highest abundance of unique OTUs detected. Whilst, J23 had the lowest microbial species richness and J07 had the lowest abundance of unique OTUs. These observations were further supported by Simpson and Shannon diversity indices (Table 2.2) and Appendix 2. However, the correlation between these observations was not estimated in the current study.

Additional alpha diversity analysis was conducted to estimate the microbial diversity between Sample Types, Location and Description (Figure 2.4-2.6). The rarefaction curves show that, there is a higher species richness, evenness and unique OTUs detected in soil and summer as compared to dung and winter (Figures 2.4 and 2.6). With regards to Location,

Spring has a higher species richness, evenness and unique OTUs whereas, Malkop has the lowest species richness, evenness and unique OUTs (Figure 2.5), further supported by Table

2.2.

2.3.3 Beta diversity

Weighted UniFrac analysis, which represents abundance between and within the different sampled types, sampled sites and sample seasonality, showed that there was variation in the microbial abundance. There was an even distribution of abundance of microbes within all sample descriptions, types and locations (Figure 2.7). Observations made from Malkop vs

Malkop showed to be statistically significant (Figure 2.7 C). This indicates that sequence depth was deeper for those particular samples but they are not divergent from other samples.

The unweighted UniFrac analysis (which represents presence and absence of unique microbial communities between and within sample description, type and location) showed that unique communities were not evenly distributed within all samples. Additionally, within

Description summer vs summer showed to be statistically significant, within Sample Type

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http://etd.uwc.ac.za/ dung vs dung showed to be statistically significant and within Location Malkop vs Malkop,

Halfmens vs Halfmens, and Malkop vs Neil showed to be statistically significant (Figure 2.8).

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*Figure produced in Qiime, with standard formatting.

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Figure 2.4 Sample Type Alpha diversity indices, A: Chao 1 diversity index showing species richness; B:

PD_whole_tree diversity index showing the phylogeny of detected microbes in ration to their abundance; C:

Observed OTUs diversity showing the number unique OTUs produced.

*Figure produced in Qiime, with standard formatting.

*Figure produced in Qiime with standard formatting

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*Figure produced in Qiime, with standard formatting.

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*Figure produced in Qiime, with standard formatting.

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Figure 2.7 Weighted UniFrac distance matrices where A is observed beta diversity of Description, B is observed beta diversity of Sample Type and C is observed beta diversity of Location. The bottom and top of each box represents the

st rd 1 and 3 quartiles whereas the whiskers represent the values outside the upper and lower quartiles. The plus signs below the whiskers represent statistically significant observations.

*Figure produced in Qiime,46 | withP a gstandard e formatting.

http://etd.uwc.ac.za/

Figure 2.8 Unweighted UniFrac distance matrices where A is observed beta diversity for Description, B is for Sample

st rd Type and C is for Location. The bottom and top of each box represents the 1 and 3 quartiles whereas the whiskers represents the values outside the upper and lower quartiles. The plus signs above and below the whiskers represent statistically significant47 | Pobservations. a g e *Figures produced in Qiime, with standard formatting.

http://etd.uwc.ac.za/ 2.4. Discussion

2.4.1 Sample Types

By using a high-throughput sequencing approach (NGS) 37 phyla, 353 families and

634 bacterial genera were identified of which Firmicutes, Planctomycetes, Bacteroidetes,

Actinobacteria, Verrucomicrobia, Chloroflexi and Proteobacteria were the most abundant phyla in the study.

Alpha diversity analysis of all samples (Figure 2.3) indicates that most of the samples collected were sufficient readers of that particular environment as rarefaction curves tend to stabilize. Figure 2.4 further supports sufficient sampling effort of both soil and dung samples.

Livestock faecal matter acts as a hub of gut microbial constituents making it a possible pollutant threat to the environment and human health. Knowledge of faecal matter and its components is of great concern for public health and environmental management of faecal disposal (Spiehs, 2007; Hutchison et al., 2005). A number of pathogenic microbes are excreted daily from both healthy carrier and infected animals, when defecating. Types of bacterial microbes that are excreted from the individual animal are dependent on three main factors; grazing routes, specific waterhole used and medical care provided to the animal.

Livestock herders change their grazing routes on a daily basis in order to allow the field time to recover. However, these grazing routes are chosen based on the proximity to a waterhole, the type of forage available, the level of safety from predators as well as the composition of the herd (discussed in Chapter 1 Sections 1.3 and 1.8). For example, some herders that specialize in goat husbandry prefer staying on the rocky mountainous areas as water sources are plenty, forage is plethora (shrubs and trees) and, competition for forage is not high as sheep cannot easily track up rocky terrain (Samuels, 2013; Ntombela, 2017). Thus,

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http://etd.uwc.ac.za/ the quality of faecal matter received from such herds are of different composition compared to herds that remain on the plains (feed on grass species).

Faecal matter quality and composition will differ between two animals from either herd

(those feeding on the koppies and those on the plains) due to a difference in forage availability.

Thus, prompting the activation of different types of enzymes and the growth of different types of gut bacteria. A study done by de Menezes et al. (2011) focused on the impact of type of forage (pasture verses mixed ration diets) on the gut microbial ecology. They revealed significant differences in the internal bacterial communities of rumen fed different diets. They also revealed that at a family level some important bacterial phyla were associated with certain diet types.

Thus, in this study the diversity of bacterial phyla found in dung samples are justified as herds are scattered throughout the landscape (according to herder preference) and consume a mixture of forage type on a weekly bases. This scenario explains why there is a statistical significance between dung samples when compared to other dung samples (dung vs dung) in

Figure 2.9, and why dung samples have a higher percentage of unclassified bacterial phyla

3.5% when compared to soil 3.3% (Figure 2.2). In other words, there are unique bacterial microbial constituent between and within dung sample types indicating dung samples differ significantly from one another.

Further analysis revealed that soil samples, when compared to dung samples, has a higher species richness, evenness and unique OTUs (Figure 2.4). According to Appendix 4 of the 57 possible pathogenic genera identified, 57 were located in soil samples and a mere 22 in dung samples. Two soil samples in particular; J24 and J31 contained the highest percentage possible pathogenic bacteria, 9% and 6% respectively.

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http://etd.uwc.ac.za/ The simplest extrapolation for this scenario is that soil samples do not only house soil bacterial microbes but also gut microbes excreted by animals that occur in that area.

