GROUNDWATER CHEMISTRY AND MICROBIOLOGY IN A

WET-TROPICS AGRICULTURAL CATCHMENT

James Stanley B.Sc. (Earth Science).

Submitted in fulfilment of the requirements for the degree of Master of Philosophy

School of Earth, Environmental and Biological Sciences, Science and Engineering Faculty.

Queensland University of Technology

2019

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ABSTRACT

The coastal wet-tropics region of north Queensland is characterised by extensive sugarcane plantations. Approximately 33% of the total nitrogen in waterways discharging into the Great Barrier Reef (GBR) has been attributed to the sugarcane industry. This is due to the widespread use of nitrogen-rich fertilisers combined with seasonal high rainfall events. Consequently, the health and water quality of the GBR is directly affected by the intensive agricultural activities that dominate the wet-tropics catchments. The sustainability of the sugarcane industry as well as the health of the GBR depends greatly on growers improving nitrogen management practices.

Groundwater and surface water ecosystems influence the concentrations and transport of agricultural contaminants, such as excess nitrogen, through complex bio-chemical and geo- chemical processes. In recent years, a growing amount of research has focused on groundwater and soil chemistry in the wet-tropics of north Queensland, specifically in regard to mobile - nitrogen in the form of nitrate (NO3 ). However, the abundance, diversity and bio-chemical influence of microorganisms in our wet-tropics groundwater aquifers has received little attention.

The objectives of this study were 1) to monitor seasonal changes in groundwater chemistry in aquifers underlying sugarcane plantations in a catchment in the wet tropics of north Queensland and 2) to identify what microbiological organisms inhabit the groundwater aquifer environment. This was completed by regular monthly & bi-monthly sampling and analysis of groundwater over 12 months through wet and dry seasons. Soil samples were also collected for microbiological analysis from a plantation paddock in the study area. It was hypothesised that denitrifying bacteria would be identified in soil and groundwater samples as a result of the - widespread application of nitrogen-rich fertilisers and that NO3 concentrations may vary with aquifer depths.

- While NO3 concentrations in groundwater consistently remained within acceptable - environmental limits (< 10 mg/L, ANZECC) throughout the study, NO3 was found to occur more frequently and in higher concentrations in some bores than historical data indicates. This - can be attributed to infrequent historical sampling and analysis. NO3 concentrations were also - variable across examined sites with 2 shallow aquifer bores displaying constantly low NO3 - concentrations, indicating that aquifer depth is not necessarily a controlling factor on NO3 concentrations. Analysis of extracted sediment cores from the location of these 2 these bores

Page | 2 revealed dense, kaolinite-rich clays overlying and underlying the aquifer material. The clays revealed strong discolouration from sulphur (S2-) and ferric iron (Fe3+) oxidation and X-Ray Diffraction (XRD) analysis showed the presence of goethite (FeO(OH)) and amorphous Fe- oxyhydroxides in the clays. Combined standing water level (SWL) data and groundwater - chemistry data suggest that the clays may be inhibiting the infiltration of NO3 and dissolved oxygen (DO) from the soil to the deeper aquifer material and that variable surface charge - associated with kaolinite clays may result in adsorption of DO and NO3 on clay surfaces. - Measured nitrite (NO2 ) concentrations and slow SWL recessions following rainfall indicate restriction of groundwater flow at one particular clay-rich site. This suggests an increased residence time for groundwater, allowing time for denitrification (DN) to progress and contributing to the creation of a DN “hot-spot”.

- Bores with very low dissolved DO and NO3 concentrations consistently displayed relatively 2- 2- 2+ higher concentrations of sulphate (SO4 ), S and Fe . In anerobic conditions, Fe-oxide 2+ minerals may release Fe ions and precipitate pyrite (FeS2) in-situ with available sulphur from 2+ hydrogen-sulphide (H2S). This would account for the high concentrations of Fe in groundwater in some aquifers and the capacity for these particular aquifers to consistently 2- produce relatively higher SO4 concentrations, compared to groundwater sites with higher DO concentrations. This process requires the presence of sufficient S2-, which is most likely sourced from agricultural inputs (through application of gypsum (CaSO4·2H2O) and fertilisers) and to some extent from lithological sources (chemically weathered parent rock material).

Metagenomic analysis of 16S ribosomal RNA (16S rRNA) from groundwater and soil samples showed that 99.58% of all classified organisms in the groundwater are bacteria, dominated by the phyla Proteobacteria, Firmicutes, Actinobacteria, Acidobacteria and Bacteroidetes, with archaea representing only ~0.42 %. The analysis was successful in classifying >80% bacteria in groundwater and soil to genus level (average: 86.71%), while an average of 38.96% of bacteria were classified to species level. This means that a large percentage of organisms remain unclassified to genus and species level. The analysis revealed a regular consortium of most-abundant bacteria across the whole catchment that are identified as being associated with common soil and plant process, dominated by the families Oxalobacteraceae, Comamondacea, Acetobacteraceae, Thermoanaerobacteraceae and the genera Janthinobacterium, Acetobacter, Curvibacter and Thermoanaerobacter). This indicates that broadscale agriculture has a direct influence on groundwater microbiota, assisted by considerable recharge from tropical rainfall. These bacterial species were present in both shallow (<10 m depth) and deep (>30 m depth)

Page | 3 aquifers, suggesting common connectivity between soil water and groundwater. Within the communities of groundwater bacteria were also identified chemo-lithotrophic organisms who 2- 2- 2- are known to utilise S , SO4 or S2O3 (thiosulphate). These chemical metabolites are sourced from anaerobic subsurface environments, through the interaction of clay mineralogy and - - agricultural chemical inputs. Bacteria associated with reduction of NO3 and NO2 occurred in lower abundances in both groundwater and soil samples. Given that a large percentage of the bacteria species remain “unclassified”, there is still uncertainty regarding the biochemical influence of groundwater and soil organisms on the process of DN. Further metagenomic research incorporating the technique of identifying the abundance of gene coding for the production of nitrite-reductases enzymes as a marker for DN would provide more useful information regarding the DN potential of specific sites.

The most abundant bacteria present in the soil samples from the paddock differed to those in groundwater samples and a marked difference was observed between most abundant soil bacteria species at different soil horizons (25 cm vs 65 cm depth). Less diversity in genus and species was observed at 65 cm depth, where the B soil horizon has a distinctly higher clay content. This indicates that variations in clay content, hence variations in permeability and capacity for gas-exchange and ion-exchange, influence bacteria species abundance and diversity. There are also indications that certain spikes in bacteria species abundance during the study period were linked to increases in dissolved chemical element concentrations in groundwater (e.g. Ca2+ & Mg2+ ) as a result of catchment-wide agricultural practices (such as the application of gypsum as a soil conditioner) following significant rainfall. It is also possible that the most abundant species of bacteria, C. lanceolatus, is having a direct effect on groundwater chemistry by means of biological precipitation of calcium-carbonate (calcite;

CaCO3).

This is the first study conducted in the Silkwood region, north Queensland, which provides a detailed look at seasonal changes in groundwater chemistry in the catchment and the first to produce any information about groundwater & soil microbiology in the region. The data from this research provides an important contribution to the emerging transdisciplinary field of groundwater microbiology. This enhances our understanding of groundwater contaminant dynamics in regions of intensive agriculture, both locally and globally.

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KEYWORDS

Groundwater - Microbiology - Chemistry - Nitrate- Aquifer - Denitrification - Metagenomics Bacteria- Tropics - Sugarcane - Sulphate - Queensland - DNA

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TABLE OF CONTENTS

Abstract ...... 2.

Keywords ...... 5.

Table of Contents ...... 6.

List of Figures ...... 7.

List of Tables ...... 11.

List of Abbreviations ...... 12.

Statement of Original Authorship ...... 14.

Acknowledgements...... 14.

Chapter 1: Introduction.…………………………………………………………..15.

1.1 Thesis Outline ...... 15.

1.2 Significance of Research ...... 16.

1.3 Research Design ...... 17.

Chapter 2: Literature Review.……………………………………………………19.

Chapter 3: Site Description.………………………………………………………36.

3.1 Climate & Rainfall ...... 37.

3.2 Geology ...... 38.

3.3 Hydrology ...... 40.

3.4 Groundwater Data ...... 41.

3.5 Groundwater Chemistry ...... 43.

3.6 Soil ...... 44.

Chapter 4: Measured Groundwater Chemistry/Sediment & Soil Analysis.…...47.

4.1 Methodology...... 47.

4.1.1 Groundwater Field Investigations and Sampling ...... 47.

4.1.2 Groundwater Laboratory Analysis ...... 52.

4.1.3 Sediment & Soil Field Investigations and Sampling ...... 54.

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4.1.4 Sediment & Soil Laboratory Analysis ...... 58.

4.2 Results & Discussion ...... 60.

4.2.1 Results & Discussion - Groundwater Chemistry Analysis...... 60.

4.2.2 Results & Discussion - Sediment & Soil Analysis ...... 93.

Chapter 5:Groundwater & Soil Microbiology....………………………………...97.

5.1 Methodology...... 97.

5.1.1 Microbiology Field Sampling...... 97.

5.1.2 Microbiology Laboratory Analysis ...... 97.

5.2 Results & Discussion- Microbiology Analysis ...... 102.

Chapter 6: Conclusion…..……………………………………...... 122.

References….…………….…….………………………………...... 125.

Appendices….…….……………….……………………………...... 144.

LIST OF FIGURES

Fig.1 Goldich’s Chemical Weathering Sequence (Page 31).

Fig.2 Location of South Johnstone catchment (Page 36).

Fig.3 Boundary of South Johnstone catchment (Page 37).

Fig.4 Township of Silkwood, the localities of El Arish, Kurrimine Beach, Cowley Beach and Japoonvale and major watercourses in the Silkwood region (Page 38).

Fig.5 Geological units in the Silkwood region (Page 39).

Fig.6 Calculated dry-season groundwater contours (Page 42).

Fig.7 Calculated wet-season groundwater contours (Page 42).

Fig.8 Piper diagram displaying historical chemical characteristics of 17 selected DNRME bores in Silkwood region (Page 44).

Fig.9 Generalised soil map (Murtha, 1986) of South and North Johnstone regions (Page 45).

Fig.10 Location of Russel-Mulgrave catchment in relation to Silkwood region (Page 48).

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Fig.11 Location of bore 11210055 in Silkwood township (Page 49).

Fig. 12 Locations of examined bores selected for groundwater sampling 2017-2018 (Page 50).

Fig.13 A Heron Dip-Meter used to monitor SWL, in conjunction with submersible 12v peristaltic pump and plastic hose for extracting groundwater samples (Page 51).

Fig.14 Location of P2R paddock and Bores P2RNE & P2RSW (Page 54).

Fig.15 DNRME map displaying digital-elevation model of drainage contours at the Silkwood P2R site (Page 55).

Fig.16 Sonic drill rig extracting sediment cores and installing bores P2RNE & P2RSW at P2R research paddock, July 2017 (Page 56).

Fig.17 Sonic drill rig extracting sediment cores and installing bores P2RNE & P2RSW at P2R research paddock, July 2017 (Page 56).

Fig.18 Examples of sediment cores extracted at P2RNE site during bore installation (Page 57).

Fig.19 Examples of sediment cores extracted at P2RNE site during bore installation (Page 57).

Fig.20 P2R paddock site (Page 58).

Fig.21 Oven-dried sediment samples prior to milling for XRD analysis preparation (Page 59).

Fig.22 SWL measurements for each examined bore Sept 2017 to Sept 2018 and rainfall data (Page 60).

Fig.23 Combined Data Logger SWL measurements and rain gauge rainfall measurements at P2R paddock site (Bores P2RNE & P2RSW) (Page 60).

Fig.24 Location of Bore 11210004 next to Liverpool Creek (Page 61).

Fig.25 Steep, vegetated bank, Liverpool Creek (Page 62).

Fig.26 P2RNE bore casing overflowing with groundwater in March 2018 following heavy rainfall (Page 63).

Fig.27 Combined measured major ion chemistry for Silkwood Bores Sept 2017-Sept 2018 (Page 64).

Fig.28 Measured Ca2+ concentrations in groundwater, 2017-2018 (Page 67).

Fig.29 Measured Mg2+ concentrations groundwater, 2017-2018 (Page 67).

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Fig.30 Measured Cl- concentrations groundwater, 2017-2018 (Page 68).

Fig.31 Measured Na+ concentrations groundwater, 2017-2018 (Page 68).

Fig.32 Location of bore 11210051 in Japoonvale valley (Page 72).

- Fig.33 Relationship between DO and NO3 in measured groundwater samples, bore 11210056 (Page 72).

- Fig.34 Graphical display of measured NO3 concentrations in Silkwood groundwater, 2017- 2018 (Page 75).

- Fig.35 Comparative magnitudes of calculated NO3 averages for each bore site, displaying spatial variations in NO3 concentrations (Page 75).

Fig.36 Major inorganic carbon species in water as a function of pH (Page 77).

Fig.37 Extracted sediment core from the P2R paddock, displaying distinctive colouration due to chemical oxidation of Fe2+ and S2- (Page 78).

Fig.38 Extracted sediment core from the P2R paddock, displaying distinctive colouration due to chemical oxidation of Fe2+ and S2- (Page 79).

2- Fig.39 Measured SO4 concentrations in Silkwood groundwater, 2017-2018 (Page 81).

2- Fig.40 Pie graph map displaying averaged relative abundances of measured SO4 , DO and - NO3 for each bore site (Page 82).

Fig.41 Minitab software cluster analysis (to 3 clusters) of measured chemical variables in groundwater, 2017-2018 (Page 86).

Fig.42 Minitab software cluster analysis (to 3 clusters) based on all observations of chemical elements in groundwater 2017-2018 (Page 86).

Fig.43 Minitab software principal component analysis (PCA) of measured chemical variables in groundwater (Page 87).

Fig.44 Calculated δ2H/δ18O (‰) based on measured values from Silkwood groundwater and rainwater, relative to SMOW (Page 91).

18 - 15 - Fig.45 δ O-NO3 (‰) / δ N-NO3 (‰) measured values from Silkwood groundwater, 2017- 2018 (Page 92).

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15 - - Fig.46 Measured δ N-NO3 values in groundwater plotted to reference values for NO3 sources (Page 93).

18 - 85 - Fig.47 Measured groundwater isotopic data (δ O-NO3 / δ N-NO3 ), Sept 2017 to Feb 2018 (Page 94).

Fig.48 Schematic diagrams for bore P2RNE (RN 183021) and P2RSW (RN 183022) (Page 95).

Fig.49 Groundwater pumped from both P2R paddock bores (Page 97).

Fig.50 Groundwater pumped from both P2R paddock bores (Page 97).

Fig.51 Aerobic and anaerobic preparatory and incubating cabinets (Page 100).

Fig.52 Vacuum flask apparatus used to filter 1 litre groundwater samples (Page 101).

Fig.53 Filtered samples agitated in TissueLyser (Page 101).

Fig.54 Colonies of culturable organisms extracted from groundwater samples (Page 103).

Fig.55 Colonies of culturable organisms extracted from groundwater samples (Page 103).

Fig.56 Colonies of culturable organisms extracted from soil samples (Page 104).

Fig.57 Estimated CFU totals based on observations of microbiological colonies (Page 104).

Fig.58 Agarose gel electrophoresis of gDNAs isolated from groundwater and soil samples (Page 105).

Fig.59 PCR product analysis using the PerkinElmer High Sensitivity LabChip kit (Page 106).

Fig.60 Edited Illumina PCA of all samples to genus level (Page 108).

Fig.61 Edited Illumina PCA of all samples to species level (Page 109).

Fig.62 Most abundant bacteria in groundwater samples, phylum to family (Page 110).

Fig.63 Combined Summary of relative abundance of classified genera and species in all Silkwood groundwater samples (Page 111).

Fig.64 Summary of relative abundance of classified genera and species in groundwater samples from Mulgrave-Russell catchment site (Page 112).

Fig.65 Most abundant bacteria in soil samples, phylum to family (Page 113).

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Fig.66 Summary of relative abundance of classified genera and species in soil samples (Page 114).

Fig.67 Measured abundances (number of reads) of sulphur-associated and nitrogen-associated bacteria species in groundwater samples (Page 117).

Fig.68 Time series graph of species identified in bore 11210045 (Page 118).

Fig.69 Time series graphs of abundant species identified in bores 11210056 (Page 120).

LIST OF TABLES

Table 1 Primary groundwater chemistry parameters and their description (Page 19).

Table 2 Weathering Reactions for Different Silicate Minerals (Page 33).

Table 3 Averaged dry-season historical SWL values for 10 selected bores (Page 41).

Table 4 Averaged wet-season historical SWL values for 10 selected bores (Page 41).

Table 5 Details of selected soil classification sites across the Silkwood region (Page 46).

Table 6 Aquifer depths and aquifer lithologies of examined bores, 2017-2018 (Page 47).

- - Table 7 Measured alkalinity values (CaCO3 and NaCO3 ), 2017-2018 (Page 64).

Table 8 Measured EC values in Silkwood groundwater bores, 2017-2018 (Page 65).

Table 9 Na+/ Cl- values calculated from the measured groundwater chemistry data, 2017-2018 (Page 69).

Table 10 Measured DO concentrations in groundwater, 2017-2018 (Page 71).

- = Table 11 Measured NO3 (mg/L) and NO2 (µg/L) concentrations in Silkwood groundwater, 2017-2018 (Page 74).

+ Table 12 Measured NH4 (mg/L) concentrations in Silkwood groundwater, 2017-2018 (Page 77).

Table 13 Measured pH values in groundwater, 2017-2018 (Page 77).

- 2- Table 14 Calculated Cl :SO4 values from measured groundwater data in the Silkwood region, 2017-2018 (79).

Table 15 Measured S2- and Fe2+ concentrations in groundwater, 2017-2018 (Page 81).

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2+ 2- Table 16 Calculated Ca /SO4 from measured groundwater chemistry, 2017-2018 (Page 84).

2+ 2+ 2- Table 17 Calculated Ca / (Ca + SO4 ) from the measured groundwater chemistry, 2017- 2018 (Page 85).

Table 18 Minitab software PCA; calculated coefficients (to 3 components) (Page 87).

Table 19 Measured total organic (TOC) and inorganic (TIC) carbon in groundwater samples (Page 89).

15 - 18 - Table 20 Measured δ N-NO3 (‰) and δ O-NO3 (‰) values from Silkwood groundwater (Page 90).

Table 21 Results of XRD analysis; quantitative mineral identification (Page 95).

Table 22 Number of reads or matches according to classifications at the taxonomic level from Kingdom to Order (Page 107).

Table 23 Summary of sample classification to genus and species taxonomic level (Page 107).

LIST OF ABBREVIATIONS

AHD - Australian height datum

ANZECC – Australian and New Zealand Environment and Conservation Council

BGL - Below ground level

CARF – Central Analytical Research Facility

DES - Department of Environment and Science

DIN - Dissolved inorganic nitrogen

DN - Denitrification

DNA - Deoxyribonucleic acid

DNRME -Department of Natural Resources, Mines and Energy

DO - Dissolved oxygen

DSITI - Department of Science, Information Technology and Innovation

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EC - Electrical conductivity

GBR - Great Barrier Reef

GWDB - Groundwater database gDNA - genomic DNA

LiDAR - Light Detection and Ranging

ORP - Oxygen redox potential

P2R - Paddock-to-Reef

PCA - Principal Component Analysis

PCR -Polymerase chain reaction

QUT - Queensland University of Technology

RH – Relative Humidity

RN - Registration number

RNA -Ribosomal DNA

SI - Système international (d'unités)

SRTM – Shuttle Radar Topography Mission

SWL - Standing Water Level

TIC - Total inorganic carbon

TOC - Total organic carbon

USGE -United States Geological Survey

WHO -World Health Organisation

XRD- X-Ray Diffraction

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STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signature: ______QUT Verified Signature Date: ______04/08/2019______

ACKNOWLEDGEMENTS

The Queensland Government’s Department of Environment and Science (DES), and Department of Natural Resources, Mines and Energy DNRME have supported this groundwater research project through generous funding.

Staff at the DNRME’s South Johnstone depot provided support through allowing ongoing access to registered DNRME groundwater bores in the region for groundwater sampling and providing equipment storage at the South Johnstone depot. MSF Sugar Pty Ltd provided on- site support in the field and access to their plantation property.

I would like to acknowledge the support of my supervisors, Dr Lucy Reading and Assoc. Prof. Junior Teo. My QUT colleagues and the CARF technical laboratory staff provided fantastic assistance and guidance.

I acknowledge the Traditional Owners of the lands where QUT now stands, pay respect to their Elders – past, present and emerging – and acknowledge the important role Aboriginal and Torres Strait Islander people continue to play within the QUT community.

Page | 14 CHAPTER 1: INTRODUCTION

The principle element of this research is the chemistry of groundwater in aquifers which exist beneath broad-scale sugarcane plantations in a catchment located in the wet-tropics of far north Queensland. Groundwater provides the vehicle for the analysis of the secondary element of this research project; the identification of microbiological organisms which inhabit the groundwater aquifers. The third element of this research project is an examination of alluvial sediments in part of the catchment which store and transport groundwater.

The interaction of these elements can have very specific consequences for the quality of groundwater, via distribution of chemical components and contaminants through agricultural and coastal marine regions. Agricultural management practices have direct physical and chemical effects on soil and groundwater, the results of which can either inhibit or strengthen future economic yields. An ancillary effect of these practices is the subsequent environmental impact on downstream communities and ecosystems.

Previous research has shown that broad-scale modern agricultural practices which utilise artificial fertilisers (such as sugarcane farming) can contribute to an accumulation of excess nutrients in surface waters and sub-surface groundwater aquifers. These excess nutrients are then transported through surface watercourses and groundwater. Subsequently, these nutrients can ultimately be discharged into coastal marine zones, where they constitute a serious environmental hazard to coastal marine ecosystems.

The analysis of groundwater microorganisms has not routinely been included in previous wet- tropics groundwater research but a growing interest in the ecological significance of these organisms and their influence on groundwater chemistry has started to emerge in recent years. This research project examines links between groundwater chemistry and microbiology in the Silkwood region - a wet-tropics catchment in north Queensland. It is first study to provide data on soil and groundwater microbiology and detailed observations of seasonal changes in groundwater chemistry in the Silkwood region.

1.1.THESIS OUTLINE

Chapter 1 provides introductory information on the significance and relevance of this research project in relation to current environmental and economic concerns, as well as formalising the research questions at the core of the study.

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Chapter 2 provides background information on specific aspects of the three fundamental factors of this research project; groundwater chemistry, groundwater microbiology and sedimentology. It also presents a review of the previously published research literature that is relevant to this research project.

Chapter 3 introduces in detail the research location for this project, including background information on climate & rainfall, geology, land use and hydrogeology.

Taking into consideration the large amount of data recorded in this project, this report has been structured to make the methods and results of the research more digestible to the reader. Hence, the methods, results and discussion sections are combined into segregated chapters for the three areas of research:

 Chapter 4: Combines Groundwater Chemistry analysis (methodology, results & discussion) with Sediment & Soil analysis (methodology, results & discussion).  Chapter 5: Presents Microbiology analysis (methodology, results & discussion).

Chapter 6 presents a summary conclusion with recommendations for further research, followed by a bibliography and appendices.

1.2 SIGNIFICANCE OF RESEARCH

The Great Barrier Reef World Heritage Area (GBRWHA) is managed by the Australian Federal Government through the Great Barrier Reef Marine Park Authority and the Queensland State Government. The Reef 2050 Water Quality Improvement Plan 2017-2022 outlines long- term plans to manage the health of the GBR. The plan is nested under the Reef 2050 Long- Term Sustainability Plan July 2018 and defines the required water quality targets for reduced sediment and nutrient loads discharging to the GBR by 2025 and identifies agriculture as the main source of excess nutrients and fine sediments. A 60% reduction in dissolved inorganic nitrogen (DIN) concentration is identified among the water quality targets for the wet-tropics region. Groundwater monitoring and analysis is not included in the implementation strategies outlined in the report.

Informing the plan is the Queensland’s Government’s 2017 Scientific Consensus Statement, which provides a review of the significant advances in scientific knowledge of water quality issues in the GBR. One of the key findings of the statement is that the GBR ecosystems

Page | 16 continue to be in poor condition and current management initiatives will not meet the water quality targets without rapid improvements to governance, program design & delivery and evaluation systems. It identifies sugarcane production areas as the largest contributors of dissolved inorganic nitrogen and pesticides.

1.3 RESEARCH DESIGN

Water quality targets for the wet-tropics region define a 70% reduction in DIN by 2025 for both the Mulgrave-Russel River and Johnstone River catchments and a 50% reduction in DIN for the Tully River catchment. These wet-tropics catchments are defined by the extensive primary agricultural industries of sugarcane and banana plantations and are separated by well- defined watersheds. Of all the wet-tropics catchments draining to the GBR, the Johnstone contributes the second largest loads of dissolved inorganic nitrogen and fine sediment (reefplan.qld.gov.au), mostly from sugarcane production.

Situated between the Johnstone River and Tully River catchments is the catchment surrounding the town of Silkwood, which is included in the South Johnstone River sub-catchment. Like the rest of the wet-tropics region, the Silkwood area is also characterized by intensive sugarcane plantations, tropical climate and high annual rainfall (3383.3 mm/y in South Johnstone). The agriculturally-developed coastal plain is composed of alluvial sediments characteristic of the wet-tropics zone but does not possess deep soil profiles (like ferrosols found in the greater Johnstone River catchment surrounding Innisfail). As the Silkwood area forms a part of the greater Johnstone River catchment, regular monitoring, testing and land use management is required to meet the 2050 water quality target of 70% reduction in DIN output.

The DNRME conducts ongoing agricultural research in the wet-tropics as part of the “Paddock to Reef” (P2R) program. The P2R program (established in 2009) is an innovative action originally implemented under the Reef Water Quality Protection Plan 2013, which collects and integrates data and information on agricultural management practices, catchment indicators, catchment loads and the health of the Great Barrier Reef. It also involves collaboration between government and industry bodies, landholders and research organisations. The P2R research program operates a project sited at a sugarcane plantation paddock near Silkwood which is owned and managed by MSF Sugar Pty Ltd. The DNRME’s technical report of studies conducted at the research paddock from 2014 to 2017 provides important paddock-scale data

Page | 17 on DIN transport in crop soil, shallow groundwater drainage and surface-water runoff (DNRME, 2017). The focus of the P2R paddock research is quantifying seasonal loss versus uptake of soil nitrogen, to improve fertiliser application management. The P2R research paddock near Silkwood provided a location for this study to conduct “paddock-scale” groundwater monitoring via the installation of 2 groundwater bores in close proximity. This means that the comprehensive the above-ground monitoring conducted by the DNRME can now be complimented by groundwater chemistry, soil mineralogy and microbiology data collected from the site. Groundwater aquifers provide a potential nitrogen transport pathway currently overlooked by the P2R program. Another benefit of utilizing the P2R site is that soil and sediment analysis could be conducted on samples that are representative of plantation conditions, without potential contamination from road runoff and local industries. Collaboration with MSF Sugar Pty Ltd and DNRME ensured regular access to the P2R site as required for the duration of the research period, as well as access to the DNRME groundwater monitoring bores throughout the catchment. The DNRME bores used in this study were selected based on accessibility and spatial distribution around the Silkwood region.

1.3.1 Research Question

The specific research problem being addressed in this project is the knowledge gap regarding - seasonal variations in groundwater chemistry and NO3 concentration and how these factors might relate to groundwater and soil microbiology in the Silkwood catchment area (or vice versa). This problem can be brought into focus by asking the following questions:

1. “Does groundwater chemistry in the Silkwood-South Johnstone catchment (including - NO3 concentration) change noticeably in response to anthropogenic activities and/or seasonal variations in rainfall over the course of a year? Are there observable spatial variations in groundwater chemistry?”

2. “What microorganisms are present in the groundwater? Do they have a potential influence on groundwater chemistry (or vice versa)?

To address these questions, it was recognised that a program of regular (monthly and bi- monthly) field sampling and analysis of groundwater in the Silkwood region over a period of 12 months would provide enough data to describe changes in the chemical environment during wet and dry seasons.

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CHAPTER 2: LITERATURE REVIEW

The dissolved chemical and biological components in groundwater determine its suitability for domestic, industrial, agricultural or recreational applications and provide indicators of the types of sediments and rock formations that store and transport it (Freeze and Cherry, 1979; Hiscock and Bense, 2014). This data can provide practical information relevant to landscape evolution, the presence of ore deposits and the sources and movements of natural or anthropogenic contaminants in groundwater and soil profiles (Freeze and Cherry, 1979).

2.1 Groundwater Chemistry

There are certain chemical parameters fundamental to the analytical assessment and classification of groundwater, as described in Table 1.

Table 1. Primary groundwater chemistry parameters and their description.

Parameter Description pH The concentration of free hydrogen (H+) ions represented as acidity or alkalinity. Electrical Conductivity (EC) The electrical conductance capacity of a solution influenced by its concentration of dissolved ions or total dissolved solids (TDS). SI unit is micro-Siemens per centimetre (µS/cm). Redox Potential The capacity of a chemical species to lose or gain electrons. SI (ORP or Eh) units are volts (V) or millivolts (mV).

Dissolved Oxygen Concentration A measurement of the dissolved gaseous oxygen present in a (DO) solution. SI units commonly are parts-per-million (ppm) or milligrams per litre (mg/L).

Dissolved Major Ion Concentration The concentration of dissolved ions in a solution, cations and

+ + 2+ 2+ 2+ 4+ 2- - - anions (e.g. Na , K , Ca , Mg , Fe , Si , SO4 , Cl , Fl etc.). SI units are ppm or mg/L.

Both DO and major ion concentrations in groundwater play an important role in determining pH, EC and redox potential (Freeze and Cherry, 1979; Appelo and Postma, 2005). Along with temperature, these chemical parameters also influence the activity, diversity and abundance of macro and microorganisms inhabiting groundwater systems. The activity of these organisms

Page | 19 can, in turn, have influential biochemical effects on the health of an ecosystem (Chappelle, 2001; Cullimore, 2007).

2.2 Nitrate

Elemental nitrogen exists as a non-metal diatomic molecule, N2, which contributes by volume 78% of the air around us (oxygen contributes 21% by volume) (Blackman et al, 2012). Nitrogen is important to living organisms as a key component in amino acids, which are the building blocks of proteins responsible for creating and maintaining enzymes, hormones, body tissue and organs (Chappelle, 2001; Blackman et al, 2012). Under ambient environmental conditions, the N2 molecule is unreactive due to the two nitrogen atoms being held together by a considerably strong triple-bond. (Blackman et al, 2012). A consequence of this lack of reactivity is that it cannot be naturally converted into organic compounds for use by biological systems without the assistance of a complex biologically-evolved enzyme, nitrogenase, which + can convert N2 to ammonia (NH3 ). (Blackman et al, 2012). The decomposition and oxidation + - of organic matter results in the conversion of NH3 to nitrate (NO3 ). This is principally accomplished by the activities of two groups of nitrifying bacteria and archaea, which gain + energy from NH3 and nitrite (NO2) in a two-step chemical process:

- + - Microbial oxidation of ammonia to nitrite: NH3 + O2 → NO2 - + 3H + 2e (eq 1)

- - + Microbial oxidation of nitrite to nitrate: NO2 - + H2O → NO3 - + 2H +2e- (eq 2)

Since the successful development of artificially-manufactured fertilizers utilizing the Haber- Bosch process in 1910 and the subsequent global expansion of large-scale monocultural farming practices (Blackman et al, 2012; Eash et al, 2016), groundwater has been intensively studied in relation to the variable effects of artificial fertilisers on both environmental and human health (Amberger and Schmidt, 1987; Boettcher et al, 1989; Tilman et al, 2002; Robinson et al, 2011; Wongsanit et al, 2015 ). In areas where intensive farming of annual leaf crops is the dominant land use, the application of fertilisers rich in nitrogen can have a considerable impact on groundwater chemistry and quality, soil fertility, microbial diversity and the health of local and regional ecosystems. This is related specifically to the environmental - transport and concentration of nitrogen in the form of nitrate (NO3 ).

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2.3 Nitrate: Significance to Health & Environment

- NO3 is identified as a significant contaminant in groundwater, related to agricultural activities and sewage disposal in densely populated regions (Freeze & Cherry, 1979; Appelo and Postma, - 2005; Hiscock and Bense, 2014). Although considered an essential plant nutrient in soils, NO3 becomes a pollutant once it is leached below the root zone and occurs in undesirable concentrations (Tate, 1995). Due to its anionic charge, and because it does not form insoluble - minerals than can precipitate out, NO3 is relatively mobile in groundwater and clay-rich saturated soils, allowing its transport and discharge to surface water (Freeze and Cherry, 1987; Appelo and Postma, 2005). This mobility contributes to its capacity to be transported readily from source to sink.

The Australian and New Zealand Guidelines for Fresh and Marine Water Quality states that - dissolved NO3 concentrations of >30 mg/L may be hazardous to animal health. The guidelines - also specify a maximum NO3 concentration of 10 mg/L in water for recreational purposes - (ANZECC, 2000). Excess biological NO3 intake in humans has been linked with mortality rates from gastric cancer (Chappelle, 2001), and the World Health Organisation (WHO) - guidelines for maximum admissible NO3 concentrations in drinking water is 50 mg/L, based on the occurrence of methaemoglobinaemia in infants (Hiscock and Bense, 2014; WHO, 2017; NHMRC, 2017).

The effects of excess loading of nitrogen in coastal and marine waters include depleted levels of oxygen caused by the decomposition of sensitive marine organisms following blooms of algae and cyanobacteria. This can negatively impact the biological structure of aquatic ecosystems (Hatfield and Follett, 2008). Corals that are regularly exposed to conditions of poor water quality (including elevated concentrations of dissolved nitrogen) have less resistance to thermal stresses which are linked to coral-bleaching (Wooldridge, 2009). This combination of factors increases the instability risks for coral reef communities facing the expected elevated temperatures associated with climate change. (Lesser, 2007; Webster et al, 2009; Wooldridge, 2009).

Given the world-wide application of nitrogen-rich fertilisers, it is not surprising that research is constantly being undertaken to monitor agricultural practices, drinking water quality and nitrate dynamics in aqueous transport domains (Brodie et al 2011; Fabro et al. 2015; Chen et - al 2016; Sadler et al. 2016). Environmental NO3 contamination continues to be a matter of concern relating to a variety of agricultural practices, in a variety of landscapes (Robinson et

Page | 21 al, 2011; Foote et al. 2014; Kyllmar et al, 2014; Kim et al. 2016; Rezaei et al. 2017). Depending - on local hydrology and geology, NO3 may accumulate in groundwater aquifers and/or be transported through saturated soils and surface run-off, into riparian zones and water-courses (Mitchell et al. 2009; Puig et al. 2016; Rasiah et al. 2013). Although soils, crops, rainfall and topography are globally variable factors, one thing in common to all studies is the intensity of modern broad-scale agricultural practices, and their capacity to enrich groundwater and surface waters with excessive concentrations of nutrients (Hutchings et al. 2005; Mitchell et al. 2009; Thorburn et al. 2011; Wick et al. 2012; Lockhart et al. 2013; Wheeler et al. 2015; Hansen et al. 2017).

- - Identifying sources of NO3 input and predicting or modelling NO3 pathways can be complicated by a variety of factors such as: the diversity of landscapes and soil types where nitrate contamination occurs; seasonal changes in catchment chemistry, water movement, soil biology and temperature; presence of various types of crops or forest which interact differently with soil bio-chemistry and groundwater. (Rasiah et al. 2003; Wexler et al. 2014; Puig et al. 2016; Kim et al. 2016; McAleer et al. 2017; Rezaei et al. 2017).

2.4 Nitrate Attenuation by Adsorption

The development of environmentally appropriate agricultural strategies requires an - understanding of the multivariate factors controlling NO3 transport. Thus, a large percentage of research focusses on direct monitoring and sampling of affected areas in agriculturally- dominated catchments. This has provided important insight into the natural capacity for certain - soils types to retain NO3 , depending on anion-exchange capacity (AEC), thereby slowing its transport potential and reducing its accumulation in environmentally-sensitive coastal and marine ecosystems. The variable-charge characteristic of clay-rich tropical soils is an important controlling factor in this process. Studies conducted in tropical areas on soils rich in Fe-oxides - (Ferrosols, Latosols), where NO3 leaches beneath the root zone of tropical sugarcane - plantations, have shown NO3 adsorption within deep, Fe-rich soil profiles (Dynia, 2000; Rasiah and Armour, 2001; Xu and Cai, 2007; Rasiah et al 2003 a). Similarly, Ryan et al (2001) and Maeda et al (2008) demonstrated the capacity for Andisols with Al-rich clay minerals to - also adsorb NO3 at depth.

In these studies, pH also plays an influence on AEC, with many tropical soils being moderately to strongly acidic (Xu et al, 2013; Xu and Cai, 2007; Rasiah,et al, 2003; Chesworth, 2008).

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Qafoku and Sumner (2001) demonstrated a direct correlation between decreased pH and - increased anion-retention (CaNO3 ) and increased AEC on soil samples from where kaolinite and amorphous Al and Fe hydroxides dominate the clay component. Harmand et al (2010) - demonstrated a positive relationship between NO3 absorption and net positive charge of kaolinite-rich soils sampled from coffee plantations on Costa Rican Acrisols.