Through trampling and desiccation of faecal matter, microbes are released into the surrounding environment; many making their way through the soil body. Soil is also considered a stable environment when compared to faecal matter (dung pots of pellets). The further down microbes travel through the soil body the more stable the environment becomes. Temperature and moisture becomes constant, and space is not a limited resource as it is in faecal matter

(Miransari, 2011; Paul, 2014). This thus allows bacterial microbes to develop and grow at rapid rates and increasing the complexity of types of bacteria found in the soil body.

However, this does not limit the movement of bacterial microbes and does not imply that bacterial microbes only move from faecal matter to soil. Bacterial microbes could easily move from the soil body to dung pots and pellets as well. The above mentioned scenario is only the most logical scenario for the Steinkopf region. As temperatures can be as high as 40°C and droughts can persist for months, thus, leaving faecal matter exposed to unfavourable conditions for long periods of time and possibly hindering the growth of bacterial microbes within faecal matter.

Therefore, soil samples are regarded as the best sample type when investigation environmental bacterial microbes because soil samples are not only composed of soil microbes but also bacterial microbes excreted from both livestock and wildlife. Whereas faecal samples are only a representation of the individual animal’s health. Thus, soil samples provide an overall view of both environmental health and animal health in that particular environment.

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http://etd.uwc.ac.za/ 2.4.2 Location

Within communal farming areas there are allocated water points and these water points are either man-made (majority is) or natural (discussed in detail in Chapter 1 Section 1.3).

Statistically it is evident that there is marked differences between the bacterial species diversity and abundance when comparing natural water points and man-made water points. It is shown in Appendix 2 that natural water points Spring and Halfmens have a similar bacterial composition and man-made water points Malkop and Neil have similar bacterial compositions.

When focusing on natural water points (Spring and Halfmens) these water points contain the highest phyla diversities and Halfmens has the highest unclassified bacterial phyla

(Appendix 2). This is attributed to the high livestock traffic within these areas during winter and, at times, summer. Herders rely on these water points as Spring contains water year round and Halfmens, being a dug-well, can be easily adjusted by herders through digging deeper for ground water.

Spring has the highest species richness, evenness and unique OTUs followed by

Halfmens (Figure 2.5). Once again this can be attributed to the fact that Spring contains water year round and herders, livestock and wildlife can always rely on this natural water point to provide water.

Analysis revealed 15 unique bacterial genera at Halfmens with six of these being potential pathogenic genera. Similarly, at Spring analysis revealed 14 unique bacterial genera with five potential pathogenic genera (Appendix 3 and Appendix 5). Further validated by

Figure 2.8 which shows a statistical significant between Halfmens vs Halfmens, indicating a difference in the absence and presence of unique species within this location.

Halfmens is one of the first water point’s livestock encounter when trekking from the summer rainfall region to the winter rainfall region. However, Halfmens is located on a koppie

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http://etd.uwc.ac.za/ and most herds only use this point as a resting area on their way to Malkop/Neil/Spring.

Therefore, few herds remain in the area (majority herds composed of goats) surrounding

Halfmens. These herds are exposed to different forage and all rely on this single water hole.

Therefore, the environmental bacterial composition located at Halfmens is so different when compared to other sites. Not only are unique gut microbes activated by the forage surrounding the water hole but, because the water hole is a dug-well, livestock are exposed to soil microbes that are found in deeper soil layers than compared to those at Malkop, Spring and Neil where livestock are only exposed to the top soil layer.

When focusing on man-made water points Appendix 2 illustrates that Neil has the lowest bacterial phyla diversity and Appendix 3 illustrates that it only has a total of three unique genera of which one is a potential pathogenic genera. This low diversity and abundance at Neil speaks to the activity at this location. Neil is a solar-windpump. However, due to lack of maintenance, it is no longer in working condition and in this way places a higher pressure on the Malkop in order to meet livestock and herders needs.

Although, Malkop has the lowest species richness, evenness and unique OTU count, it does, however, have the highest count of unique genera (42 genera) and the highest count of potential pathogenic genera (21 genera). This is further validated with Figure 2.7 and Figure

2.8, which shows statistical significance between Malkop vs Malkop. The clear variation between the two man-made water points (Neil versus Malkop) is evident in Figure 2.8 where a statistical significance is seen between Malkop vs Neil.

The high levels of potential pathogenic bacterial genera located at these man-made water holes speaks to the lack of maintenance at these water holes and the importance of natural water holes. Pathogenic bacteria are not as prevalent at natural water holes because herders

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http://etd.uwc.ac.za/ tend to clean these waterholes removing all muck and pollutants surrounding the waterhole.

Natural waterholes also tend to have built in maintenance systems.

In almost all natural environments pathogen suppressiveness plays a key role (Larkin et al., 1996; Löbmann et al., 2016; Mwendwa, 2018). Wildlife such as birds actively remove disease carrying insects or worms from the waterhole while certain fungal/plant interactions prevent pathogenic bacteria from thriving in the environment through either releasing a toxin to fight the bacteria or preventing the bacteria from entering the environment in the first place.

Bacterial taxa such as Solibacillus silvestris and Exiguobacterium mexicanum, found at natural water points, both Halfmens and Spring, are bacterial taxa currently under investigation for their pathogen inhibitory traits (Gupta et al., 2012; Shanthakumar et al., 2015; Markande et al.,

2018).

Man-made waterholes on the other hand are maintained by the municipality who is responsible for repairs, cleaning of the system as well as upgrades to the system. However, in this particular communal rangeland, the municipality has limited funds and man-power and can only maintain water points for a certain period after which it is neglected and becomes non- operational.

2.4.3 Description

When looking at the category Description, all samples were divided into two subcategories; Summer and Winter. Samples were collected in both summer and winter seasons within the winter rainfall region because herder move their livestock seasonally (Chapter 1

Section 1.8). Thus implying an absence and presence of livestock within the winter rainfall area on a seasonal basis. This migratory pattern can clearly be seen in both alpha and beta diversity indices. Summer is represented as the highest species richness, evenness and unique

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http://etd.uwc.ac.za/ OTUs present when compared to winter (Figure 2.6). This could be justified by the higher percentage of unclassified bacterial phyla in summer (3.9%), compared to winter (2.7%).

Within the high number of unique OUTs detected 17 possible pathogenic genera are present in summer compared to the 15 possible pathogenic genera present in winter. Beta diversity indices refer to an uneven distribution within sample descriptions and a statistical significance within summer vs summer. Indicating not only that summer has a higher presence of unique microbial bacterial communities but that there is a great significance between the various types of summer samples.

The higher summer diversity and abundance can be contributed by two factors: low competition and bacterial temperature tolerances. Without livestock present in the summer season within the winter rainfall region, bacterial microbes are not disturbed through trampling or increases in competition for natural resources such as organic matter, water and space.

During the summer season bacterial communities are able to grow and stabilize and be a robust community by winter. This resting period also allows for more complicated bacterial species to develop once the pioneer species have settled. Secondly, bacterial species have a higher tolerance to high temperatures than they do to low temperatures seen in the study done by

Pietikäinen et al. (2005) and Lapara et al. (2000) which proved that soil bacteria had optimum growth rates between 25-45°C and an opposite tendency was observed at lower temperatures.

2.5. Conclusion

This study describes how useful high-throughput sequencing techniques, such as Next-

Generation sequencing, are in order to produce large volumes of information from small sample sizes. It also allowed for identification of bacterial abundance and diversity and comparing these within three main categories. This method detected differences in abundance

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http://etd.uwc.ac.za/ and diversity when comparing summer samples to winter samples, man-made water points to natural water points and soil to dung. This would not have been possible if conventional methods were applied as they are laborious, time consuming, some are costly, prone to human error and some bacterial species cannot be cultured using current techniques (Pontes et al.,

2007; Boughner and Singh, 2016).

Soil was found to be the best sample type for such investigations as it takes into account the variation of individuals occupying the environment instead of being bias to a single individual. Within the study area, summer samples were of greater diversity and abundance and contained high levels of potential pathogenic bacteria. Therefore, it is suggested that all herders who use the winter rainfall area, at any time in the year, rather relocate livestock to the summer rainfall area during summer seasons and move them back to the winter rainfall area during winter seasons. This will decrease the risk of animals being infected with potential zoonotic bacteria. Maintenance of communal rangelands are also of concern when comparing man-made water points to natural water points. Although natural water points are freely available and can be maintained by herders there is no guarantee that water will be available at those sites. This leaves herders reliant on man-made water points that is poorly maintained and acts as a breeding ground to potential pathogenic bacteria. This is a real risk for both livestock and herder as contact with these waters and surrounding area could cause health problems.

Further studies should therefore be conducted to understand the interaction at man-made water points in detail and to prove the importance as soil as a sample type over dung.

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http://etd.uwc.ac.za/ Chapter 3 Identification and characterization of bacterial microbes within the

Steinkopf communal rangeland: Is bacterial pathogenesis a threat?

3.1 Introduction

In the past few years, global expansion has been placing an increased burden on agriculture to meet globalised increasing demand for livestock products (Coker et al., 2011).

One unintended consequence has been the emergence and spread of transboundary animal diseases and, more specifically, the resurgence and emergence of zoonotic diseases (refer to

Chapter 1 Section 1.4 for further details on zoonosis and the impacts on agriculture). Nearly two thirds of human pathogens and three quarters of emerging and re-emerging diseases are of a zoonotic origin (Coker et al., 2011). Emerging diseases include avian influenza, sever acute respiratory syndrome (SARS) and bovine spongiform which have impacted on economies through substantial media coverage, medical care and at times preventing income through tourism (Coker et al., 2011; Murray and Dazak, 2013).

New infectious diseases have become more apparent due to changes in nutrition in both livestock and humans, rapid urbanisation, deforestation, encroachment on wildlife, shifts in land use and most importantly increased and improper management of agricultural and trade practices (Coker et al., 2011; Murray and Dazak, 2013). Similarly, what was once regarded as ancient diseases such as rabies, anthrax, brucellosis and bovine tuberculosis have now become re-emerging diseases because of similar factors including an increase in transboundary travels and transmission of pathogens between wildlife and domestic animals (Coker et al., 2011).

Increasing complexity of food chains and different systems of food production and preparation are also leading to rising importance of food-borne zoonosis such as Salmonella,

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http://etd.uwc.ac.za/ Campylobacter and Escherichia coli infections which are linked to livestock production and meat processing (Coker et al., 2011).

The first livestock sector revolution largely affected ruminant animals, through advances in livestock production methods and marketing systems, and caused many diseases, especially in developed countries. These diseases were controlled through veterinary practices, increased education and investment in research. However, many overlooked diseases, such as bovine tuberculosis and brucellosis, later caused large-scale human mortality (Coker et al.,

2011). The second livestock revolution has been parallel to the increased production of mixed farming models, increasing contact among humans, livestock and wildlife. This change has happened due to increased settlement developments, farming in previous wildlife territories and increased tourism in remote areas (Coker et al., 2011; Murray and Dazak, 2013).

This amplified contact has heightened the possibility of transmission of infections from wildlife to livestock and humans. Reviews suggest that many emerging human pathogens originate from wildlife and the increased intensity of livestock production only aggravates this transmission process (Coker et al., 2011; Murray and Dazak, 2013).

An interconnected world means drastic changes in ecosystems that result in unpredictable opportunities for microbe transmission. Many pathogens persist in multi-host systems making the identification of infection and the reservoir crucial for devising effective interventions. Failure to establish this understanding can hamper policy formulation and lead to ineffective or counter-productive control measures with costly implications for socially, or economically important populations (Viana et al., 2014).

According to the World Health Organisation, listed under Disease Outbreak Reports, in the past 23 years South Africa has bared burden to a few of these disease pandemics. In 1996 between the periods of 8 November- 28 November South Africa was on full alert after a patient

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http://etd.uwc.ac.za/ from Gabon entered the country carrying Ebola haemorrhagic fever and infected a nurse. Due to fast action taken by medical staff only two fatalities were listed.

Three years later, in 1999, the Kruger National Park lost a lot of wildlife due to an outbreak of Rift Valley Fever; three veterinarians were infected by exposed wildlife tissue. No human fatalities reported.

On the 27th of May 2010 German and South African authorities alerted the South

African republic with another scare of Rift Valley Fever (RVF), after a German tourist returned home and was admitted to hospital. A few days before the tourist arrived in the country 172 cases and 15 deaths were reported by the Department of Health South Africa in the Free-State

Province, Eastern Cape Province, Northern Cape Province, Western Cape Province and North

West Province. Most of these cases had been tracked back to infected livestock and wildlife.