- NO3 adsorption and interaction with clay-rich sediments is a process that can be readily observed and measured in controlled laboratory experiments (Li and Bowman, 2001; Soares, 2002; Mohsenipour et al, 2015; Tribe et al, 2012). These studies are commonly conducted for developing environmental remediation techniques for wastewater, sewerage and sites with high - concentrations of NO3 and other oxyanions and provide empirical data that is useful in supporting field observations and analysis of collected groundwater and soil samples.

2.5 Nitrate in Groundwater

Thorburn et al (2002) conducted a study on 1454 groundwater bores along the coast in north- eastern Australia. This study incorporated the work of Sunners, 1993 in the Bundaberg region - and measured NO3 concentrations in 1031 bores from 1997-1998. The results showed that - only 3% of all bores studied displayed NO3 levels > 50 mg/L with a further 11% of bores - displaying NO3 levels > 20 mg/L. It was found that certain regions of elevated levels (> 20 - mg/L) displayed no general trend in NO3 concentrations, although there was substantial - variation within bores between times of sampling. This highlights the “hot spot” nature of NO3 concentrations observed in some environmental field studies. Thayalakumaran et al (2008) also - observed NO3 hot spots in their study of groundwater bores in the lower Burdekin floodplain. Bernard-Jannin et al (2017) observed that riverbank geomorphology plays an important role in - the spatio-temporal distribution of NO3 hot spots, with dilution and DN controlled by - hydrological flow paths and residence time. These studies indicate that although NO3 transport occurs on a regional/catchment scale, understanding the controls of localised influences (sediments and aquifer materials, geology, biochemistry, land-use practices, hydrology and - connectivity) is necessary in understanding environmental NO3 attenuation mechanisms.

Vegetated riparian zones represent an important interface between agricultural land, groundwater and surface water systems, where hydraulic connectivity and nutrient exchange can be measured. Some studies indicate these zones to be a useful tool for intercepting nutrient runoff, despite heterogeneity of interactive surface water-groundwater processes (Krause et al,

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2009; Pert et al, 2010). Woodward et al (2009) suggest that riparian zones could be useful - buffers to NO3 leaching depending on the influence of hydrology and processes affected by the fluctuation of aquifers between anoxic and oxic conditions. However, Connor et al (2013), showed that the hydrology of a 150 m-wide riparian zone in the Mulgrave River catchment, north Queensland, could be very dynamic; seasonal changes in groundwater “standing water level” (SWL) and rapid variations in groundwater gradients altered size and locations of groundwater discharge zones. It is estimated that under such fluctuating conditions, residence - time of groundwater in riparian zones is not long enough to allow significant removal of NO3 via processes like DN (or uptake into biomass). Conolly et al (2015) also studied the Mulgrave- - Russell catchment in tropical north Queensland and found noticeable increases in NO3 and - - NO2 concentration with distance downstream, indicating discharge of NO3 -rich groundwater into watercourses. Their study concluded that riparian zones have a variable influence, but not - influential enough to mitigate NO3 contamination without also reducing fertiliser input.

- Studies which combine measurements of NO3 flux in both soils as well as groundwater provide a clearer picture of how both domains interact and affect the potential of a system’s capacity - to store or leach NO3 and other contaminants. Rasiah et al (2012) have recorded a positive - correlation between rising SWL and NO3 -N concentrations in groundwater beneath sugarcane paddocks in Queensland’s wet-tropics region, with receding SWLs corresponding with - - decreasing NO3 - N concentration in groundwater and subsequent transport of NO3 from groundwater to surface water.

- In 2003, Rasiah et al also showed this relationship (fluctuating SWL and NO3 -N concentration) - positively correlating with fluctuating NO3 -N adsorption in clay-rich soil profiles under sugarcane during the north Queensland wet season, based on monitoring of SWLs and - measured NO3 concentrations in groundwater and soil core samples from piezometers installed up to a depth of 10 m.

2.6 Microbial Activity in Groundwater

Groundwater bores contain a variety of microorganisms that exist in the natural local environment, as well as microorganisms introduced from the installation & development of the bore itself (Cullimore, 2007). Sometimes their physical presence is apparent; microbial colonies can form noticeable biofilms upon the surface of the water, well-screens and bore walls (Cullimore, 2007). In groundwaters having relatively high concentrations of Fe and Mn

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(> 0.5-1.5 ppm), such biofilms and slimes from communities of Fe-Mn-reducing bacteria can cause plugging up or corrosion of the bore wells (Cullimore, 2007).

Depending on the concentrations of dissolved oxygen, organic carbon and other nutrients present within aquifers, microorganisms may colonise the surfaces of sediment grains and particles or exist suspended in groundwater (Griebler & Leuders, 2009).

Gram-negative bacteria (those with thin cell walls) are found extensively in groundwater systems (particularly the genus Pseudomonas), contributing an important component of the subsurface biota. This also includes members of the genera Azotobacter, Rhizobium, Alcaligenes, Flavobacterium and Bordetella (Chappelle, 2001). Azotobacter and Rhizobium are well known as nitrogen-fixing bacteria in soils and shallow groundwater environments, + converting N2 to NH3 . Nitrosomonas and Nitrobacter are two genera of bacteria associated + with the nitrifying of NH3 to NO3 (as described in equations 1 & 2, respectively).

Microorganisms catalyse nearly all the important redox reactions that occur in groundwater (Freeze and Cherry, 1979). However, there is acknowledgement that the abundance, diversity and bio-geo-chemical interactions of microorganisms in groundwater aquifers is still poorly understood (Cullimore, 2007; Griebler and Lueders, 2009; Butterbach-Bahl et al, 2013).

To sustain the hundreds of chemical reactions required every second to maintain metabolic processes, bacterial cells utilise enzymes as catalysts; their influence decreases the required activation energies of the reactions (Freeze and Cherry, 1979; Chappelle 2001). The activity of - the nitrate-reductase enzyme is inhibited by the presence of oxygen, which is why many NO3 - reducing bacteria become restricted to anaerobic (<2% DO) environments (Chappelle, 2001).

Bacteria and other microorganisms can thrive, or at least survive, within a range of temperatures (0°C to >45°C) and acid or alkaline environments (Chappelle, 2001). They can utilise both organic and inorganic compounds as energy sources and substitute various anion- complexes as terminal electron-acceptors, depending on the environment (Freeze and Cherry, 1979; Chappelle, 2001)

- A very important point to remember is that some processes that mitigate NO3 contamination are catalysed by microorganisms in soil and water. (Freeze and Cherry, 1979; Bergwall, 1995; Tate, 1995; Chappelle, 2001; Granger et al 2008; Zeng et al. 2016). This is often acknowledged by researchers, but the presence and activity of relevant microbiological species is often assumed without being clinically examined and quantified (Boettcher et al. 1990;

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Thayalakumaran et al. 2007; Jahangir et al. 2012; Bernard-Jannin et al. 2016; Jessen et al, 2017; McAleer et al, 2017).

Recording a precise and accurate report on groundwater microbiological communities is problematic (Cullimore, 2007; Larned, 2012). The standard methods of groundwater analysis cannot be expected to provide a truly reliable representation of the chemical and microbiological conditions which exist in-situ many metres below ground level. Drilling and extraction methods disturb the soil and create an artificially-oxygenated “redox front” immediately surrounding the groundwater well. Bacterial communities will typically colonize these zones, especially around the well-screen, which may further influence the chemistry of groundwater as it gradually moves through aquifer material into the bore. Also, the extraction of groundwater during sampling may influence not only the concentration of dissolved oxygen in the sample, but also the measured pH and temperature values (Cullimore, 2007; Lehman, 2007).

Widespread experimental research has been done on the ability of microorganisms to complete important chemical processes under controlled laboratory conditions (“batch” experiments), where distinct bacteriological strains can be identified and examined (Soares et al. 1988; Peng et al. 2015; Dong et al. 2009; Wang et al. 2015; Su et al. 2015; Soares, 1999; Bosch et al. 2018). These experiments are often conducted for identifying applications suitable for wastewater management and bio-remediation of contaminated land. Empirical data on microbiological interaction with contaminants and dissolved ions derived from such experiments can be used to help understand the processes affecting chemical conditions recorded in environmental field observations. Environmental research focusing on groundwater and soil contamination commonly utilizes soil samples, groundwater samples and sediment cores extracted directly from field sites, which then are examined in laboratory conditions to identify specific microorganisms using genetic DNA-extraction methods, or to observe chemical indicators of processes such as DN (Lehman et al. 2001; Bengtsson and Bergwall, 1995; Lehman, 2007; Lee et al, 2016; Yao et al. 2016).

A few important research papers specifically look at Queensland’s groundwater microbiology (Wakelin et al, 2011; Korbel and Hose, 2011; Perna et al 2012; Korbel et al, 2017). They highlight the need to combine groundwater microbiology data with groundwater physico- chemistry to better understand groundwater ecology and the need to employ reliable field

Page | 26 sampling methods and purging of wells to eliminate the risk of species abundance and diversity being influenced by communities existing in borehole water (Korbel et al, 2013).

2.7 Nitrate Attenuation by Denitrification

Depending on groundwater residence time, certain chemical reactions may occur which can - contribute to the mitigation of NO3 excess in both soil and groundwater. DN is the process - whereby microbial activity catalyses the reduction of NO3 to N2 (g) (Appelo and Postma, 2005; Tate, 1995). The reaction involves the transfer of electrons along a reaction pathway with metastable intermediate nitrogen species (Appelo and Postma, 2005):

- - NO3 (aq) -> NO2 (aq) -> NO (enzyme complex) -> N2O (g) -> N2 (g) (Eq 3)

The endpoint gaseous species of N2O and N2 may be lost to the atmosphere through transport - in the unsaturated zone (Figure 10). The result is that NO3 concentration in the system is minimalised through this bio-chemical activity. The subordinate process of dissimilatory reduction of nitrate to ammonium (DNRA) is also possible, with a potential benefit being that ammonium can potentially be adsorbed on negatively-charged clay surfaces and retained in the soil profile (Appelo and Postma, 2005):

+ + NO3 + 4H2 + 2H -> NH4 + 3H2O (Eq 4)

Denitrifying microorganisms exploit organic sources as electron-donors (heterotrophic DN) or inorganic sources (autotrophic DN). During heterotrophic DN, bacteria utilize organic carbon- rich substances like methanol and ethanol as carbon sources which donate electrons. The carbonaceous substrate becomes oxidised to carbon dioxide during nitrate reduction:

- - 5CH3OH + 6NO3 -> 3N2 + 7H2O + 5CO2 + 6OH (Eq 5)

This highlights the importance of dissolved organic carbon as a factor influencing DN in soils and groundwater (Thayalakumaran et al. 2007; O’Reilly et al. 2012). In the upper 2m of the unsaturated zone, organic carbon is the most common electron donor in the DN process (O’Reilly et al. 2012). In environments lacking organic carbon sources, autotrophic DN may proceed in the presence of elemental electron-donors found in mineral phases, such as sulphides like FeS2 (pyrite):

- + 2+ 2- 5FeS2 + 14NO3 +4H -> 7N2 + 5Fe + 10SO4 + 2H20 (Eq 6)

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This may be catalysed by the activity of specific bacterial strains such as Thiobacillus denitrificans. DN may also result via the oxidation of ferrous iron in groundwater, resulting in the formation of Fe-oxides (e.g. goethite):

2+ - ½ + 5Fe + NO3 + 7H2O -> 54FeOOH + N2 +9H (Eq 7)

Microorganisms involved the DN process are often facultative anaerobes which usually require - an anoxic environment. In an oxygen-depleted environment, the available NO3 may become - the electron-acceptor in the DN reaction. In environments where all NO3 or DO have been 2- - consumed, SO4 and metal oxides like MnO2 , Fe(OH)2 may substitute as electron-acceptors (Freeze and Cherry, 1979).

Certain chemical factors can be used as indicators of a soil or groundwater environment’s suitability for DN. A combination of low DO concentrations (<2 mg/L) and low redox (Eh) values (< 250 mV) is reported to be favourable for the occurrence of DN (Freeze and Cherry, 1979; Korom, 1992).

Despite decades of research, the interacting processes controlling DN in the environment, are still difficult to accurately measure and predict (Korom, 1992; Butterbach-Bahl et al, 2013). Groffman et al (2006) state that DN is “… a miserable process to measure. Available methods are problematic for a variety of reasons; they change substrate concentrations, disturb the physical setting of the process, lack sensitivity, or are prohibitively costly in time and expense.

Most fundamentally, it is very difficult to quantify the dominant end product (N2) of denitrification given its high back-ground concentration in the environment. Quantification of denitrification is also hindered by high spatial and temporal variation in the process, especially in terrestrial environments.” A common laboratory method used to measure DN involves the injection of acetylene (C2H2) into a sealed sediment/soil core to measure the accumulation of

N2O as a product of DN, as C2H2 inhibits the reduction of N2O to N2 (see Equation 5) (Balderston et al, 1976). This method has provided a great deal of empirical data on DN rates, - which is particularly useful when examining ecosystems with high NO3 levels. However, problems associated with this method include the recognition that C2H2 also inhibits the - production of NO3 via nitrification (resulting in underestimation of DN rates) (Mosier, 1980) and the fact that disturbed samples are separated from the physical environment which can play a huge role in regulating the bio-geochemical processes influencing DN.

Mass-balance methods have been used for a long time to estimate the flux of nitrogen inputs and outputs on a catchment or regional scale (Allison, 1955; Pribyl et al, 2005; Groffman et al,

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2006), but are limited in their ability to provide quantitative information on DN rates unless the inputs, outputs and storages of a system can be thoroughly constrained. This is especially problematic in sediment and groundwater aquifer systems that display a great deal of heterogeneity and doesn’t take into account localised hydrological factors contributing to the hot spot effect.

Although comparable modern agricultural techniques are utilised from temperate to tropical latitudes, they are applied across a variety of geographical zones and bioregions. Seitzinger et al provide a global perspective in their 2006 paper, “Denitrification across Landscapes and Waterscapes: A Synthesis”. Their findings indicated that DN rates (kg N.km-2/yr) in terrestrial soils and groundwater are, on average, approximately one-tenth the per-area rates of DN in lakes, rivers, estuaries, continental shelves or oceanic oxygen-minimum zones. This acknowledges the need for strategies to increase potential DN in terrestrial sources, to prevent nitrogen export to coastal zones. However, the effectiveness of such strategies is affected by our ability to confidently monitor and model DN.

- A common method of examining and reporting on environmental NO3 dynamics and DN - involves analysis of stable oxygen and nitrogen isotopes in NO3 samples (Freeze and Cherry, 1979; Appelo and Postma, 2005; Pasten-Zapata et al. 2013; Kim et al. 2015). This method can be useful in providing a more accurate analysis of contaminant pathways and potential sources - of NO3 in the environment (Kendall and McDonnell, 1998). Isotopic studies indicate that a - correlation between a decrease in NO3 concentration and an increase in the heavier oxygen 18 15 - and nitrogen isotopes ( O, N) in NO3 should be observed as an indication of DN, as the lighter isotopes (16O and 14N) are favoured during this bio-chemical process (Chapelle, 2001).

Isotopic ratios (e.g. 15N/14N) using the delta (δ) notation describe how much an isotopic ratio from a sample (R sample) deviates from a common reference standard (R standard) and are expressed in per mil (‰):

훿 푅푠푎푚푝푙푒 −푅푠푡푎푛푑푎푟푑 (Eq 8) 푠푎푚푝푙푒 = ×1000 푅푠푡푎푛푑푎푟푑

- Gormly and Spalding provided a platform for referencing NO3 source identification in their 1979 paper “Sources and Concentrations of Nitrate-Nitrogen in Ground Water of the Central 15 - Platte Region, Nebraska”, correlating spatial variations in δ N-NO3 concentrations with well- - depth measurements and NO3 concentrations. Building upon their work, researchers have since 15 - 18 - utilised the δ N-NO3 reference scale as well as δ O-NO3 values as a tool for identifying

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- potential sources of NO3 in groundwater, soils and surface waters (Kendall and McDonnell, 1998; Amberger and Schmidt, 1988; Durka et al. 1994; Xue et al. 2009; Osaka et al. 2006; Reynolds-Vargas et al. 2006; Hosono et al. 2013; Minet et al. 2017). However, it is accepted 15 - 15 that there is an overlap in δ N-NO3 values for some potential sources, most notably the δ N- - NO3 values for fertilised/unfertilised soil-nitrogen and nitrogen derived from animal wastes (Gormly and Spalding 1979; Kendall and McDonnell, 1998). Also, there is no comparable 18 - reference scale for deriving sources of O-NO3 (Kendall and McDonnell, 1998). This reduces confidence in the utilisation of isotopic data alone, so incorporating other analytical factors is a requirement.

Isotopic analysis is also routinely used to determine groundwater sources, using the stable isotopes oxygen 18 (18O) and deuterium (2H) in water samples (Appelo and Postma, 2005; Hiscock and Bense, 2014). The main isotope ratios comprising water molecules are 18O/16O and 2H/1H. Various physical reactions can influence the concentration of different isotopic ratios, resulting in the processes known as isotopic fractionation. During water evaporation, atmospheric condensation and then rainfall, the isotopic ratios 18O/16O and 2H/1H become fractionated; water evaporated form the ocean becomes depleted in the heavier isotopes 18O and 2H and enriched in the lighter isotopes 16O and 1H. In shallow groundwater systems, 18O and 2H are hardly affected by chemical processes and remain useful tracers, indicative of the isotopic composition of the precipitation entering soils and groundwater systems (Appelo and Postma, 2005; Hiscock and Bense, 2014).

Deep ocean water has a uniform isotopic composition globally, so provides a reproducible standard against which to measure other waters. This common standard is known as Vienna Standard mean Ocean Water (VMOW).

Understandably, temperature and geography are important controlling factors on the isotopic ratios of water. Analyses of precipitation from different locations around the Earth shows that 18O and 2H concentrations correlate to the equation (Appelo and Postma):

δ2H‰ = 8*δ18O‰ + 10 (Eq 9)

This is known as the meteoric water line (after Craig 1961 and Dansgaard, 1964). Deviation of 18O and 2H concentrations from the meteoric water line can be used to examine the effects of evaporation, precipitation and infiltration on groundwater and surface waters. A noticeable aspect of the historical published scientific literature on groundwater isotope research is that the majority of historical data available is derived from research in northern-hemisphere

Page | 30 locations with cooler, temperate climates and rainfall patterns which differ to those in tropical regions (Kendall and McDonnell, 1998).

2.8 Sediment Mineralogy: Implications for groundwater Chemistry

Alluvial deposits laid down by fluvial processes (rivers and streams) provide important supply sources of groundwater (Boggs, 2011). These materials (gravels, sands, clays, silts) have not been altered by cementation, pressure or high temperatures, so maintain their porosity and permeability and constitute the physical components of water-bearing aquifers and water- impeding aquitards (Freeze and Cherry, 1987; Boggs, 2011). Lithological factors controlling the nature of water-bearing aquifers and water-impeding aquitards are mineral composition, grain size and packing and permeability. The stratigraphic relationships between localised deposits of materials with high hydraulic conductivity and materials of low conductivity determine the preferential pathways that transport groundwater and dissolved chemical elements from zones of groundwater recharge to zones of discharge (Freeze and Cherry, 1987; Hiscock and Bense, 2014).

The geochemical nature of sedimentary deposits reflects the mineralogy of the rocks and parent material from which they originate (Boggs, 2011; Hefferan and O’Brien, 2010). The chemical weathering of parent material causes the dissolution and removal of mineral components in rocks, but certain minerals are more resistant to weathering than others. In 1938, Goldich recognised a sequence reflecting which minerals have greater stability at the Earth’s surface, and hence greater resistance to chemical weathering (Figure 1).

Figure 1. Goldich’s chemical weathering sequence.

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The rate of chemical weathering is an important factor controlled by geography; tropical regions with higher temperature, topography and rainfall generate faster rates of chemical weathering and erosion. Dissolved atmospheric carbon dioxide (CO2) in precipitation forms carbonic acid in water (H2CO3) which dissociates to produce hydrogen and carbonate ions (Hefferan and O’Brien, 2010):

+ - CO2 + H2O <-> H2CO3 <-> H + HCO3 (Eq 10)

The increase in H+ ions relative to OH- makes meteoric waters more acidic and therefore stronger chemical weathering agents. Organic acids from biochemical processes provide another environmental source of protons, as plants release H+ ions when taking cations from soils (Eash et al, 2016). The reaction between H+ and OH- ions with primary silicate minerals (hydrolysis) releases soluble cations and silicic acid, resulting in the formation of secondary clay minerals like kaolinite (Table 2). A noticeable effect of silicate weathering reactions which form clay minerals is the consumption of acid (H+ protons from carbonic acid dissolution), and therefore an increase in pH (Appelo and Postma, 2005; Hefferan and O’Brien, 2010; Perkins, 2014).

Kaolinite clays are characteristic of warm, tropical or sub-tropical regions with intense chemical weathering processes. They are associated with acid soils showing a positive surface charge and therefore a strong anion exchange capacity (AEC) rather than the cation exchange capacity (CEC) typical of most other negatively-charged clay soils. This variable charge quality of kaolinite is due to the acceptance of H+ ions on the edges of the plate-like kaolinite crystal structure during the dissolution of hydroxyl ions (OH-) in lower pH environments, resulting in a net positive charge (Eash et al, 2016).

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Table 2. Chemical weathering reactions for different silicate minerals contributing to the chemistry of clays and sediments.

Weathering Reactions for Different Silicate Minerals 2+ - CaCO3 + H2O + CO2 -> Ca (aq) + 2HCO3 (aq) (Carbonation) (calcite)

+ + 2KAlSi3O8 +2H (aq) + 9H2O -> H4Al2Si2O9 +4H4SiO4 +2K (aq) (orthoclase) (kaolinite) (silicic acid)

+ + 2NAlSi3O8 +2H (aq) + 9H2O -> H4Al2Si2O9 +4H4SiO4 +2K (aq) (albite) (kaolinite) (silicic acid)

2- + 2FeS2 + 15/2O2 + 4H2O -> Fe2O3 +4SO4 (aq) + 8H (aq) (pyrite) (hematite)

+ + 2+ 2K(Mg2Fe) (AlSi3)O10(OH)2 + 10H + 0.5O2 + 7H2O -> Al2Si2O5(OH)4 +2K 4Mg + 2Fe(OH)3 + 4H4SiO4 (biotite) (kaolinite) (silicic acid)

The chemical characteristics (pH and availability of exchangeable anions and cations) of the clays, sands and soils which constitute the products of chemical weathering influence the redox reactions that may occur in groundwater and soils. These geochemical influences in turn have an impact on the activities and abundances of microorganisms inhabiting groundwater and soil ecosystems, and the attenuation or transport of contaminants though aquifers and saturated zones, to surface waters and discharge zones.

2.9 Wet -Tropics Research

- Over the last 15 years, an increasing amount of research into DN and NO3 contamination has been conducted in Australia. However, a large percentage of research into nitrate transport and DN has been conducted in temperate climates, where crop types, temperature, rainfall and soil types contribute noticeably different influences on the environment, compared to tropical - climates. Recent studies have examined the adverse impact that NO3 other nutrients have on the health of the Great Barrier Reef marine zone (Devlin and Brodie, 2005; Hunter and Walton 2008; Hutchings et al, 2005; Mitchell et al 2009; Webster et al, 2009; Brodie et al 2010). They highlight that agricultural and urban development in catchments poses a significant environmental hazard to inshore coral reef communities and marine water quality, via input from terrestrially-derived nutrients and sediments. They highlight the impact of nitrogen in the - form of nitrate (NO3 ) and other nutrients on the ecosystems of the GBR marine zone through

Page | 33 outwash from seasonal high-rainfall events. These studies focused mainly on nitrogen and phosphorus concentrations and suspended sediment loads in surface waters. The literature links seasonal high-rainfall to significant outwash events into coastal marine zones. 30% of total nitrogen inputs to Great Barrier Reef (GBR) ecosystems is derived from river runoff (Devlin and Brodie, 2005), with sugarcane cropping being a major contributor (DSITI, 2016).

Humid wet-tropic regions are characterised by relatively faster biological and geological chemical processes, including the cycling of nutrients (Xu et al, 2012; Houlben et al. 2015). Combined with typically higher rainfall, wet-tropic catchments can display lower residence times for groundwater (Connor et al. 2012), depending on topography. This may inhibit the - - ability of chemical processes which mitigate NO3 contamination (such as NO3 removal - through DN) to reach completion, allowing increased NO3 outwash to marginal marine areas - (Houlben et al. 2015). A detailed understanding of groundwater chemistry and NO3 activity in agricultural catchments is crucial for the future management of important bioregions that incorporate wetlands, creeks and marine ecosystems like the GBR.

- Several papers examining DN and NO3 retention in north Queensland’s wet and dry tropics have mainly focused on the Tully, Burdekin, Mulgrave-Gordonvale and North Johnstone catchments (Rasiah et al 2003; Thayalakumaran et al 2007; Mitchell et al 2009; Thorburn et al 2011; Connor et al 2012; Rasiah et al 2013; Connolly et al 2015). Although agricultural land use and annual rainfall in the Silkwood catchment is similar to that of the North Johnstone catchment, deep (~1-10 m) Fe-rich soil profiles are absent in the Silkwood catchment. DNRME groundwater bore report data indicates a significant heterogeneity of clay layers and alluvial aquifer material in the Silkwood region. This could contribute to variable hydrological site - conditions, which may contribute to potential NO3 hot spots.

The South Johnstone-Silkwood region represents a noticeable research/data gap in our knowledge regarding groundwater dynamics in the tropical north Queensland. Rasiah et al incorporated a few bore sites near Japoonvale and Silkwood in their 2011 study on soluble phosphate dynamics, but most sites were located on the northern side of the Basilisk Range drainage divide, in the South Johnstone -Innisfail region. The local Liverpool Creek catchment around Silkwood has been mostly ignored. Conceptual models on the region’s geological and alluvial groundwater systems (combined with those of the greater South Johnstone river catchment) have previously been compiled by the Queensland government (DSITI, DNRME, 2013). Semi-regular groundwater sampling and analysis is conducted by the DNRME, but to

Page | 34 date no intensive research has been conducted on measuring spatio-temporal groundwater - chemistry data and NO3 values in the Silkwood area over the course of a year. Nor has the microbiology of groundwater and soil in the region been analysed.

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CHAPTER 3: SITE DESCRIPTION

This thesis examines a section of one of the wet-tropics catchments abutting the GBRWHA; the Silkwood region of the South Johnstone catchment (Figures 2 & 3). Since European settlement in the early 1900s, the Silkwood area has been extensively cleared of native vegetation, leaving remnant riparian belts along watercourses and saturated marginal-marine zones.

Figure 2. Location of South Johnstone catchment (boundary in red), far north Queensland.

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Figure 3. Boundary of Johnstone River catchment (in red).

3.1 CLIMATE & RAINFALL

Located approximately 90 km south of Cairns and 200 km north of Townsville is the South Johnstone River drainage sub-catchment, which constitutes the southern section of the greater Johnstone River drainage catchment (Figure 3). Average monthly temperatures in the region range from 24°C to 31°C, and the mean monthly 9 am relative humidity rages from 72% to 86%.

This region is one of the wettest in Australia, with perennial watercourses and variable rainfall occurring across short distances due to distinct local changes in topographical orientation and elevation. Orographic rainfall is produced close to the coast, where mountain ranges intercept warm, moist south-easterly air streams. Average annual rainfall at Innisfail ranges from 3769 mm/a to 4800 mm/a while the average annual rainfall in the South Johnstone catchment is 3383.3 mm/a. The South Johnstone catchment’s wet season is traditionally January to March, with June to October being distinctly drier (<600 mm/a). Less than 20 registered rainfall gauges operate in the South Johnstone catchment, and there is scarce data available on evaporation.

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The alluvial plain of the South Johnstone basin covers an area of approximately 300 km2 and is bordered to the west by the irregular, northerly-trending, rainforest-covered Walter Hill Range and to the east by low, coastal, swampy lagoons and beach sands (including Maria Creek National Park and Kurrimine Beach National Park). The catchment is drained by Liverpool Creek (which runs from Japoonvale to Cowley Beach) and by Maria Creek in the south (which drains the Maria Creek National Park wetlands and meets the coastline immediately south of Kurrimine Beach) (Figure 4).

The headwaters of all the catchments in the region are in high-rainfall range areas and local rivers can experience severe flooding during the wet season. Consequently, low-lying coastal areas can become flood-affected and crops inundated. Although sugarcane is more resilient to flooding than other crops, extensive waterlogging can result in poor yields. Agricultural surface and sub-surface drainage works have been constructed in the area to promote quicker surface runoff.

Figure 4. Displaying the township of Silkwood, the localities of El Arish, Kurrimine Beach, Cowley Beach and Japoonvale and major watercourses in the Silkwood region

3.2 GEOLOGY

The groundwater-bearing material of the South Johnstone basin is predominantly Cainozoic flood plain alluvium (Figure 5). Based on interpretations from government reports (DSITI,

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2013), two significant paleochannels exist in the Silkwood area; one trending eastward, generally coincident with the valley of Liverpool Creek and one trending northwest from El Arish. Both intercept the coast between Cowley Beach and Kurrimine. Historical bore lithology data indicates that significant thicknesses of alluvium occur in the valleys of Liverpool Creek and Maria Creek with a maximum thickness of > 80 m in the axes of the paleochannels (DSITI & DNRME, 2013). The alluvium material throughout the catchment is quite heterogeneous; dominated by clays but ranging in texture from silt to coarse sands and gravels. Beds and lenses of sand and gravel are commonly only 2-3 m thick. Quaternary dune sands constitute the coastal margin.

Significant basement rock units are the underlying Proterozoic-Palaeozoic Barnard Metamorphics (upper Greenschist-Lower Amphibolite mica-schist, biotite-gneiss, quartzite and phyllite) and the extensive Devonian Hodgkinson Formation (mudstone, arenite, chert and basalt) which is exposed as the Walter Hill range to the west. Another significant formation with minor outcrops in the basin is the Late Carboniferous-Permian Tully Granite Complex (biotite-granite, hornblende-biotite granodiorite, minor quartz gabbro). Elsewhere in the region (to the north and west of the South Johnstone catchment) the Tertiary Atherton Basalt contributes to the chemistry of Krasnozem soils and provides fractured aquifer material.

Figure 5. Displaying geological units in the Silkwood region; (HF: Hodgkinson Fm; TG: Tully Granite; Qa: Quaternary Alluvium; QS: Quaternary Sands; MBGC: Mission Beach Granite Complex; COC: Cowley Ophiolite Complex).

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3.3 HYDROLOGY

Due to the high rainfall and surface water, irrigation from production bores has not been a requirement in the South Johnstone catchment. Previous data has identified the overall quality of groundwater in the catchment to be suitable for domestic and livestock purposes. General flow direction of groundwater in the region is eastward towards the coastline (DSITI, 2013).

The chief source of data for groundwater flow and quality is measurements from drilling logs and groundwater bores (monitoring wells) throughout the catchment, with the earliest recorded bore observations dating from 1981. There are numerous registered private bores in the Silkwood region (>100), but they are unreliable data sources due to most receiving little, if any, regular monitoring for groundwater quality or water levels. Bores operated by DNRME have been monitored more frequently than private bores, and therefore provide more useful data. However, there is inconsistency regarding the timing and frequency of monitoring.

DNRME bore data indicates that standing water level (SWL) fluctuations correspond closely to the rainfall trend, suggesting that recharge is reasonably fast and groundwater residence time short. Some deviations from this correlation may be due to localised topographical and lithological factors. Monitoring wells are screened at various depths below ground level and consequently tap groundwater from a variety of aquifer material (clay-rich, sand-gravel, decomposed metamorphic rocks). DNRME bore data indicates that some bores located close to granitic basement rocks (Tully Granite, Mission Beach Granite Complex) display higher proportions of sand, gravel and decomposed granite with some fine quartz gravel. There is no data available regarding flow rates from bores, so a comparative analyses of aquifer hydraulic properties and influence of proximity to watercourses cannot be made. Also, the heterogeneous nature of the alluvium material makes correlating aquifer lithologies between bores very difficult (the most reliable and workable representation of the sub-surface geology aquifer material is based on historical bore lithology reports (DSITI, 2013).

There is limited historical reporting of any groundwater use for irrigation in the South Johnstone catchment. A 2010 Department of Environment and Resource Management (DERM) survey identified up to 450 groundwater extraction facilities in the South Johnstone catchment, but most of these are situated outside of the Silkwood area. 139 of these were not previously registered on the government’s groundwater database. Based on the survey, agricultural use of groundwater for irrigation of sugarcane amounts to only 1.55% of the total extracted in the South Johnstone catchment (DSITI, 2013).

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3.4 GROUNDWATER DATA

Historical SWL data for the Silkwood region is available from Groundwater Explorer which derives data from the Queensland Government’s Groundwater Database (GWDB). This database provides information on lithology, aquifer type & depth, water chemistry, groundwater levels and installation dates etc. for each registered groundwater monitoring bore.

Groundwater contour maps for the Silkwood region (Figures 6 & 7) were generated using ArcMap computer software, based on the averaged SWL values (measured depth below ground level (BGL)) from 10 selected bores in both dry-seasons and wet-season over 4 years. These values were corrected for elevation to create groundwater contours (Table 3 & 4).

Table 3. Averaged dry-season historical SWL values (metres BGL) for 10 selected bores in the Silkwood region (reference elevation for correction: AHD. ASL = “Above Sea Level”).

Height ASL (m) Bore ID (RN) 4/06/2012 2/07/2014 3/06/2015 29/06/2016 Average Corrected from Elevation 11210051 -8.86 -9.47 -8.55 -9.28 -9.04 20.56 11210056 -3.30 -3.72 -5.29 -3.34 -3.94 18.67 11210004 4.74 -6.36 -8.19 5.35 -6.16 12.04 11210055 -2.00 -2.70 -3.92 -2.42 -2.76 11.24 11210032 -2.78 -3.08 -4.00 -2.71 -3.14 8.86 11210029 -2.46 -3.32 -4.00 -2.97 -3.19 8.01 11210045 -3.56 -3.92 -4.97 -3.66 -4.03 6.57 11210040 -3.53 -3.95 -4.20 -3.72 -3.85 5.25 11210041 -1.89 -2.17 -2.88 -1.90 -2.21 3.29 11210075 -3.24 -3.59 -4.17 -3.33 -3.58 0.22

Table 4. Averaged wet-season historical SWL values (metres BGL) for 10 selected bores in the Silkwood region (reference elevation for correction: AHD. ASL = “Above Sea Level”).

Height ASL (m) Bore ID (RN) 7/03/2012 13/03/2013 5/03/2014 11/03/2015 Average Corrected from Elevation 11210051 -7.94 -8.95 -8.86 -9.43 -8.80 20.81 11210056 -2.70 -3.12 -3.08 -3.64 -3.14 19.48 11210004 -4.32 -4.91 -5.22 -5.64 -5.02 13.18 11210055 -1.50 -2.16 -2.20 -2.88 -2.19 11.82 11210032 -2.01 -2.56 -2.45 -3.08 -2.54 9.46 11210029 -1.89 -2.56 -2.70 -3.12 -2.57 8.63 11210045 -2.95 -3.82 -3.55 -4.20 -3.63 6.97 11210040 -3.05 -3.74 -3.51 -3.82 -3.53 5.57 11210041 -1.40 -1.86 -1.70 2.24 -1.80 3.70 11210075 -2.64 -3.10 -3.16 -3.70 -3.15 0.65

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Figure 6. Calculated dry-season groundwater contours, indicating the regional hydraulic gradient from west to east. Values in yellow indicate height ASL (m) and correspond to bore locations.

Figure 7. Calculated wet-season groundwater contours, indicating the regional hydraulic gradient from west to east. Values in yellow indicate height ASL (m) and correspond to bore locations.

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3.5 GROUNDWATER CHEMISTRY

An accurate representation of the groundwater chemistry from Silkwood DNRME bores in response to seasonal fluctuations each year cannot be created, due to infrequent historical sampling and analysis (it is worth noting that there is no historical data regarding dissolved oxygen (DO) concentrations in groundwater). Depending on what time of year the bores are sampled and analysed, the effects of significant environmental events (cyclones; El- Nino/Southern-Oscillation etc.) may not be accurately recorded. As the primary land-use in the Silkwood region is intensive sugarcane farming, the application of N-rich fertilisers is a - seasonal occurrence that may contribute to increased NO3 concentrations that are not being accurately recorded due to the infrequency of groundwater analysis. The graphs in Appendix - 2 display the historical data for NO3 concentrations in DNRME bores in the Silkwood region, based on historical sampling and analysis.