However, by the 11th of May 2010 German authorities alerted South Africa to a misdiagnosis of RVF and instead diagnosed the victim with Rickettsia. Despite this, no other Rickettsia cases were reported in South Africa. However, the RVF tally increased to 186 confirmed cases and

18 deaths.

Lastly, in 2018 a multi-sectoral task force was put into operation to address the case of

Listeriosis, a food-borne disease. Between 1 January 2017 through 24 April 2018, 1024 laboratory-confirmed listeriosis cases had been reported by the National Institute for

Communicable Diseases (NICD) from all provinces, of whom 200 of them died.

It can be noted that the above disease outbreaks are between 1996 to 2018 links to the same century as public Global Warming awareness. Scientists were aware of Global Warming since the early 19th century (Svante, 1896; Nuemann, 1985), however, the effects of Global

Warming only become apparent in the 1960’s when the warming effect of carbon dioxide gas become increasingly convincing. In addition to the trends in annual temperatures and rainfall,

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http://etd.uwc.ac.za/ the impact of rainfall in extreme precipitation events became a high priority (Kruger and

Nxumalo, 2017).

One could only question if these changes have an effect on environmental bacterial microbial composition.

Over recent decades, the mean global climatic range has changed significantly specifically with reference to surface temperature. Despite surface temperatures that proved to be increasing annually, the rate of precipitation became more sporadic (Donat et al., 2013).

Many studies have been done to determine the global pattern of precipitation, yet only basic patterns have been found. These patterns do not necessarily hold true to what we are currently experiencing (Donat et al., 2013; McKellar et al., 2014). One such study is Kruger and

Nxumalo (2017). They used historical climate data (1921-2015) of South Africa to try to determine future precipitation patterns. They found that Precipitation has been and will continue to decrease in the far north-east and increase in the western regions, agreeing with a study done by McKellar et al. (2014).

Therefore, the aims of this aspect of the study were:

1. Identify and characterize bacterial microbes in the broader Steinkopf communal

rangeland, winter and summer rainfall regions, using Next-generation sequencing.

2. Determine if seasonality contributes to the composition of environmental bacterial

communities.

3. Determine which of these bacterial species are potentially pathogenic.

4. Describe the importance of these potentially pathogenic bacteria within the

communal rangeland.

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http://etd.uwc.ac.za/ 3.2 Methods and Materials

3.2.1 Study site and collection

For this aspect of the study samples were collected at random, working (in-use) water holes in both the summer rainfall region and the winter rainfall region (Figure 3.1) during the corresponding rainy season. Within the winter rainfall region only four water holes were being used and within the summer rainfall region eight water holes were being used. Sampling occurred on days a day or two after the area received rain and one soil and one dung sample was extracted from each site. A list of samples can be found in Appendix 6.

3.2.2 DNA processing

For DNA extraction, Amplification of V3 and V4 region of 16S rRNA, Metagenomic data preparation and Statistical analysis see Chapter 2, sections 2.2.2 – 2.2.5.

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Orange dots representing summer rainfall areas sampling sites.

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http://etd.uwc.ac.za/ 3.3 Results

3.3.1 Classification of bacterial OTUs

For this section of the study eight winter rainfall samples and 12 summer rainfall samples were analysed; one dung and one soil sample from each available working water point.

A total number of 240579 winter rainfall sequence reads and 816668 summer rainfall sequence reads were analysed. Data provided by each sample type was combined with the correlating sample from the specific water point it was collected from. Phylum level analysis (Figure 3.2) revealed that the Firmicutes, Bacteroidetes, Verruccomicrobia and Planctomycetes were the four most prevalent phyla assemblages present throughout all samples. However, the proportion distribution between sites vary; within the summer rainfall region Planctomycetes

(21 %), Firmicutes (20 %), Bacteroidetes (17 %) and Verrucomicrobia (16 %) whereas within the winter rainfall region Planctomycetes (33 %), Firmicutes (15 %), Bacteroidetes (14 %) and

Verrucomicrobia (7 %). Appendix 7 allows for identification of phyla between individual sites and has shown that the above mentioned phyla are present at relatively high percentages at all sites (between 4 % - 30 %) except at site W2 where Firmicutes and Verrucomicrobia have percentage distributions of 0.9 % and 0.3% respectively. W2 is also the only winter site that has the phyla Thermi present, at 0.2 % (Appendix 7). Spirochaetes, Elusimicrobia, WS2,

Chlorobi, SBR1093 and GN02 are phyla that are only present in the summer rainfall region and not in the winter rainfall region.

Family level analysis revealed that samples were dominated by Isosphaeraceae,

Verrucomicrobiaceae, Ruminococcaceae, Gemmaaceae, and Pirellulaceae.

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http://etd.uwc.ac.za/ 120

100

4 60 4

40 3

20 2

1 0 Summer Winter

Firmicutes Bacteroidetes Verrucomicrobia Actinobacteria

Planctomycetes Unclassified Proteobacteria Chloroflexi Acidobacteria TM7 Cyanobacteria Euryarchaeota Synergistetes Fibrobacteres Gemmatimonadetes Lentisphaerae Nitrospirae Armatimonadetes WPS-2 OD1 SR1 Tenericutes [Thermi] [Parvarchaeota]

FBP Spirochaetes Elusimicrobia WS2

Chlorobi SBR1093 GN02

Figure 3.2 Relative abundance of the four most prevalent bacterial phyla derived from 16S rRNA next-

generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland.

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http://etd.uwc.ac.za/ Distribution of these bacterial families (Figure 3.3) differ between regions. Aside from

Isosphaeraceae which has the same percentage presence (6.7%) at both winter and summer rainfall regions. Verrucomicrobiaceae has the highest percentage distribution in the summer rainfall regions (summer 15% and winter 6.6%) followed by Ruminococcaceae (summer

10.8% and winter 8%). However, within the winter rainfall region Gemmataceae and

Pirellulaceae have higher percentage distributions in the winter rainfall region (winter 8.2% and 5.4%, respectively and summer at 2.3% and 4% respectively). Besides a few differences in percentage distribution all bacterial families identified in the summer rainfall region is present in the winter rainfall region as well. Bacterial family distribution between sample sites can be found in Appendix 8.

Genus level analysis revealed uneven distribution of genera between summer and winter rainfall regions (Figure 3.4). Despite this, there were three genera abundant in both summer and winter rainfall regions; Akkermansia, Gemmata, and Planctomycetes. Within the summer rainfall region Akkermansia had a higher distribution to that in the winter rainfall region (summer 14.3 % and winter 6.5 %). However, Gemmata and Planctomycetes had higher winter rainfall distributions than summer rainfall, (winter 7 % and 1.8 % respectively) (summer

2.3 % and 1.3 % respectively). The level of unclassified genera is also profound compared to unclassified phyla and family levels. The summer rainfall region had the highest level of unclassified genera (4.5 %) and winter had the lowest (3.2 %). Pirellula (0.95 %) and

Caldilinea (0.6 %) were only present in the winter rainfall region. Whereas Oscillospira (0.3

%), Methanobrevibacter (0.3 %), Candidatus Xiphinematobacter (0.3 %), Pyramidobacter (0.3

%), and Fibrobacter (0.2 %) are only present in the summer rainfall region. Distribution of genera between sample sites are located in Appendix 9.

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0 Summer Winter Gemmataceae Ruminococcaceae Verrucomicrobiaceae Lachnospiraceae

Pirellulaceae Unclassified [Paraprevotellaceae] Isosphaeraceae

Ellin6075 Synergistaceae RF16 Planctomycetaceae Caldilineaceae Bacteroidaceae Micrococcaceae Rhodobacteraceae Clostridiaceae Sphingomonadaceae

Figure 3.3 Relative abundance of the three most prevalent bacterial families derived from 16S rRNA next- generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland.

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http://etd.uwc.ac.za/ Further analysis of Illumina MiSeq Summary reports revealed 3 pathogenic species

(Table 3.1) of which one was a plant pathogen, one human pathogen, and one multi-host

pathogen (infects both animals and humans).

Table 3.1 Summary of pathogenic bacterial species found in soil and faecal matter samples collected in the

Steinkopf communal rangelands.

Interaction Sample Sample Pathogen Region Type ID Animal Human Plant

Escherichia albertii Winter Rainfall Dung J29 +

Janthinobacterium agaricidamnosum Summer Rainfall Dung J53 + Kocuria assamensis + + Summer Rainfall Soil J12

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0 Summer Winter Akkermansia Gemmata Unclassified Planctomyces YRC22 Prevotella Pirellula Ruminococcus Clostridium Paludibacter 5-7N15 Arthrobacter Microbispora Oscillospira Methanobrevibacter Muricauda Candidatus Xiphinematobacter Pyramidobacter Fibrobacter Methanocorpusculum Microbispora Caldilinea

Figure 3.4 Relative abundance of the four most prevalent bacterial genera derived from 16S rRNA next- generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland.

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http://etd.uwc.ac.za/ 3.4 Discussion

By doing an environmental microbial comparison between the winter and summer rainfall regions one would be able to establish the state of health of the region, the benefit of the transhumance patterns livestock to that region and if there are potential pathogenic reservoirs within that region.

But in order to do this, certain environmental factors need to be taken into account.

Bacterial microbiota and parasites can only thrive in a given environment for example if certain soil factors are favourable. These being pH, Temperature, C/N ratio and wetting regime

(Mawdsley et al., 1995). A study conducted by Rousk et al. (2010) tested the effect of soil pH on both fungal and bacterial soil taxa and found that both relative abundance and diversity of soil bacteria was strongly influenced by pH and that bacterial levels nearly doubled between pH 4 and pH 8. A similar study by Rousk and Bååth (2007) tested the effect of C/N ratios on the growth rate of both fungal and bacterial soil taxa and found that addition of N to the soil body had a negative effect on the growth rate of bacterial microbes.

However, for this study environmental temperature and rainfall regime (seasonality) was taken into account and used to compare the bacterial microbial communities in the winter rainfall region and summer rainfall region.

3.4.1 Identification of bacterial taxa

When comparing the bacterial communities found in the winter and summer rainfall regions at a phylum level four phyla were detected as dominant in both regions. Firmicutes,

Bacteroidetes and Verrucomicrobia were of higher abundance in the summer rainfall region whereas Planctomycetes had a higher abundance in the winter rainfall region (Figure 3.2).

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http://etd.uwc.ac.za/ There is a great difference in the diversity of detected phyla in each region with summer rainfall having the highest diversity of 41 phyla, and winter a lower diversity at 35 phyla.

The above mentioned phyla are regarded as common soil microbes and many studies have been done (Belnap, 2001; Nagy et al., 2005) to identify and classify these taxa. But the role of these taxa within the soil body are still largely understudied.

The phyla Planctomycetes has the highest abundance in both winter (33 %) and the summer (21 %) rainfall regions. This phylum was first proposed in 2001 and was comprised of a single class, Planctomycetia, and two orders, Planctomycetales and Brocadiales (Garrity and Holt, 2001). However, in 2006 it was reclassified and had been designated to a superphylum integrating the phyla Planctomycetes, Chlamydiae and Verrucomicrobia

(Wagner and Horn, 2006). Within the current classification Planctomycetes comprises 3 orders,

Phycisphaerales, Plantomycetes and Candidatus Brocadiales, housing a total of 11 genera and

14 species (Lage and Bondoso, 2011). The members of this phylum is characterised with a slow growth rate which can be further hindered by fast growing and persistent taxa. The second most common genera in both the summer and winter rainfall region was Gemmata (summer at

2.3 % and winter at 7 %) which is a genus within the phyla Planctomycetes.

Firmicutes is the second highest phyla in terms of abundance and diversity within both the summer (20 %) and the winter (15 %) rainfall regions (Figure 3.2). It is a bacterial phyla that occupies a large range of habitats and can be both beneficial and detrimental in these diverse settings (Haakensen et al., 2008). Firmicutes are regarded as one of the largest bacterial phyla comprised of three classes: Bacilli, Clostridia and Erysipelotrichi (Logan and De Vos,

2009). The classes Bacilli and Clostridia are endospore forming bacteria (endospore-forming

Firmicutes; EFF) but do, however, differ in their respiration strategies; Bacilli are primarily aerobic bacteria and Clostridia are primarily anaerobic. This endospore forming abilities allow

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http://etd.uwc.ac.za/ these bacterial taxa a survival advantage as they are able to incubate in a dormant stage until environmental factors are favourable enough to encourage growth (Yung, 2006). This further allows them the advantage to persist in environments for longer periods of time and inhibiting other taxa from surviving within the given environment.