Classification of groundwater chemical facies can be conducted using the method after Piper, 1944 (Appelo and Postma, 2005; Hiscock and Bense, 2014), whereby the concentrations of major ions in mg/L are converted to mmol/L-1 by multiplying by the formula atomic weight. The concentrations of each major ion in mmol/L-1 are then multiplied by their respective ionic charge to convert mmol/L-1 to meq/L. The meq/L values of each major ion, based on the complete historical values from 5 DNRME bores (11210038, 11210051, 11210055, 11210056, and 11210075) were plotted on a ternary diagram (Piper diagram) as a percent of total meq/L per bore, using AquaChem software (Figure 8). These bores in particular were chosen due to having the most consistent enough historical chemistry data, compared to other bores in the - area. Figure 2 indicates a dominant NaCl water type with mixed Ca-MgCl and NaHCO3 water chemistry. The NaCl dominance can be a result of seawater intrusion but is also consistent with a region where precipitation is influenced by seawater chemistry (Freeze and Cherry, 1979). Armstrong (2006) showed that a similarity in chemical ratios of Na:Cl in local rainfall and groundwater on Bribie Island, South East Queensland confirmed precipitation as the primary source of recharge of NaCl and Mg-NaCl groundwater types. McMahon (2004) found that

NaCl and Na-HCO3 groundwater types were dominant in the deltaic groundwater aquifer of the Burdekin River Delta, north Queensland and concluded that marine aerosols were a contributing influence to measured Na:Cl ratios of close to 1 in groundwater.

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Figure 8. Piper diagram displaying historical chemical characteristics of 17 selected DNRME bores in Silkwood region. Full data set in Appendix 2.

3.6 SOIL

Murtha (CSIRO, 1986) classified several soil types in the Silkwood region as “poorly drained soils formed on alluvium” (see Figure 9), particularly the Bulgun series (“mottled, gradational or occasionally texture-contrast soils with prominent dark A1 horizon”).

Using DNRME data provided from the QldGlobe database, more detail regarding soil properties at specific sites located closest to the 17 selected bores describes the soil profile characteristics across the Silkwood region (Table 5). Mottling effects are typically discolourations indicative of a redox-horizon caused by groundwater level fluctuation, resulting in mineral oxidation (Eash et al, 2016).

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Figure 9. Generalised soil map (Murtha, 1986) of South and North Johnstone regions, from Tully to Innisfail.

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Table 5. Details of selected soil classification sites across the Silkwood region (QldGlobe, DNRME).

CORRESPONDING SOIL DESCRIPTION NEARBY BORE LOCATION 11210051 ICL Site 518. Described 1986 (no classification) A: clay loam, sandy. B: loam fine sandy (<2% mottles) C: loamy fine sand. 11210056 ICL Site 558. Described 1986 (no classification) A: sandy clay loam. B: sandy clay loam. 11210055 ICL Site 590. Described 1986 (no classification) A: sandy clay loam. B: sandy clay 11210032 ICL Site 695. Described 1986 (no classification) A: sandy clay loam. B: sandy light clay (common 10-20% mottles) C1: coarse sandy loam (2-10% mottles) C2: medium clay (20-50% mottles). 11210029 ICL Site 611. Described 1986 (no classification) A: sandy clay loam. B: sandy light clay (10-20% mottles) C1: coarse sandy loam (2-10% mottles) C2: medium clay (20-50% mottles) 11210045 ICL Site 353. Described 1986 (no classification) A: clay loam, fine sandy. B21: light clay (10-20% mottles) B22: light clay (20-50% mottles). 11210040 ICL Site 609. Described 1986 (no classification) A: sandy clay loam. B21: sandy clay loam. B22: sandy clay loam. 11210041 ICL Site 707. Described 1986 (no classification) A: sandy loam. B21: coarse sandy clay loam (Common 10- 20% Sub rounded Quartz Small pebbles 2- 6 mm). B22: coarse sandy clay loam (Common 20- 50% sub rounded Quartz Small pebbles 2-6 mm). 11210075 ICL Site 641. Described 1986 (no classification) A: sandy clay loam. B21: sandy loam (<2% mottles). B22: sandy loam (10-20% mottles). C1: sandy loam (2-10% mottles). 11210031 ICL Site 727. Described 1986 (no classification). A: Coarse sandy clay loam. B21: Coarse sandy loam. B31: loamy coarse sand. B32: loamy sand (2-10% mottles). 11210035 ICL Site 704. Described 1986 (no classification). A: Coarse sandy loam. B: Coarse sandy clay loam, clay. 11210037 ICL Site 638. Described 1986 (no classification). A: Light sandy clay. B21: Clay loam, fine sandy (10-20% mottles). C1: fine sandy loam (20-50% mottles). 11210046 ICL Site 353. Described 1986 (no classification) A: clay loam, fine sandy. B21: light clay (10-20% mottles) B22: light clay (20-50% mottles). 11210052 ICL Site 520. Described 1986 (no classification). A: clay loam. B21: clay loam. B22: clay loam, fine sandy (2-10% mottles). 11210057 ICL Site 768. Described 1986 (no classification). A: Sandy clay loam. B21: sandy clay. B22: coarse sandy clay. (common 10-20% small quartz pebbles; 2-6mm).

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CHAPTER 4: MEASURED GROUNDWATER CHEMISTRY/ SEDIMENT & SOIL ANALYSIS

4.1 METHODOLOGY

This chapter describes the research project’s methods used for sampling and analysing groundwater and sediment & soil samples from September 2017 to September 2018.

4.1.1 GROUNDWATER FIELD INVESTIGATIONS & SAMPLING

Six DNRME groundwater bores were selected for direct field sampling, (Table 6, Figure 12), based on spatial distribution across the region and site accessibility. Another 2 bores were newly installed in July 2017 at the P2R research paddock, bringing the total number of bores examined in this project to eight. These two new bores installed in July have recently been designated DNRME registration numbers (RN) 183021 and 183022. However, for this report they are referred to as bore P2RNE and P2RSW, respectively.

Table 6. Aquifer depths and aquifer lithologies of examined bores, 2017-2018. Depths are recorded in metres below ground level (BGL).

BORE ID (RN) BORE SCREEN AQUIFER AQUIFER MATERIAL DEPTH DEPTH DEPTH 11210004 10.00 6.00-9.00 6.00-9.50 Clayey, coarse, gravelly sands (Liverpool creek Alluvium) 11210040 10.54 6.50-9.50 5.00-8.00 Clayey, silty, fine/coarse sand. 11210041 34.00 17.10-19.10 17.00-21.00 Clay, silt, fine/coarse sand. 11210045 48.00 38.40-40.40 38.40-40.40 Fine/medium quartz, deco metamorphics, gravel, clay (Hodgkinson Fm). 11210051 66.70 10.50-12.50 11.80-13.80 Medium sand to fine gravel (Johnstone River Alluvium). 11210056 42.00 31.70-33.70 31.70-33.70 Metamorphics (Hodgkinson Fm). 183021 (P2RNE) 14.00 8.80-11.80 9.00-11.00 Medium/coarse clayey sand, fine quartz (~4-5 m). 183022 (P2RSW) 15.00 9.00-12.00 9.00-11.00 Fine/medium clayey sand, fine quartz (~2 mm).

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A sampling time-line was created allowing for bi-monthly field trips during the dry season and monthly trips during the summer wet season to monitor changes in groundwater chemistry. A total of 10 sampling trips were completed from September 2017 to September 2018:

 2017: 22nd September 10th November 1st December 15th December.  2018: 12th January 16th February 23rd March 11th May 20th July 21st September.

Due to field conditions, bore 11210056 was inaccessible in February and July 2018. Bore 11210004 ran dry of water in January and September 2018. This has resulted in some data gaps in the figures and tables presented in section 4.2 -Results & Discussion.

In addition to the eight bores chosen for regular groundwater sampling, samples were also collected in July 2018 from two other bores; a bore in the wet-tropics Russel-Mulgrave catchment near Gordonvale, ~75 km north of Silkwood (bore 149747, Figure 10) and bore 11210055 which is located in the middle of Silkwood township (Figure 11). These were sampled specifically for microbiological research and are discussed further in Chapter 5.

Figure 10. Location of Gordonvale in Russel-Mulgrave catchment in relation to Silkwood region.

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Figure 11. Location of bore 11210055 in Silkwood township.

4.1.1.1 SWL Measurements, Rainfall, Groundwater Sampling

A Heron electric dip-meter was used to record the groundwater level (SWL) at each bore before pumping. Data-loggers installed in bores P2RNE and P2RSW also provide continuous SWL monitoring data. Rainfall measurement data was provided from a DNRME rain gauge installed at the P2R paddock.

A 12 V peristaltic pump was used to extract water from each bore, connected to a flexible plastic sampling hose (Figure 13). Each bore was purged of 3 x volume to remove static bore water before sampling and ensure a more accurate representation of aquifer water. Samples were collected into rinsed 1 litre polypropylene bottles. Field duplicate samples were collected for analysis and field blanks using deionised water. The Heron dip-meter was attached to the pump to assist in monitoring depth of pump during groundwater extraction.

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Figure 12. Locations of examined bores selected for groundwater sampling 2017-2018.

4.1.1.2 Water Quality Measurements

Measurements of groundwater quality parameters were recorded at each bore using a calibrated WTW portable multimeter: electrical conductivity (EC µS/cm), dissolved oxygen (DO mg/L), redox potential (ORP mV), acidity/alkalinity (pH) and temperature (°C). An in-flow chamber connected to the pump hose was used to assist in accuracy of measurements. Sampling into rinsed polypropylene bottles commenced after the observed values of groundwater quality parameters had stabilised. Other physical parameters recorded at each bore site included: observed colour and clarity of groundwater; changes in land use (e.g. harvesting, fertilizer or lime application, tilling etc.); observable effects of recent flooding or surface water flow.

All sample collection and preservation methods were conducted according to Australian standard guidelines (DNRME WMO018; Monitoring and Sampling Manual 2009 Environmental Protection (Water) Policy 2009(2013 version); Groundwater Sampling and Analysis – A Field Guide Geoscience Australia 2009). Accordingly, groundwater samples were immediately stored at refrigerated temperatures (<4°C) using ice, ice-bricks and clean polystyrene boxes. Samples were cold-freighted to Brisbane to be stored in cold-room refrigeration at QUT Gardens Point campus for laboratory analysis. Samples intended for

Page | 50 laboratory analysis of cations were preserved with HNO3 (equivalent to 2 mL per 1 L of sample).

Figure 13. A Heron Dip-Meter used to monitor SWL, in conjunction with submersible 12 V peristaltic pump and plastic hose for extracting groundwater samples (Bore RN 11210004 pictured).

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4.1.2 GROUNDWATER LABORATORY ANALYSIS

Chemical analysis of groundwater samples was conducted at QUT’s Central Analytical Research Facility (CARF) laboratories, Gardens Point campus. Samples were prepared in duplicate with deionised-water blanks. Where required for cation and anion analysis, samples were filtered through a 0.45 µm filter membrane.

Analysis of major cations (Na+, K+, Ca2+, Mg2+, Mn2+, Al3+, Si4+, Fe2+, S2-) was completed using Inductively Coupled Plasma Optical Emission Spectroscopy (Perkin Elmer Optima ICP- - - - 2- - OES). Analysis of major anions (Fl , Br , NO3 , SO4 , PO4 ) was completed using Ion Chromatography (Thermo Scientific Dionex D211 RFIC Ion Chromatograph). The method detection limits vary for each dissolved ion and are calculated from 3 x the standard deviation of deionised water blanks. Quality control measures during major ion analysis included the use of certified reference material (CRM) of different dilutions, as well as sample duplicates and deionised water blanks.

- - + Analysis of dissolved inorganic nitrogen (DIN) species (NO3 , NO2 , NH4 ) was conducted on filtered samples using a Thermo Scientific Gallery Automated Chemistry Analyser.

Alkalinity analysis was conducted on unfiltered samples using a Mettler Toledo Auto-Titrator. Analysis of total organic carbon (TOC), total inorganic carbon (TIC) and total nitrogen (TN) was conducted on unfiltered samples using a Shimadzu TOC-V Analyzer and TNM-1 measuring Unit.

Field duplicate samples were included in laboratory analysis. The resulting data for each sample (concentrations in ppm/mg/L) is a calculated average of sample and duplicate.

2 18 15 - 15 14 18 - 18 16 Stable isotope analyses for H and O, δ N-NO3 ( N/ N) and O-NO3 ( O/ O) were conducted by staff at the DES laboratory, Dutton Park, Brisbane (Eco-Sciences Precinct). 1 litre of sample was used for groundwater isotopic analysis, and 500mL of sample was used for rainwater analysis. For 2H and 18O, the variation in natural abundance of the stable isotopes were determined in filtered (0.45 um) water samples using a Thermo Fisher FLASH HT Plus Elemental Analyser coupled to a Thermo Fisher Delta V Advantage Isotope Ratio Mass Spectrometer with each sample run in triplicate. The method for analyses was performed as per the Elemental Analyser operating manual (Thermo Fisher, 2010), following protocols and calculations detailed in Carter and Barwick (2011). Instrument results were corrected for linearity, drift and normalised against IAEA (International Atomic Energy Agency) certified

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15 - reference materials. The natural abundance of δ N in NO3 salts extracted from ground water was determined by combustion in a Thermo-Fisher Flash 2000 HT plus Elemental Analyser.

The combustion product of N2 (N2 post reduction) gases were separated and transferred to a Thermo-Fisher Delta-V Advantage IRMS (Isotope Ratio Mass Spectrometry) for isotopic ratio 15 - determination. A sample of Incitec Pivot CK 50/50 fertiliser was locally-sourced for δ N-NO3 18 - and O-NO3 analysis.

Additional isotopic analysis of 2H and 18O in groundwater and rainwater samples was conducted at QUT’s CARF, using the Los Gatos Liquid Water Isotope Analyzer (Los Gatos Research; San Jose, California, USA).

4.1.2.1 Statistical Analysis

Statistical analysis of groundwater chemical parameters was completed using Minitab analytical computer software (Minitab Inc.). Principal Component analysis (PCA) and cluster analysis are two standard statistical tests used in groundwater chemistry analysis to identify relationships between multiple variables in groundwater chemistry data (Sing et al. 2005; Spanos et al. 2015; Gibbons, Bhaumik and Aryal, 2009). The cluster analysis used a combined correlation coefficient distance and complete-linkage (hierarchical) clustering method. The PCA utilised a standard correlation matrix method.

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4.1.3 SEDIMENT & SOIL FIELD INVESTIGATIONS AND SAMPLING

2 new groundwater bores were installed at the P2R research paddock in July 2017 (Figure 14), for monitoring groundwater chemistry, including nitrate concentrations, at depths > 4 m BGL. Their installation location was decided based on estimated groundwater flow direction from south-west to north-east, towards Buckley’s Creek. This was derived from historical Digital- Elevation-Model (DEM) topographical data (USGS, Figure 15) and DNRME LiDAR data (2014). The 2 bores were installed at the south-west and north-east corners of the P2R site and have been designated the registration numbers 183022 and 183021, respectively. However, in this thesis they are referred to as P2RSW and P2RNE.

Figure 14. Location of P2R paddock (indicated by yellow polygon) and Bores P2RNE & P2RSW (approx. 2.5 km east of Silkwood township)

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P2RNE

P2RSW

Figure 15. Contour map indicating topographical changes in elevation at P2R paddock site. Location of bores P2RNE & P2RSW indicated on map by red markers. Figure generated using ArcMap from USGS DEM data (SRTM 1 Arc-second, 2014).

Bore installation was completed using a Sonic LX 600 Sonic Drill Rig (Figures 16 & 17) which recovered sediment cores in 1 m lengths (Figures 18 & 19). Logging of soil/sediment cores was conducted on site for both bore locations, observing physical properties corresponding to various depths. Some shallow water (~3 m below ground level (BGL)) was encountered during the drilling of bore P2RNE, indicating the presence of a shallow, perched aquifer. For both bores P2RNE and P2RSW, significant water was encountered at approximately 9 m BGL.

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Figures 16 & 17. Sonic drill rig extracting sediment cores and installing bores P2RNE & P2RSW at P2R research paddock, July 2017.

4.1.3.1 Sediment Sampling

14 sediment samples were collected during bore installation in July 2017 on site at the P2R paddock, from core sections corresponding to various depths of the 2 new bores (from 3 m to 12 m). (Note: sediment samples were only collected from the P2R research paddock site, not from any other locations in this study). Sediment samples were collected into clip-seal plastic sample bags, clearly labelled with date and sample IDs and stored with groundwater samples at <4°C for transport to QUT Gardens Point Campus for storage. Field logs of each section of extracted sediment core were recorded during installation, noting physical characteristics.

4.1.3.2 Soil Sampling

Soil samples were collected at the P2R research paddock in May 2018. 3 sites were identified for sampling; 2 were chosen for proximity to the P2R site groundwater bores while still being within the cultivated paddock and 1 site was located approximately in the middle of the paddock. The P2R is an experimental research paddock, divided into 5 different sections monitored for variable fertilizer types and rates of application, to observe crop response and nitrogen retention in soil and shallow water. Each soil sampling site corresponds to a different experimental fertilizer application (Figure 20)

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2 distinct upper soil horizons are present at the P2R paddock; a clay-rich black A-Horizon down to approximately 55-60 cm, overlying a denser, B-Horizon of clay. For each of the 3 soil sampling sites, one sample was collected from ~25 cm depth and one from ~65 cm to represent each layer. A steel hand-auger was used to manually extract soil samples. Samples were collected into rinsed 500 mL polypropylene bottles and clean clip-seal plastic sample bags and immediately stored with groundwater samples at <4°C for transport to QUT Gardens Point Campus for cold-room storage.

Figures 18 & 19. Examples of sediment cores extracted at P2RNE site during bore installation.

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Figure 20. P2R paddock site; Yellow arrows indicate locations of soil sample extractions from 25 cm and 65 cm depth. Different fertiliser applications correspond to each sampling site: NE = 160 kg N/ha Enhanced Efficiency Fertiliser (EEF); MID = 100 kg N/ha Urea; SW = Nil N.

4.1.4 -SEDIMENT & SOIL LABORATORY ANALYSIS

X-ray diffraction (XRD) analysis was performed on sediment samples from both bores at the P2R research paddock, for quantitative mineral identification. All preparations and analyses were conducted at QUT’s CARF laboratory facilities.

Sediment samples for XRD analysis were first air-dried then oven-dried (40°C, over 24 hrs) to remove water (Figure 21). These samples were then individually crushed and sieved (from 1 mm to 710 µm) before being pulverised to ~450 µm using a RockLabs benchtop ring-mill. Samples were then micronized with ethanol, decanted into petri dished and oven dried (40°C for 1 hour). The residual powder was pressed into discs and loaded onto slides for XRD analysis using a PANalytical Multi-Purpose Diffractometer.

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Figure 21. Oven-dried sediment samples (corresponding to different bore depths) prior to milling for XRD analysis preparation.

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4.2 RESULTS & DISCUSSION

4.2.1. GROUNDWATER CHEMISTRY ANALYSIS

4.2.1.1 Rainfall and SWL

SWL data collected from field measurements and data-loggers installed in bores P2RNE & P2RSW were combined with rainfall data collected from the DNRME P2R paddock rain gauge (Figures 20 & 21)

Figure 22. SWL measurements for each examined bore Sept 2017 to Sept 2018 and rainfall data recorded from Silkwood P2R site rainfall gauge (rainfall data available to 15/08/2018).

Figure 23. Combined Data Logger SWL measurements and rain gauge rainfall measurements at P2R paddock site (Bores P2RNE & P2RSW). SWL measurements on primary vertical axis are corrected for height of bore casing (P2RNE: 0.65 m above ground level; P2RSW: 0.78 m above ground level). Red triangle markers indicate sampling dates.

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The averaged SWL values based on historical data (Table 2, Chapter 3, section 3.4) suggest little variation in response to wet and dry seasons, whereas the measured SWL values in Figure 22 demonstrate a more distinct fluctuation in response to rainfall events (particularly bores 11210004 and 11210041). This may be due to averaging of the data combined with the infrequency of historical measurements (the historical data displays SWL measurements recorded only once or twice a year, as opposed to monthly measurements). SWLs peaked as rainfall increased in February –March 2018. According to BOM rainfall records, wet season rains are typical for March, with minor preceding events in November, December common. A relatively dry spell over 3-4 weeks in December 2017-January 2018 and again in September 2018 resulted in a decrease in SWLs, with bore 11210004 running dry (making sample collection impossible).

Bore 11210004 is located close to the edge of the steep, vegetated bank of Liverpool Creek (Figures 24 & 25).

Figure 24. Location of Bore 11210004 next to Liverpool Creek.

Figure 25. Steep, vegetated bank, Liverpool Creek. (Location of bore 11210045 is next to vehicle in right of photo).

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The bore is screened at 6-9 m BGL and depth of bore is 10 m BGL. The depletion of water in the bore during dry periods suggests that the SWL may fall below the depth of the bore. During pumping, bore 11210004 regularly displayed a pumping flow rate that was relatively lower/less stable than that of other bores sampled in this study (pumping required to stop several times to allow recharge).

Figure 23 displays the high SWLs recorded at the P2R paddock site. P2RSW SWLs remained shallow (<2 m BGL), while P2RNE maintained a SWL above ground for nearly the whole year and was observed to overflow in March 2018 (Figure 26), indicating the regular degree of saturation experienced by the clay-rich alluvial sediment in the local area. No other bores in the study recorded such high SWLs, even at aquifer depths shallower that those at the P2R paddock (e.g. bores 11210004 and 11210040 are both screened at 6 m and 6.50 m BGL, respectively, while the P2R site bores are screened at 9 m BGL). An exception being bore 11210041, which displayed an above-ground SWL in response to significant rainfall events only in February-March 2018.

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Figure 11, displaying the recorded SWL and rainfall from bore data loggers and rainfall gauge at the P2R paddock, also indicates a rapid response in SWL to significant rainfall events, with longer recession periods following.

Figure 26. (Right) P2RNE bore casing overflowing with groundwater in March 2018 following heavy rainfall.

This may be attributable to the dense clay layers observed above and below the aquifer material at the P2R site. As the local soil and sediment becomes saturated by rainfall, the lower permeability due to high clay content may restrict groundwater flow, resulting in an increase in pressure through the preferential pathway of the more permeable aquifer material at ~9 m BGL. This “bottleneck” effect would result in a rapid SWL rise in the bore, but the less-permeable nature of the saturated clays would still slow the transport of water through the subsurface environment, resulting in a slow recession in SWL. It was observed during pumping that the P2R bores displayed a flow rate similar to that displayed by bore 11210004, with pumping required to pause several times to allow recharge. This may be due to the presence of less- permeable clays restricting groundwater flow in the aquifer matrix and surrounding sediment. Bore 11210040 is the closest DNRME bore to the P2R paddock; lithological data recorded during its installation indicates a very clay-rich profile overlying gravelly, sandy aquifer material at 6-9 m BGL, but no other bores are located close enough to correlate any lateral continuity of sediment layers beneath these paddocks.

4.2.1.2 Groundwater Chemistry

The Piper diagrams in Figures 8 & 27 indicate a dominant NaCl water type with mixed Ca- - MgCl and NaHCO3 water chemistry. The NaCl dominance is consistent with a coastal region where precipitation may be influenced by seawater chemistry (Freeze and Cherry, 1979). Bore - 11210045 displays a distinct Ca/NaHCO3 water type and bore P2RSW displays a mix between

NaCl and Ca(HCO3)2 and /Na(HCO)3 water types.

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Figure 27. Combined measured major ion chemistry for Silkwood Bores Sept 2017-Sept 2018 (AquaChem, Piper Diagram). Full major ion dataset provided in Appendix 5.

Table 7. Measured alkalinity values (CaCO3 and Na2CO3), 2017-2018.

Alk CaCO3 (ppm) Bore ID (RN) Date: 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 2.8 6.8 37.5 4.9 2.0 4.0 10.6 10/11/2017 < DL 6.2 36.1 5.3 2.4 2.2 9.8 1/12/2017 3.8 6.3 38.6 2.1 5.7 2.0 7.7 15/12/2017 4.4 6.1 40.5 5.3 6.9 1.7 6.0 12/01/2018 < DL 6.3 46.5 5.6 8.8 2.6 6.0 16/02/2018 4.4 5.7 29.6 5.8 NA 1.6 4.8 23/03/2018 4.5 6.5 31.0 6.0 3.3 1.3 6.4 11/05/2018 4.9 5.0 38.7 6.0 3.3 1.4 6.1

Alk Na₂CO3 (ppm) Bore ID (RN) Date: 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 3.0 7.2 39.7 5.2 2.2 4.2 11.2 10/11/2017 0.0 6.6 38.2 5.7 2.6 2.3 10.4 1/12/2017 4.0 6.7 40.8 2.3 6.1 2.2 8.2 15/12/2017 4.7 6.5 42.9 5.6 7.3 1.8 6.4 12/01/2018 < DL 6.6 49.3 5.9 9.3 2.7 6.3 16/02/2018 4.6 6.0 31.4 6.2 NA 1.7 5.1 23/03/2018 4.7 6.9 32.8 6.4 3.5 1.4 6.8 11/05/2018 5.2 5.3 41.0 6.3 3.5 1.5 6.4

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Bore 11210045 is the deepest of the measured bores, with an aquifer depth of 38m BGL. It also displays a relatively higher pH, higher EC, highest concentrations of dissolved cations (Na+, Ca2+, Mg2+, K+; see Appendix 5) and highest measured alkalinity (Table 7). Sediment geochemistry plays an important role in the influencing the concentrations of major, minor and trace elements in groundwater from the dissolution of various minerals (Freeze and Cherry, 1979). The mineralogical composition of the aquifer material at bore 11210045 incorporates decomposed metamorphic basement material from the Barnard Metamorphics (gneisses and mica-schist), which may contribute dissolved cations from the dissolution of minerals in the parent rock material. The weathering of silicate minerals (e.g. k-feldspar, biotite) from the - geological source may also contribute to increased concentrations of HCO3 (Table 2, Chapter 2) and therefore increased alkalinity values.

Measured EC (Table 8) provides an immediate representation of the total inorganic dissolved solids (TDS) present in groundwater solutions, the typical major ionic constituents being Na+, 2+ 2+ + - 2- - Ca , Mg , K , HCO3 , SO4 and Cl (Freeze and Cherry, 1979).

These EC values are low for groundwater (an EC range of 20-100 µS/cm is consistent with rain water and fresh water streams (Sanders, 1998)), suggesting the rapid infiltration (recharge) and transport of low-TDS rainwater throughout the catchment. Measured EC values remain fairly constant throughout the study period, with only bore 11210045 displaying any degree of noticeable fluctuation (with values peaking in January 2018, coincident with peaks in Ca2+, Mg2+ concentrations). Bore P2RNE displays a significantly higher EC value for September 2017, possibly due to disturbance of clay-rich sediments during bore installation in July 2017.

Table 8. Measured EC values in Silkwood groundwater bores, 2017-2018, including uncertainty range of values for EC probe (± 0.5%).

EC (µS/cm) Bore ID (RN) Date: 11210004 ± 11210040 ± 11210041 ± 11210045 ± 11210051 ± 11210056 ± P2RNE ± P2RSW ± 22/09/2017 52.8 0.3 52.8 0.3 64.8 0.3 109.1 0.5 54.5 0.3 67.0 0.3 115.0 0.6 57.5 0.3 10/11/2017 55.0 0.3 49.8 0.2 62.1 0.3 87.5 0.4 54.6 0.3 57.5 0.3 50.8 0.3 57.2 0.3 1/12/2017 59.8 0.3 48.4 0.2 63.6 0.3 98.0 0.5 53.0 0.3 59.3 0.3 53.0 0.3 56.0 0.3 15/12/2017 57.5 0.3 46.7 0.2 62.5 0.3 110.9 0.6 52.3 0.3 57.5 0.3 51.0 0.3 52.5 0.3 12/01/2018 48.2 0.2 61.1 0.3 112.0 0.6 51.2 0.3 61.1 0.3 49.9 0.2 52.5 0.3 16/02/2018 62.0 0.3 51.3 0.3 61.0 0.3 89.0 0.4 48.8 0.2 50.0 0.3 49.5 0.2 23/03/2018 62.2 0.3 52.5 0.3 67.7 0.3 92.5 0.5 47.2 0.2 34.6 0.2 51.8 0.3 50.7 0.3 11/05/2018 58.7 0.3 47.8 0.2 65.6 0.3 98.0 0.5 48.0 0.2 49.3 0.2 49.3 0.2 47.5 0.2 20/07/2018 57.7 0.3 48.0 0.2 63.3 0.3 109.5 0.5 53.4 0.3 51.0 0.3 50.2 0.3 21/09/2018 51.5 0.3 62.9 0.3 109.8 0.5 55.8 0.3 58.0 0.3 51.3 0.3 50.0 0.3

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4.2.1.3 Groundwater Chemistry - Ca2+, Mg2+, Na+ and Cl-

Measured groundwater chemical data shows that around December 2017-January 2018, there were noticeable increases in concentration of dissolved Ca2+, Mg2+, Na+ and Cl- (Appendix 5; Figures 28, 29, 30 & 31). This may be an influence of fertiliser and lime/acidity-regulator applications (dolomite; CaMgCO3) (Adams and Evans, 1989) to paddocks around this time. Fertiliser was applied in the region around November 2017 in anticipation of early summer wet-season rainfall events; a recorded rainfall event in November (Figures 20 & 21) precedes the increase in Ca2+, Mg2+, Na+ and Cl- concentrations. Common sugarcane fertilisers known to be used in the region are urea (CH4(N2O)), potassium-sulphate (K2SO4) and ammonium- + sulphate (NH4)2SO4. Other commonly used sugarcane fertilisers in Queensland contain Na and Cl-, such as “muriate of potash” (potassium-chloride, KCl) (BSES, CANEGROWERS,

2008) and sodium nitrate (NaNO3).

Liming and acidity-regulator application is observed to take place at different sites at different times throughout the year, depending on local requirements. Gypsum (CaSO4·2H2O) is known to be used regularly in the region. Measured groundwater data shows that bore 11210045 maintains the highest Ca2+ concentration throughout the year, which may be at least partially attributed to geochemical influences from aquifer material (Castanier et al, 1999).

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Figure 28. Measured Ca2+ concentrations in groundwater, 2017-2018.

Figure 29. Measured Mg2+ concentrations groundwater, 2017-2018.

Na+ and Cl- concentrations across the catchment also display a distinct increase in December 2017, but whereas most Na+ concentrations display very little fluctuation for the remainder of the study period, Cl- concentrations remain elevated and display more fluctuation across sites (Figures 30 & 31).

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Figure 30. Measured Cl- concentrations groundwater, 2017-2018.

Figure 31. Measured Na+ concentrations groundwater, 2017-2018.

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The “source-rock deduction’ method as described by Hounslow (1995) is utilised to gain insight into possible sources and geochemical influences on groundwater chemistry by comparing ratios of dissolved ions. Table 9 displays Na+/ Cl- values calculated from the measured groundwater chemistry data (Appendix 5). According to the method, Na+ = Cl- indicates the source is halite (NaCl; from the ocean, via rainfall) dissolution (the values closest to “1” are highlighted by bold outline boxes). As Na+ may be derived from more sources than Cl-, a result of Na+ < Cl- (cells highlighted green in Table 8) indicates either an analytical error or the water is derived from brines, or reverse ion-exchange is occurring (Hounslow, 1995):

2Na+ + Ca-Clay -> Ca2+ + 2Na-Clay (Eq.12)

A result of Na+ > Cl- (cells highlighted red) indicates a source other than halite, such as albite

(NaAlSi3O8) or natural softening of water via ion-exchange:

Ca2+ + 2Na-Clay -> 2Na + + Ca-Clay (Eq.13)

Table 9. Na+/ Cl- values calculated from the measured groundwater chemistry data, 2017-2018

Date: 1121004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 0.9 1.0 0.7 2.8 1.1 0.6 0.5 1.4 10/11/2017 0.9 0.8 0.6 2.3 0.9 0.5 0.8 1.2 1/12/2017 0.8 0.8 0.7 1.9 0.5 0.9 0.6 1.0 15/12/2017 0.5 0.6 0.4 1.8 0.6 0.6 0.5 0.8 12/01/2018 0.6 0.4 2.0 0.6 0.7 0.5 0.8 16/02/2018 0.6 0.6 0.5 1.7 0.7 0.5 0.8 23/03/2018 0.6 0.6 0.5 1.6 0.6 0.4 0.5 0.8 11/05/2018 0.6 0.6 0.5 1.7 0.7 0.4 0.5 0.8 20/07/2018 0.6 0.7 0.6 1.9 0.7 0.6 0.9 21/09/2018 0.6 0.5 1.9 0.7 0.5 0.5 0.8

Hounslow states this method is not infallible and, as it is based on lithological source- deduction, does not take into account possible anthropogenic inputs of mineral elements. Based on the method, the data from Table 8 indicates that near start of the study period (September- November 2017) natural rainfall may be an influential Na+ and Cl- source, (according to the Na+/ Cl- being close to, or equal to, 1), but this is not replicated in the rest of the data set during periods of high rainfall following January 2018. Without considering possible anthropogenic inputs, Hounslow’s method ascribes analytical error as a potential reason for a ratio of Na+ < Cl-. However, an alternative source for increased Cl- concentrations from December 2017 may be the KCl from sugarcane fertilisers. Given that Ca2+ and Mg2+ concentrations also rapidly

Page | 69 increase around the same time (December 2017; Figures 28 & 29) this may be an indication of a rapid catchment-wide shift in chemistry away from environmental background influences (i.e. mineralogical and meteorological sources) to anthropogenic influences, explained by the application of fertilisers and soil-conditioning/pH-altering agricultural products around that period (November-December 2017).

The calculated ratios for bore 11210045 in Table 9 suggest that Na+-rich silicate minerals are influential sources in that aquifer (this is consistent with other measured groundwater data displaying relatively higher major ion concentrations for that site (Appendix 5)) and may be influenced by the mineralogy of the aquifer material (Table 6). The calculated ratios in Table 9 also reflects the relatively higher concentrations of Na+ in bore P2RSW that contribute to its

NaHCO3 chemical characterisation in the Piper diagram (Figure 27), which is also consistent with the measured alkalinity data in Table 7. In both P2R bores, alkalinity, DO, Fe2+ and TIC concentration levels all peak at the beginning of the study period (September 2107) then decrease. As both bores were installed in July 2017, it is possible that these chemical parameters are indicators of the disturbance of the clay-rich sediments during bore installation, which then stabilise over the following months. The data regarding bore P2RSW in Figure 31 may suggest a lithological Na+ source when considered in this context (Na+ > Cl-), which is also consistent with the consistently higher Na+, Ca2+ and Mg2+ concentrations in groundwater sampled from bore 11210045.

4.2.1.4 Groundwater Chemistry: DO

DO concentrations (DO) are variable across the sampled sites (Table 10), with some sites indicating influx of oxygenated waters. (Freeze and Cherry, 1979). Bore 11210051 has consistent DO values > 3.20 mg/L. It is the deepest of bores in the study (66.70 m BGL, Table 6) but is screened at 9-11 m BGL. Lithology from bore report data shows that underlying the screened zone is ~29 m of alluvial material incorporating coarse sand and gravel (Appendix 6). Although its lateral extent is unknown, such material is likely to have a permeability and hydraulic conductivity suitable enough to store and transport water, indicating that bore 11210051 may draw on aquifer material of significant vertical extent compared to the other bores. It is also worth considering that local topography can be an important influence on recharge (Toth, 1963; Schaller and Fan, 2009; Condon and Maxwell, 2015). With bore 11210051 being located in a narrow valley surrounded by forested recharge zones of higher

Page | 70 topography (Figure 32), recharge may be rapid due to concentrated rainfall infiltration, bringing oxygenated waters into the aquifer material.

Table 10. Measured DO concentrations in groundwater, 2017-2018.

DO mg/L Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 4.50 4.50 2.90 0.80 4.60 5.60 1.34 2.40 10/11/2017 3.55 2.11 2.45 0.55 3.84 3.66 0.25 0.14 1/12/2017 2.30 2.13 2.63 0.13 4.60 4.27 0.35 0.16 15/12/2017 2.09 2.03 2.66 0.05 4.17 0.09 0.25 0.11 12/01/2018 2.70 2.68 0.06 3.98 0.04 0.25 0.22 16/02/2018 1.81 1.10 2.99 0.15 4.25 0.30 0.10 23/03/2018 1.40 1.10 2.69 0.13 3.31 3.31 0.40 0.14 11/05/2018 0.98 0.73 2.02 0.23 4.45 3.01 0.4 0.08 20/07/2018 2.05 1.09 2.9 0.09 3.82 0.5 0.59 21/09/2018 3.28 2.62 0.06 3.45 0.07 0.54 0.35

There are also indications of DO decline in response to significant rainfall events in February- March 2018 (Bores 11210004, 11210040 with corresponding declines in redox potential; Appendix 4), indicating a more reduced environment. Historical bore report data (DNRME, Appendix 6) shows that these shallow aquifers (Table 6, section 4.1.1) are overlain with clays, which, in times of rainfall, may inhibit the infiltration of oxygen and other gaseous species due to relatively lower permeability. The relationship observed between clays and groundwater chemistry is explored further in section 4.2.2.

Bore 11210056 displayed a significant reduction on DO during the dry period of December 2017 and an increase in DO around the time of significant rainfall, indicating recharge with oxygenated waters detectable at a depth of ~32 m BGL. The data also shows a strong - correlation between DO and NO3 (Figure 33).

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Figure 32. Location of bore 11210051 in Japoonvale valley.

- Figure 33. Relationship between DO and NO3 in measured groundwater samples, bore 11210056.