The Bacteroidetes phyla is a phenotypically diverse group of Gram-stain-negative rods that do not form endospores. The phylum contains four classes: Bacteroidia, Cytophagia,

Flavobacteria and Sphingobacteria. The genera Rhodothermus, Salinibacter and Thermonema are represented in this group as well (King et al., 1923). The phylum Verrucomicrobia is related to the phyla Chlamydiae, Lentisphaerae and Planctomycetes. It is comprised of three classes:

Verrucomicrobiae, Opitutae, and Spartobacteria. Analysis revealed that the genus Akkermansia was the most abundant genus in the study, in both the winter (6.5 %) and summer (14.3 %) rainfall regions. This genus is part of the Verrucomicrobia phyla and was first proposed in

2004.

Linking back to environmental factors, a study done by Pietikäinen et al. (2005) on the effect of temperature on fungal and bacterial soil taxa found that bacterial growth increased at higher temperatures and started decreasing at lower temperatures. While optimal bacterial growth temperature ranges between 25°C – 30°C, bacteria continued growing at temperatures higher than 30°C. A different study conducted by Bapiri et al. (2010), with regards to dry- rewetting cycles and its effect on fungal and bacterial growth, demonstrated that bacteria grew better in environments where rainfall and soil moisture were somewhat constant throughout the bacterial life cycle. However, in areas where the soil experienced a dry-rewetting cycle bacterial growth rate increased rapidly but then severely reduced. These studies are all explanations for the bacterial composition pattern seen in the Steinkopf communal rangeland.

The summer rainfall region contains a higher abundance and diversity of bacterial species because temperature is optimal during summer ranging between 30°C and above and rainfall

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http://etd.uwc.ac.za/ patterns are not as intense and regular as they are in the winter rainfall region. Within the Nama

Karoo biome (summer rainfall region) droughts are common and, although rainfall ranges from

100 mm to 500 mm, rainfall is normally low for the region. Therefore, one can presume that the summer rainfall region is a better environment to house bacterial taxa than the winter rainfall region due to these two environmental factors that appear to be favourable for bacterial growth.

One can only further extrapolate that the summer rainfall region will continue to be a breeding hub for unique bacterial species and potential pathogenic bacterial species because veld observation show signs of desiccation; and community members have been tracking drastic changes in precipitation, predicting that they have not received sufficient rains in approximately 5 years. If this drought continues possible corridors could be unintentionally created, by migrating livestock, between the summer and winter rainfall regions as conditions within the winter rainfall region might become more favourable for bacterial growth.

However, through this study one is not able to determine if the interaction between environmental bacterial microbes ad global warming is a positive or negative response. An attempt at a literature review in order to answer the question produced poor results as microbes live in very diverse communities that interact with other organisms and the environment in ways. Therefore, scientists have found it difficult to include microbes in climate change modeling as many variable factors need to be taken into account.

One study that has been setting the foundation for microbial and climate change studies is Junpeng et al. (2015). In this study they aimed to identify microbes sensitive to climate change and identify the key factors shifting microbial communities. They found that shifts in temperature and plant biomass drastically impacted the composition of environmental microbial communities.

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http://etd.uwc.ac.za/ Therefore, with Steinkopf located on the cusp of two biomes it is challenging to compare environmental microbial communities in terms of seasonality as they are influenced by many various and opposing factors.

3.4.2 Pathogenic and unclassified taxa

Further analysis of the bacterial composition in the winter and summer rainfall regions revealed a total of 3 pathogenic bacterial species of which one is a multi-host species (infect animals and humans) (Kocuria assamensis 0.12 %) one infect only humans (Escherichia albertti 0.12 %), and one plant pathogen (Janthinbacterium agaricidamnosum 0.11 %), as well as a high percentage of unclassified bacteria within both regions. Unclassified bacterial genera in the summer rainfall region was 4.5 % and 3.2 % in the winter rainfall region. As described above, the summer rainfall region has environmental factors favourable to bacterial growth and this is a reason there is a higher count of unclassified bacterial taxa in the summer rainfall region. These percentages might seem miniscule. But by not being able to identify and classify these bacterial taxa there is a big threat of the unknown. Some of these unclassified taxa might be an environmental threat or a pathogenic bacterial species that has the potential to spread throughout the communal rangelands and leaving both herders and scientists unaware of the cause of the issue.

Janthinbacterium agaricidamnosum is a bacterial species that acts as a plant pathogen and was identified in the summer rainfall region. J. agaricidamnosum causes soft-rot tissue in white button mushrooms (Agaricus bisporus) and is thus responsible for a lot of agricultural losses (Grauper et al., 2015). Kocuria assamensis was identified in the summer rainfall region.

K. assamensis is a ubiquitous species and forms part of the natural skin and mucous membranes in both humans and animals (Kandi et al., 2010). Organisms in this genus are Gram-positive,

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http://etd.uwc.ac.za/ coagulase-negative coccoid actinobacteria belonging to the family Micrococcaceae and order

Actinomycetales (Savini et al., 2010). K. assamensis has become a recent emerging pathogen when medical and laboratory staff realized that it was able to cause clinical infections and diseases and as such many reported cases had been misdiagnosed as laboratory scientist overlooked K. assamensis as a pathogen due to its status as a general skin and mucous membrane bacterial species (Savini et al., 2010; Kandi et al., 2010). Sporadic reports in literature have dealt with infections by Kocuria species mostly in compromised hosts with serious underlying conditions. Nonetheless, infections and diseases caused by these species have been highly neglected, given that misidentification by phenotypic assays has presumable affected estimates of the prevalence over the years (Savini et al., 2010). A further cause of concern is that guidelines for therapy of the illnesses caused are non-existent mostly due to the lack of knowledge of how these species replicate or knowledge about growth inhibition in the presence of antibiotics. The species behavior has been compared to that of Staphylococcus, however this has caused confusion and misidentification between the two species (Savini et al., 2010).

Escherichia albertii was first described by Janda et al. (1999) with a group of five diarrhoeagenic bacterial strains that were removed, from infected minors, by staff at the

Diarrheal Disease Research Centre in Bangladesh. Each of these five strains were isolated from stool specimens. Initially the strains were identified as the species Hafnea alvei by the API 20E system. Later Huys et al. (2003) corrected the misidentification and described one such strain to Escherichia albertii, following the identification of features similar to Escherichia coli.

Under the current classification E. albertti is regarded as an emerging bird and human gastrointestinal pathogen (Ooka et al., 2013).

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http://etd.uwc.ac.za/ 3.5 Conclusion

The study finding in the Steinkopf communal farming rangeland emphasised the importance of understanding environmental factors and its influence on the constantly growing environmental microbiota and possible pathogeneses. The summer rainfall region has been illustrated as a region capable of being a potential bacterial reservoir to both pathogenic and non-pathogenic bacterial taxa. The level of unclassified bacterial taxa might seem minor but the possible bacterial taxa that could be present is of troubling concern. Despite the low incidence rate of the pathogenic bacteria and the low number of pathogenic bacteria present: if environmental factors were to become favourable for those pathogens, they could easily spread within the environment, among livestock herds and eventually to humans. Herds migrate from one region to the other for good reason. However, this migratory pattern to the summer rainfall region could be harmful for both livestock and humans. Currently there is no clear danger or threat but the potential of one developing cannot be ignored.

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http://etd.uwc.ac.za/ *Chapter 4 General conclusion

This study gives a clear understanding of communal farming systems and the intricate links needed to have a healthy running system which contributing to the growth of the community and the greater economy. It further adds knowledge to both environmental bacterial microbiota and potential pathogenesis within this particular rangeland and show-cases the usefulness of using a high-throughput sequencing platform such as Next-generation sequencing. Furthermore adding to the success and reliability of using the V3-V4 hypervariable regions for bacterial studies, and highlighting the importance of sample type choices

Choices made based on sample type are of great importance because it influences the type of data that will be made available for analysis purposes, which ultimately influences the results of the study. The current study questions were based off of environmental observation readings. Thus dung and soil samples were chosen as sample types because research shows that both faecal matter and soil are mediums in which bacterial microbes and, especially pathogenic microbes, can be transported in from one host to another (Kruse et al., 2004; Su et al., 2012; Morand et al., 2014; Soliman et al., 2017). Analysis of these sample types revealed that soil was a better reader of environmental health as it had a greater diversity and abundance of both environmental and gut bacterial microbes. A final conclusion was reached that faecal matter samples are derived from individual animals and soil samples are a composition of environmental microbes and many individual animal microbes, excreted by animals that potentially used the area and defecated or urinated or bleed or lost bodily fluids through injury while in the area (Chapter 2).

The comparison between natural and man-made water holes revealed natural water holes to be a superior choice for both herder and livestock’s health and safety. Firstly, because

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http://etd.uwc.ac.za/ of natural systems in place to prevent the growth of pathogenic bacteria, bacterial inhibitor species like Solibacillus silvestris and Exiguobacterium mexicanum, were found in the winter rainfall region. Secondly, human maintenance is more reliable at natural water holes than at man-made water holes. A lack of municipal help with maintenance of man-made water holes have left many solar- and wind-pumps broken and left stagnant; allowing the growth and development of complex bacterial species. Whereas with natural water holes, herders constantly clean the surrounding environment removing all litter and ensuring the water hole is kept clean (Chapter 2).

Seasonality also plays a big role in the life-cycle of environmental microbes

(Pietikäinen et al., 2005; Bapiri et al., 2010). In the Steinkopf communal rangeland seasonality is an important factor because with change in season 85 % of herders migrate their livestock to a different seasonal rangeland based on rain (Ntombela, 2017). The study firstly illustrated that within the winter rainfall region it was safer for livestock to graze and hydrate themselves as the diversity and abundance of bacterial taxa were lower than in the summer rainfall region.

This is supported by a study done by Bapiri et al. (2010) where they monitored bacterial growth rate in response to dry-rewetting soil cycles and found that bacterial growth drastically dropped after a dry-rewetting cycle. Over the past few years, the winter rainfall region has been receiving more annual rain than the summer rainfall region.

When comparing the summer rainfall region to winter rainfall region once again the winter rainfall region was considered safer because it had a lower bacterial taxa abundance and diversity in comparison to the summer rainfall region. This being because temperature and rainfall regimes are optimal for bacterial growth in the summer rainfall region (Pietikäinen et al., 2005; Bapiri et al., 2010). The levels of unclassified bacterial taxa in the summer rainfall region are also a cause for concern because there are many possible threats that could be within those unknown taxa (Chapter 3).

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http://etd.uwc.ac.za/ Finally, although only three pathogenic bacteria were found, of which two were possible livestock and human bacterial pathogen species (Escherichia albertii and Kocuria assamensis, it still needed to be treated as possible threats. Despite having low abundance percentages, if environmental factors were to change and become favourable they could easily multiply and become a real threat.

Therefore, this study has shown that the Steinkopf communal farming rangeland is not currently at risk of pathogenesis but it does have the potential to host pathogenic bacterial taxa.

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http://etd.uwc.ac.za/ Appendix 1 Sample list of the winter rainfall region and its corresponding subdivision

Sample ID Sample Type Location Description

J.05 soil Halfmens summer J.06 soil Halfmens summer J.26 soil Halfmens winter J.27 dung Halfmens winter J.04 dung Halfmens winter J.07 dung Halfmens summer J.03 dung Halfmens summer J.10 soil Spring summer J.44 soil Spring summer J.39 soil Spring winter J.40 dung Spring winter J.41 dung Spring winter J.14 dung Spring summer J.11 dung Spring summer J.31 soil Malkop winter J.17 soil Malkop summer J.24 soil Malkop summer J.29 dung Malkop winter J.30 dung Malkop winter J.23 dung Malkop summer J.25 dung Malkop summer J.21 soil Neil summer J.18 soil Neil summer J.35 soil Neil winter J.15 dung Neil summer J.16 dung Neil summer