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An insight into why the chemistry of bore 11210056 responds so dynamically compared to the other studied sites might arise with further examination of its local lithology and hydrology. Lithological data from DNRME bore reports indicate that the upper 29 m of sediment at the site of bore 11210056 is composed of clay, silt and fine sand to fine gravel, which is the same description of the aquifer material at 31-36 m BGL (Schematic bore diagrams displayed in Appendix 6). Immediately below the aquifer material is firm clay and metamorphic basement rock. The upper 29 m of sediment in this locality of the catchment may have a relatively higher hydraulic conductivity compared to less-permeable clays overlying aquifers in the other studied bore sites, due to the sand and gravel content within the alluvium, providing a more rapid transport medium for infiltration of rainfall to groundwaters. Recharge might therefore be relatively fast compared to other aquifers in the catchment.

The bores which displayed consistently lower DO are 11210045, P2RNE and P2RSW. Considering the aquifer depth at bore 11210045 (38.40 m BGL), it may not be surprising that the groundwater is the most depleted of DO. But the aquifer supplying the two P2R bores is only 9 m BGL yet displays DO comparable to 11210045. This can be explained by the presence of denser, less permeable clay alluvium in the local region of the P2R paddock; when the SWLs are high and the soils less permeable, oxygen diffusion through sediments may be reduced (Freeze and Cherry, 1979; Appelo and Postma, 2005).

- 4.2.1.5 Groundwater Chemistry: NO3

- NO3 concentrations in all bores remained < 10 mg/L, within acceptable environmental limits (50 mg/L, ANZECC) (Table 11; Figure 34). Bores 11210004 and 11210041 maintained the - highest NO3 concentrations while bores 11210045, P2RNE and P2RSW consistently displayed - the lowest NO3 concentrations throughout the year (averages of 0.42 mg/L, 0.80 mg/L and 0.01 mg/L respectively) (Figure 34). Aquifer depth does not appear to be a major controlling factor; the P2R aquifers are relatively shallow (~9 m BGL) while the aquifer supplying bore - 11210045 is at a depth of 38 m BGL. Fluctuations of NO3 concentrations were variable across all sites:

-  11210004 displays a slight increase in NO3 in Feb-Mar 2018 (period of increased rainfall).

-  11210051 displays a decrease/dilution in NO3 in Feb-Mar 2018.

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-  11210056 displays a significant decrease in NO3 in December 2017, but due to the site being inaccessible on 2 occasions, a complete record is not available.

-  P2RNE and P2RSW also display a slight increase in NO3 in Feb-Mar 2018.

-  11210041 NO3 remains constant throughout 2017-2018.

These results differ to observations by Rasiah et al (2012) who recorded a noticeable positive - correlation between rising SWL and NO3 -N concentrations in groundwater beneath sugarcane paddocks in the wet-tropics Mulgrave River catchment.

- Table 11. Measured NO3 (mg/L) and NO2(µg/L) concentrations in Silkwood groundwater, 2017-2018. NA = Groundwater sampling unavailable.

NO3 (mg/L) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 5.94 3.28 6.58 1.83 6.50 6.42 0.91

NO2 (µg/L) Bore ID (RN ) Date Sampled: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017

- The variations in DO and NO3 concentrations across all groundwater aquifers highlights the - variable nature of groundwater chemistry on a catchment scale. NO3 concentrations in groundwater can be influenced by the different types and amounts of fertilisers applied on paddocks throughout the region. The heterogeneity of the local lithology and sediment type - may also influence the concentrations of both DO and NO3 in groundwater, depending on AEC capacity and the presence (or lack) of dense clays which may adsorb, or inhibit the infiltration - of, NO3 .

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- Historical data indicates that NO3 in bores 11210045 and 11210056 have remained below detection limits since 2002 (Appendix 1). However, the data shows that over 2017-2018 shows - that these bores displayed measurable NO3 concentrations ranging from 1.83 mg/L to 6.42 L - mg/L This suggests that a certain percentage of NO3 in the catchment is going unnoticed every year and is potentially being discharged into surface waters.

- Figure 34. Graphical display of measured NO3 concentrations in Silkwood groundwater, 2017-2018.

- Figure 35. Comparative magnitudes of calculated NO3 averages for each bore site, displaying spatial variations in NO3 concentrations (values are mg/L) (ArcMap).

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- - 4.2.1.6 Groundwater Chemistry: NO2 , NH4

- Environmental NO2 commonly exists in trace level concentration (Appelo and Postma, 2005) but can be used as an indicator of DN if occurring in relatively elevated concentrations. Table - 11 shows that across the catchment nitrite concentrations (NO2 ) remained below detection limits (< 0.02 µg/L), except for 2 occurrences:

- 1. Analysis revealed measurable NO2 in all samples for November 2017. A possible explanation is that DN had occurred in the samples before laboratory analysis despite refrigerated storage. Although it is possible that DN rates can be rapid under certain - conditions, there is no definite explanation for the increased NO2 in November 2017. - 2. Bore P2RNE consistently displayed measurable NO2 2017-2018 as well as consistently - - lower NO3 . NO2 peaked in February 2018 (12.75 µg/L), coincident with the only other - noticeable NO2 peak for the nearby bore 11210040 (9.89 µg/L). This also coincides with a marked decline in redox potential, from 305 mV to 145 mV and a decline in DO concentrations.

Chemical conditions for both P2R bores generally favour the possibility of DN occurring (very low DO and average redox potential < 250+ mV) (Korom, 1992; Appelo and Postma, 2005), so it is likely that the overall observed changes in groundwater chemistry at bore P2RNE is indicative of DN occurrence. The presence of dense clays at the location of bores 183021 and 183022 may restrict and slow-down groundwater flow. This would increase groundwater residence time, potentially long enough to allow biologically-catalysed DN to occur.

+ NH4 concentrations remained low (< 0.1 mg/L) or below detection level at most sites 2017- + 2018, which may suggest that NH4 is retained in soil profiles (and taken up by crops) and not + transported to groundwater. The only sites displaying a consistent measurable NH4 are the P2R paddock sites (Table 12). The nearby bore 11210040 also displays detectable + concentrations of NH4 during the periods of increased rainfall (March 2018).

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+ Table 12. Measured NH4 (mg/L) concentrations in Silkwood groundwater, 2017-2018.

NH4 (mg/L) Bore ID (RN) Date: 11210040 P2RNE P2RSW 22/09/2017 < DL 0.13 0.06 10/11/2017 0.07 0.11 0.05 1/12/2017 < DL 0.10 0.05 15/12/2017 < DL 0.12 0.06 12/01/2018 < DL 0.13 0.06 16/02/2018 0.20 0.11 0.06 23/03/2018 0.12 0.12 0.06 11/05/2018 0.15 0.11 0.04 20/07/2018 < DL 0.14 0.14 21/09/2018 0.02 0.09 0.07 4.2.1.7 Groundwater Chemistry - pH

The application of acidity-regulators to paddocks does not appear to have an influence on groundwater acidity. Groundwater pH values remained in a consistent range for each site during 2017-2018 (Table 13). The groundwater can be classified as moderately acidic, with values ranging from 4.1 to 6.3. The overall calculated average is 5, which is consistent with carbonic acid (H2CO3) being the major species of dissolved inorganic carbon (Figure 36) (Freeze and Cherry, 1979; Hiscock and Bense, 2014).

Table 13. Measured pH values in groundwater, 2017-2018. Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 183021 183022 22/09/2017 4.8 4.8 5.2 6.3 5.1 5.1 3.8 5.7 10/11/2017 4.1 5.2 4.8 5.6 4.5 4.2 4.7 5.7 1/12/2017 4.5 5.0 5.0 6.1 5.0 3.8 5.0 5.5 15/12/2017 4.7 5.0 4.7 6.1 5.1 4.9 5.1 5.4 12/01/2018 4.6 5.0 6.3 5.0 5.2 5.0 5.4 16/02/2018 4.6 4.9 5.0 4.0 4.9 4.7 5.5 23/03/2018 4.3 4.9 5.0 6.0 5.0 5.0 4.8 5.2 11/05/2018 4.6 5.0 5.0 6.3 5.0 5.0 4.8 5.5 20/07/2018 4.7 4.9 5.9 6.3 5.0 4.9 5.4 21/09/2018 5.0 4.9 5.9 4.6 4.6 4.9 5.5

Figure 36. Major inorganic carbon species in water as a function of pH (Hiscock and Bense, 2014).

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Bore 11210045 displays a rapid drop in pH to 4.0 in February 2018. This coincides with the onset of significant rainfall and may be explained by increased H2CO3 (refer to equation 10). Bore 11210056 displays variable pH values during the study period, along with variable major ion chemistry. Bore P2RNE displayed an acidic pH of 3.8 in September 2017. During the installation of the P2R bores in July 2017, a vadose-monitoring-zone installation was completed on the same paddock, approximately 45 m from the P2RNE bore. This required the drilling of 2 bores approximately 5 m deep at an angle of 35°. The sediment cores extracted from the paddock at this site displayed distinct colouration from Fe2+ (to Fe3+) and S2- oxidation (Figures 37 & 38). Rapid aeration of S2-- rich soils quickly reduces pH and can produce the pale-yellow pigment of Jarosite (end member formula: KFe3(SO4)2(OH)6), while Fe-oxides goethite (FeOOH) and hematite (Fe2O3) produce red and brown colouration (Lynn and Pearson, 2000). These chemical oxidation reactions also contribute to observed colour mottling at redox zones coinciding with fluctuating groundwater levels in soils (as described in Chapter 3, section 3.6).

The disturbance of clay-rich, S2-- bearing sediments can also reduce

pH, through the formation of H2SO4 (sulphuric acid) (DER, 2015; Eash et al, 2016). This may potentially have influenced the pH of groundwater at this site.

Figure 37 (left) Extracted sediment core from the P2R paddock, displaying distinctive colouration due to chemical oxidation of Fe2+ and S2-.

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Figure 38 (below).Extracted sediment cores from the P2R paddock, displaying distinctive colouration due to chemical oxidation of Fe2+ and S2-.

A 2009 Western Australian government study into groundwater interactions with acid sulphate soils in the low-lying, coastal Torbay catchment found that certain groundwater sites were affected by acid sulphate soils, based on observations including - 2- low pH (5-5.5) and low Cl :SO4 ratio in groundwater sampled from shallow observation bores (<5 m) close to Lake Powell (Kilminster, 2009). Table 14 displays - 2- calculated Cl :SO4 values from measured groundwater data in the Silkwood region, with the 2- P2R paddock sites showing consistently lower values due to higher SO4 concentrations.

- 2- Although 11210045 also displays relatively low Cl :SO4 values the consistently higher concentrations of Ca2+, Mg2+ and Na+ in groundwater from that site indicate the presence of geochemical acidity buffers from mineralogical sources, which contributes to the relatively higher pH values of bore 11210045.

- 2- Table 14. Calculated Cl :SO4 values from measured groundwater data in the Silkwood region, 2017-2018.

Bore ID (RN) Date: 1121004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 15.42 41.03 0.00 5.09 48.59 0.00 3.19 2.15 10/11/2017 4.97 8.53 47.85 4.18 9.23 0.00 1.88 0.00 1/12/2017 7.94 4.96 20.69 4.03 17.55 6.78 1.91 2.18 15/12/2017 6.38 7.61 21.31 3.62 6.57 1.91 1.87 2.21 12/01/2018 8.36 20.81 3.59 6.62 1.90 1.76 2.24 16/02/2018 4.90 10.76 32.95 2.47 7.39 1.95 2.26 23/03/2018 4.65 8.58 12.83 2.28 5.54 7.32 1.81 2.18 11/05/2018 5.79 7.10 11.94 3.71 4.38 14.56 2.70 2.14 20/07/2018 0.00 5.82 16.40 3.58 7.73 2.05 2.65 21/09/2018 8.11 14.39 3.02 6.74 1.45 1.96 2.26

There are 5 potential origins that may explain the presence of S2- in the clays at the Silkwood sites (Ayers, 1991; Rickard, 1997; Rickard and Luther, 2007; Miao et al, 2011):

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1. Sedimentary pyrite (FeS2) formed during eustatic sea level fluctuations (Rickard and Luther, 2007; Pasquier et al, 2017); (pyrite is widespread in continental margin sediments where terrestrial detritus supplies Fe (Kastner, 1999), most effectively in the form of coatings of oxyhydroxides on clays as well as particulate/colloidal oxyhydroxides (Reitner and Theil, 2011).

2. FeS2 precipitated in-situ in reduced saturated environment (Rickard, 1997). 3. Detrital sulphide elements; oxidation of weathering products from geological parent material (including sulphide minerals) (Rickard and Luther, 2007). 4. Sulphur content in N-rich fertilisers (a common component in modern artificial fertilisers; lowers soil pH and assists in chlorophyll formation (Sugar Research 2- Australia Ltd, 2018); oxidised to SO4 by soil microorganisms). 2- 2- 5. SO4 input from precipitation (atmospheric SO4 formed from sulphur-dioxide

(SO2) is closely associated with acid rain in regions of industrial activity where 2- elevated SO4 concentrations occur due to anthropogenic input (Freeze and Cherry, 1979; Appelo and Postma, 2005). Seawater chemistry also contributes 2- - SO4 to precipitation, along with Cl , but there is scarce data available regarding 2- SO4 in precipitation in Australia).

2- 4.2.1.8 Groundwater Chemistry - SO4 sulphate

2- While SO4 in all bores is well below contaminant levels (< 250 mg/L), there is a noticeable 2- variation in SO4 across the studied sites. Figures 39 and 40 show that both P2R bores and 2- bore 11210045 maintained the highest SO4 in the study, with bore 11210056 again displaying 2- dynamic fluctuation in concentrations. A noticeable increase in SO4 occurs in December 2017 and again in February-March 2018 (when significant rainfall events increased). Some of this 2- may be attributable to the application of fertilisers and soil acidity-regulators containing SO4 (e.g. gypsum). The data also shows that bores 11210045, P2RNE and P2RSW also had correspondingly higher sulphur concentrations (S) and iron concentrations (Fe) (Table 15). S2- 2- 2- over time is similar to SO4 , although with some variation (e.g. a noticeable S increase in July 2018).

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2- Figure 39. Measured SO4 concentrations in Silkwood groundwater, 2017-2018.

2- 2- The bores displaying the highest SO4 (Fig.39) also displayed the highest S and Fe (Table 15) - also displayed the lowest DO and NO3 concentrations. This characteristic is distinctly - 2- displayed in figure 40, which shows the averaged DO, NO3 and SO4 concentrations for each bore, based on measured values 2017-2018. Bores 11210045, P2RNE and P2RSW stand out 2- as having the highest SO4 concentrations and lowest DO concentrations.

Table 15. Measured S2- and Fe2+ concentrations in groundwater, 2017-2018

S¯ Bore ID (RN ) Date Sampled: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 0.30 0.32 0.05 0.62 0.37 0.17 1.42 1.23 10/11/2017 0.56 0.32 0.08 0.69 0.39 0.31 1.46 1.25 1/12/2017 0.67 0.32 0.09 0.54 0.18 0.28 1.22 0.96 15/12/2017 0.51 0.46 0.25 0.89 0.47 1.58 1.51 1.68 12/01/2018 0.38 0.25 0.90 0.54 1.70 1.46 1.21 16/02/2018 0.57 0.34 0.20 1.13 0.37 1.47 1.10 23/03/2018 0.74 0.43 0.39 1.19 0.41 0.38 1.36 1.29 9/05/2018 0.71 0.39 0.34 1.31 0.40 0.43 1.71 1.37 20/07/2018 0.10 0.36 0.04 0.88 0.31 1.36 1.10 21/09/2018 0.24 0.13 0.64 0.34 1.85 1.36 1.13

Fe²⁺ Bore ID (RN ) Date Sampled: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 0.01 0.02 0.02 0.18 0.01 0.02 1.55 2.32 10/11/2017 0.09 0.09 0.04 0.19 0.04 0.14 1.30 2.18 1/12/2017 0.17 0.01 0.02 0.17 0.05 0.01 1.38 2.32 15/12/2017 0.02 0.01 0.01 0.37 0.01 0.00 1.27 0.22 12/01/2018 0.01 0.01 0.48 0.01 0.06 1.42 1.93 16/02/2018 0.01 0.22 0.01 0.09 0.01 0.64 1.92 23/03/2018 0.01 0.03 0.01 0.10 0.01 0.06 0.54 1.76 9/05/2018 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.94 20/07/2018 0.00 0.01 0.01 0.26 0.03 0.59 1.52 21/09/2018 0.00 0.00 0.43 0.00 0.05 0.54 1.39

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2- Figure 40. Pie graph map displaying averaged relative abundances of measured SO4 , DO and - NO3 for each bore site.

Bores 11210045, P2RNE and P2RSW display chemical condition known to be favourable for DN to occur (reduced environment; Eh <250+ mV, DO < 2 mg/L) (Freeze and Cherry, 1979) - 2- Jahangir et al, 2012) as well as relatively lower NO3 and higher SO4 concentrations. It is 2- possible that DN is playing a role in the formation of SO4 (oxidation of sulphides) at the - 2+ expense of NO3 (reduction by microbial DN), with sulphide minerals or thiosulphate (S2O3 ) - acting as electron-donors and NO3 being the electron acceptor in reduced environments where there is a deficit of DO (Chung et al, 2014; Jessen et al 2017).

- - 2- Bore 11210041 displays the highest Cl and NO3 and lowest SO4 , S and Fe (an opposite trend to bores 11210045, P2RNE and P2RSW). Like the other bores with consistently relatively - higher NO3 (11210004 and 11210051), it also displays relatively higher DO (again, an opposite trend to bores 11210045, P2RNE and P2RSW).

Depending which chemical reaction pathways are utilised, the process of DN catalysed by soil 2- and groundwater bacteria can result in the production of both SO4 and Fe-oxides (Eq. 6 & 7). - Previous groundwater studies have highlighted the negative correlation between NO3 and 2- SO4 in reduced environments (Jahangir et al, 2012; Jessen et al, 2017), mainly in relation to

DN via oxidation of S and Fe from pyrite (FeS2). However, in the absence of pyrite-rich

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- 2- sediments, the processes involving NO3 attenuation in the presence of SO4 are not so clear- - 2- cut. Rasiah et al (2003) recorded a negative correlation between NO3 and SO4 in ferrosol soil profiles under sugarcane in the South Johnstone catchment. Their study examined seven clay- rich sediment cores (to 12.5 m depth) with deep (>10 m depth) soil profiles. At depths >2 m, 2- - - SO4 decreased rapidly while Cl and NO3 concentrations increased. The clay percentage of - - the soils increased down-profile, with increased Cl and NO3 retention, indicating a 2- - - hierarchical preference for anion adsorption in the form of: SO4 > Cl > NO3 . It was also 2- found that SO4 values (mg/kg) in soil profiles under fertilised plantations were not markedly 2- higher than those recorded under rainforest soils profiles. The study concluded that the SO4 2- content precedes current land use and is most likely derived from retention of historical SO4 - - in precipitation, while the Cl and NO3 are derived from fertiliser input. However, no isotopic 18 2- analysis was performed for source determination (e.g. δ O- SO4 ) and the conclusion regarding rainfall input was drawn mainly from calculations based on observations from a 2- study conducted in NSW (Ayers and Manton, 1991) and assumed complete retention of SO4 2- over ~6000 years. Very little information is available regarding SO4 in rainfall (and rainfall chemistry generally) in Australia.

2+ 2 4.2.1.9 Groundwater Chemistry - Fe & SO4

The measured Fe2+ values in the P2R bores are consistently above the accepted environmental limits of < 0.3 mg/L. This is likely due to the anaerobic quality of the groundwater, reducing the ferric iron (Fe2+) from clay oxides to significant concentrations of Fe2+ (Hounslow, 1995). 2+ Where aqueous H2S is present in reduced waters, the Fe may precipitate as a sulphide, either pyrite or marcasite (an unstable orthorhombic polymorph of pyrite) (Eq. 11) (Hounslow, 1995). Rickard (1997) states that this pathway of pyrite precipitation is the most rapid (up to ~50% of the reaction may be completed within 24 hrs) and that the hydrogen gas generated may contribute alternative metabolic sources for microorganisms.

FeS (s) + H2S (aq) = FeS2 + H2(g) (Eq.11)

This process of sulphide production sheds more light on the dynamic relationship between groundwater and clay-sediment chemistry and microbiology and the dynamic interaction of oxygen, sulphur and iron in saturated and aqueous domains. FeS2 production in the presence of aqueous H2S also provides another possible DN pathway, supporting the observed negative - 2- correlation between NO3 and SO4 in some sites.

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4.2.1.10 Source Deduction

The source-rock deduction method as described by Hounslow (1995) can be applied to identify 2- possible sources of SO4 in the groundwater samples.

2+ 2- Table 16 displays Ca /SO4 calculated from the measured groundwater chemistry data. 2+ 2- Gypsum is identified as a source if Ca = SO4 (the values closest to “1” are highlighted by 2+ 2- bold outline boxes in Table 16). A result of Ca > SO4 indicates a source other than gypsum, such as dolomite or calcite or silicate minerals (boxes highlighted red in colour scale). A result 2+ 2- 2+ of Ca < SO4 indicates pyrite oxidation, or Ca removal via calcite precipitation (boxes highlighted green in colour scale).

2+ 2- Table 16. Calculated Ca /SO4 from measured groundwater chemistry, 2017-2018.

Ca/SO4 1121004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 2.6 11.1 4.0 16.0 0.4 0.3 10/11/2017 1.5 2.3 6.4 2.6 3.1 0.3 1/12/2017 1.8 1.2 2.5 1.7 4.0 1.7 0.2 0.1 15/12/2017 1.0 1.6 2.3 2.1 1.4 0.6 0.1 1.3 12/01/2018 1.6 1.9 2.3 1.5 0.9 0.1 0.1 16/02/2018 1.1 1.9 4.0 1.1 1.6 0.1 0.1 23/03/2018 0.9 1.3 2.4 0.9 0.9 1.8 0.1 0.1 11/05/2018 1.2 1.5 2.1 1.8 0.9 3.5 0.1 0.1 20/07/2018 1.4 1.9 1.9 1.7 0.1 0.1 21/09/2018 1.4 1.4 1.9 1.5 0.5 0.1 0.1

2+ 2+ 2- Table 17 displays Ca / (Ca + SO4 ) calculated from the measured groundwater chemistry data. According to the “source-rock deduction’ method, a value = 0.5 indicates gypsum dissolution (values highlighted by boxes in Table 17). A value < 0.5 (at pH of < 5.5) indicates pyrite oxidation (boxes highlighted green in colour scale). A value > 0.5 indicates calcium sources other than gypsum (carbonates or silicate minerals; highlighted in red).

A trend may be observed in both Tables 16 and 17: that gypsum as a source is indicated in some sites just prior to and during the period of significant rainfall (February-March 2018). The application of gypsum in the region of bore 11210004 might also account for the consistently low pH values in groundwater from the shallow aquifer at that site, with the result being the formation of H2SO4.

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2+ 2+ 2- Table 17. Calculated Ca / (Ca + SO4 ) from the measured groundwater chemistry, 2017- 2018.

Ca/(Ca+SO4) 1121004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 0.7 0.9 1.0 0.8 0.9 1.0 0.3 0.2 10/11/2017 0.6 0.7 0.9 0.7 0.8 1.0 0.2 1.0 1/12/2017 0.6 0.5 0.7 0.6 0.8 0.6 0.1 0.1 15/12/2017 0.5 0.6 0.7 0.7 0.6 0.4 0.1 0.6 12/01/2018 0.6 0.7 0.7 0.6 0.5 0.1 0.1 16/02/2018 0.5 0.7 0.8 0.5 0.6 0.1 0.1 23/03/2018 0.5 0.6 0.7 0.5 0.5 0.6 0.1 0.1 11/05/2018 0.5 0.6 0.7 0.6 0.5 0.8 0.1 0.1 20/07/2018 1.0 0.6 0.7 0.7 0.6 0.1 0.1 21/09/2018 0.6 0.6 0.6 0.6 0.3 0.1 0.1

Pyrite oxidation is indicated for both P2R bores (except for a couple of outliers at the P2RSW bore, which might be consistent with the relatively higher mineral content and alkalinity at the start of the study period, following bore installation in July 2017). Similar to the trend seen in Table 16 relating to Na+ concentration, there appears a trend of increased Ca2+ at the start of the study year (September 2017). The source-rock method indicates a combination of both natural silicate mineral sources and applied dolomite.

4.2.1.11 Statistical Analysis

The statistical analyses support the observations based on the measured groundwater chemistry data. The results of both cluster analysis and principal-component analysis indicate:

 Bore 11210045 displays chemical properties distinct from the other sites (Figures 42 & 43)

 Bores P2RNE and P2RSW have distinct chemical properties but do display some overlap with the other sites (Figure 42 & 43)

 Bore 11210056 displays a more dynamic, fluctuating chemistry (Figure 43).

-  A correlation between DO and NO3 (Figure 42).

2-  A correlation between SO4 , S and Fe (Figure 42).

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The PCA (Table 18) highlights the dominant dissolved ions in the groundwater samples, whose concentrations (although relatively different across sites) show little fluctuation over the course of the study period (see Appendix 5). These are the major 1st level components: Na, K, Si and

CaCO3. These are among the most common dissolved ions found in groundwater (Freeze and Cherry, 1987) and their consistent concentrations can be attributed to their presence in the mineralogy of the clays, sands, gravels and decomposed rocks that form the aquifer materials in the region and the processes of chemical weathering that liberate them from the parent material.

Figure 41. Minitab software cluster analysis (to 3 clusters) of measured chemical variables in groundwater, 2017-2018.

Figure 42. Minitab software cluster analysis (to 3 clusters) based on all observations of chemical elements in groundwater 2017-2018. Group 1 (blue cluster) = observations of bores P2RNE & P2RSW with overlap from bore 11210056. Group 3 (green cluster) = observations

Page | 86 of bore 11210045. Group 2 (red cluster) = observations of all other bores, with some overlap from P2RNE & P2RSW.

Figure 43. Minitab software PCA of measured chemical variables in groundwater. Diagram has been edited to indicate corresponding field site IDs.

Table 18. Minitab software PCA; calculated coefficients (to 3 components) related to chemical variables. Highlighted are the most significant coefficient values in each component.

PC1 PC2 PC3 Na 0.382 -0.159 0.085 K 0.366 -0.128 -0.092 Ca 0.275 -0.240 0.344 Mg 0.188 -0.359 0.024 Fe 0.094 0.402 -0.103 Si 0.348 0.039 0.204 Al 0.005 0.212 0.357 Mn 0.115 -0.017 0.468 S 0.253 0.355 -0.091 Cl -0.008 -0.107 -0.291 SO4 0.232 0.335 -0.169 NO3 -0.284 -0.278 0.116 DO -0.315 -0.206 0.241 TOC -0.142 0.237 0.295 TIC -0.057 0.321 0.429 Caco3 0.380 -0.185 0.052

nd 2+ 2+ 2- 2- The major 2 level principal components are shown to be Mg , Fe , S and SO4 . This is 2+ 2- consistent with the already discussed influence of Fe , S and SO4 ; how their concentrations in relation to other chemical parameters serve to define the chemical characteristics of groundwater from specific aquifers in the region. The definition between these sites is

Page | 87 described in the results of the statistical cluster analysis and PCA (i.e. the distinction of sites 11210045, 11210056, P2RNE and P2RSW from the other sites). The distinction of the chemical characteristics of bore 11210045 is also evident from the Piper Diagram analysis in Figure 14. The negative value for PC2 coefficient Mg2+ (-0.359) indicates that the sites which display negative PCA scores (11210004, 11210040, 11210041 and 11210051) will tend to have 2+ 2+ 2- 2- a greater influence from Mg than the other dominant PC2 eigenvectors (Fe , S and SO4 ), Fig. 43 indicates that this is particularly relevant to sites 11210041 and 11210051. However, the PC2 eigenvector value -0.359 does not represent a significantly higher magnitude of 2- 2- influence than the values 0.355 (S ) and 0.335 (SO4 ). Together with the observation that many PCA data points for bores 11210004, 11210040 and 11210051 cluster close to zero (Fig.43), it can be concluded that the range of difference between these variables (Mg2+, Fe2+, 2- 2- S and SO4 ) is not significant, except for bore 11210041 which consistently displayed higher 2+ 2- 2- measured concentrations of Mg than S and SO4 (Figs 29 and 39; Table 15).

4.2.1.12 Groundwater Chemistry: TIC & TOC

Data from all sites displays measured TIC concentrations greater than TOC, although there is a noticeable temporal correlation between the two parameters with values reaching lowest values in December 2017 and peaking in February 2018 (Table 19). This indicates that rainfall events influence the transport of both TIC and TOC into groundwater across the catchment (peaks coinciding with rainfall events displayed in Figures 20 & 21). Previous research has shown a connection between increased TOC concentrations and rainfall recharge events in (Thayalakumaran et al, 2015) and consequent temporal fluctuations in shallow groundwater levels (Lyon et al, 2011). Thayalakumaran et al (2015) recorded very high TOC levels in groundwater in the sugarcane-dominated Burdekin catchment (up to 82 mg/L), attributed to a combination of crop residues and applied organic fertilisers produced from sugarcane mill by- products. The measured TOC values in Silkwood groundwater (Table 18) are within the range expected for fresh groundwater (1-3 ppm) (Kalbitz et al, 2000), indicating that TOC is mostly retained and processed in the upper saturated soil zones and most likely sourced from organic matter in overlying soil profiles, including decomposing sugarcane leaves, sap and roots (crop residues).

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Table 19. Measured total organic (TOC) and inorganic (TIC) carbon in groundwater samples. TOC (ppm) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 1.41 0.77 0.72 0.35 0.83 0.38 0.40 0.24 10/11/2017 0.87 0.46 0.52 0.20 0.76 0.46 0.61 0.45 1/12/2017 0.9 0.7 0.7 0.7 0.6 0.5 0.6 0.5 12/01/2018 1.1 0.9 0.0 0.5 1.4 1.5 0.5 16/02/2018 2.6 1.6 1.1 0.8 1.2 0.8 0.5 23/03/2018 1.15 1.07 0.80 0.68 1.07 1.27 0.64 0.46 11/05/2018 1.81 1.10 1.22 1.13 1.40 1.26 1.12 0.10 20/07/2018 2.0 1.6 2.1 0.9 2.0 1.2 1.6

TIC (ppm) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 22.17 12.45 14.15 14.90 15.23 6.10 9.70 11.38 10/11/2017 10.27 6.96 9.12 13.30 12.87 4.67 6.06 6.61 1/12/2017 0.95 1.06 1.87 9.01 1.42 2.09 0.81 3.44 12/01/2018 7.36 9.76 14.44 3.88 2.17 3.38 6.07 16/02/2018 25.03 15.04 15.60 13.46 16.82 9.81 10.36 23/03/2018 11.04 9.11 9.17 9.95 11.94 5.74 5.99 5.87 11/05/2018 22.21 11.14 13.14 13.70 15.11 13.78 9.39 9.09 20/07/2018 21.56 12.36 13.38 15.91 15.68 7.84 9.60

Although the outdated practice of sugarcane paddock burning in preparation for harvest in no longer practiced in the wet-tropics, crops are still sometimes burnt occasionally in the district to combat plant parasites in the root zone. Recalcitrant soil carbon produced as charcoals may also be a contributor to the TOC infiltrating the groundwater system.

4.2.1.13 Groundwater Isotopic Analysis: SMOW, δ2H/δ18O (‰)

Analysis of δ2H and δ18O data from water samples shows that the measured values are consistent with waters sourced from warm-climate/tropical-subtropical sources close to the coast, as the samples show small depletions of δ18O relative to V-SMOW (average: -3.92 ‰) (Dansgaard, 1964; Appelo and Postma, 2005; Hiscock and Bense, 2014). This is displayed in Figure 44 as δ2H/δ18O (‰) relative to V-SMOW. There is a tight cluster of data with some sites displaying minor temporal variances in δ18O values and one significant outlier from site P2RSW. From the two rainwater samples, one from February 2018 falls within the range of the groundwater samples. The rainwater sample from July 2018 is a distinct outlier. The cause of this difference may be due to seasonal influences, with the July sample indicating an evaporation trend during the dry period which followed the wet season.

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Figure 44. δ2H/δ18O (‰) based on measured values from Silkwood groundwater and rainwater, relative to SMOW, combined with calculated Global Meteoric Water Line (δ2H = 8* δ18O + 10; Appelo and Postma, 2005) (Appendix 7)

Only 2 rainwater samples were included in the analysis, both collected from the P2R paddock where bore P2RNE and P2RSW are located. The sample from February 2018 plots within the data cluster of groundwater samples (Figure 44), which would suggest an isotopic relationship between atmospheric rain water and groundwater. This is consistent with the chemical characterisation of groundwaters in Figures 8 & 27, indicating the influence of seawater (NaCl) on groundwater chemistry via recharge from rainfall. The 2nd rainwater sample from July 2018 plots very differently and may be indicative of increased evaporation (Appelo and Postma, 2005) occurring during the dry season which followed May 2018. Alternatively, any outliers in the dataset may be a result of analytical error, or even unknown biologically-mediated fractionation processes.

15 - 4.2.1.14 Groundwater Isotopic Analysis: δ N-NO3

18 - 15 - δ O-NO3 (‰) / δ N-NO3 (‰) from groundwater samples were analysed over the period September 2017 to February 2018 (Table 20, Figure 45). Some samples were not able to be - analysed due to insufficient sample amount (very low NO3 concentration in the groundwater sample).

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15 - Figure 45 describes the measured δ N-NO3 data (Table 20) in reference to published literature - 15 which provides a guideline for identifying possible NO3 sources via δ N analysis (Gormly and Spalding, 1979; Kendall and McDonnell, 1998).

15 - 18 - Table 20. Measured δ N-NO3 (‰) and δ O-NO3 (‰) values from Silkwood groundwater (IS = Insufficient amount of sample to complete analysis).

δ¹⁵N-NO₃ (‰) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN 22/09/2017 11.8 5.3 8.1 15.6 7.4 8.4 -5.0 -18.5 10/11/2017 5.3 8.5 10.6 IS 5.7 8.8 10.9 IS 1/12/2017 6.3 6.6 11.8 IS 6.4 5.9 7.9 6.7 15/12/2017 9.5 10.8 11.1 IS 6.2 11.6 10.9 2.9 12/01/2018 7.6 10.9 IS 6.3 10.9 IS IS 16/02/2018 4.5 8.8 8.9 IS 6.2 10.2 IS -20.0

δ¹⁸O-NO₃(‰) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN 22/09/2017 34.6 36.4 35.4 36.2 35.7 32.2 33.8 35.3 10/11/2017 36.6 39.9 36.2 IS 39.7 35.0 34.0 IS 1/12/2017 33.5 35.3 34.5 IS 33.7 33.9 41.0 36.3 15/12/2017 31.7 31.9 33.5 IS 36.0 31.8 32.3 34.6 12/01/2018 37.0 36.7 IS 39.8 37.4 IS IS 16/02/2018 21.1 24.0 22.9 IS 22.2 24.5 IS -4.2

18 - 15 - Figure 45. δ O-NO3 (‰) / δ N-NO3 (‰) measured values from Silkwood groundwater, 2017-2018.

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15 - - Figure 46. Measured δ N-NO3 values in groundwater plotted to reference values for NO3 sources.

15 - The blue rectangle in Figure 46 indicates where the majority of δ N-NO3 data clusters from the groundwater samples (2.90 -11.80 (‰)). There are 3 distinct outlier data points indicates - by the red rectangles (-18.50 (‰), -5.00 (‰) and 15.60 (‰)). The data indicates potential NO3 source material mainly derived from natural, organic soil N, with some overlap from animal waste sources.

There is scarce data publicly available regarding isotopic compositions of fertilisers used in 15 - 18 - Australia. An analysis of the δ N-NO3 and δ O-NO3 composition of the CK 50/50 (N & K) sugarcane fertiliser shows a δ15N value of 0.6 (‰) and δ18O value of 64.6 (‰). This δ15N value is consistent with previous research conducted on isotopic fertiliser compositions in both Europe and the USA, which report the average δ15N value range of synthetic fertilisers to be 0 ± 2 (‰) (Bateman and Kelly, 2007; Michalski et al 2015). These studies also report a δ18O value range of 23 ± 3 (‰) for synthetic fertilisers and 55 ± 5 (‰) for natural nitrate fertilisers. 15 - The outlying δ N-NO3 measured value in this data set of -5 (‰) corresponds with the reported - value range for rainfall - sourced NO3 (Kendall and McDonnell, 1998; Xue et al, 2009; Osaka et al, 2010). The outlying measured value of -18.5 (‰) cannot be sufficiently explained and may have been the consequence of unidentified chemical processes which occurred in the sample during storage.

18 - There is no empirical reference scale for δ O-NO3 values based on previous scientific 18 - research. However, studies indicate that elevated δ O-NO3 values (> 50 (‰)) correspond to

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- atmospheric NO3 in precipitation (Durka et al, 1994; Kendall and McDonnell, 1998; Xue et al, 2009; Osaka et al, 2010). Relatively high δ18O values (~ 30-50 (‰)) have been previously reported in spring waters as fractionated through-fall water in forested temperate catchments (Amberger and Schmidt, 1987; Bottcher et al, 1990; Durka et al, 1994; Kendall and 18 - McDonnell, 1998; Osaka et al, 2010). Most available δ O-NO3 data come from studies conducted in relatively cooler, temperate regions.

The measured δ18O value of the CK 50/50 sugarcane fertiliser (64.6 (‰)) seems high in comparison to the average δ18O value range reported from overseas studies (23 ± 3 (‰)) and 18 - is more consistent with atmospheric/rainfall δ O-NO3 values. It is not known whether this would be a typical value for the sampled fertiliser or if it indicates experimental error, as a 15 - 18 range of fertiliser samples were not analysed in this report, so the reported δ N-NO3 and δ O- - NO3 from the CK 50/50 aren’t necessarily considered reliable.

- There appears to be no noticeable temporal or spatial trend in the isotopic NO3 data; Figure 44 shows that the values cluster around a range of values, rather than trace a trend over time. 15 18 Although δ N values show some variance, δ O values consistently fall within the range of 31.7-41 across all sampled sites (Table 20). However, δ18O values show a noticeable decrease - in February 2018, possibly indicating a change in source of NO3 , more consistent with artificial fertiliser.

There is no noticeable trend in the isotopic data indicating the occurrence of DN. Assuming 15 18 initial δ N and δ O values consistent with the aforementioned researched average fertiliser 15 18 values, an increase in δ N and δ O concentrations due to microbially-catalysed DN should be observed. Figure 47 displays a theorised DN path (green arrow), assuming a linear δ15N: δ18O fractionation ratio of 0.51, based on the research of Chen & MacQuarrie (2005). The red dotted square indicates an assumed averaged isotopic fertiliser composition in the ranges of: δ15N = 0 ± 2 (‰) and δ18O value = 23 ± 3 (‰). Overall, it is possible that the data displays both the - 15 - influence of atmospheric NO3 from rainfall and the fractionation of fertiliser δ N-NO3 and 18 - δ O-NO3 , due to bio/geo-chemical processes during and after rainwater infiltration, however a clear DN pathway of increased isotopic concentrations cannot be observed.

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18 - 85 - Figure 47. Measured groundwater isotopic data (δ O-NO3 / δ N-NO3 ), Sept 2017 to Feb 2018. The data points sitting below the dotted green DN line correspond to samples from Feb 2018.

4.2.2 SEDIMENT & SOIL ANALYSIS

Both P2R bores intercept an aquifer at ~9 m BGL, composed of clayey sand (Figure 49). The aquifer thickness is approximately 3 m, which is typical of aquifer material in the region (DSITI, DNRM, 2013). The bores are located ~270 m apart. XRD analysis (Table 21) reveals the main clay composition to be kaolinite with illite/muscovite. Field observations during bore installation confirm the noticeable presence of muscovite throughout. Along with goethite and hematite, amorphous hydroxide minerals are present throughout, particularly at the 4-5 m depth region. The inverse relationship observed between weight % of quartz and clay minerals is indicative of aquifer depth due to increased content of coarser sandy material.

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Table 21. Results of XRD analysis; quantitative mineral identification (values in weight percent). Sediments sampled from cores during bore installation (P2RNE & P2RSW).

Bore ID & Sample Depth Quartz Kaolinite Illite/Muscovite Magnetite Goethite K-Feldspar Siderite Hematite Amorphous P2RNE 3-4m 39.3 36.4 12.2 3.7 1.8 6.5 P2RNE 4-5m 30.4 26.5 17.4 1.1 4.3 7.1 13.2 P2RNE 9m (Aq) 73.6 13.3 3.6 3.1 6.5 P2RNE 10m (Lower Aq) 87.5 7.2 trace 4.1 1.2 0.0 P2RNE 12m 33.6 31.6 17.7 3.7 4.1 9.5 P2RSW 4-5m 9.2 47.3 19.9 23.7 P2RSW 5-7m 15.0 37.0 17.9 0.8 4.0 5.7 19.7 P2RSW 8-9m (Aq) 71.5 12.7 7.0 4.1 0.9 3.9 P2RSW 10-11m(Aq) 86.3 8.3 1.5 1.9 0.8 1.3 P2RSW 11-12m 37.6 26.5 18.4 6.0 1.9 9.6

Based on field observations of extracted cores during bore installation at the P2R paddock, new bore diagrams were created for bores P2RNE and P2RSW (Figure 48). In both diagrams, the coarser-grained aquifer material occurs at 9-12 m BGL, overlain with dense clays. This is consistent with the mineralogical data retrieved from XRD analysis in Table 21. Borehole screens were installed at ~ 9 m BGL to access groundwater. P2RNE and P2RSW are only 275m apart and located on the same paddock. Not surprisingly, it appears that they access the same aquifer, hence some of their chemical groundwater qualities are similar (e.g. low concentrations - 2- 2- 2+ of NO3 and DO and relatively higher concentrations of SO4 , S and Fe ).

Figure 48. Schematic diagrams for bore P2RNE (RN 183021) and P2RSW (RN 183022).

4.2.2.1 Links to Groundwater Chemistry

The P2R paddock bores are characterised by groundwater with relatively higher concentrations 2- of Fe, SO4 , S, (Figure 39; Table 15) very low (to the point of anoxia) concentrations of DO - (Table 10) and only very low concentrations of NO3 (Table 11). Given that the overlying land

Page | 95 use is the same as elsewhere in the catchment, geochemical influences from the clay-rich sediments may exert a strong influence on this chemistry.

Clay minerals, amorphous oxides and hydroxides are amongst the most common adsorbents in subsurface environments (Hounslow, 1995). Amorphous hydroxides can have a chemical influence that belies their concentration due to their habit of coating other minerals (Jenne, 2- 1968; Hounslow, 1995). Kaolinite clay makes it a suitable anion exchanger of both SO4 and - + NO3 (Hounslow, 1995) due to its variable-charge when H ions are accepted on the edges of the plate-like kaolinite crystal structure during the dissolution of hydroxyl ions (OH-) in lower - pH environments (Eash et al, 2016). It is possible that NO3 is being adsorbed on exchange sites at the kaolinite clay surface, which would account for its low concentration in groundwater samples. Along with kaolinite and amorphous oxides, goethite (FeO(OH)) commonly - contributes to adsorption of SO4 , but in reduced conditions this can reverse (Hounslow, 2- 1995).SO4 may be formed in-situ as a result of biologically oxidation by bacteria. In sulphur- rich, anaerobic environments, the formation of precipitated FeS2 (as discussed in section 4.2.9) 2- from ferrous mono-sulphides is a potential way to supply new ingredients for SO4 formation via biological oxidation (by sulphide-associated bacteria). In such anoxic conditions, Fe2+ is likely to be in solution and available to contribute to the process. The source-rock deduction method (Table 17, section 4.2.9) supports the idea that the sediments in the P2R paddock area are sources of pyrite formation.

If pyrite is present only as framboidal crystals (5-20 μm), its structure is irregular and its size too small to be detectable by XRD analysis (encompassing it with the Fe-amorphous fraction). Pyrite in framboidal form is common in coastal sediments (Ohfuji and Rickard, 2003; Wilkin and Barnes, 1997). The potential sources of sulphur in soil and sediment have been discussed in section 4.2.7.

It was observed during pumping of groundwater that both bores P2RNE and P2RSW consistently produced turbid, clay-rich water with the appearance of Fe-rich clay content (Figure 49 & 50) and groundwater chemistry analysis confirms that these bores consistently displayed relatively higher Fe2+ concentrations (above accepted environmental limits of < 0.3 mg/L (ANZECC); Table 15, section 4.2.8). The natural iron content of the alluvium is derived from chemical weathering of the parent geological material (contributing to the red soil colour in the region). Road and quarry cuttings in outcrops of the Hodkinson Formation throughout the area display a distinctly red muscovite-rich schist which has been used as ballast for

Page | 96 sugarcane railway tracks. Products form the weathering of the parent geological material are also evident in the XRD analysis (illite/muscovite and associated micas are a weathering product of K-feldspar and kaolinite is a product of both (Table 2, section 2)).

The only data available relating to alluvial sediments in the region (below soil profile depths of 2 m BGL) is from historical bore reports which do not provide quantitative mineralogical information. However, the analysis of extracted sediment cores from the P2R paddock has shown that mineralogical analysis can provide insight into the geochemical influences on groundwater chemistry,

Figures 49 & 50. Groundwater pumped from both P2R paddock bores

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CHAPTER 5: GROUNDWATER & SOIL MICROBIOLOGY

5.1 METHODOLOGY

5.1.1 - MICROBIOLOGY FIELD SAMPLING

The eight bores sampled during this project for groundwater chemistry analysis also provided groundwater samples for microbiological analysis. In addition to these, two other groundwater bores were sampled for microbiological analysis in July 2018: bore 11210055 in Silkwood township and bore 149747 in the Russel-Mulgrave catchment, north of Silkwood. The intention of including these two bores was to examine whether groundwater analysis might reveal distinctly taxonomic differences in groundwater microbiota due to their locations (i.e. within an urban zone as opposed to within sugarcane plantations or within a completely separate catchment), in comparison to the other eight bores.

Groundwater samples from each bore were collected into rinsed 1 litre polypropylene bottles and immediately refrigerated to < 4°C for transportation and stored away from the light. To retain sediment, samples were not filtered. Samples were then cold-freighted to Brisbane and stored in QUT’s CARF facilities cold room at 0.4°C Soil samples collected from the P2R paddock in May 2018 were also used for microbiological analysis.

5.1.2 - MICROBIOLOGY LABORATORY ANALYSIS

To collect data regarding the identification and abundance of microbiological species inhabiting groundwater samples, specific genetic analyses were conducted at QUT’s CARF Genomics laboratory.

5.1.2.1 Cultivating Organisms under Aerobic & Anaerobic Conditions

Initial attempts at cultivating and observing culturable organisms from groundwater samples were conducted on samples collected from the Silkwood region in July, September and November 2017. Groundwater samples underwent serial dilution in sterile water before plating onto nutrient-agar (NA) plates. The following steps were completed for each sample in a Gelaire Class 2 laminar flow biosafety cabinet:

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1. Nutrient agar (NA) solid medium was prepared from 28 g NA powder per 1 L distilled water. This mixture was then autoclaved at 122°C for 30 minutes. 2. Once cooled to ~50°C, about 20 mL of the NA was poured into each polystyrene petri dish (polystyrene 9 mm x 15 mm) and left to solidify. 3. Groundwater sample bottles were lightly shaken to ensure homogeneous mixing of water and sediment. 4. 10 mL of each water sample was pipette-extracted into a collection flask. 5. Extracted samples were then diluted 4 times to 10-1, 10-2, 10-3, 10-4 dilutions, using sterilized water. 6. Each NA plate was inoculated with 200 µL of diluted sample. 7. NA plates were incubated at 37°C for 24 hours and checked for bacterial growth. The total amount of individual bacterial colonies observed were counted on each NA plate and expressed as colony-forming units (CFU). These were used to estimate the total amount of CFU per 1 litre of sample. 8. Material from selected colonies was isolated and transferred to fresh NA plates, using the “pick and patch” plating method. These were then incubated as per step 7 and stored at 4°C.

Similar to the groundwater samples, the soil samples collected from the P2R paddock in September and November 2017 were also checked for bacterial growth and CFU counts. 1 g of soil sample was diluted in 99 mL of sterilised water and vigorously shaken to mix in a borosilicate glass flask (Cappuccino and Sherman, 2002). Samples then underwent dilutions before plating onto NA plates.

Cultivation on NA plates was also performed in anaerobic conditions using a Coy anaerobic biological safety chamber (Figure 51) (agar plates for anaerobic growth contained thioglycolate). Well isolated colonies were picked and “patched” onto new plates, incubated at 37°C overnight and stored at 4°C until required.

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Figure 51. Aerobic (left) and anaerobic (right) preparatory and incubating cabinets.

5.1.2.2 Genomic DNA Extraction from Groundwater and Soil Samples

To determine total abundance and diversity of culturable and non-culturable microorganisms, samples were selected for total genomic-DNA (gDNA) extraction and analysis using a Qiagen EasyDNA Powerwater gDNA isolation kit. Groundwater samples were vacuum-filtered through a 0.45 µm filter membrane (Figure 52). The filter membrane was then loaded into a 5 mL beaded tube with 1 mL lysis buffer solution for agitation and centrifugation. Prior to proceeding with agitation according to Qiagen protocol, samples were subjected to a brief agitation in a Qiagen TissueLyser II at 12 Hz/s for 20 seconds to ensure thorough mixing of lysis buffer solution on beads and filter membrane (Figure 53). Following centrifugation, the supernatant, containing post-lysis cell material and DNA, was collected and processed according to the Qiagen EasyDNA Powerwater kit method.

The Qiagen gDNA isolation kit method captures isolated gDNA material on a fine gauze filter in a spin column. The final centrifugation of the spin column with 100 µL of elution buffer solution allowed the flow-through of gDNA with elution buffer from the filter into a 2 mL collection Eppendorf tube. To assist in concentration of gDNA material, 50 µL of elution buffer was used instead of 100 µL.

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The eluted 50 µL gDNA solution was checked for quality and concentration using a NanoDrop Microvolume Spectrophotometer and the yield expressed in ng/µL. gDNA quality was verified using 1% (w/v) agarose-gel electrophoresis. 5 μL of each sample, combined with 2.5 µL of 6 x DNA loading dye, was loaded onto a 1% gel and ran for 38 minutes at 100 V. Imaging of agarose gels was carried out using the GeneSys computer program and lightbox chamber.

Figure 52. (Left) Vacuum flask apparatus used to filter 1 litre groundwater

Figure 53. (Below) Cell lysis for the filtered samples agitated in TissueLyser.

The Qiagen EasyDNA Powerwater kit was also used to isolate gDNAs from soil samples collected from the P2R paddock site in May 2018. The method for soil was altered by inserting ≤ 1 g soil sample into the beaded tube, without filtration. The same steps were then followed as per the water samples.

5.1.2.3 Metagenomics 16S RNA Gene (V3-V4 Amplicon) Amplification and Sequencing

16S ribosomal RNA gene sequences (V3-V4 hypervariable region) were isolated according to the protocols outlined in the Illumina 16S Metagenomic Sequencing Library Preparation manual (Illumina Inc.). Milli-Q water blanks were used during sample preparations to monitor for cross-contamination. PCR was performed in an Eppendorf Master Cycler-Pro thermal cycler, using the following program:

 1 cycle: 95°C for 3 minutes

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 25 cycles: 95°C for 30 seconds, 55°C for 30 seconds, 72°C for 30 seconds,  1 cycle: 72°C for 5 minutes  Hold at 45°C

Illumina forward and reverse primer sequence adaptors were attached using the Nextera XT Index Kit and PCR clean-up and DNA library denaturing were completed as per the Illumina manual. A PerkinElmer High Sensitivity LabChip kit was used to verify DNA sizes. The sample library was analysed using the MiSeq Reporter software, which classified organisms based on the Greengenes database (http://greengens.Ibl.gov/).

The final MiSeq report was uploaded to the Illumina Base-Space website and analysed using the Illumina 16S Metagenomics application. The BaseSpace app 16S Metagenomics analyses DNA from amplicon sequencing of prokaryotic 16S small subunit rRNA genes. The read classification is performed using a high-performance version of the Ribosomal Database Project (RDP) Classifier, which provides rapid and accurate classification of 16S rRNA sequences using a Naïve Bayes taxonomic classification algorithm (Wang et al, 2007). The Greengenes database provides taxonomic classification using multiple published taxonomic databases and provides screening for chimeras (DeSantis et al, 2006).

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5.2 RESULTS & DISCUSSION

5.2.1 Initial Observation of Culturable Organisms

The culturing of organisms on NA plates, from diluted samples, revealed 3 morphologically- distinct bacterial communities. These are presented in Figure 54 and Figure 55: 1) yellow- pigmented, opaque, irregular-to-circular colonies ≤ 1 cm dimeter, 2) small, white, irregular, opaque colonies ≤ 7 mm diameter and 3) large, white, opaque, irregular-to-circular colonies ≥ 1 cm diameter. Similar colonies were observed for communities cultured from soil samples, although the distinctly yellow-pigmented colonies were absent (Figure 56). Soil samples provided much higher estimated abundance of bacterial colonies than groundwater samples. This is displayed in Figure 57. Given the incubation times, it’s likely that only fast-growing organisms were isolated, therefore the CFU counts are probably underestimates.

Figure 54. Colonies of culturable organisms isolated from groundwater samples

Figure 55. Colonies of culturable organisms extracted from groundwater samples, indicating sample site ID.

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Figure 56. Colonies of culturable organisms isolated from soil samples, indicating sample site ID.

200000000

180000000

160000000 140000000

120000000

100000000

80000000

Estimated CFU Litre / 60000000

40000000

20000000

0

11210040 11210041 11210045 11210056 P2RNE P2RSW NE25 NE65 MID25 MID65 SW25 SW65

Figure 57. Estimated CFU totals based on observations of microbiological colonies. Soil sample CFU/L estimates are displayed as grey data bars; groundwater samples are black data bars

Attempts to culture organisms under anaerobic conditions proved unsuccessful, even after using thioglycolate agar as an oxygen-inhibitor, except for one attempt using groundwater sampled from bore 11210045 in January 2018.

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The use of solid media (e.g. NA plate) to grow culturable organisms that exist in the environmental samples was found to be easy and effective. However, it is generally accepted that < 2% of microorganisms sampled from the environment can be successfully cultured under laboratory conditions (Wade, 2002). These isolated organisms remained unidentified as the main objective of this study was to examine total abundance and diversity in samples using a metagenomics approach as discussed below.

5.2.2 gDNA Extraction

The Qiagen PowerWater kit proved useful for extracting gDNA from groundwater that appeared to have less particulates. NanoDrop analysis revealed low concentrations of gDNA in the range of 1.70 ng/µL to 33.50 ng/µL from a total of 37 samples. However, following checking gDNAs using agarose electrophoresis, the quality of gDNAs (Figure 58) from 31 samples were considered suitable for subsequent PCR reactions for library preparation. Size- determination from observing the 1Kb “ladder” in the far left well in each gel indicates gDNA sizes corresponding to ~10000 bp. Amendments such as adding an agitation step during cell lysis using the TissueLyser and using 50 µL instead of 100 µL to elute the product may have aided in improving the yield. In comparison, the water blanks included during PCR processing indicated no signs of contamination when verified using the LabChip analysis.

Figure 58. Agarose gel electrophoresis of gDNAs isolated from groundwater and soil samples.

The Biochip analysis shown in Figure 59 confirmed the low concentrations of gDNAs, but the detectable peaks at ~650 bp indicated that they were of suitable quality before continuing onto Illumina sequencing. Responses that displayed high amounts of distortion due to possible

Page | 105 contamination or gDNA degradation were omitted from final analysis, bringing the total number of usable samples to 29.

Figure 59. PCR product analysis using the PerkinElmer High Sensitivity LabChip kit.

5.2.3 16S Metagenomics Analysis utilising Illumina Base-Space online application and GreenGenes Database Classification.

Following PCRs and assessment, a total of 29 samples from sampling sites in the Silkwood area were of good quality and processed for metagenomic analysis. The initial results of the different classifications at the taxonomic levels of Kingdom, Phylum, Class and Order, based on matches to the GreenGenes database, and according to the number of reads, are shown in Table 23. The number of reads represents abundance. Only the top 9 rankings within each taxon are included in Table 23 (see Appendix 8). However, the results provided by Illumina incorrectly classifies Bacteria, Archaea and Viruses under Kingdom, but should be as 3 Domains.

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The overall Q-Score for the analysis was 38. >81.1% of analysis reads reported a Q-score of ≥ 30, indicating an error probability close to approximately 1 in 10,000. A Q-Score of 30 with an error probability of 1 in 1,000 was considered a benchmark in next-generation sequencing, indicating that virtually all reads were of high quality with no ambiguities.

Table 22. Number of reads or matches according to classifications at the taxonomic level from kingdom to order. (Based on the Illumina report, the first column is incorrectly titled as Kingdom, but should be tilted Domain).

Kingdom Reads Phylum Reads Class Reads Order Reads Bacteria 12716567 Proteobacteria 7423123 Betaproteobacteria 4159595 Burkholderiales 3634361 Archaea 53381 Firmicutes 1546359 Alphaproteobacteria 1951027 Rhodospirillales 1540504 Viruses 280 Actinobacteria 667479 Clostridia 1061820 Thermoanaerobacterales 651417 Acidobacteria 664788 Gammaproteobacteria 878900 Actinomycetales 595529 Bacteroidetes 458015 Actinobacteria 631263 Acidobacteriales 437812 Verrucomicrobia 379734 Acidobacteria 437812 Bacillales 392762 Chloroflexi 160318 Bacilli 425381 Pseudomonadales 328084 Nitrospirae 133295 Deltaproteobacteria 316155 Sphingobacteriales 314938 Planctomycetes 101104 Sphingobacteriia 314938 Clostridiales 308134

Table 24 shows that the Illumina BaseSpace analysis was successful in classifying and finding matches to >80% bacteria in groundwater and soil to genus level, while only 37.17 % of bacteria were classified to species level. These results have identified that approximately 60% of organisms remained unclassified to species level. “Unclassified” refers to the 16S rRNA (V3-V4 region) gene sequences that did not find matches to the RNA gene sequences in the current version of the GreenGenes database.

Table 23. Summary of sample classification to genus and species taxonomic level. Samples highlighted in green indicate soil samples from P2R paddock.

Number of Species Classified to Genus Level Classified to Species Level Site ID Field Sample Date Identified (%) (%) 11210004 1/12/2017 942 89.42 40.47 11210004 9/05/2018 748 89.72 41.00 11210040 12/01/2018 993 88.17 76.97 11210041 12/01/2018 847 90.96 42.75 11210045 1/12/2017 992 81.55 53.02 11210045 12/01/2018 555 91.05 58.09 11210045 16/02/2018 614 85.95 33.23 11210045 23/03/2018 727 73.57 34.17 11210045 9/05/2018 1,123 84.40 27.16 11210056 1/12/2017 901 93.10 41.01 11210056 23/03/2018 692 71.28 35.66 11210056 9/05/2018 825 86.56 10.80 P2RNE 12/01/2018 475 88.28 50.35 P2RNE 9/05/2018 899 95.31 14.96 P2RSW 10/11/2017 587 91.74 27.83 P2RSW 15/12/2017 1,144 88.62 61.08 P2RSW 12/01/2018 533 95.64 6.55 NE 25 9/05/2018 988 84.14 42.32 NE 25 9/05/2018 1,165 72.01 34.30 MID 25 9/05/2018 869 74.93 38.38 MID 25 9/05/2018 658 78.55 43.03 SW 25 9/05/2018 1,160 70.21 39.90 SW 25 9/05/2018 775 73.98 37.00 SW 65 9/05/2018 842 79.57 18.02 SW 65 9/05/2018 921 69.14 49.37 149747 (Mulgrave) 20/07/2018 516 93.66 15.19 149747 (Mulgrave) 20/07/2018 555 88.4 31.05

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5.2.4 Illumina 16S Metagenomic Analysis –Statistical Analysis

The Illumina Base Space metagenomics analysis application generates a Principal Coordinates (PC) statistical analysis which measures differences in the distribution of taxonomic classifications between samples, up to each taxonomic level. The Illumina PC analysis report for classification to the genus level showed a spread of data with the variance in clustering of samples from the same sites indicative of the changes in genus diversity over time (Figure 60). For example, samples from bore 11210045 are located in nearly all the different clusters, indicating noticeable changes in genus diversity over time. Samples from bore 11210004, collected in December 2017 and May 2019 plotted together in the purple cluster, indicating very little change in genus diversity between those sampling times. Most soil samples from ~25 cm depth are located in the green cluster, which suggested a spatial similarity in genus diversity across the paddock. However, soil samples collected from ~65 cm depth appeared outside of the green cluster, suggesting genus diversity occurred at different depths in the soil profile.

Figure 60. Illumina PC analysis of all samples to genus level. The original PC graph from Illumina has been edited to identify the specific field site IDs for each sample.

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Similar to Figure 60, the results from the Illumina PC analysis at the species level is shown in Figure 61. According to Fig. 61, distinct groupings relating to soil sample sites were identified. For example, all soil samples from ~25 cm depth except for one outlier were found grouped together (Fig 61, green and yellow clusters). In comparison, soil samples from ~65 cm depth grouped separately, indicating a vertical species distribution change in the soil profile (Fig 61, blue cluster). Compared to Figure 60, there is less spread of data, with most samples located closer together in the two main red and yellow clusters, suggesting less temporal variance in species diversity.

Figure 61. Illumina PC analysis of all samples to genus level. The original PC graph from Illumina has been edited to identify the specific field site IDs for each sample.

5.2.5 Major Taxa Abundance & Diversity- Groundwater

Metagenomic analysis of 16S ribosomal RNA (16S rRNA) from groundwater in the Silkwood catchment classified 99.58% of all organisms as bacteria (dominated by the phyla

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Proteobacteria, Firmicutes, Actinobacteria, Acidobacteria and Bacteroidetes), with archaea representing only ~0.42 %. Figure 62 displays the most abundant microorganisms discovered in groundwater samples, classified by taxon from phylum to family

Figure 62. Most abundant bacteria in groundwater samples, shown at the taxa levels of phylum to family.

5.2.6 Genus and Species Abundance & Diversity- Groundwater

The most abundant species of microbial identifications from groundwater samples are shown in Figure 63. The key species reported to be associated with a variety of soil, water and plant processes include Chitinophaga soli, Janthinobacterium agaricidamnosum, Acidovorax temperans, Methylotenera versatilis, Curvibacter lanceolatus and Ralstonia insidiosa. (Willems et al, 1990; Lincoln et al, 199; Lee et al 2008; Xu et al, 2016) (Figure 63; Appendix 9).

Some of the most abundant species identified have been reported as obligate anaerobes, such as Thermoanaerobacter inferii, Moorella glycerine and Oxalobacter vibrioformis, whereas Phenylobacterium lituiforme is a facultative anaerobe (Dehning and Schink, 1989; Kanso,

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2004). Some species identified in the groundwater samples have been previously described as thermophilic, or moderately thermophilic, from sources such as hot springs, and sewerage sludges (e.g. Moorella glycerini, Phenylobacterium lituiforme, Tepidanaerobacter syntrophicus, Desulfurispora thermophila and members of the Thermoanaerobacter phylum) (Slobodkin et al, 1997; Kanso, 2004; Sekiguchi et al, 2006; Kaksonen et al, 2007; Sekiguchi et al, 2008).

Even though most of the closely matched microorganisms have been reported to grow under standard laboratory conditions, very little is understood about their actual roles, mechanisms

Groundwater Genera - Relative Abundance Groundwater Species - Relative Abundance Percentage (%) Top 25 Genera Percentage (%) Top 25 Species Janthinobacterium 19.65 Curvibacter lanceolatus 16.93 Curvibacter 18.08 Ralstonia insidiosa 9.85 Acetobacter 17.77 Janthinobacterium agaricidamnosum 7.79 Ralstonia 4.04 Methylotenera versatilis 7.69 Pseudomonas 3.80 Chitinophaga soli 5.63 Rhodoferax 3.18 Demequina aurantiaca 5.51 3.11 Methylotenera Curvibacter gracilis 5.43 Acidovorax 2.92 Acidovorax temperans 5.27 Gluconobacter 2.49 Chryseobacterium indologenes 5.17

Chryseobacterium 2.49 Oxalobacter vibrioformis 4.88 Species Genus Chitinophaga 2.26 Pseudomonas marginalis 2.88 Azospirillum 2.26 Pseudomonas entomophila 2.60 Demequina 2.22 Gluconobacter morbifer 2.01 Oxalobacter 1.94 Rhodococcus qingshengii 1.88 Alkanindiges 1.92 Edaphobacter modestus 1.87 Pedosphaera 1.75 Janthinobacterium lividum 1.83 Rubrivivax 1.38 Pelotomaculum isophthalicicum 1.83 Candidatus Koribacter 1.25 Hydrogenophaga defluvii 1.68 Candidatus Solibacter 1.23 Acidovorax delafieldii 1.64 Sulfuritalea 1.17 Thermoanaerobacter inferii 1.48 Rhodococcus 1.14 Phenylobacterium lituiforme 1.48 Sphingomonas 1.08 Escherichia albertii 1.27 Aquabacterium 1.05 Moorella glycerini 1.16 Burkholderia 0.94 Methylocaldum tepidum 1.14 Thermodesulfovibrio 0.87 Candidatus Scalindua brodae 1.13 of growth and contributions to the ecosystem at their natural habitats.

Figure 63. The top 25 most abundant classified genera and species in Silkwood groundwater samples.

The hierarchy of the most abundant bacteria from the Russel-Mulgrave catchment samples is shown in Figure 64. The Mulgrave site samples show far less diversity, being dominated by Janthinobacterium agaricidamnosum, which is also among the most abundant species in the Silkwood groundwater samples. The Janthinobacterium genus has been demonstrated in previous studies to have high degree of 16S rDNA sequence similarity to the Oxalobacter genus and J. agaricidamnosum is known to cause a soft rot disease of the cultivated mushroom Agaricus bisporus (Lincoln et al, 1999). The high relative humidity (RH) of the natural tropical

Page | 111 environment, similar to artificial mushroom-cropping environments which are kept at a high RH (~90% ), may be an influential factor on the abundance of this species (Lincoln et al, 1999).

The genus Chromobacterium and related species Aquitalea magnusonii are strongly represented. The betaproteobacterium C. subtsugae has been previously isolated from forest

Mulgrave Groundwater Genera - Relative Abundance Mulgrave Groundwater Species - Relative Abundance Percentage (%) Top 25 Genera 0 20 40 60 80 100 Percentage (%) Top 25 Species Janthinobacterium 89.84 0 50 100 Chromobacterium 2.11 Aquitalea 1.28 Janthinobacterium agaricidamnosum 67.47 Oxalobacter vibrioformis Burkholderia 1.22 5.36 Chromobacterium subtsugae 4.84 Oxalobacter 1.16 Aquitalea magnusonii 4.33 Cupriavidus 1.11 Chromobacterium aquaticum 2.53 Polynucleobacter 0.42 Burkholderia ubonensis 1.99 Vogesella 0.39 Cupriavidus laharis 1.94 Candidatus Solibacter 0.28 Aquitalea denitrificans 1.51 Hydrogenophaga 0.25 Chromobacterium piscinae 1.38 Methyloversatilis 0.20

Genus Hydrogenophaga defluvii 1.00 Species Pedosphaera 0.20 Methyloversatilis universalis 0.93 Azospirillum 0.18 Sporotomaculum syntrophicum 0.75 Sporotomaculum 0.16 Burkholderia brasilensis 0.71 Dechloromonas 0.14 Dechloromonas hortensis 0.65 Acetobacter 0.13 Cupriavidus basilensis 0.58 Candidatus Koribacter 0.12 Vogesella perlucida 0.56 Edaphobacter 0.11 Edaphobacter modestus 0.53 Pelagicoccus 0.11 Methylobacillus glycogenes 0.49 Moorella 0.11 Chromobacterium violaceum 0.46 Haliangium 0.11 Rhodocyclus purpureus 0.43 Methylobacillus 0.11 Chondromyces pediculatus 0.41 Thermodesulfovibrio 0.10 Cupriavidus metallidurans 0.40 Curvibacter 0.10 Moorella glycerini 0.39 Rhodocyclus 0.09 Pelotomaculum isophthalicicum 0.36 soil in Maryland, USA, and known to be toxic to Colorado potato beetle larvae and other insects (Martin et al, 2007).

Figure 64. The top 25 most abundant classified genera and species in groundwater samples from Mulgrave-Russell catchment site.

5.2.7 Major Taxa Abundance & Diversity- Soil

The most abundant microorganisms discovered in groundwater samples (classified by taxon from phylum to family) were also identified in the soil samples (Figure 65). The family Firmicutes was shown to have a greater abundance in the soil samples compared to groundwater (see Figure 62).

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Figure 65. Most abundant bacteria in soil samples, shown at the taxa levels phylum to family.

5.2.8 Genus and Species Abundance & Diversity- Soil

The microbial diversity and abundance in soil samples collected from the P2R paddock, were also analysed and the results from the top 25 genera and species, are shown in Figure 67. There are differences in abundance levels between 25 cm and 65 cm, with a clear decrease in abundance at 65 cm compared to the 25 cm depth (Fig. 66). It was expected that soil depth and composition would have an impact on the diversity and abundance of the microbial communities found at these sites.

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Soil (~25cm) Genera - Relative Abundance Soil (~25cm) Species-Relative Abundance Percentage (%) Top 25 Genera Percentage (%) Top 25 Species 12.23 Thermoanaerobacter Thermoanaerobacter inferii 20.45 Bacillus 10.97 Bacillus mucilaginosus 10.22 Candidatus Koribacter 10.23 Edaphobacter modestus 9.06 Candidatus Solibacter 7.62 Moorella glycerini 5.26 Pedosphaera 6.92 Bacillus atrophaeus 4.75 Janthinobacterium 5.25 Demequina aurantiaca 4.09 Edaphobacter 4.92 Tepidanaerobacter syntrophicus 4.08 Azospirillum 4.00 Thermovenabulum ferriorganovorum 3.83 Curvibacter 3.73 Ammonifex thiophilus 3.62 Thermogemmatispora 3.47 Anaerobaculum thermoterrnum 3.34 Saccharopolyspora 3.09 Curvibacter lanceolatus 2.91

Genus Moorella 3.04 Chthoniobacter flavus 2.78 Species Thermodesulfovibrio 2.54 Thermodesulfovibrio aggregans 2.76 Demequina 2.29 Pelotomaculum isophthalicicum 2.70 Tepidanaerobacter 2.19 Brachyspira ibaraki 2.65 Candidatus Liberibacter 2.07 Rhodothermus clarus 2.52 Thermovenabulum 2.06 Actinokineospora inagensis 2.16 Desulfovibrio 2.02 Janthinobacterium agaricidamnosum 1.81 Ammonifex 1.95 Peptoniphilus coxii 1.81 Anaerobaculum 1.80 Chondromyces pediculatus 1.77 Actinoallomurus 1.78 Sporotomaculum syntrophicum 1.65 Chthoniobacter 1.54 Candidatus Scalindua brodae 1.56 Pelotomaculum 1.45 Azospirillum rugosum 1.47 Brachyspira 1.42 Thermodesulfovibrio thiophilus 1.38 1.37 Anaeromyxobacter 1.41 Desulfomonile tiedjei

Soil (~65 cm) Genera - Relative Abundance Soil (~65cm) Species-Relative Abundance Percentage (%) Top 25 Genera Percentage (%) Top 25 Species Acetobacter Tepidanaerobacter syntrophicus 19.18 Arthrobacter 14.25 54.22 Ammonifex thiophilus 12.01 Tepidanaerobacter 5.12 Desulfurispora thermophila 8.14 Gluconobacter 3.43 Calothrix parietina 8.06 Ammonifex 3.20 Arthrobacter psychrochitiniphilus 7.66 Desulfurispora 2.17 Gluconobacter morbifer 7.07 Calothrix 2.15 Anaerobaculum thermoterrnum 5.22 Thermoanaerobacter 1.63 Amycolatopsis pigmentata 3.73 Anaerobaculum 1.39 Candidatus Rhabdochlamydia crassificans 2.83 Amycolatopsis 1.39 Kineosporia mikuniensis 2.69 2.60 Tanticharoenia 1.24 Gluconobacter krungthepensis Gemmata obscuriglobus 2.38

Genus Candidatus Solibacter 1.11 Species Pelotomaculum isophthalicicum 2.01 Geobacillus 1.03 Escherichia albertii 1.96 Gemmata 0.88 Geobacillus gargensis 1.86 Candidatus Rhabdochlamydia 0.88 Geothrix fermentans 1.83 Luteibacter 0.81 Paenibacillus filicis 1.69 Escherichia 0.78 Geobacillus thermoglucosidans 1.54 Kineosporia 0.72 Rhodobium gokarnense 1.35 Paenibacillus 0.62 Sulfobacillus yellowstonensis 1.12 Bacillus 0.57 Thermoanaerobacter inferii 1.06 Azospirillum 0.56 Acidobacterium capsulatum 1.04 Pelotomaculum 0.54 Candidatus Amoebophilus asiaticus 1.02 0.49 Geothrix Moorella glycerini 1.00 0.42 Methylobacterium Arthrobacter soli 0.93 Anaerobacillus 0.41

Figure 66. The top 25 most abundant classified genera and species in soil samples, from 25 cm and 65 cm depth BGL.

There were two distinct soil types found at the P2R paddock where the samples were collected. Depending on the depth, the composition of the two soil horizons labelled A and B, were analysed with the subsequent discovery of some dominant bacterial species;

 The A horizon (upper 40-50 cm), was noticeable more friable than the B horizon and represents the extent of the sugarcane root zone, containing more humic content. Interestingly, the most abundant genus discovered here, Thermoanaerobacter, has been described to include thermophilic anaerobes that have been isolated from hot

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springs (Wiegel and Ljungdahl, 1981; Euzeby, 1997). Not surprisingly, Bacillus mucilaginosus has been described as a common soil bacteria which has demonstrated to have the ability to break down silicate materials and to solubilize potassium from minerals in soil, making it useful in bio-fertiliser applications (Basak and Biswas, 2008; Yang et al, 2015; Liu et al, 2017). Edaphobacter modestus (Acidobacter phylum) has previously been isolated from a calcareous alpine soil and is adapted to slightly acidic conditions (Koch et al, 2008). Moorella glycerini has been described as a thermophilic, anaerobic bacterium which was isolated from a hot spring in 2- Yellowstone National Park in the USA and was observed to reduce thiosulfate (S2O3 ) to elemental sulfur (S2-) (Slobodkin et al, 1997).