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http://etd.uwc.ac.za/ Appendix 3 List of rare bacterial genera and its distribution between samples. All genera denoted with an * are potential pathogenic genera J03 Halfmens Dung Summer Syntrophomonas J04 Halfmens Dung Winter Veillonella * J05 Halfmens Soil Summer Kutzneria Frankia Catellatospora * Actinopolymorpha * Actinoallomurus Pelotomaculum J06 Halfmens Soil Summer Selemonas J07 Halfmens Dung Summer Williamsia * Anaerobacillus Pigmentiphaga * J26 Halfmens Soil Winter Dethiobacter * Lutispora Haliscomenobacter J10 Spring Soil Summer Lysobacter J14 Spring Dung Summer Cetobacterium J39 Spring Soil Winter Inquilinus * Azospirillum Thalassospira * Crocinitomix Candidatus Amoebophilus Fulvivirga Jiangella Nocardia * Methanolobus J41 Spring Dung Winter Planococcus Thermoanaerobacterium * Caulobacter * J17 Malkop Soil Summer Clavibacter Microlunatus * Larkinella * Nitrospira Nostocoida * Labrys * Roseomonas * Deinococcus * J23 Malkop Dung Summer Christensenella * Coprobacillus J24 Malkop Soil Summer Serinicoccus Sanguibacter Salinicoccus *

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http://etd.uwc.ac.za/ Staphylococcus * Facklamia * Trichococcus Moryella * Shuttleworthia * Tissierella Soehngenia Halomonas * Pseudomonas * J29 Malkop Dung Winter Truepera Alicyclobacillus J30 Malkop Dung Winter Sphingobacterium * J31 Malkop Soil Winter Lutibacterium * Erythromicrobium Erythrobacter Citromicrobium * Sulfitobacter * Marivita * Pleomorphamonas Methylosinus Acidaminobacter * Leptolunbya * Cyanobacterium Cylindrospermopsis Thermobaculum Oscillochloris Litorilinea Flavihumibacter Dyadobacter Nonomuraea J34 Neil Dung Winter Methanosaeta Plesiomonas * Garciella

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http://etd.uwc.ac.za/ Appendix 4 List of 57 potential pathogenic bacteria detected during analysis

Acidaminobacter, Acinetobacter, Actinoallomurus, Actinotalea, Anaerobiospirillum, Bacillus,

Bacteroides, Brevibacterium, Campylobacter, Catellatospora, Caulobacter, Cellumonas,

Christensenella, Citromicrobium, Clostridium, Corynebacterium, Deinococcus, Dietzia,

Enterococcus, Facklamia, Fusobacterium, Georgenia, Gordonia, Halomonas, Helicobacter,

Inquilinus, Labrys, Larkinella, Leptolunbya, Lutibacterium, Marivita, Microlunatus, Moryella,

Mycobacterium, Nocardia, Nostocoida, Pigmentiphaga, Plesiomonas, Prevotella,

Pseudomonas, Rhodococcus, Roseomonas, Salinicoccus, Shewanella, Shuttleworthia, Slackia,

Solibacillus, Sphingobacterium, Sphingomonas, Staphylococcus, Streptococcus, Sulfitobacter,

Thermoanaerobacterium, Thalassospira, Treponema, Veillonella, Williamsia.

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J03 J04 J05 J06 J07 J10 J11 J14 J15 J16 J17 J18 J23 J24 J26 J27 J29 J30 J31 J34 J35 J37 J39 J40 J41 J44

Appendix 5 Representation of distribution of possible pathogenic bacterial genera. Malkop samples J24

(soil) has the highest percentage (9%) followed by J31 (soil) (6%) whereas Halfmens sample J07 (dung) has the lowest distribution (1%).

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http://etd.uwc.ac.za/ Appendix 6 Sample list of winter rainfall versus summer rainfall regions

Sample ID Sample Type Location Description

J.12 Soil KB2 Summer

J.13 Dung KB2 Summer

J.53 Dung KB3 Summer

J.65 Soil KB3 Summer

J.99 Dung KB4 Summer

J.100 Soil KB5 Summer

J.101 Dung KB5 Summer

J.104 Dung KB7 Summer

J.105 Soil KB7 Summer

J.106 Soil KB8 Summer

J.107 Dung KB8 Summer

J.108 Soil KB4 Summer

J.26 Soil Halfmens Winter

J.04 Dung Halfmens Winter

J.39 Soil Spring Winter

J.41 Dung Spring Winter

J.31 Soil Malkop Winter

J.29 Dung Malkop Winter

J.35 Soil Neil Winter

J.34 Dung Neil Winter

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100

0 S.1 S.2 S.3 S.4 S.5 S.6 W.1 W.2 W.3 W.4

Firmicutes Bacteroidetes Verrucomicrobia Actinobacteria Planctomycetes Unclassified Proteobacteria Chloroflexi Acidobacteria TM7 Cyanobacteria Euryarchaeota Synergistetes Fibrobacteres Gemmatimonadetes Lentisphaerae

Nitrospirae Armatimonadetes WPS-2 OD1 SR1 Tenericutes [Thermi] [Parvarchaeota] FBP Spirochaetes Elusimicrobia WS2 Chlorobi SBR1093 GN02

Appendix 7 Relative abundance of the four most prevalent bacterial phyla derived from 16S rRNA next-

generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland

*S denotes summer rainfall region; W denotes winter rainfall region

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70,0

60,0

50,0

40,0

30,0

20,0

10,0

0,0 S.1 S.2 S.3 S.4 S.5 S.6 W.1 W.2 W.3 W.4 Gemmataceae Ruminococcaceae Verrucomicrobiaceae Lachnospiraceae

Pirellulaceae Unclassified [Paraprevotellaceae] Isosphaeraceae Ellin6075 Synergistaceae RF16 Planctomycetaceae

Caldilineaceae Bacteroidaceae Micrococcaceae Rhodobacteraceae Clostridiaceae Sphingomonadaceae

Appendix 8 Relative abundance of the three most prevalent bacterial families derived from 16S rRNA next- generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland

*S denotes summer rainfall region; W denotes winter rainfall region

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0 S.1 S.2 S.3 S.4 S.5 S.6 W.1 W.2 W.3 W.4

Akkermansia Gemmata Unclassified Planctomyces YRC22 Prevotella Pirellula Ruminococcus Clostridium Paludibacter 5-7N15 Arthrobacter Microbispora Oscillospira Methanobrevibacter Muricauda Candidatus Xiphinematobacter Pyramidobacter Fibrobacter Methanocorpusculum Microbispora Caldilinea Appendix 9 Relative abundance of the four most prevalent bacterial genera derived from 16S rRNA next- generation sequencing of soil and dung samples extracted from the winter and summer rainfall regions of

Steinkopf communal rangeland

*S denotes summer rainfall region; W denotes winter rainfall region

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