 The B horizon (≥ 65 cm BGL) is a dense clay with less organic matter present, which experiences normal water saturation during the year. The most abundant species discovered here, Tepidanaerobacter syntrophicus, has been described as an anaerobic, moderately thermophilic bacterium originally isolated from sewage sludge in Japan (Sekiguchi, 2006). The second most abundant species Ammonifex thiophilus has been described as extremely thermophilic, anaerobic, and facultatively chemolithoautotrophic originally isolated from a hot spring in Russia (Miroshnichenko et al, 2008).

- 5.2.9 Links to Groundwater Chemistry: NO3 and DN

- - Species that are known to utilise NO3 or NO2 are present in the samples, mostly in the groundwater samples. These microorganisms identified included: Denitratisoma oestradiolicum, Nitrospira moscoviensis, Thiohalorhabdus denitrificans, Aquitalea denitrificans, Glaciecola nitratireducens, Denitratisoma oestradiolicum, Hydrogenophilus + denitrificans, Nitrosococcus watsoni (NH4 reducer), Nisaea nitritireducens, Steroidobacter denitrificans (soil) (Burghate and Ingole, 2014). However, most of these bacteria were not classified within the most abundant species or genera (see Figure 64). An exception is the occurrence of Nitrosococcus watsonii and Denitratisoma oestradiolicum in the top 10 most abundant species from groundwater sampled at bore 11210056 in May 2018.

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2- 5.2.10 Links to Groundwater Chemistry: SO4

A noticeable chemical characteristic of the measured groundwater data is the observed link 2- between low DO concentrations and relatively higher SO4 concentrations. 2- 2- 2- Chemolithoautotrophic species known to utilise S , SO4 or S2O3 (thiosulphate) for metabolic processes and energy were present in the groundwater and soil samples. They were present in higher abundance and diversity than DN-related bacteria (Figure 67) and sometimes found in the top 25 most abundant species and genera. The species included: Thermodesulfatator atlanticus, Thermodesulfovibrio aggregans, Desulfurispirillum alkaliphilum, Thermodesulfovibrio thiophilus, Desulfomonile tiedjei, Desulfurispora thermophila, Paracoccus sulfuroxidans, Thiobacillus sajanensis, Sulfobacillus yellowstonensis, and Desulfonatronum thiosulfatophilum. Members of the genera 2- Thermoanaerobacter and Anaerobaculum are also chemolithotrophs associated with SO4 and 2- S2O3 reduction (Wiegel and Ljungdahl, 1981; Maune and Tanner, 2011; Patel and Hugenholtz, 2015). These species, with the exception of Paracoccus sulfuroxidans, have been previously described as facultative or obligate anaerobes. Another common characteristic of most of these bacteria is their identification as thermophiles (e.g. Thermodesulfatator atlanticus (isolated from a Mid-Atlantic Ridge hydrothermal vent), Thermodesulfovibrio aggregans, Thermodesulfovibrio thiophilus, Thiobacillus sajanensis (isolated from hot springs) and Sulfobacillus yellowstonensis) (DeWeerd et al, 1990; Dul’tseva et al, 2006; Kaksonen et al, 2007; Liu et al, 2006; Sorokin et al, 2007; Sekiguchi et al, 2008; Sorokin et al, 2011). Sulphur is among the major components in geothermal waters, in the form of dissolved 2- 2- 2- SO4 , sulphides, S2O3 and sulphite (SO3 ) (Kaasalainen and Stefánsson, 2011). The Silkwood region groundwater cannot be considered an optimal thermophile environment (>50°C), with measured temperatures ranging from 25°C to 27°C. This suggests that temperature is not necessarily a critical controlling factor on environment suitability for these identified species, provided that stable sources of sulphurous metabolites are available in anaerobic conditions.

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Measured Abundances of Sulphur-Associated Species Measured Abundances of Nitrogen- Associated Species Reads Reads 0 50000 100000 0 4000 8000 12000

Thermodesulfovibrio aggregans 49582 Denitratisoma 10366 Thermodesulfovibrio thiophilus 26108 oestradiolicum Desulfurispora thermophila 25803 Nitrospira 8301 Desulfomonile tiedjei 24702 moscoviensis Thermodesulfatator atlanticus 17496 Sulfobacillus yellowstonensis 16092 Nitrosococcus watsoni 5961 Desulfonatronum thiosulfatophilum 5339 Steroidobacter Desulfurispirillum alkaliphilum 3180 4792 denitrificans

Desulfovibrio psychrotolerans 1901 Species Species 1875 Thiohalorhabdus Desulfotomaculum indicum 4330 Desulfacinum subterraneum 1746 denitrificans Thiomonas thermosulfata 1445 Aquitalea denitrificans 3647 Desulfovibrio carbinolicus 1410 Desulfosarcina ovata 1311 Glaciecola 3468 Desulfotomaculum salinum 1263 nitratireducens Desulfuromonas svalbardensis 1262 Desulfovibrio putealis 1128 Nisaea nitritireducens 2712 Desulfovibrio butyratiphilus 1119 Hydrogenophilus Desulfofrigus oceanense 1026 1078 denitrificans Desulfovibrio oryzae 1020

Figure 67. Measured abundances as number of reads of bacteria associated with sulphur vs nitrogen utilisation bacteria species in groundwater samples. X-axis values are measured reads from Illumina sequencing analysis, indicating species abundance.

5.2.11 Temporal Changes in Species Abundance

The 5 groundwater samples from bore 11210045 (Figure 68) provide an opportunity to examine changes in species diversity and abundance at one sampling site over a 6 months sampling period. Several species showed increased abundance in January 2018, of which Curvibacter lanceolatus, a bacterium normally found in soil (Zhang et al, 2017), returned the highest number of reads, accounting for 22.86% of all identified species in the sample. P. marginalis and R. qingshengii also showed high relative abundance, but not as high as C. lanceolatus. Groundwater chemistry data for January 2018 showed there was an increase in concentrations of Cl+, Ca2+, Mg2+ and Fe2+ in bore 11210045, with a corresponding increase in EC, while DO concentration was at its lowest (0.06 mg/L).

The catchment-wide increase in Ca2+ and Mg2+ concentrations observed in the groundwater data (see Figures 28 & 29) was potentially a result from the application of lime in the form of

Page | 117 dolomite (CaMgCO3) to plantation paddocks in the region. Previous reports have found that rapid growth in microbiological activity (Haynes, 1984; Haynes and Naidu, 1998), resulting in increased microbial biomass (Badalucco et al, 1992; Haynes and Naidu, 1998), occurred following the application of lime to acid soils.

120000

100000 Curvibacter lanceolatus

80000

60000

Reads Pseudomonas marginalis

Pseudomonas entomophila

40000 Janthinobacterium agaricidamnosum Rhodococcus qingshengii

Janthinobacterium agaricidamnosum Thermoanaerobacter inferii 20000

0 1/12/2017 12/01/2018 16/02/2018 23/03/2018 9/05/2018

Figure 68. Time series graph of species identified in bore 11210045. Y-axis values are measured reads from Illumina sequencing analysis, indicating species abundance.

Some bacterial species are known to induce the precipitation of calcium-carbonate (calcite; - CaCO3 ) (Zammit et al, 2011; Xu et al, 2016; Chaparro-Acuña et al, 2018). C. lanceolatus is - one of a number of soil microbes that precipitate CaCO3 and is among the abundant species in the samples isolated during December 2017-January 2018. C. lanceolatus was one of the top 2 most abundant species in samples from bores 11210041, 11210051 and P2RNE and within the top ten most abundant species from bores 11210004 and 11210056 in December 2017, coinciding with peaks in alkalinity and Ca2+ concentration in measured groundwater chemistry at that time. Under laboratory conditions, C. lanceolatus was demonstrated to precipitate - 2+ 2+ CaCO3 and Mg-rich calcite crystals in the presence of Ca and Mg -enhanced growth (Zhang et al, 2017; Zhang et al (2), 2017). This suggests that increased levels of Ca2+ and Mg2+ in the - environment have the potential to facilitate biological precipitation of CaCO3 which in turn - - raises alkalinity. However, CaCO3 in the presence of dissolved HCO3 can also result in the - 2+ formation of HCO3 and the liberation of free Ca ions (Appelo and Postma, 2005):

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2+ - CaCO3 + H2CO3 -> Ca + 2HCO3 (Eq.13)

2+ - Relatively higher alkalinity values, Ca concentrations and HCO3 concentrations were displayed in measured groundwater chemistry data for bores 11210045 and P2RSW (Table 7; Figures 28 & 29, Chapter 4, section 4.2). It is possible that the dissolved carbonate anions in the groundwater samples resulted from a combination of geochemical and biological processes. Interestingly, Sanchez-Roman et al (2007) studied 19 species of moderately halophilic bacteria - which also precipitated CaCO3 and Mg-calcite. Although the same species were not found in the Silkwood groundwater samples, it raises the possibility that amongst the “unclassified” - species, some may have contributed to CaCO3 precipitation in response to the slightly elevated levels of Na2+ and Cl- observed in December 2017.

Other abundant species classified in the January 2018 sample included the soil bacteria Pseudomonas marginalis (a pathogen known to cause soft rot in a wide variety of harvested fruits and vegetables (Achbani et al, 2014), the carbendazim (fungicide)-degrading Rhodococcus qingshengii (Xu et al, 2007), the common soil bacterium Acidovorax delafieldii (Comamonadaceae family) and the anaerobic, oxalate-reducing Oxalobacter vibrioformis.

The most abundant classified bacterial species in 3 groundwater samples from bore 11210056 were also assessed over time and are shown in Figure 69. A noticeable increase in species abundance occurs in December 2017, during the period of low rainfall. The most abundant identified species is Acidovorax temperans, a biofilm-forming bacterium common in activated waste water (Boycheva et al, 2015), accounted for 20.31% of identified species in the sample. Also showing high abundance was M. versatilis isolated from lake sediments in Seattle, USA (Lapidus et al, 2011), and C. lanceolatus and O. vibrioformis, both isolated from anoxic freshwater sediments (Dehning and Shink, 1989). Measured groundwater chemistry data shows - that November-December 2017 was a period of relatively higher DO and NO3 concentrations. Following significant rainfall events in February and March 2018, there is a noticeable decline - in species abundance (Figure 69) and a significant decline in NO3 concentration from 5.10 mg/L to 0.70 mg/L. A. temperans was still present as the most abundant identified species but accounted for only 5.65 % of total classified species in the sample.

These trends would suggest that rapid changes in particular groundwater chemistries had a direct influence on species abundance. Groundwater chemistry in May 2018 displayed a significant increase in dissolved-carbon concentrations. Consequently, and a new suite of bacteria emerged as the dominant microorganisms. The new species included: Gluconobacter

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+ morbifer, Gluconobacter krungthepensis, Escherichia albertii, the NH4 -reducing Nitrosococcus watsonia (Campbell et al, 2011) and the oestradiol-degrading denitrifier Denitratisoma oestradiolicum (Fahrbach et al, 2006)

25000

Acidovorax temperans

20000

Methylotenera versatilis

15000

Curvibacter lanceolatus

Oxalobacter vibrioformis Reads

10000 Curvibacter gracilis Gluconobacter morbifer Acidovorax temperans Chitinophaga soli Pelotomaculum isophthalicium

5000 Gluconobacter krungthepensis

0 1/12/2017 23/03/2018 9/05/2018

Figure 69. Time series graphs of abundant species identified in bores 11210056. Y-axis values are measured reads from Illumina sequencing analysis, indicating species abundance.

5.2.12 Related Wet-Tropics Research – Comparison to the Tully-Murray Catchment

The wet-tropics Tully-Murray catchment is located approximately 30 km south of Silkwood and separated from the Silkwood catchment by the Walter Hill range. Like the Silkwood region, it is characterised by rainfall, soil types, land use, SWL fluctuations and soil saturation similar to those of the Silkwood region. Wakelin et al (2011) published a study on groundwater microbiology in the Tully-Murray catchment, employing 1 m-deep lysimeters and 7-11 m-deep groundwater bores to collect water samples underlying sugarcane and banana plantations.

Their analysis using 16S ribosomal RNA gene sequencing found that the groundwater bacterial community was dominated by Proteobacteria. Mainly Alphaproteobacteria and Betaproteobacteria, with the families Burkholderiaceae and Comamonadaceae strongly represented and also Burkholderiales and Neisseriales the dominant orders. Similarly, the alphaproteobacteria and betaproteobacteria were dominant in the groundwater sampled in the Silkwood region. Oxalobacteraceae and Comamonadaceae were the dominant family taxa,

Page | 120 and Burkholderiales the most abundant order. Neisseriales was not among the highset-ranked orders.

Initial conclusions based on such comparisons suggested that North Queensland’s wet-tropics catchments displayed very similar dominant microbiological organisms in groundwater systems, as climate and rainfall patterns, soil types and agricultural plantations are also similar throughout the region. However, there is a marked difference between the organisms identified in the Silkwood catchment to those in the Murray-Tully catchment. The microbiological communities in groundwater have been previously reported to contain very low levels of the Gram-negative Firmicutes and Actinobacteria (Wakelin et al, 2011). However, this study found both phyla are among the most abundant in the Silkwood groundwater samples, whereas very low levels were detected in the Murray-Tully groundwater samples.

Wakelin et al (2011) did manage to identify a clear distinction between groundwater and soil- water bacteria in the Murray-Tully study. However, during this study the biochemical soil properties were found to have a greater influence on groundwater microbiota in the Silkwood catchment. Actinobacteria include some of the most common bacteria in soils (Huggett and O’Grady, 2014) and previous research conducted by Sharmin et al (2013) found that the bacterial taxa within 3 Australian sugarcane processing plants were dominated by Firmicutes, along with Proteobacteria, Bacteriodetes and Chloroflexi. All four of these phyla are among the most abundant in the Silkwood groundwater and soil samples discovered during this study. These findings indicate significant infiltration and/or connectivity between soil water and groundwater aquifers throughout the Silkwood catchment; the soil profile would retain cellulose and sugars derived from sugarcane plant residues, which provides the energy source for the abundant soil bacteria. The same dominant bacteria species occur at aquifer depths of < 10 m and > 35 m in different locations.

16S rDNA sequencing was also used by Korbel et al (2017) to identify microorganisms in samples collected from groundwater wells in the Condamine, Gwydir, Naomi and Macquarie- Bogan catchments located in southern Queensland and north-west New South Wales. These areas are different to far north Queensland not only in climate and rainfall patterns, but also the aquifers in the catchments are mainly composed of sandstones, siltstones and shales with gravel and clay. Dominant agricultural practices are cattle grazing and cotton growing while SWL decline due to drawdown from groundwater extraction is not uncommon. Groundwater bores sampled in the study by Korbel and co (2017) were screened at depths of 10-25 m BGL. The

Page | 121 study examined microbial communities in both aquifer and bore environments and found that unpurged bore-water samples were dominated by bacteria from the orders Burkholderiales, Neisseriales, Actinomycetales, Pseudomondales, Xanthomonadales and Sphingomonadales. In contrast, aquifer-sourced groundwater samples contained significantly higher abundances of archaea from the orders Nitrosopumilales and Nitrososphaerales and bacteria from the order Anaerolineales and Acidobacteria.

The results from this study indicated that the bacteria sampled from Silkwood groundwater classified to the taxonomic level of order are different to those sampled from the groundwater reported in the Korbel et al (2017) study of southern Queensland and north-west New South Wales. However, there were some similarity to findings reported by Korbel et al. (2017), as Burkholderiales, Actinomycetales, Pseudomondales, Xanthomonadales and Neisseriales were among the most abundant orders in Silkwood groundwater (Appendix 8).

Therefore, the comparison between the far north Queensland catchments the southern Queensland catchments suggests that climate, aquifer material and land use all contributed to the types and abundances of microbial communities present in groundwater.

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CHAPTER 6: CONCLUSION

A growing amount of research in recent years has focused on groundwater and soil chemistry in the wet-tropics of north Queensland. The accumulating data from these studies contributes to a greater understanding of the transport of agriculturally-derived contaminants in tropical catchments around the world, particularly in regions of intensive sugarcane farming. However, information on groundwater microbiology and aquifer ecology is still limited, despite acknowledgement that bio-chemical processes are an important influence on groundwater chemistry. A transdisciplinary approach to research is required to provide a more detailed interpretation of the interacting geological, environmental and biological factors which control the fate of contaminants in our catchments. With a more complete understanding, better strategies can be developed to manage our natural resources.

This study is the first to provide a detailed examination of seasonal changes in groundwater chemistry and SWL measurements within the Silkwood area catchment, based on monthly and bi-monthly sampling and analysis. Over the course of the year-long study, the measured groundwater chemistry data did show noticeable changes in response to rainfall events and, potentially, anthropogenic inputs. Spatial variations in groundwater chemistry were also evident.

Rapid SWL responses to rainfall trends are indicative of fast infiltration of precipitation and suggest a relatively low residence time for groundwater throughout the catchment. The - measured groundwater chemistry data shows that concentrations of DO and NO3 in groundwater aquifers can increase in response to rainfall. The occurrence of both these dissolved chemical elements in aquifers at depths greater than 30 m BGL indicates hydrologic connectivity between the soil zone and deeper alluvium. However, areas in the catchment rich - in kaolinite clays can display anoxic groundwater with very low NO3 concentrations (at times 2+ 2- below detection level) and elevated Fe and SO4 concentrations. The anion-adsorption capacity of the Fe-oxide-rich clays and their low-permeability combined with anthropogenic input of S2- (via fertilisers and gypsum) may promote the in-situ precipitation of Fe-sulphides and anoxic conditions distinctly affect groundwater chemistry and quality by increasing dissolved Fe2+ concentrations. This study was restricted to one site for soil and sediment sample collection, which is informative but not comprehensive. Unfortunately, the opportunity for mineralogical analysis of sediment requires that cores be extracted, which is only possible during bore installation or drilling. A more thorough analysis of geochemical influences on

Page | 123 groundwater in the Silkwood catchment would be possible in future studies if sediment extraction from multiple sites is available.

- NO3 concentrations in groundwater remained well within accepted environmental limits throughout the study, but in some aquifers were higher than would be expected based on available historical groundwater chemistry data. This is due to the infrequency of historical sampling and analysis, which does not provide month-to-month groundwater analysis. Although groundwater isotope analysis and microbiology analysis revealed no conclusive - evidence for the occurrence of DN, measured NO2 concentrations at one site characterised by dense clays (Bore P2RNE) were indicative of the presence and activity of DN. The lower permeability of the clay may inhibit rapid groundwater transport following rainfall events, allowing enough time for autotrophic DN to progress in the anoxic groundwater conditions.

This is the first study to identify and analyse groundwater and soil microorganisms in the Silkwood region. It was initially expected that denitrifying bacteria would be present within the most abundant species classified in this study, given the broadscale sugarcane agriculture and use of nitrogen-rich fertilisers. However, known denitrifying species were identified in the groundwater in much smaller abundances than bacteria who are identified in previous research as being anaerobic, thermophilic, sulphur/sulphide/sulphate-users. Their presence correlates with anoxic aquifer conditions where S2- and Fe-sulphides are readily available as electron- donors. NO3- or NO2- reductase enzyme/gene identification with metagenomic microbiological analysis in future studies may provide more quantitative data regarding DN potential in groundwater and soil samples.

The most abundant species of bacteria identified in the groundwater from all sampled sites have associations with soil, water and plants (Chitinophaga soli, Janthinobacterium agaricidamnosum, Acidovorax temperans, Methylotenera versatilis, Curvibacter lanceolatus, Ralstonia insidiosa). That these species were identified in groundwater samples from aquifers at depths greater than 30 m BGL suggests a hydrologic connectivity between soil zones and deeper groundwater aquifers. This displays the widespread influence that agricultural activities have on the subsurface environment throughout the catchment. There is a possibility that certain species may have a noticeable influence on the groundwater chemistry (e.g. the calcite- precipitating C. lanceolatus), but with most of the bacteria species in the analysis being “unclassified,” there remains a significant gap in our understanding of the full diversity and the complex bio-chemical processes at work. Collection and analysis of samples from a larger

Page | 124 number of groundwater bores and soil sites in the area would provide a more comprehensive analysis.

As sugarcane growers and government continue to trial nitrogen management practices, it would be worthwhile conducting further studies which integrate chemistry and microbiology, utilising the monthly/bi-monthly sampling and analysis approach. This would provide a more detailed understanding of the potential for DN in plantation zones, particularly if focused on soil and surface water bio-chemistry. Groundwater analysis is not considered an important strategy in the Queensland government’s Reef 2050 Water Quality Improvement Plan 2017- 2022. However, this study shows that agricultural activities can have an impact on groundwater chemistry and microbiology. Without regular monitoring, a confident assessment cannot be made of the groundwater contribution to nitrogen discharge from catchments. Equally, the contribution of groundwater systems to nitrogen attenuation can be better understood though a combination of bio-chemical and geo-chemical analyses. Such information has implications for improving resource management strategies to meet environmental targets for contaminant reduction.

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APPENDICES

- Appendix 1: NO3 , Historical Data (GWDB).

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Appendix 2: Groundwater Major Ion Chemistry -Historical Data (mg/L)

Bore RN ID Na K Mg Ca Cl SO4 HCO3 11210051 7.1 1.2 0.8 1.2 9.4 9.4 9.4 11210051 6.6 1.8 1.3 2.9 8.6 8.6 8.6 11210051 3.7 0.1 0.9 1.5 5.4 5.4 5.4 11210051 4.2 1 1.7 1.9 7 7 7 11210051 4 1.3 1.2 1.5 6.7 6.7 6.7 11210051 5 1.4 1.5 1.8 7.8 7.8 7.8 11210051 5 1.3 1.4 1.6 7.8 7.8 7.8 11210051 5 1.4 1.4 1.8 7.4 7.4 7.4 11210051 5 1.3 1.2 1.6 6.8 6.8 6.8 11210056 22.2 2.4 4.2 9.9 7 7 0 11210056 22.5 3.6 4.6 9.4 7.7 7.7 0.5 11210056 22.1 3.2 4 9.7 8.8 8.8 0.3 11210056 21.8 2.2 4.3 10 6.4 6.4 0 11210056 22 2.6 4 9.8 6.8 6.8 0 11210056 11 2.6 2.3 7 8.1 8.1 1.1 11210056 12 2.2 2.1 5.4 7.2 7.2 1 11210056 12 2.2 2 4.7 6.9 6.9 1.8 11210056 13 2.5 2.1 5.6 6.7 6.7 1.6 11210056 10 1.9 1.5 4.1 6.3 6.3 2.3 11210056 13 2.3 2.2 5.4 7 7 1.7 11210056 5 1.2 0.8 2.1 5.3 5.3 4.4 11210056 15 2.4 2.4 6.5 7 7 2.4 11210055 4.1 3.9 1.5 2.5 9.1 8.3 6.7 11210055 35.5 4.9 3.7 5.1 8.6 34 3.9 11210055 4.3 3 1.5 2 6.6 6.6 0.4 11210055 4.3 3 2 2.7 8.4 8 0 11210055 4.2 4.2 1.9 2.6 8.2 8.6 0.8 11210055 4.2 3.6 1.8 2.5 7.8 8.2 0.4 11210055 4.1 3.5 1.6 2.3 7.4 8 0.4 11210055 4.1 3.7 1.8 2.3 7.5 7.8 0.6 11210055 4 4 1.6 2.1 8.1 7.4 0 11210075 8.1 2.1 1 0.9 14 0.00 12 11210075 8.5 2 0.4 0.5 14 0.00 3.8 11210075 9.6 1.7 0.4 0.7 14.8 3.90 0.8 11210075 9.2 1.5 0.7 0.3 11.1 0.8 8.8 11210075 7.8 1.1 0.8 0.5 11.8 0 3.2 11210075 7.4 2.3 0.5 0.4 11.7 0.9 4 11210075 7.8 1.5 0.9 0.4 13.3 0.6 6.3 11210075 7.7 1.5 0.8 0.3 11.2 0.5 9.2 11210075 7.5 0.8 0.9 0.3 11.6 0.5 7.7 11210075 7.5 1.4 0.9 0.9 12.7 0.6 7.3

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11210075 7.6 1.5 0.8 0.4 12.1 0.5 5.9 11210075 7.2 1 0.9 0.4 11.3 0.9 8.8 11210075 6.9 1.3 0.8 0.3 11.2 0.7 10 11210075 7 1.4 0.7 0.3 12 0 12 11210075 7 1.6 0.8 0.3 13 0 8 11210075 7 1.5 0.8 0.3 12 1 10 11210075 7 1.6 0.8 0.4 12 1.1 11 11210075 7 1.5 0.8 0.5 12 1 10 11210075 10 1.5 1.8 1.8 18 1.6 9 11210075 7 1.6 0.8 0.4 11 0 12 11210075 7 1.5 0.8 0.3 12 1 12 11210038 13 3.8 1.9 1.3 8 26.5 3 11210038 10.5 5.9 2.1 1.4 7.9 1.9 0 11210038 8.3 2.7 1.7 1.1 12 0 4.3 11210038 8 3.4 1.7 1.2 9.5 0 15.4 11210038 9.8 2.4 1.8 1.2 12.7 1.6 25.4 11210038 9.9 1.6 1.6 1.7 7.5 1.4 25 11210038 9.7 2.2 1.7 1.1 7.6 1.2 25.4

Appendix 3: Measured Groundwater Chemistry Data.

Redox (ORP) mV 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 NA NA NA NA NA NA NA NA 10/11/2017 315 224 320 168 196 225 163 115 1/12/2017 307 322 300 190 270 254 200 160 15/12/2017 330 320 270 35 280 280 130 108 12/01/2018 305 298 40 363 251 210 151 16/02/2018 407 145 355 150 347 268 140 23/03/2018 152 222 316 163 350 233.4 262 152 11/05/2018 276 197 259 120 270 275 234 95 20/07/2018 345 291 292 100 314 254 190 21/09/2018 328 305 77 266 214 250 194

EC µS/cm 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 52.8 52.8 64.8 109.1 54.5 67.0 115.0 57.5 10/11/2017 55.0 49.8 62.1 87.5 54.6 57.5 50.8 57.2 1/12/2017 59.8 48.4 63.6 98.0 53.0 59.3 53.0 56.0 15/12/2017 57.5 46.7 62.5 110.9 52.3 57.5 51.0 52.5 12/01/2018 48.2 61.1 112.0 51.2 61.1 49.9 52.5 16/02/2018 62.0 51.3 61.0 89.0 48.8 50.0 49.5 23/03/2018 62.2 52.5 67.7 92.5 47.2 34.6 51.8 50.7 11/05/2018 58.7 47.8 65.6 98.0 48.0 49.3 49.3 47.5 20/07/2018 57.7 48.0 63.3 109.5 53.4 51.0 50.2 21/09/2018 51.5 62.9 109.8 55.8 58.0 51.3 50.0

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Bore ID (RN) δ²H-H₂O (‰) Bore ID (RN) Temp. (⁰C) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW 22/09/2017 -18.8 -25.6 -20.6 -19.3 -18.7 -14.3 -19.6 -16.0 22/09/2017 27.0 27.0 26.0 26.0 26.0 27.0 26.0 27.0 10/11/2017 -16.8 -22.7 -20.7 -23.1 -18.7 -18.9 -21.0 -18.2 10/11/2017 26.8 27.0 27.0 26.0 25.0 27.0 28.0 28.0 1/12/2017 -15.9 -17.4 -17.6 -19.7 -20.1 -18.0 -20.0 -16.9 1/12/2017 27.0 27.0 25.6 25.0 26.0 25.0 27.0 27.0 15/12/2017 -22.0 -20.3 -16.4 -19.6 -18.7 -17.7 -20.2 -16.4 12/01/201815/12/2017 29.0 -24.427.0 -17.026.0 IS26.0 -19.326.0 IS26.0 IS27.0 IS26.0 16/02/201812/01/2018 -21.8 -21.027.0 -17.026.0 -21.927.0 -18.526.0 26.0 -20.127.0 -20.026.0 -20.0 16/02/2018 27.6 26.9 26.0 26.0 26.0 26.5 27.0 23/03/2018 NA NA NA NA NA NA NA NA 23/03/2018 26.0 26.0 26.0 25.0 26.0 26.0 26.0 27.0 11/05/2018 NA NA NA NA NA NA NA NA 11/05/2018 27.0 26.0 26.0 25.0 25.0 25.0 25.7 26.0 20/07/2018 -16.0 -16.4 -15.1 -20.3 -17.5 -19.2 -15.8 13.8 20/07/2018 25.6 26.0 25.8 23.0 23.0 26.0 26.0 21/09/2018 5.0 4.9 5.9 4.6 4.6 4.9 5.5 21/09/2018 27.0 26.6 25.0 24.8 27.0 25.5 27.5 δ¹⁸O-H₂O (‰) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN 22/09/2017 -4.5 -5.6 -5.0 -5.5 -3.9 -3.5 -4.6 -4.1 10/11/2017 -2.7 -2.1 -3.6 -6.6 -3.7 -1.5 -3.4 -2.5 1/12/2017 -3.6 -3.4 -3.9 -4.5 -5.2 -4.5 -5.4 -3.9 15/12/2017 -4.3 -4.6 -3.5 -5.0 -4.7 -4.1 -4.6 1.9 12/01/2018 -5.8 -2.3 IS -4.6 IS IS IS 16/02/2018 -2.8 -3.0 -2.1 -2.9 -4.9 -5.9 -4.2 -4.2 23/03/2018 NA NA NA NA NA NA NA NA 11/05/2018 NA NA NA NA NA NA NA NA 20/07/2018 -3.8 -3.8 -3.8 -3.6 -4.5 -3.8 -4.2 -3.8 1.4 21/09/2018 5.0 4.9 5.9 4.6 4.6 4.9 5.5

2 18 Appendix 4: Measured δ H-H2O (‰) δ O-H2O (‰) and values from Silkwood groundwater and rain (NA= No analysis completed; IS = Insufficient amount of sample to complete analysis).

Appendix 5: Measured Major Ion Data, Silkwood Groundwater

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SAMPLE SAMPLE DATE DATE

22/09/2017 CATIONS ppm 22/09/2017 ANIONS ppm

Na K Ca Mg Fe Si Al Mn S Fl Cl Br SO4

11210004 4.44 1.30 0.83 1.31 0.01 5.43 0.15 0.06 0.30 11210004 0.00 4.87 0.00 0.32

11210040 5.29 1.37 1.49 0.48 0.02 2.16 0.06 0.01 0.32 11210040 0.00 5.54 0.00 0.13

11210041 5.04 1.87 0.88 2.61 0.02 5.37 0.05 0.02 0.05 11210041 0.00 6.88 0.00 0.00

11210045 14.76 2.98 4.12 2.59 0.18 14.54 0.04 0.05 0.62 11210045 0.00 5.22 0.00 1.03

11210051 5.63 1.52 1.68 1.20 0.01 7.11 0.04 0.02 0.37 11210051 0.00 5.08 0.00 0.10

11210056 5.52 1.54 2.54 1.09 0.02 3.85 0.06 0.06 0.17 11210056 0.00 8.64 0.00 0.48

P2RNE 4.75 2.11 0.98 0.88 1.55 7.70 0.16 0.01 1.42 P2RNE 0.00 8.65 0.00 2.71

P2RSW 7.62 1.46 0.81 0.47 2.32 9.78 0.21 0.02 1.23 P2RSW 0.00 5.42 0.00 2.52

10/11/2017 Na K Ca Mg Fe Si Al Mn S 10/11/2017 Fl Cl Br SO4

11210004 4.38 1.06 1.52 0.86 0.09 3.99 0.32 0.08 0.56 11210004 0.00 4.88 0.00 0.98

11210040 4.10 1.11 1.47 0.47 0.09 2.49 0.20 0.01 0.32 11210040 0.00 5.35 0.00 0.63

11210041 4.00 1.36 0.91 2.58 0.04 5.34 0.16 0.02 0.08 11210041 0.00 6.86 0.00 0.14

11210045 12.19 2.20 3.25 2.15 0.19 11.78 0.14 0.04 0.69 11210045 0.00 5.33 0.00 1.27

11210051 4.67 1.16 1.67 1.26 0.04 7.42 0.14 0.02 0.39 11210051 0.00 5.03 0.00 0.55

11210056 4.02 1.11 2.14 0.96 0.14 5.19 0.39 0.06 0.31 11210056 0.00 7.48 0.48 0.00

P2RNE 4.26 1.71 0.84 0.88 1.30 7.74 0.18 0.01 1.46 P2RNE 0.00 5.39 0.00 2.88

P2RSW 6.36 1.19 0.72 0.43 2.18 9.68 0.17 0.02 1.25 P2RSW 0.00 5.19 2.26 2.26

1/12/2017 1/12/2017 Fl Cl Br SO4

Na K Ca Mg Fe Si Al Mn S 11210004 0.00 7.43 0.32 0.94

11210004 6.19 1.54 1.67 0.98 0.17 4.09 0.16 0.08 0.67 11210040 0.00 6.99 0.00 1.41

11210040 5.45 1.56 1.66 0.61 0.01 2.49 0.06 0.01 0.32 11210041 0.00 9.22 0.00 0.45

11210041 6.47 1.96 1.14 2.86 0.02 5.41 0.05 0.02 0.09 11210045 0.00 7.42 0.00 1.84

11210045 14.27 2.77 3.19 2.40 0.17 12.34 0.09 0.04 0.54 11210051 0.00 9.73 0.00 0.55

11210051 5.31 1.50 2.22 1.24 0.05 4.57 0.16 0.05 0.18 11210056 0.00 6.87 0.32 1.01

11210056 6.25 1.52 1.68 1.45 0.01 7.60 0.05 0.02 0.28 P2RNE 0.00 7.43 0.00 3.89

P2RNE 4.82 2.14 0.62 1.09 1.38 8.58 0.41 0.01 1.22 P2RSW 0.00 7.17 0.00 3.28

P2RSW 6.99 1.41 0.39 0.45 2.32 10.22 0.70 0.02 0.96

15/12/2017 Na K Ca Mg Fe Si Al Mn S 15/12/2017 Fl Cl Br SO4

11210004 3.81 1.06 1.18 1.21 0.02 4.91 0.19 0.08 0.51 11210004 0.00 7.19 0.29 1.13

11210040 4.00 1.17 1.52 0.45 0.01 2.21 0.04 0.01 0.46 11210040 0.00 7.17 0.30 0.94

11210041 4.05 1.41 0.98 2.52 0.01 5.13 0.03 0.02 0.25 11210041 0.00 9.25 0.30 0.43

11210045 12.96 2.51 4.16 2.62 0.37 16.16 0.03 0.05 0.89 11210045 0.00 7.30 0.29 2.02

11210051 4.15 1.08 1.47 1.20 0.01 7.22 0.03 0.02 0.47 11210051 0.00 6.85 0.29 1.04

11210056 5.81 1.39 2.77 1.08 0.00 11.22 0.02 0.07 1.58 11210056 0.00 8.99 0.29 4.72

P2RNE 3.64 1.65 0.52 0.94 1.27 7.80 0.12 0.02 1.51 P2RNE 0.00 7.55 0.28 4.04

P2RSW 5.80 1.48 4.24 1.55 0.22 11.57 0.27 0.17 1.68 P2RSW 0.00 7.25 0.28 3.28

Appendix 3 continued

12/01/2018 Fl Cl Br SO4

Page | 149

12/01/2018 Na K Ca Mg Fe Si Al Mn S 11210004 NA NA NA NA

11210004 NA NA NA NA NA NA NA NA NA 11210040 0.00 7.47 0.00 0.89

11210040 4.28 1.06 1.42 0.53 0.01 2.31 0.05 0.01 0.38 11210041 0.00 9.23 0.00 0.44

11210041 4.00 1.43 0.83 2.77 0.01 5.34 0.02 0.02 0.25 11210045 0.08 7.25 0.00 2.02

11210045 14.21 2.77 4.55 2.85 0.48 17.53 0.02 0.06 0.90 11210051 0.00 6.97 0.00 1.05

11210051 4.24 1.11 1.53 1.24 0.01 7.28 0.03 0.02 0.54 11210056 0.00 8.89 0.00 4.67

11210056 6.02 1.45 4.37 1.61 0.06 11.86 0.27 0.18 1.70 P2RNE 0.00 7.48 0.00 4.25

P2RNE 3.73 1.71 0.46 0.95 1.42 7.73 0.05 0.02 1.46 P2RSW 0.00 7.18 0.00 3.20

P2RSW 5.52 1.17 0.34 0.38 1.93 9.72 0.03 0.02 1.21

16/02/2018 Fl Cl Br SO4

16/02/2018 Na K Ca Mg Fe Si Al Mn S 11210004 0.00 7.02 0.00 1.43

11210004 4.34 1.10 1.55 0.80 0.01 3.78 0.00 0.08 0.57 11210040 0.00 8.60 0.00 0.80

11210040 4.82 1.22 1.54 0.47 0.22 2.25 0.00 0.00 0.34 11210041 0.00 9.42 0.00 0.29

11210041 4.36 1.41 1.15 2.59 0.01 5.06 0.00 0.02 0.20 11210045 0.00 6.91 0.00 2.80

11210045 11.56 2.36 3.15 2.12 0.09 10.83 0.00 0.03 1.13 11210051 0.00 6.56 0.00 0.89

11210051 4.27 1.12 1.46 1.20 0.01 6.58 0.00 0.01 0.37 11210056 NA NA

11210056 NA NA NA NA NA NA NA NA NA P2RNE 0.00 7.41 0.00 3.79

P2RNE 4.03 1.79 0.32 1.05 0.64 7.28 0.05 0.01 1.47 P2RSW 0.00 7.04 0.00 3.11

P2RSW 5.81 1.18 0.34 0.37 1.92 8.82 0.00 0.01 1.10

23/03/2018 Na K Ca Mg Fe Si Al Mn S 23/03/2018 Fl Cl Br SO4

11210004 4.68 1.24 1.44 0.86 0.01 0.83 0.13 0.08 0.74 11210004 0.00 7.61 0.47 1.64

11210040 5.28 1.29 1.35 0.55 0.03 0.69 0.04 0.01 0.43 11210040 0.00 9.10 0.47 1.06

11210041 5.17 1.52 1.85 2.17 0.01 0.42 0.00 0.02 0.39 11210041 0.00 9.88 0.48 0.77

11210045 11.46 2.68 2.80 2.14 0.10 1.27 0.00 0.04 1.19 11210045 0.00 7.18 0.46 3.15

11210051 4.16 1.21 1.14 1.16 0.01 0.56 0.00 0.01 0.41 11210051 0.00 6.72 0.46 1.21

11210056 2.65 1.09 1.45 0.70 0.06 0.40 0.12 0.03 0.38 11210056 0.00 6.07 0.46 0.83

P2RNE 3.93 1.91 0.35 1.05 0.54 1.42 0.07 0.02 1.36 P2RNE 0.00 7.68 0.46 4.23

P2RSW 5.88 1.37 0.38 0.52 1.76 1.33 0.01 0.01 1.29 P2RSW 0.00 7.19 0.46 3.30

11/05/2018 Fl Cl Br SO4

11/05/2018 Na K Ca Mg Fe Si Al Mn S 11210004 0.00 7.56 0.00 1.31

11210004 4.25 1.36 1.54 0.77 0.00 3.76 0.00 0.08 0.71 11210040 0.00 7.93 0.00 1.12

11210040 4.74 1.69 1.63 0.38 0.00 2.17 0.00 0.00 0.39 11210041 0.00 9.81 0.00 0.82

11210041 5.28 1.52 1.73 1.95 0.00 4.59 0.00 0.02 0.34 11210045 0.00 7.88 0.00 2.13

11210045 13.41 2.97 3.79 2.12 0.00 15.99 0.00 0.03 1.31 11210051 0.00 6.74 0.00 1.54

11210051 4.49 1.43 1.31 1.01 0.00 7.04 0.00 0.00 0.40 11210056 0.00 9.43 0.00 0.65

11210056 3.84 1.45 2.24 0.84 0.00 4.44 0.00 0.07 0.43 P2RNE 0.00 8.20 0.00 3.04

P2RNE 4.24 2.31 0.29 0.91 0.00 7.92 0.00 0.00 1.71 P2RSW 0.00 7.31 0.00 3.42

P2RSW 6.04 1.49 0.20 0.34 0.94 9.92 0.00 0.00 1.37

Appendix 3 continued

20/07/2018 Fl Cl Br SO4

Page | 150

20/07/2018 Na K Ca Mg Fe Si Al Mn S 11210004 0.00 7.58 0.00 0.00

11210004 4.44 1.33 0.82 1.12 0.01 3.50 0.05 0.01 0.10 11210040 0.00 6.41 0.00 1.10

11210040 4.79 1.16 1.58 0.45 0.01 2.14 0.02 0.00 0.36 11210041 0.00 7.94 0.00 0.48

11210041 4.66 1.43 0.93 2.63 0.01 5.20 0.01 0.01 0.04 11210045 0.00 7.76 0.00 2.16

11210045 14.54 2.59 4.08 2.57 0.26 15.72 0.01 0.04 0.88 11210051 0.00 6.97 0.00 0.90

11210051 4.83 1.13 1.50 1.18 0.03 6.91 0.01 0.01 0.31 11210056 NA NA NA NA

11210056 P2RNE 0.00 7.35 0.00 3.59

P2RNE 4.27 1.65 0.26 0.97 0.59 7.61 0.12 0.00 1.36 P2RSW 0.00 6.92 0.00 2.61

P2RSW 6.10 1.66 0.26 0.37 1.52 9.37 0.02 0.00 1.10

21/09/2018 Na K Ca Mg Fe Si Al Mn S 21/09/2018 Fl Cl Br SO4

11210004 NA NA NA NA NA NA NA NA NA 11210004 NA NA NA NA

11210040 4.96 0.88 1.42 0.47 0.00 2.36 0.04 0.00 0.24 11210040 0 8.50 0.44 1.05

11210041 4.43 1.25 0.85 2.54 0.00 5.46 0.01 0.02 0.13 11210041 0 8.88 0.46 0.62

11210045 13.41 2.31 4.31 2.45 0.43 14.67 0.02 0.07 0.64 11210045 0 7.03 0.42 2.32

11210051 4.68 0.98 1.59 1.28 0.00 7.44 0.03 0.01 0.34 11210051 0 7.18 0.42 1.07

11210056 4.23 1.00 2.61 0.88 0.05 9.90 0.08 0.11 1.85 11210056 0 8.26 0.47 5.68

P2RNE 4.11 1.47 0.29 0.95 0.54 7.67 0.03 0.01 1.36 P2RNE 0 7.59 0.46 3.86

P2RSW 5.75 0.95 0.24 0.33 1.39 9.73 0.02 0.01 1.13 P2RSW 0 7.18 0.46 3.18

Appendix 6: Schematic diagrams of bores examined in Silkwood region, based on lithological bore report data.

Depth (m) RN 11020004 Depth (m) RN 11210040 0.00 0.00 Topsoil 2.00 Wet sandy clay Clay

Clay

6.00 3.50 8.00 Gravelly, sandy clay Clay, sand, silt Clayey, coarse sand 9.00 5.00 9.50 Coarse sand grading into clay Clayey, silty sand 10.00 Firm clay (fine-coarse) (Aquifer) 8.00 8.00 Clay 10.00 10.55

Depth (m) RN 11210041 Depth (m) RN 11210045 0.00 0.00 2.00 Silty clay, sand 3.50 Silty clay 4.00 Clay 7.00 Silty clay Clay, silt, fine-coarse sand Clay, silt, sand 13.00 14.00 Clayey, silty sand & gravel 21.00 17.00 Clay, quartz, gravel, sand Silty, sandy clay Fine quartz, gravel, clay 26.00 21.00

Metamorphics Sandy clay with coarse quartz throughout

34.00 31.00 Fine-coarse quartz, gravel, sand, clay

36.00 38.00 Coarse sand, clay 39.00 Medium quartz, deco-metamorphic gravel, clay 40.00 Sandy clay

Clay, fine gravel, deco-metamorphics 47.00 48.00 Metamorphics

Page | 151

Depth (m) RN 11210051 Depth (m) RN 11210056 0.00 0.00 2.00 Sandy clay, fine-coarse quartz 4.00 Hard clay with mica 5.00 Clay with coarse sand, gravel-size quartz Clay, silt, fine sand to fine gravel 9.00 Hard clay with mica 11.00 Medium sand to fine gravel with clay 14.00 Fine sand with gravel-sized quartz 17.00 Fine sand to gravel-sized, deco-rock material 21.00 Soft clay with coarse sand, fine gravel 23.00 Clay, medium-coarse sand Clay with fine-coarse gravel 29.00 31.00 Clay, silt

37.00 Clay, silt, fine sand to fine gravel 38.00 Gravel, coarse sand with clay 36.00 40.00 Clay 37.00 Firm silty clay 43.00 Soft silt to fine sand 45.00 Medium-coarse sand with clay clasts Metamorphics 42.00

Coarse sand to fine, gravel-sized deco-metamorphics, some clay

66.70

P2RNE Depth (m) P2RSW 0.00 0.00 Clay 2.00 Silty clay 2.00 3.20 Clay Hard clay 4.00 Silty clay

Hard clay Hard, Fe-rich clay

7.70 9.00 Medium-coarse clayey sand 10.00 Medium-coarse clayey sand 9.00 11.00 Sand with clay Fine-medium clayey sand 12.00 Sandy clay 11.00 Sandy clay 12.00 Sandy clay with muscovite

Page | 152

2 18 Appendix 7: Measured δ H-H2O (‰) δ O-H2O (‰) and values from Silkwood groundwater and rain

δ²H-H₂O (‰) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN 22/09/2017 -18.8 -25.6 -20.6 -19.3 -18.7 -14.3 -19.6 -16.0 10/11/2017 -16.8 -22.7 -20.7 -23.1 -18.7 -18.9 -21.0 -18.2 1/12/2017 -15.9 -17.4 -17.6 -19.7 -20.1 -18.0 -20.0 -16.9 15/12/2017 -22.0 -20.3 -16.4 -19.6 -18.7 -17.7 -20.2 -16.4 12/01/2018 -24.4 -17.0 IS -19.3 IS IS IS 16/02/2018 -21.8 -21.0 -17.0 -21.9 -18.5 -20.1 -20.0 -20.0 23/03/2018 NA NA NA NA NA NA NA NA 11/05/2018 NA NA NA NA NA NA NA NA 20/07/2018 -16.0 -16.4 -15.1 -20.3 -17.5 -19.2 -15.8 13.8 21/09/2018 5.0 4.9 5.9 4.6 4.6 4.9 5.5

δ¹⁸O-H₂O (‰) Bore ID (RN) Date: 11210004 11210040 11210041 11210045 11210051 11210056 P2RNE P2RSW RAIN 22/09/2017 -4.5 -5.6 -5.0 -5.5 -3.9 -3.5 -4.6 -4.1 10/11/2017 -2.7 -2.1 -3.6 -6.6 -3.7 -1.5 -3.4 -2.5 1/12/2017 -3.6 -3.4 -3.9 -4.5 -5.2 -4.5 -5.4 -3.9 15/12/2017 -4.3 -4.6 -3.5 -5.0 -4.7 -4.1 -4.6 1.9 12/01/2018 -5.8 -2.3 IS -4.6 IS IS IS 16/02/2018 -2.8 -3.0 -2.1 -2.9 -4.9 -5.9 -4.2 -4.2 23/03/2018 NA NA NA NA NA NA NA NA 11/05/2018 NA NA NA NA NA NA NA NA 20/07/2018 -3.8 -3.8 -3.8 -3.6 -4.5 -3.8 -4.2 -3.8 1.4 21/09/2018 5.0 4.9 5.9 4.6 4.6 4.9 5.5 (NA= No analysis completed; IS = Insufficient amount of sample to complete analysis).

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Appendix 8: 16S Illumina Metagenomic Analysis -Classification to Taxa, Relative Abundances.

KINGDOM PHYLUM CLASS Bacteria 12716567 Proteobacteria 7423123 Betaproteobacteria 4159595 Archaea 53381 Firmicutes 1546359 Alphaproteobacteria 1951027 Viruses 280 Actinobacteria 667479 Clostridia 1061820

Acidobacteria 664788 Gammaproteobacteria 878900

Bacteroidetes 458015 Actinobacteria 631263

Verrucomicrobia 379734 Acidobacteria 437812

Chloroflexi 160318 Bacilli 425381

Nitrospirae 133295 Deltaproteobacteria 316155

Planctomycetes 101104 Sphingobacteriia 314938

Synergistetes 77768 Pedosphaerae 216598

Spirochaetes 73592 Solibacteres 209500

Chlamydiae 57778 Flavobacteriia 135407

Cyanobacteria 47275 Nitrospira 133295

Thermotogae 46008 Ktedonobacteria 89822

Thermi 45356 Synergistia 77768

Tenericutes 44003 Anaerolineae 64264

Crenarchaeota 36547 Planctomycetia 59319

Armatimonadetes 25442 Chlamydiia 57778

Gemmatimonadetes 19937 Opitutae 57557 Thermodesulfobacteria 17496 Spartobacteria 48660

Euryarchaeota 13902 Thermotogae 46008

Caldithrix 8772 Deinococci 45356

Chlorobi 5702 Mollicutes 44003

Deferribacteres 4104 Brocadiae 41485

Chrysiogenetes 3180 Methylacidiphilae 37769

Fusobacteria 2110 Spirochaetes 36901

DNA 280 Thermoprotei 36476

Fibrobacteres 97 Brachyspirae 35979

Elusimicrobia 38 Nostocophycideae 27709

Chthonomonadetes 20010

Gemmatimonadetes 19937

Thermodesulfobacteria 17496

Holophagae 17459

Verrucomicrobiae 12576

Acidimicrobiia 9527

Page | 154

Thermoleophilia 9272

Epsilonproteobacteria 8854

Caldithrixae 8772

Synechococcophycideae 8728

Methanomicrobia 8380

Oscillatoriophycideae 8023

Chlorobia 5443

Bacteroidia 5192

Halobacteria 4807

Fimbriimonadetes 4413

Deferribacteres 4104

Nitriliruptoria 3585

Chrysiogenetes 3180

Thermobacula 2289

Fusobacteria 2110

Chloroflexi 1144

Armatimonadia 985

Leptospirae 648

Methanobacteria 507

Erysipelotrichi 435

Dehalococcoidetes 404

Group II 280

Ignavibacteria 259

Fibrobacteria 97

Archaeoglobi 90

Thaumarchaeota 70

Elusimicrobia 38

Thermococci 22

Rubrobacteria 5

Methanococci 5

ORDER FAMILY Burkholderiales 3634361 Oxalobacteraceae 1893430 Rhodospirillales 1540504 Comamonadaceae 1629602 Thermoanaerobacterales 651417 Acetobacteraceae 1267215 Actinomycetales 595529 Thermoanaerobacteraceae 534806 Acidobacteriales 437812 Bacillaceae 308990 Bacillales 392762 Rhodospirillaceae 258192 Pseudomonadales 328084 Koribacteraceae 255805 Sphingobacteriales 314938 Chitinophagaceae 220423 Clostridiales 308134 Chromatiaceae 219863 Chromatiales 261442 Pedosphaeraceae 216598 Pedosphaerales 216598 Solibacteraceae 209500 Solibacterales 209500 Pseudomonadaceae 183016

Page | 155

Rhizobiales 190124 Methylophilaceae 179662 Methylophilales 179662 Acidobacteriaceae 175307 Rhodocyclales 166679 Rhodocyclaceae 166679 Flavobacteriales 135407 Cellulomonadaceae 149145 Nitrospirales 133295 Moraxellaceae 144919 Myxococcales 119076 Peptococcaceae 139294 Sphingomonadales 108674 Flavobacteriaceae 135407 Thermogemmatisporales 89822 Sphingomonadaceae 108418 Synergistales 77768 Thermodesulfovibrionaceae 90585 Desulfovibrionales 66384 Thermogemmatisporaceae 89822 Enterobacteriales 61369 Pseudonocardiaceae 86007 Anaerolineales 61309 Micrococcaceae 76651 Caulobacterales 58452 Clostridiaceae 71962 Chlamydiales 57778 Nocardiaceae 64591 Methylococcales 57411 Hyphomicrobiaceae 62748 Xanthomonadales 56576 Burkholderiaceae 61686 Chthoniobacterales 48660 Enterobacteriaceae 61369 Gemmatales 48161 Anaerolinaceae 61309 Neisseriales 47758 Caulobacteraceae 58452 Syntrophobacterales 46748 Desulfovibrionaceae 58004 Thermotogales 46008 Methylococcaceae 57135 Desulfuromonadales 43616 Chthoniobacteraceae 48660 Brocadiales 41485 Rhizobiaceae 48647 Deinococcales 40760 Neisseriaceae 47758 Methylacidiphilales 37769 Thermovenabulum 47368 Brachyspirales 35979 Thermomonosporaceae 46236 Thermoproteales 34504 Thermotogaceae 46008 Opitutales 28935 Paenibacillaceae 43878 Coriobacteriales 27202 Anaerobaculaceae 43580 Gallionellales 26860 Rhodothermaceae 43226 Spirochaetales 26297 Nitrospiraceae 42651 Lactobacillales 25843 Brocadiaceae 41485 Oceanospirillales 25240 Deinococcaceae 40760 Stigonematales 23100 Rhabdochlamydiaceae 39544 Pelagicoccales 22035 Xanthomonadaceae 38761 Acholeplasmatales 21795 Methylacidiphilaceae 37769 Mycoplasmatales 21001 Myxococcaceae 36336 Bifidobacteriales 20448 Brachyspiraceae 35979 Chthonomonadales 20010 Ectothiorhodospiraceae 35656 Gemmatimonadales 19937 Thermoproteaceae 34504 Legionellales 19390 Polyangiaceae 33863 Rhodobacterales 17835 Actinosynnemataceae 32185 Thermodesulfobacteriales 17496 Geobacteraceae 31030 Holophagales 17459 Opitutaceae 28935

Page | 156

Alteromonadales 16540 Syntrophaceae 27515 Thiotrichales 14026 Coriobacteriaceae 27202 Hydrogenophilales 13468 Gallionellaceae 26860 Verrucomicrobiales 12576 Thermoactinomycetaceae 26728 Borreliales 10404 Spirochaetaceae 26297 Planctomycetales 10153 Cystobacteraceae 25827 Bdellovibrionales 9678 Isosphaeraceae 23547 Acidimicrobiales 9527 Rivulariaceae 23100 Solirubrobacterales 9272 Pelagicoccaceae 22035 Campylobacterales 8854 Acholeplasmataceae 21795 Caldithrixales 8772 Gemmataceae 21541 Pseudanabaenales 8728 Mycoplasmataceae 21001 Chroococcales 7527 Bifidobacteriaceae 20448 Rickettsiales 7355 Chthonomonadaceae 20010 Methanosarcinales 6477 Gemmatimonadaceae 19937 Chlorobiales 5443 Leuconostocaceae 19344 Bacteroidales 5192 Synergistaceae 18848 Halobacteriales 4807 Sphingobacteriaceae 18801 Puniceicoccales 4588 Bradyrhizobiaceae 18524 Thermales 4575 Haliangiaceae 18498 Fimbriimonadales 4413 Xanthobacteraceae 18012 Nostocales 4407 Flexibacteraceae 17895 Thiohalorhabdales 4330 Halomonadaceae 17636 Deferribacterales 4104 Sinobacteraceae 17568 Euzebyales 3585 Thermodesulfobacteriaceae 17496 Chrysiogenales 3180 Holophagaceae 17459 Caldilineales 2890 Caldicellulosiruptoraceae 17407 Entotheonellales 2693 Sulfobacillaceae 16094 Thermobaculales 2289 Veillonellaceae 15671 Thermicanales 2146 Streptosporangiaceae 15601 Fusobacteriales 2110 Hydrogenophilaceae 13468 Natranaerobiales 2078 Kineosporiaceae 13010 Exiguobacterales 1839 Verrucomicrobiaceae 12576 Methanomicrobiales 1800 Aminiphilaceae 12531 Kiloniellales 1742 Waddliaceae 11795 Desulfurococcales 1462 Alcaligenaceae 11097 Cerasicoccales 1281 Beijerinckiaceae 10723 Desulfobacterales 1172 Borreliaceae 10404 Thiobacterales 1156 Planctomycetaceae 10153 Armatimonadales 985 Syntrophobacteraceae 10095 Pirellulales 946 Piscirickettsiaceae 10067 Roseiflexales 918 Actinomycetaceae 10040 Nitrosomonadales 680 Coxiellaceae 9980 Pasteurellales 649 Acidimicrobiaceae 9278

Page | 157

Leptospirales 648 Microbacteriaceae 9241 Methanobacteriales 507 Bdellovibrionaceae 9002 Erysipelotrichales 435 Legionellaceae 8834 Halanaerobiales 418 Caldithrixaceae 8772 Dehalococcoidales 404 Pseudanabaenaceae 8728 Anaeroplasmatales 309 Rhodobacteraceae 8015 (Microviridae) 280 Conexibacteraceae 7716 Oscillatoriales 273 Methylobacteriaceae 7659 Ignavibacteriales 259 Mycobacteriaceae 7261 Chloroflexales 226 Eubacteriaceae 7048 Entomoplasmatales 226 Methylocystaceae 6949 Vibrionales 119 Desulfobacteraceae 6849 Fibrobacterales 97 Saprospiraceae 6579 Methanocellales 97 Carboxydocellaceae 6425 Archaeoglobales 90 Ruminococcaceae 6241 Sphaerochaetales 65 Oceanospirillaceae 6162 Aeromonadales 51 Phormidiaceae 6145 Nitrososphaerales 48 Streptomycetaceae 5937 Elusimicrobiales 38 Micromonosporaceae 5799 Cenarchaeales 22 Heliobacteriaceae 5669 Thermococcales 22 Chlorobiaceae 5443 Gemellales 21 Rickettsiaceae 5368 Sulfolobales 8 Desulfonatronumaceae 5342 Turicibacterales 6 Corynebacteriaceae 5323 Methanococcales 5 Alteromonadaceae 5044 Rubrobacterales 5 Methanosarcinaceae 5040 Acidithiobacillales 4 Shewanellaceae 5020 Desulfitobacterales 4 Nocardioidaceae 4817 Herpetosiphonales 1 Halobacteriaceae 4807 Desulfurellales 1 Puniceicoccaceae 4588

Thermaceae 4575

Amoebophilaceae 4493

Fimbriimonadaceae 4413

Nostocaceae 4379

Campylobacteraceae 4355

Thiohalorhabdaceae 4330

Streptococcaceae 4303

Glycomycetaceae 4137

Deferribacteraceae 4104

Thiotrichaceae 3894

Phyllobacteriaceae 3881

Euzebyaceae 3585

Bacteroidaceae 3537

Lachnospiraceae 3358

Page | 158

Dietziaceae 3292

Chrysiogenaceae 3180

Intrasporangiaceae 3124

Staphylococcaceae 3106

Syntrophomonadaceae 2937

Caldilineaceae 2890

Nannocystaceae 2797

Entotheonellaceae 2693

Hyphomonadaceae 2658

Thermobaculaceae 2289

Parachlamydiaceae 2288

Actinopolysporaceae 2271

Desulfuromonadaceae 2260

Pseudoalteromonadaceae 2245

Thermicanaceae 2146

Propionibacteriaceae 2137

Symbiobacteriaceae 2090

Helicobacteraceae 2059

Leptotrichiaceae 1996

Anaplasmataceae 1957

Planococcaceae 1943

Exiguobacteraceae 1839

Kiloniellaceae 1742

Anaerobrancaceae 1499

Solirubrobacteraceae 1463

Desulfurococcaceae 1462

Dethiosulfovibrionaceae 1379

Methanosaetaceae 1318

Cerasicoccaceae 1281

Porphyromonadaceae 1197

Thiobacteraceae 1156

Peptostreptococcaceae 1110

Lactobacillaceae 1001

Armatimonadaceae 985

Brucellaceae 954

Pirellulaceae 946

Desulfohalobiaceae 930

Methanomicrobiaceae 912

Kouleothrixaceae 855

Pelobacteraceae 821

Catenulisporaceae 776

Nitrosomonadaceae 680

Bacteriovoracaceae 676

Nitrospinaceae 659

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Pasteurellaceae 649

Leptospiraceae 648

Listeriaceae 601

Contubernalisaceae 575

Litoricolaceae 538

Geodermatophilaceae 534

Methanoregulaceae 525

Desulfobulbaceae 513

Methanobacteriaceae 507

Dermabacteraceae 449

Francisellaceae 447

Erysipelotrichaceae 435

Brevibacteriaceae 408

Aerococcaceae 406

Dehalococcoidaceae 404

Enterococcaceae 394

Aurantimonadaceae 389

Bartonellaceae 385

Halanaerobiaceae 367

Sanguibacteraceae 314

Anaeroplasmataceae 309

Alcanivoracaceae 300

Yaniellaceae 297

Microviridae 280

Frankiaceae 275

Chroococcaceae 270

Alicyclobacillaceae 270

Microcystaceae 263

Ignavibacteriaceae 259

Iamiaceae 245

Gordoniaceae 241

Tsukamurellaceae 240

Psychromonadaceae 237

Saccharospirillaceae 233

Cryptosporangiaceae 228

Entomoplasmataceae 226

Crenotrichaceae 226

Flammeovirgaceae 159

Chloroflexaceae 148

Carnobacteriaceae 130

Vibrionaceae 119

Erythrobacteraceae 119

Idiomarinaceae 112

Sporolactobacillaceae 106

Page | 160

Fusobacteriaceae 104

Methanocellaceae 97

Fibrobacteraceae 97

Archaeoglobaceae 90

Bogoriellaceae 81

Oscillochloridaceae 78

Methanocorpusculaceae 73

Sporichthyaceae 71

Sphaerochaetaceae 65

Prevotellaceae 64

Roseiflexaceae 61

Halobacteroidaceae 51

Nocardiopsaceae 51

Aeromonadaceae 50

Nitrososphaeraceae 48

Dermacoccaceae 47

Gomphosphaeriaceae 47

Ekhidnaceae 46

Elusimicrobiaceae 38

Halothiobacillaceae 34

Promicromonosporaceae 33

Williamsiaceae 33

Ferrimonadaceae 29

Scytonemataceae 24

Thermococcaceae 22

Cenarchaeaceae 22

Gemellaceae 21

Cyanobacteriaceae 16

Paraprevotellaceae 14

Rhodobiaceae 13

Hahellaceae 12

Sulfolobaceae 8

Methanospirillaceae 8

Patulibacteraceae 7

Turicibacteraceae 6

Rubrobacteraceae 5

Methanocaldococcaceae 5

Desulfitobacteraceae 4

Acidithiobacillaceae 4

Odoribacteraceae 4

Desulfomicrobiaceae 4

Chlamydiaceae 4

Moritellaceae 3

Leptospirillaceae 2

Page | 161

Rikenellaceae 1

Dehalobacteriaceae 1

Pasteuriaceae 1

Succinivibrionaceae 1

Herpetosiphonaceae 1

Desulfurellaceae 1

GENUS SPECIES (To a minimum of 100 reads) Janthinobacterium 1469798 Curvibacter lanceolatus 347954 Acetobacter 1061175 Thermoanaerobacter inferii 239309 Curvibacter 925536 Janthinobacterium agaricidamnosum 236471 Thermoanaerobacter 273566 Ralstonia insidiosa 196897 Candidatus Koribacter 255805 Methylotenera versatilis 155939 Bacillus 247427 Demequina aurantiaca 145436 Pedosphaera 216598 Edaphobacter modestus 129526 Candidatus Solibacter 209500 Bacillus mucilaginosus 116705 Ralstonia 203067 Chitinophaga soli 115720 Azospirillum 186092 Curvibacter gracilis 113355 Pseudomonas 182907 Oxalobacter vibrioformis 105385 Methylotenera 158643 Acidovorax temperans 101085 Rhodoferax 155767 Chryseobacterium indologenes 98328 Demequina 148514 Moorella glycerini 77332 Acidovorax 140202 Tepidanaerobacter syntrophicus 74547 Gluconobacter 135421 Pelotomaculum isophthalicicum 64524 Edaphobacter 131850 Ammonifex thiophilus 57444 Chryseobacterium 119979 Pseudomonas marginalis 54214 Chitinophaga 118139 Bacillus atrophaeus 51297 Alkanindiges 115904 Thermodesulfovibrio aggregans 49582 Oxalobacter 105665 Pseudomonas entomophila 48918 Thermodesulfovibrio 90465 Thermovenabulum ferriorganovorum 47368 Thermogemmatispora 89822 Gluconobacter morbifer 46153 Moorella 84154 Anaerobaculum thermoterrnum 43577 Rubrivivax 75011 Rhodothermus clarus 43226 Tepidanaerobacter 74547 Chthoniobacter flavus 41884 Saccharopolyspora 68163 Candidatus Scalindua brodae 37853 Sulfuritalea 65218 Brachyspira ibaraki 35979 Pelotomaculum 64580 Hydrogenophaga defluvii 35358 Arthrobacter 60875 Rhodococcus qingshengii 35234 Burkholderia 58557 Janthinobacterium lividum 34523 Desulfovibrio 58004 Phenylobacterium lituiforme 31290 Ammonifex 57444 Acidovorax delafieldii 30742 Sphingomonas 54621 Chondromyces pediculatus 30396 Rhodococcus 53979 Actinokineospora inagensis 29324 Aquabacterium 50607 Peptoniphilus coxii 28704

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Thermovenabulum 47368 Sporotomaculum syntrophicum 28329 Candidatus Liberibacter 45506 Planifilum fimeticola 26268 Chthoniobacter 44699 Thermodesulfovibrio thiophilus 26108 Rhodoplanes 44127 Escherichia albertii 26087 Anaerobaculum 43580 Desulfurispora thermophila 25803 Rhodothermus 43226 Desulfomonile tiedjei 24702 Geobacillus 43095 Azospirillum rugosum 23967 Nitrospira 42651 Candidatus Rhabdochlamydia crassificans 23789 Deinococcus 40760 Azospirillum palatum 23267 Cupriavidus 40622 Calothrix parietina 22973 Actinoallomurus 40518 Methylocaldum tepidum 22279 Escherichia 39708 Marinitoga okinawensis 21616 Candidatus Rhabdochlamydia 39544 Novosphingobium stygium 20436 Hydrogenophaga 38910 Burkholderia brasilensis 19158 Candidatus Scalindua 37854 Candidatus Tammella caduceiae 18735 Novosphingobium 37778 Gluconobacter krungthepensis 18443 Candidatus Methylacidiphilum 37769 Ectothiorhodospira haloalkaliphila 18100 Phenylobacterium 36634 Methylobacillus glycogenes 18058 Brachyspira 35979 Thermogemmatispora foliorum 17795 Anaeromyxobacter 35686 Anaerolinea thermolimosa 17720 Thermocladium 32896 Thermodesulfatator atlanticus 17496 Methylomonas 32417 Rhodoferax antarcticus 17240 Chondromyces 32355 Sphingomonas oligophenolica 17104 Peptoniphilus 31443 Sulfobacillus yellowstonensis 16092 Geobacter 31030 Cupriavidus taiwanensis 16016 Clostridium 30942 Thermogemmatispora onikobensis 15972 Actinokineospora 29345 Deinococcus yavapaiensis 15386 Limnohabitans 29266 Sphingomonas asaccharolytica 14588 Opitutus 28935 Cohnella soli 14330 Sporotomaculum 28329 Geothrix fermentans 14056 Tanticharoenia 27769 Segetibacter aerophilus 13952 Gallionella 26860 Bifidobacterium bombi 13578 Planifilum 26273 Rhodoferax ferrireducens 13533 Slackia 25930 Rhodovibrio sodomensis 13384 Cystobacter 25827 Aminiphilus circumscriptus 12531 Desulfurispora 25814 Methyloversatilis universalis 12477 Marinitoga 25543 Kineosporia mikuniensis 12465 Desulfomonile 24865 Pseudomonas tremae 11278 Paucibacter 24664 Limnohabitans planktonicus 11267 Treponema 24376 Pseudomonas azotoformans 11198 Methylocaldum 24079 Treponema porcinum 11059 Calothrix 23100 Burkholderia ubonensis 10921 Pelagicoccus 22035 Leptothrix discophora 10603 Gemmata 21541 Rhodococcus baikonurensis 10405

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Ectothiorhodospira 21497 Arthrobacter psychrochitiniphilus 10382 Paenibacillus 21408 Denitratisoma oestradiolicum 10366 Chromobacterium 21083 Granulicella tundricola 10244 Mycoplasma 21001 Micrococcus yunnanensis 10144 Cohnella 20271 Propionivibrio dicarboxylicus 10138 Bifidobacterium 20210 Uliginosibacterium gangwonense 9971 Chthonomonas 20010 Amycolatopsis pigmentata 9718 Gemmatimonas 19937 Runella limosa 9475 Singulisphaera 19498 Actinomyces naturae 9415 Candidatus Tammella 18735 Geobacter pickeringii 9251 Haliangium 18498 Thiobacillus sajanensis 9019 Methylobacillus 18288 Dokdonella fugitiva 8731 Anaerolinea 17932 Cupriavidus basilensis 8631 Thermodesulfatator 17496 Erwinia soli 8503 Geothrix 17459 Leptolyngbya laminosa 8466 Caldicellulosiruptor 17407 Caldanaerobacter hydrothermalis 8381 Sulfobacillus 16094 Actinoallomurus yoronensis 8363 Bradyrhizobium 16003 Nitrospira moscoviensis 8301 Pedobacter 15964 Megasphaera hominis 8175 Candidatus Phytoplasma 15908 Sutterella sanguinus 8110 Amycolatopsis 15258 Gemmata obscuriglobus 8041 Segetibacter 15138 Gemmatimonas aurantiaca 7851 Polynucleobacter 14338 Geobacillus gargensis 7770 Propionivibrio 14312 Delftia lacustris 7757 Rhodovibrio 13820 Chromobacterium aquaticum 7399 Micrococcus 13735 Candidatus Phytoplasma brasiliense 7376 Thiobacillus 13468 Methylomonas methanica 7288 Kushneria 13013 Aquitalea magnusonii 7275 Kineosporia 12778 Moraxella caviae 7243 Aminiphilus 12531 Mycoplasma insons 7026 Methyloversatilis 12490 Longilinea arvoryzae 6974 Acinetobacter 12204 Vogesella perlucida 6865 Anaerobacillus 12042 Chromobacterium subtsugae 6625 Giesbergeria 11806 Comamonas kerstersii 6605 Waddlia 11795 Burkholderia phenoliruptrix 6434 Pelomonas 11532 Carboxydocella ferrireducens 6412 Desulfosporosinus 11251 Nitrosococcus watsoni 5961 Granulicella 11122 Magnetospirillum magnetotacticum 5864 Aquitalea 11053 Pseudomonas teessidea 5736 Leptothrix 10868 Arthrospira fusiformis 5606 Erwinia 10592 Hydrocarboniphaga daqingensis 5592 Denitratisoma 10572 Caldithrix palaeochoryensis 5576 Nocardia 10524 Skermanella aerolata 5517 Borrelia 10404 Acinetobacter antiviralis 5486

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Roseospira 10214 Herbaspirillum chlorophenolicum 5427 Planctomyces 10153 Opitutus terrae 5407 Uliginosibacterium 10025 Desulfonatronum thiosulfatophilum 5339 Comamonas 9989 Dechloromonas hortensis 5296 Runella 9475 Anaerobacillus alkalilacustre 5156 Actinomyces 9452 Acidovorax wohlfahrtii 5070 Delftia 9149 Lewinella marina 5033 Dokdonella 9067 Methylonatrum kenyense 4947 Streptosporangium 9011 Pelomonas puraquae 4839 Bdellovibrio 9002 Steroidobacter denitrificans 4792 Vogesella 8908 Geobacillus thermoglucosidans 4584 Thiocapsa 8820 Coraliomargarita akajimensis 4583 Legionella 8804 Acidobacterium capsulatum 4564 Caldithrix 8772 Candidatus Amoebophilus asiaticus 4493 Acidimicrobium 8685 Acidisoma tundrae 4483 Rhodospirillum 8626 Clostridium thermosuccinogenes 4474 Leptolyngbya 8535 Thiohalorhabdus denitrificans 4330 Oenococcus 8436 Bellilinea caldifistulae 4315 Caldanaerobacter 8381 Serratia entomophila 4312 Arthrospira 8348 Pelagicoccus croceus 4210 Methylophaga 8319 Mycoplasma timone 4155 Magnetospirillum 8287 Chromobacterium piscinae 4132 Hydrocarboniphaga 8234 Treponema zioleckii 4111 Megasphaera 8175 Deferribacter autotrophicus 4102 Sutterella 8128 Heliorestis baculata 4084 Polaromonas 7943 Blastochloris gulmargensis 4027 Conexibacter 7716 Flavisolibacter ginsengisoli 3975 Methylobacterium 7659 Pectinatus cerevisiiphilus 3903 Beijerinckia 7526 Rhodocyclus purpureus 3874 Acidobacterium 7297 Pedobacter kwangyangensis 3802 Mycobacterium 7261 Clostridium caenicola 3731 Moraxella 7247 Treponema paraluiscuniculi 3657 Herbaspirillum 7152 Candidatus Koribacter versatilis 3653 Luteibacter 7138 Aquitalea denitrificans 3647 Syntrophobacter 7133 Paenibacillus filicis 3644 Longilinea 7065 Euzebya tangerina 3585 Acetobacterium 7046 Beijerinckia mobilis 3529 Methylosinus 6888 Beijerinckia derxii 3527 Roseomonas 6870 Glaciecola nitratireducens 3468 Fervidobacterium 6791 Ectothiorhodospira imhoffii 3378 Sphingobium 6642 Propionivibrio pelophilus 3342 Lewinella 6579 Allochromatium palmeri 3342 Carboxydocella 6425 Cupriavidus pauculus 3329 Variovorax 6257 Peptococcus niger 3328

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Dechloromonas 6165 Geobacter toluenoxydans 3320 Nitrosococcus 5963 Xanthobacter polyaromaticivorans 3301 Acidisphaera 5802 Desulfurispirillum alkaliphilum 3180 Aquicella 5753 Sphaerisporangium rubeum 3108 Heliorestis 5651 Sphingomonas wittichii 3038 Skermanella 5648 Nocardia roseoalba 3027 Acholeplasma 5620 Zoogloea oryzae 3016 Flavobacterium 5597 Actinoallomurus luridus 2910 Weissella 5561 Caldilinea tarbellica 2890 Chlorobaculum 5443 Chlorobaculum limnaeum 2868 Hymenobacter 5397 Cupriavidus laharis 2785 Rickettsia 5368 Luteibacter anthropi 2741 Azoarcus 5358 Pseudomonas syncyanea 2730 Desulfonatronum 5342 Thermoanaerobacterium islandicum 2718 Corynebacterium 5323 Nisaea nitritireducens 2712 Flavisolibacter 5304 Bacteroides denticanum 2642 Azorhizobium 5292 Bifidobacterium subtile 2617 Steroidobacter 5139 Kribbella ginsengisoli 2594 Marinomonas 5081 Methanolobus taylorii 2502 Shewanella 5020 Variovorax boronicumulans 2449 Methylonatrum 4947 Bdellovibrio exovorus 2348 Psychrobacter 4800 Legionella shakespearei 2346 Acidisoma 4704 Thauera selenatis 2318 Coraliomargarita 4583 Bradyrhizobium pachyrhizi 2314 Thauera 4558 Thermobaculum terrenum 2285 Candidatus Amoebophilus 4493 Halorhodospira halochloris 2217 Fimbriimonas 4413 Oleomonas sagaranensis 2179 Serratia 4373 Comamonas odontotermitis 2154 Blastochloris 4342 Syntrophobacter wolinii 2151 Thiohalorhabdus 4330 Actinopolyspora indiensis 2138 Bellilinea 4319 Thioalkalivibrio jannaschii 2122 Alkaliphilus 4298 Gallionella ferruginea 2083 Streptomyces 4262 Fervidobacterium islandicum 2075 Streptococcus 4210 Asticcacaulis taihuensis 2060 Glycomyces 4137 Geobacter argillaceus 2051 Deferribacter 4102 Treponema bryantii 2047 Kaistobacter 4066 Methylomonas scandinavica 2025 Desulfotomaculum 4020 Symbiobacterium toebii 2012 Oscillospira 4011 Variovorax paradoxus 2000 Nevskia 3916 Phyllobacterium catacumbae 1977 Pectinatus 3903 Johnsonella ignava 1940 Rhodocyclus 3896 Ralstonia pickettii 1904 Candidatus Xiphinematobacter 3885 Desulfovibrio psychrotolerans 1901 Petrotoga 3863 Niastella koreensis 1885

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Niastella 3858 Nocardia niigatensis 1876 Leuconostoc 3689 Desulfotomaculum indicum 1875 Hyphomicrobium 3662 Micrococcus luteus 1842 Euzebya 3585 Chryseobacterium taichungense 1831 Thiomonas 3558 Leucothrix mucor 1782 Bacteroides 3537 Nevskia soli 1772 Zoogloea 3522 Desulfacinum subterraneum 1746 Candidatus Brocadia 3482 Magnetospirillum bellicus 1741 Glaciecola 3468 Pseudomonas rhodesiae 1703 Xanthobacter 3434 Ehrlichia ovina 1689 Paracoccus 3432 Marinitoga hydrogenitolerans 1666 Allochromatium 3344 Thermosipho ferriphilus 1649 Rickettsiella 3338 Novosphingobium yangbajingensis 1638 Peptococcus 3328 Cupriavidus metallidurans 1636 Pedomicrobium 3316 Ralstonia detusculanense 1632 Devosia 3303 Rhodobium gokarnense 1619 Dietzia 3292 Brevundimonas staleyi 1609 Parvibaculum 3272 Treponema calligyrum 1601 Labrys 3218 Campylobacter canadensis 1597 Desulfurispirillum 3180 Telmatospirillum siberiense 1585 Sphaerisporangium 3179 Haliangium ochraceum 1583 Lysobacter 3088 Thiomonas perometabolis 1569 Prosthecobacter 3074 Isosphaera pallida 1561 Actinomadura 2995 Ralstonia solanacearum 1541 Sterolibacterium 2945 Arcobacter marinus 1535 Telmatospirillum 2944 Sporosarcina pasteurii 1521 Syntrophomonas 2937 Chromatium weissei 1521 Ramlibacter 2907 Bacillus herbersteinensis 1492 Rubritalea 2899 Anaerobranca zavarzinii 1488 Caldilinea 2890 Novosphingobium subterraneum 1473 Xenophilus 2824 Bacillus amyloliquefaciens 1470 Nannocystis 2795 Pedobacter koreensis 1445 Thermoanaerobacterium 2718 Thiomonas thermosulfata 1445 Nisaea 2713 Acinetobacter xiamenensis 1434 Oleomonas 2702 Bradyrhizobium cytisi 1420 Candidatus Entotheonella 2693 Desulfovibrio carbinolicus 1410 Kribbella 2674 Luteibacter yeojuensis 1385 Brevundimonas 2672 Marinomonas basaltis 1370 Meiothermus 2648 Oscillospira eae 1366 Asticcacaulis 2519 Pseudomonas cinnamophila 1347 Methanolobus 2514 Azoarcus evansii 1331 Halomonas 2510 Phenylobacterium falsum 1318 Hydrogenophilus 2450 Desulfosarcina ovata 1311 Staphylococcus 2450 Pelomonas saccharophila 1308

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Thermobaculum 2289 Aquicella siphonis 1304 Actinopolyspora 2271 Thiorhodococcus pfennigii 1299 Pseudoalteromonas 2245 Cerasicoccus arenae 1279 Campylobacter 2225 Fervidobacterium pennivorans 1269 Halorhodospira 2217 Desulfotomaculum salinum 1263 Candidatus Protochlamydia 2212 Desulfuromonas svalbardensis 1262 Thermicanus 2146 Maricaulis indicus 1248 Thioalkalivibrio 2123 Arthrobacter soli 1244 Symbiobacterium 2090 Bacillus aryabhattai 1235 Thiothrix 2089 Sphingomonas melonis 1234 Helicobacter 2059 Sphingomonas yabuuchiae 1231 Phyllobacterium 2029 Helicobacter suncus 1201 Desulfosarcina 2000 Hyphomicrobium aestuarii 1198 Sebaldella 1995 Solirubrobacter soli 1189 Micromonospora 1993 Exiguobacterium profundum 1181 Arcobacter 1955 Actinoallomurus amamiensis 1178 Johnsonella 1951 Candidatus Liberibacter africanus 1167 Methylocella 1948 Aeromicrobium ponti 1163 Phaeobacter 1921 Thiobacter subterraneus 1156 Thermus 1864 Blastomonas natatoria 1144 Exiguobacterium 1839 Syntrophomonas sapovorans 1138 Faecalibacterium 1814 Polynucleobacter cosmopolitanus 1131 Leucothrix 1782 Desulfovibrio putealis 1128 Isosphaera 1773 Hymenobacter xinjiangensis 1126 Spirochaeta 1762 Desulfovibrio butyratiphilus 1119 Desulfacinum 1747 Terracoccus luteus 1114 Thalassospira 1742 Acinetobacter rhizosphaerae 1109 Collimonas 1698 Marichromatium gracile 1105 Ehrlichia 1689 Bacillus horneckiae 1102 Rhodobium 1678 Chitinophaga ginsengisoli 1085 Thermosipho 1653 Hydrogenophilus denitrificans 1078 Rathayibacter 1643 Niabella soli 1077 Actinocatenispora 1638 Delftia tsuruhatensis 1077 Curtobacterium 1637 Dysgonomonas wimpennyi 1071 Alishewanella 1599 Bacillus subtilis 1064 Sporosarcina 1561 Spirochaeta aurantia 1053 Dyadobacter 1539 Propionispora hippei 1044 Chromatium 1521 Azovibrio restrictus 1043 Candidatus Glomeribacter 1493 Meiothermus granaticius 1032 Microbacterium 1490 Ramlibacter tataouinensis 1026 Anaerobranca 1488 Desulfofrigus oceanense 1026 Solirubrobacter 1463 Chromobacterium violaceum 1025 Desulfobacter 1463 Desulfovibrio oryzae 1020 Mesorhizobium 1454 Thioalkalimicrobium sibiricum 1019

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Desulfobacca 1438 Bradyrhizobium yuanmingense 1015 Sediminibacterium 1412 Clostridium thermoalcaliphilum 1008 Desulfurococcus 1412 Limnobacter litoralis 1001 Amaricoccus 1406 Pseudomonas moraviensis 995 Agrobacterium 1366 Methylobacterium mesophilicum 994 Terracoccus 1356 Segetibacter koreensis 981 Aeromicrobium 1346 Paenibacillus alginolyticus 965 Niabella 1346 Methylosinus pucelana 955 Sorangium 1344 Brevibacillus ginsengisoli 940 Tepidimonas 1336 Actinomadura cremea 938 Thiorhodococcus 1334 Arthrobacter halodurans 937 Dethiosulfovibrio 1329 Lewinella lutea 929 Methanosaeta 1318 Actinoallomurus purpureus 925 Hyphomonas 1317 Mesorhizobium camelthorni 924 Azohydromonas 1299 Erwinia mallotivora 923 Desulfuromonas 1298 Inquilinus ginsengisoli 916 Kibdelosporangium 1296 Bacillus pseudofirmus 898 Cerasicoccus 1281 Saccharopolyspora shandongensis 893 Maricaulis 1252 Paracoccus sulfuroxidans 889 Stenotrophomonas 1249 Novosphingobium acidiphilum 887 Ancylobacter 1232 Herbaspirillum magnetovibrio 868 Atopobium 1230 Clostridium alkalicellulosi 859 Blautia 1179 Chondromyces apiculatus 849 Thiobacter 1156 Variovorax soli 838 Blastomonas 1146 Atopobium fossor 832 Marichromatium 1105 Amaricoccus macauensis 831 Sphingobacterium 1104 Acholeplasma palmae 828 Enterobacter 1089 Methylobacterium goesingense 823 Dysgonomonas 1073 Bacillus velezensis 817 Dactylosporangium 1071 Azohydromonas australica 807 Azovibrio 1053 Pelobacter acetylenicus 806 Microtetraspora 1047 Streptomyces roseogilvus 801 Brevibacillus 1046 Selenomonas infelix 797 Propionispora 1046 Thiobacillus thiophilus 795 Desulfofrigus 1026 Dactylosporangium maewongense 792 Thioalkalimicrobium 1019 Sphingobium abikonense 785 Limnobacter 1017 Methylocella silvestris 781 Propionibacterium 1013 Novosphingobium hassiacum 779 Armatimonas 985 Polaromonas jejuensis 777 Sphingopyxis 960 Geobacter hydrogenophilus 774 Ochrobactrum 948 Syntrophobacter fumaroxidans 760 Pirellula 946 Roseomonas terpenica 754 Rhizobium 934 Candidatus Protochlamydia amoebophila 752 Inquilinus 924 Lautropia mirabilis 734

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Caloramator 922 Propionivibrio limicola 726 Desulfuromusa 911 Hyphomicrobium vulgare 721 Pseudonocardia 896 Candidatus Phytoplasma prunorum 719 Rhodanobacter 894 Thermobacillus xylanilyticus 705 Coxiella 868 Neisseria mucosa 705 Pigmentiphaga 868 Prosthecobacter dejongeii 704 Virgibacillus 866 Limnohabitans curvus 694 Kouleothrix 855 Pelagicoccus mobilis 684 Methanogenium 842 Citricoccus muralis 657 Smithella 829 Kaistobacter terrae 655 Pelobacter 821 Bradyrhizobium liaoningense 655 Microbulbifer 812 Thermoanaerobacter kivui 655 Selenomonas 797 Desulfotomaculum thermoacetoxidans 647 Achromobacter 792 Burkholderia seminalis 633 Dyella 789 Sphingomonas sanxanigenens 628 Saccharomonospora 787 Rhodanobacter lindaniclasticus 626 Agromyces 778 Limnohabitans parvus 625 Catenulispora 776 Bacteriovorax litoralis 621 Citricoccus 756 Sphingobium japonicum 619 Lautropia 734 Dyella ginsengisoli 618 Gramella 730 Microbacterium aurum 617 Thermobacillus 705 Thalassospira tepidiphila 614 Neisseria 705 Geobacter pelophilus 613 Candidatus Blochmannia 681 Hydrogenophaga taeniospiralis 613 Methylibium 680 Acetobacter pasteurianus 609 Acidiphilium 676 Cycloclasticus oligotrophus 606 Bacteriovorax 676 Micromonospora halophytica 603 Nitrospina 659 Oscillatoria corallinae 602 Terriglobus 659 Roseomonas mucosa 595 Leptospira 648 Acinetobacter junii 592 Arenimonas 629 Rubrivivax gelatinosus 591 Kitasatospora 629 Leptospira licerasiae 590 Nitrosovibrio 628 Geobacter uraniireducens 590 Emticicia 623 Burkholderia terrae 586 Afipia 618 Pseudomonas fragi 584 Methylomicrobium 615 Mycobacterium pinnipedii 581 Cycloclasticus 606 Peptoniphilus ivorii 578 Oscillatoria 602 Thauera terpenica 575 Methanohalophilus 600 Candidatus Contubernalis alkalaceticum 575 Caulobacter 593 Ferrimicrobium acidiphilum 557 Candidatus Contubernalis 575 Streptomyces lazureus 549 Ferrimicrobium 557 Methanosaeta concilii 549 Dolichospermum 545 Chryseobacterium culicis 548 Litoricola 538 Mycoplasma iguanae 547

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Phaeospirillum 532 Actinocatenispora silicis 542 Lentzea 528 Roseomonas lacus 534 Pseudoxanthomonas 524 Sphingobacterium bambusae 534 Marinobacter 515 Pelagicoccus albus 531 Actinocorallia 514 Rhodococcus percolatus 529 Desulfonauticus 507 Thauera mechernichensis 523 Listeria 507 Arthrobacter stackebrandtii 520 Luteimonas 500 Agrobacterium tumefaciens 519 Methanobacterium 486 Psychrobacter halophilus 519 Nostoc 485 Curtobacterium albidum 516 Tetrasphaera 480 Desulfovibrio piger 513 Nocardioides 472 Devosia ginsengisoli 510 Macrococcus 470 Desulfonauticus autotrophicus 507 Rhodobacter 461 Rhodoplanes cryptolactis 497 Marinospirillum 456 Vogesella indigofera 493 Brachybacterium 447 Amycolatopsis ultiminotia 492 Francisella 447 Listeria innocua 490 Streptacidiphilus 438 Propionibacterium acnes 486 Alkalibacillus 423 Actinocorallia herbida 483 Schlegelella 422 Bradyrhizobium jicamae 481 Bosea 422 Azospirillum brasilense 478 Erysipelothrix 421 Dietzia cinnamea 476 Mitsuaria 420 Desulfosporosinus hippei 476 Lactobacillus 418 Azospirillum zeae 466 Desulfonatronovibrio 411 Bacillus safensis 464 Methanolinea 410 Amaricoccus kaplicensis 461 Brevibacterium 408 Caloramator mitchellensis 458 Mannheimia 404 Acinetobacter gerneri 456 Dehalogenimonas 402 Aquicella lusitana 454 Xanthomonas 401 Brevundimonas olei 452 Thiocystis 399 Emticicia oligotrophica 451 Agrococcus 396 Arenimonas malthae 451 Elizabethkingia 390 Paenibacillus chondroitinus 451 Aurantimonas 389 Burkholderia vietnamiensis 447 Cellulomonas 387 Francisella hispaniensis 447 Ruminococcus 386 Niastella populi 442 Bartonella 385 Pseudomonas lundensis 438 Marinococcus 382 Sterolibacterium denitrificans 437 Azospira 378 Sphingopyxis granuli 429 Syntrophus 372 Curtobacterium pusillum 426 Leucobacter 370 Rhodococcus yunnanensis 425 Halanaerobium 367 Schlegelella aquatica 422 Alkalibacterium 366 Erysipelothrix muris 421 Sinomonas 363 Mitsuaria chitosanitabida 420

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Hydrocoleum 360 Corynebacterium tuberculostearicum 419 Pediococcus 351 Acholeplasma cavigenitalium 418 Myxococcus 341 Arcobacter skirrowii 417 Nonomuraea 330 Actinomadura yumaensis 417 Microvirgula 329 Pseudomonas clemancea 414 Luteolibacter 326 Methylobacterium longum 412 Phycicoccus 321 Bacillus butanolivorans 410 Arcanobacterium 320 Haliangium tepidum 410 Geodermatophilus 319 Tetrasphaera australiensis 409 Sanguibacter 314 Mycobacterium kansasii 406 Providencia 313 Mannheimia caviae 404 Pilimelia 310 Kouleothrix aurantiaca 401 Olivibacter 309 Burkholderia lata 399 Corallococcus 307 Atopobium minutum 398 Asteroleplasma 301 Virgibacillus salexigens 398 Alcanivorax 300 Planctomyces limnophilus 393 Herminiimonas 299 Prosthecobacter fluviatilis 392 Yaniella 297 Novosphingobium mathurense 391 Salinispora 281 Elizabethkingia meningoseptica 388 Microvirus 280 Sphingomonas insulae 385 Frankia 275 Oscillospira guilliermondii 384 Thiorhodospira 270 Sphingomonas dokdonensis 379 Chroococcus 270 Gluconobacter kondonii 379 Alicyclobacillus 270 Alkaliphilus crotonatoxidans 378 Nitrobacter 270 Pseudomonas veronii 372 Microcystis 263 Chryseobacterium joostei 368 Neorickettsia 261 Luteimonas terricola 368 Actinomycetospora 261 Thauera linaloolentis 367 Lentibacillus 259 Paenibacillus caespitis 365 Ignavibacterium 259 Sphingomonas yunnanensis 364 Pontibacillus 251 Sphingobacterium shayense 364 Iamia 245 Methylobacterium komagatae 363 Anaerococcus 243 Flavobacterium frigidimaris 358 Gordonia 241 Flavobacterium antarcticum 358 Sediminibacillus 241 Nocardia yamanashiensis 358 Tsukamurella 240 Prosthecobacter vanneervenii 355 Psychromonas 237 Microtetraspora glauca 352 Saccharospirillum 232 Streptomyces danangensis 348 Oerskovia 230 Clostridium nitrophenolicum 347 Tenacibaculum 229 Peptoniphilus methioninivorax 343 Cryptosporangium 228 Novosphingobium aromaticivorans 340 Crenothrix 226 Pedobacter hartonius 339 Flectobacillus 221 Actinoallomurus iriomotensis 337 Thermomonas 218 Pseudomonas putida 337

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Desulfosporomusa 215 Bradyrhizobium elkanii 335 Negativicoccus 213 Methylocella palustris 335 Salinibacterium 212 Erwinia papayae 334 Actinopolymorpha 211 Desulfovibrio gracilis 334 Stenoxybacter 208 Pseudomonas oryzihabitans 333 Desulfobulbus 207 Mycobacterium conspicuum 324 Spirosoma 205 Bacillus cereus 322 Amphritea 203 Pseudomonas panipatensis 320 Lysinibacillus 202 Caldicellulosiruptor bescii 319 Natronincola 196 Pseudomonas plecoglossicida 318 Defluvibacter 194 Agrococcus versicolor 316 Desulfitobacterium 193 Demequina globuliformis 313 Kineococcus 183 Luteolibacter algae 312 Yonghaparkia 182 Sanguibacter suarezii 312 Kosmotoga 181 Cupriavidus oxalaticus 308 Modestobacter 175 Roseomonas massiliensis 307 Catellatospora 174 Corallococcus exiguus 305 Sulfurospirillum 174 Cystobacter armeniaca 302 Marinobacterium 168 Phenylobacterium koreense 299 Knoellia 163 Cystobacter badius 299 Tetragenococcus 163 Sinomonas atrocyanea 297 Pontibacter 161 Alcanivorax indicus 297 Flammeovirga 159 Flavobacterium succinicans 295 Muricauda 155 Marinobacter arcticus 295 Polaribacter 151 Sphingomonas elodea 295 Chloroflexus 148 Burkholderia acidipaludis 284 Ruegeria 148 Microvirus Enterobacteria phage PhiX174 280 Ammoniphilus 146 Pseudomonas agarici 280 Aminobacter 144 Corynebacterium minutissimum 278 Enterococcus 144 Agrobacterium viscosum 278 Mesoplasma 140 Pedomicrobium australicum 278 Trichodesmium 139 Desulfovibrio tunisiensis 277 Phormidium 138 Syntrophomonas cellicola 271 Lachnospira 135 Clostridium hveragerdense 271 Kocuria 135 Dolichospermum curvum 271 Parapedobacter 133 Propionibacterium humerusii 270 Actinoplanes 131 Agromyces salentinus 270 Enhydrobacter 129 Devosia geojensis 269 Rubellimicrobium 128 Frankia alni 269 Prauserella 125 Rhodococcus kyotonensis 269 Janibacter 124 Shewanella pneumatophori 268 Methanococcoides 121 Sphingobium faniae 267 Cryocola 120 Myxococcus fulvus 267 Salinicoccus 119 Bartonella rochalimae 266

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Plesiomonas 118 Dietzia papillomatosis 266 Anoxybacillus 118 Hyphomonas hirschiana 263 Shinella 116 Microcystis panniformis 263 Acidocella 115 Chroococcus minutus 262 Vibrio 114 Pseudomonas borealis 262 Pseudidiomarina 112 Neorickettsia helminthoeca 261 Candidatus Methanoregula 112 Chondromyces crocatus 260 Candidatus Magnetobacterium 110 Cupriavidus campinensis 257 Thermosinus 108 Phenylobacterium mobile 257 Cellvibrio 100 Methanosaeta pelagica 255 Fibrobacter 97 Niabella aurantiaca 249 Methanocella 97 Beijerinckia indica 249 Geotoga 94 Nocardioides islandensis 248 Brochothrix 94 Caulobacter crescentus 248 Kaistia 91 Pedobacter westerhofensis 247 Carnobacterium 90 Clostridium frigoris 246 Archaeoglobus 90 Bdellovibrio bacteriovorus 246 Lactococcus 90 Clostridium saccharoperbutylacetonicum 246 Porphyromonas 90 Aurantimonas litoralis 241 Mycoplana 88 Mycobacterium arupense 241 Zhihengliuella 88 Acidiphilium symbioticum 240 Serinicoccus 87 Desulfovibrio vietnamensis 240 Entomoplasma 86 Sediminibacillus halophilus 240 Trabulsiella 86 Acidovorax anthurii 240 Sporolactobacillus 84 Micrococcus thailandicus 236 Thermococcus 84 Pseudoxanthomonas mexicana 236 Kutzneria 83 Lentibacillus salinarum 234 Georgenia 81 Rhodococcus equi 233 Phascolarctobacterium 81 Lentzea flavoverrucoides 231 Myroides 79 Cohnella thermotolerans 230 Oscillochloris 78 Dehalogenimonas lykanthroporepellens 230 Deefgea 77 Corynebacterium imitans 228 Oligella 74 Methylobacterium gregans 227 Desulfocapsa 73 Oerskovia ginkgo 225 Desulforhopalus 73 Microvirgula aerodenitrificans 225 Candidatus Endobugula 71 Azospira restricta 224 Sporichthya 71 Roseospira thiosulfatophila 224 Methanocorpusculum 70 Enterobacter aceae 223 Pandoraea 69 Bacillus alkalogaya 222 Virgisporangium 69 Methanohalophilus mahii 221 Klebsiella 68 Candidatus Blochmannia castaneus 219 Helcococcus 68 Geobacter chapelleii 214 Planococcus 67 Pseudoxanthomonas sacheonensis 213 Haloferax 66 Litoricola lipolytica 213

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Actinobaculum 66 Escherichia coli 213 Sphaerochaeta 65 Negativicoccus succinicivorans 213 Prevotella 64 Zoogloea resiniphila 213 Veillonella 63 Legionella taurinensis 212 Hylemonella 62 Candidatus Phytoplasma pini 211 Roseiflexus 61 Thermomonas dokdonensis 210 Verrucosispora 58 Clostridium magnum 210 Aneurinibacillus 58 Sphingomonas azotifigens 209 Hirschia 57 Variovorax dokdonensis 209 Balneimonas 56 Actinomadura echinospora 206 Vulcanisaeta 56 Bacillus ginsengihumi 204 Mogibacterium 56 Amphritea atlantica 203 Methanosarcina 56 Sphingopyxis witflariensis 199 Chelatococcus 55 Candidatus Liberibacter solanacearum 199 Shimazuella 54 Sphingomonas hunanensis 199 Sedimentibacter 52 Actinomadura rubrobrunea 198 Erythrobacter 52 Azospirillum picis 196 Desulfotalea 51 Kushneria indalinina 196 Nocardiopsis 51 Clostridium aestuarii 195 Erythromicrobium 50 Polynucleobacter rarus 194 Parachlamydia 50 Methylomonas fodinarum 193 Jiangella 49 Roseospira visakhapatnamensis 193 Nitrosospira 49 Curtobacterium citreum 192 Tolumonas 49 Desulfosarcina variabilis 191 Aminobacterium 49 Dietzia alimentaria 191 Candidatus Nitrososphaera 48 Pseudonocardia kongjuensis 190 Arthronema 48 Providencia alcalifaciens 189 Symploca 48 Stenotrophomonas geniculata 187 Thermotoga 48 Burkholderia caribensis 187 Dermacoccus 47 Lentzea waywayandensis 185 Snowella 47 Peptoniphilus asaccharolyticus 184 Halanaerobacter 47 Rhodoplanes roseus 184 Fulvivirga 46 Saccharopolyspora cebuensis 181 Pimelobacter 45 Pigmentiphaga kullae 179 Viridibacillus 44 Microbacterium pygmaeum 179 Alicycliphilus 44 Acidovorax facilis 178 Propionigenium 42 Caulobacter vibrioides 177 Denitrobacter 42 Desulfovibrio caledoniensis 177 Caldisphaera 41 Desulfosporosinus acidiphilus 177 Trichococcus 40 Desulfotomaculum putei 176 Brenneria 38 Psychrobacter phenylpyruvicus 175 Elusimicrobium 38 Arthrobacter rhombi 174 Roseococcus 38 Sphingobium chungbukense 174 Actinoalloteichus 37 Kosmotoga arenicorallina 173

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Bergeyella 37 Acidiphilium acidophilum 172 Facklamia 36 Sphingomonas ginsenosidimutans 172 Arsenophonus 36 Dactylosporangium roseum 168 Halothiobacillus 34 Litoricola marina 168 Dickeya 34 Marinobacterium sediminicola 168 Fusobacterium 34 Erwinia dispersa 167 Blastococcus 34 Planctomyces maris 166 Williamsia 33 Rhodococcus marinonascens 166 Methylopila 33 Devosia insulae 165 Vagococcus 33 Streptacidiphilus griseus 164 Methanofollis 32 Rhodanobacter thiooxydans 164 Sagittula 31 Shewanella upenei 163 Candidatus Jettenia 31 Tetragenococcus doogicus 163 Friedmanniella 30 Acetobacter orleanensis 163 Luteococcus 30 Actinopolymorpha rutila 163 Lampropedia 29 Caloramator uzoniensis 162 Ferrimonas 29 Nitrospina gracilis 162 Tepidibacter 29 Pontibacter niistensis 161 Microcoleus 29 Actinomadura viridilutea 161 Mycetocola 28 Pontibacillus halophilus 160 Desulfococcus 27 Arcanobacterium phocae 159 Planomicrobium 27 Flammeovirga pacifica 159 Rothia 26 Microbulbifer okinawensis 158 Azomonas 26 Legionella cherrii 157 Nitrincola 25 Marinomonas brasiliensis 155 Tatlockia 25 Paenibacillus pocheonensis 155 Rhodovulum 25 Clostridium histolyticum 155 Pleomorphomonas 25 Muricauda lutimaris 155 Scytonema 24 Paenibacillus taiwanensis 153 Ethanoligenens 23 Syntrophomonas bryantii 152 Halobaculum 23 Exiguobacterium aurantiacum 152 Nitrosopumilus 22 Sphingomonas leidyia 151 Pseudaminobacter 22 Herbaspirillum huttiense 150 Pullulanibacillus 22 Arcanobacterium bernardiae 150 Desulfobacterium 21 Achromobacter insolitus 150 Gemella 21 Pilimelia columellifera 150 Anaeromusa 21 Rhodococcus fascians 149 Paenisporosarcina 20 Clostridium cavendishii 149 Methanosalsum 19 Pseudomonas collierea 148 Gillisia 19 Chloroflexus aurantiacus 148 Palaeococcus 19 Pseudomonas stutzeri 148 Rhodobaca 19 Legionella waltersii 148 Heliobacterium 18 Mesorhizobium amorphae 147 Aquimonas 18 Crenothrix polyspora 147

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Chelativorans 18 Acinetobacter calcoaceticus 147 Rheinheimera 17 Clostridium cadaveris 147 Pseudobutyrivibrio 16 Sphingomonas roseiflava 145 Sinorhizobium 16 Agrobacterium larrymoorei 145 Tindallia 16 Mycobacterium vanbaalenii 144 Cellulosimicrobium 15 Sphingomonas mali 144 Jeotgalicoccus 15 Pseudomonas coronafaciens 143 Adlercreutzia 15 Pseudomonas fluorescens 143 Methanoculleus 15 Chitinophaga skermanii 143 Fructobacillus 15 Syntrophobacter sulfatireducens 142 Cyanobacterium 15 Sphingomonas soli 142 Eggerthella 15 Hymenobacter ocellatus 140 Eubacterium 14 Rhodococcus erythropolis 140 Paraprevotella 14 Mesoplasma entomophilum 140 Tetrathiobacter 14 Chromobacterium haemolyticum 139 Natronococcus 13 Bacillus foraminis 139 Afifella 13 Rhizobium alamii 138 Zhouia 13 Tepidimonas ignava 137 Halorubrum 13 Roseomonas terrae 137 Jannaschia 13 Pseudoalteromonas gracilis 137 Nesterenkonia 13 Devosia yakushimensis 136 Lutibacterium 12 Paenibacillus donghaensis 136 Halochromatium 12 Lachnospira pectinoschiza 135 Psychroserpens 12 Comamonas composti 135 Desulfovermiculus 12 Brevundimonas bullata 135 Pasteurella 12 Paracoccus marinus 135 Hahella 12 Phaeospirillum fulvum 135 Thermacetogenium 12 Emticicia ginsengisoli 134 Dethiobacter 11 Actinomadura chibensis 134 Ignatzschineria 11 Parapedobacter koreensis 133 Terribacillus 11 Kushneria aurantia 133 Planktothricoides 11 Pseudomonas mosselii 133 Haladaptatus 10 Desulfosporomusa polytropa 132 Crossiella 10 Hydrogenophilus hirschii 132 Yersinia 10 Pseudomonas benzenivorans 132 Paludibacter 10 Paenibacillus contaminans 131 Promicromonospora 10 Polaribacter butkevichii 131 Desulfotignum 9 Pseudomonas brenneri 131 Rhodothalassium 9 Bradyrhizobium japonicum 131 Methanospirillum 8 Micromonospora rifamycinica 131 Thiomicrospira 8 Novosphingobium nitrogenifigens 129 Anaeroplasma 8 Enterobacter hormaechei 126 Alteromonas 8 Corynebacterium accolens 126 Rummeliibacillus 8 Mycoplasma edwardii 126

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Parascardovia 8 Staphylococcus caprae 126 Thermobispora 7 Sphingobium yanoikuyae 126 Acidianus 7 Sphingomonas panni 125 Patulibacter 7 Polynucleobacter difficilis 124 Isoptericola 7 Actinocatenispora thailandica 124 Haloanella 7 Clostridium carboxidivorans 122 Coprococcus 7 Chromobacterium pseudoviolaceum 122 Morganella 7 Propionibacterium microaerophilum 122 Desulfofaba 7 Desulfobulbus elongatus 122 Anabaenopsis 7 Labrys wisconsinensis 122 Salegentibacter 6 Methanococcoides methylutens 121 Pseudochrobactrum 6 Aminobacter aminovorans 121 Gloeotrichia 6 Lysinibacillus parviboronicapiens 120 Psychroflexus 6 Halomonas almeriensis 120 Methanosphaera 6 Treponema succinifaciens 119 Zobellia 6 Sphingomonas japonica 119 Methanomethylovorans 6 Anaeromyxobacter dehalogenans 119 Turicibacter 6 Desulfosporosinus lacus 118 Dialister 6 Novosphingobium capsulatum 118 Candidatus Regiella 5 Marinomonas foliarum 118 Xylella 5 Clostridium lundense 117 Bizionia 5 Catenulispora cavernae 116 Methanocaldococcus 5 Rickettsia monacensis 116 Anaerostipes 5 Ancylobacter rudongensis 115 Couchioplanes 5 Hyphomicrobium hollandicum 115 Rubrobacter 5 Legionella rowbothamii 115 Puniceicoccus 5 Stenotrophomonas retroflexus 114 Soehngenia 5 Methylosinus acidophilus 114 Sarcina 5 Knoellia aerolata 114 Candidatus Phlomobacter 5 Chryseobacterium oranimense 111 Marivita 5 Corynebacterium appendicis 111 Roseateles 5 Achromobacter arsenitoxydans 111 Pseudoclavibacter 5 Mesorhizobium septentrionale 110 Nitratireductor 4 Pedomicrobium manganicum 110 Oribacterium 4 Pseudomonas poae 110 Desulfitobacter 4 Pseudomonas parafulva 110 Natronomonas 4 Kitasatospora paranensis 109 Aquimarina 4 Chryseobacterium soli 108 Novispirillum 4 Micrococcus flavus 108 Acidithiobacillus 4 Corynebacterium sundsvallense 107 Aerococcus 4 Stenotrophomonas maltophilia 107 Mechercharimyces 4 Rhodococcus phenolicus 106 Microlunatus 4 Mycobacterium smegmatis 105 Desulfomicrobium 4 Pseudomonas proteolytica 103

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Sodalis 3 Clostridium taeniosporum 103 Acidaminobacter 3 Mycobacterium interjectum 103 Anaerofilum 3 Stenotrophomonas pavanii 102 Pyrococcus 3 Pseudomonas mendocina 102 Oceanospirillum 3 Salinicoccus luteus 101 Moritella 3 Chryseobacterium gleum 101 Umboniibacter 3 Ochrobactrum pseudogrignonense 101 Akkermansia 3 Sphingomonas phyllosphaerae 100

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Appendix 9: 16S Illumina Metagenomic Analysis – Most Abundant species per sample, per sample date.

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Appendix 9 continued

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Appendix 9 continued

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Appendix 9 continued

Sample ID Date Sample ID Date SW 65 (soil) 09/05/2018 SW 65 (soil) 09/05/2018 1 Unclassified 388233 1 Unclassified 58546 2 Tepidanaerobacter syntrophicus 14351 2 Calothrix parietina 8548 3 Ammonifex thiophilus 12156 3 Desulfurispora thermophila 7594 4 Arthrobacter psychrochitiniphilus 7991 4 Tepidanaerobacter syntrophicus 5983 5 Gluconobacter morbifer 7447 5 Amycolatopsis pigmentata 3705 6 Anaerobaculum thermoterrnum 4810 6 Paenibacillus filicis 1683 7 Candidatus Rhabdochlamydia crassificans 2887 7 Geobacillus thermoglucosidans 1579 8 Gluconobacter krungthepensis 2729 8 Rhodobium gokarnense 1427 9 Kineosporia mikuniensis 2204 9 Pelotomaculum isophthalicicum 1137 10 Escherichia albertii 2015 10 Acidobacterium capsulatum 1032 11 Geothrix fermentans 1817 11 Gemmata obscuriglobus 1021 12 Geobacillus gargensis 1625 12 Candidatus Amoebophilus asiaticus 1003

Sample ID Date Sample ID Date SW 25 (soil) 09/05/2018 747(Mulgrave) 20/07/2018 1 Unclassified 392102 1 Unclassified 292687 2 Thermoanaerobacter inferii 43332 2 Janthinobacterium agaricidamnosum 27848 3 Bacillus mucilaginosus 15205 3 Chromobacterium subtsugae 3220 4 Edaphobacter modestus 14955 4 Oxalobacter vibrioformis 2595 5 Thermovenabulum ferriorganovorum 11130 5 Aquitalea magnusonii 2474 6 Ammonifex thiophilus 7881 6 Chromobacterium aquaticum 1826 7 Tepidanaerobacter syntrophicus 7245 7 Cupriavidus laharis 1167 8 Moorella glycerini 6334 8 Burkholderia ubonensis 992 9 Brachyspira ibaraki 6204 9 Chromobacterium piscinae 745 10 Chthoniobacter flavus 6137 10 Aquitalea denitrificans 664 11 Bacillus atrophaeus 4535 11 Hydrogenophaga defluvii 517 12 Anaerobaculum thermoterrnum 4486 12 Methyloversatilis universalis 498

Sample ID Date 747(Mulgrave) 20/07/2018 1 Unclassified 145952 2 Janthinobacterium agaricidamnosum 43593 3 Oxalobacter vibrioformis 3083 4 Aquitalea magnusonii 2115 5 Chromobacterium subtsugae 1909 6 Burkholderia ubonensis 1117 7 Aquitalea denitrificans 933 8 Cupriavidus laharis 885 9 Chromobacterium aquaticum 857 10 Chromobacterium piscinae 719 11 Hydrogenophaga defluvii 541 12 Methyloversatilis universalis 483

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