Examining Accelerated Ecosystem Development in the Ecological Restoration of Mined Land

Robert James Scanlon, BSc (Hons) (UoN)

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Environmental Science

October 2020

This research was supported by an Australian Government Research Training Program (RTP) Scholarship Title page image: Landscape of the Ravensworth Operations restoration area, which the main Experimental Site was situated in. Photo taken in February 2018 by Brenton Hubert.

Statement of Originality I hereby certify that the work embodied in the thesis is my own work, conducted under normal supervision. The thesis contains no material which has been accepted, or is being examined, for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made. I give consent to the final version of my thesis being made available worldwide when deposited in the University’s Digital Repository, subject to the provisions of the Copyright Act 1968 and any approved embargo.

Robert J. Scanlon

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Acknowledgements

‘No thesis is undertaken in isolation’ – Nigel Fisher (2010)

This thesis is no different. My research is preceded by a huge amount of work from CSER and other researchers around the world: I feel like a small explorer standing on the shoulders of giants.

The largest thanks I need to give for this work is to Carmen Castor who has mentored me for more than six years, and I’ve needed every single bit she could give! With her support, I have been able to work on improving my ability to think critically, communicate effectively and slowly alleviate my plant blindness. Without the long hours that she put in to assisting me in the field, identifying plants, reminding me to take notes, reading my scrawl and debating with me on the interpretation of every little result, this work would have been a shadow of what it is. I cannot thank her enough for the hours of her life that she has devoted.

I have been blessed to have the support of three other great supervisors. Anita Chalmers has been an amazing supervisor throughout the entire journey. She has encouraged me, provided me with detailed feedback asap, guided me through moral dilemmas, taught me about teaching, and when necessary cracked the whip (thank you for doing so!). I really appreciate all that she has given me over this journey. Suresh Subashchandrabose has helped introduce me to the world of microbiology, an area that I had always dreamed of exploring. It has been an amazing gift to see into Suresh’s analytical mind as he patiently answered my questions and ‘powered my rocket’. Megh Mallavarapu has provided me with wisdom throughout this journey. On several occasions, he found critical flaws in my logic and provided solutions so simple that I had overlooked them.

There has also been a fantastic group of academics from across the university that have supported me. Greg Hancock has been a great mentor and friend since my early undergraduate years; I have really appreciated his guidance and wisdom throughout my journey. Greg’s labs have proven to be an invaluable and flexible space; sharing them with Abe Gibson and others has been fantastic. Thanks to Kim Colyvas for guiding me through statistics, being patient, paying strong attention to detail, and curiosity. These virtues have

really helped me learn. Thanks to Steve Lucas for many discussions and support, particularly early in my studies where I was introduced to techniques I didn’t realise were possible. Thanks also to Richard Yu, Joe Enright, Craig Evans and Ian Grainge for allowing me to use the labs/glasshouse space and the quick and easy support I recieved whenever there have been issues. Thanks to Margaret Platell for guiding me through new techniques in PRIMER. Inside the Conservation Science Research Group, I’ve been privileged to work alongside some great minds in Matt Hayward, Andrea Griffin, John Clulow, Michael Mahony, Kaya Klop-Toker, Alex Callan and Ryan Witt. Thanks for driving a great group, covering such a wide range of topics, pressing us to think about big and small challenges and taking me in even though I wasn’t working on frogs!

Two other academics have really helped along the way. Kate Newman has not only provided gDNA data on spoil for this study but has also taught me a number of lessons about work/life balance and how to be a good PhD candidate. I almost certainly wouldn’t be here without her. Mike Cole is one of the main people responsible for laying the groundwork that led to my work. He has always probed and prodded me in ways that challenge my understanding and drives me to improve.

Thank you to all my supervisors and academics for giving me the freedom to dream big and be creative with this PhD. It has really made the experience very positive; I am extremely glad that I undertook this endeavour.

I wish to thank both of the reviewers of this thesis for providing usefull and detailed feedback that improved this document. I also wish to thank Catherine Greenwood for copyediting this document to improve its quality.

A crucial factor of this PhD was funding. The largest contributor to this project was the Australian Government through the Research Training Program Scholarship; this gave me the financial peace of mind to really focus on the project; I couldn’t have done anything without it. Thank you to the staff at Mt Owen Complex (Glencore and Thiess) and Ravensworth Operations (Glencore) coal mines for the substantial funding provided to me, the logistical

v support, and answering my overly technical questions. This work has been supported over many years by many individuals, but specifically this project wouldn’t have been possible without Monique Pollock, Brenton Hubert and Garry from Ravensworth, and Hubert Mhangami, Linda Lunnon and Ned Stephenson from Mt Owen. Thank you!

Thank you to Global Renewables for the financial support and the discussions I’ve had with their technical manager, Michael Bonanno. Michael has been supporting this research for many years and has repeatedly demonstrated an ability to comprehend and constructively improve my work. Thank you for all your commentary and support; I think one day you should embark on a PhD of your own!

There is a range of students, post-docs and support workers who have come through the Conservation Science Research Group over the years. These colleagues have been mental health support, critiques, chefs, explorers, artists, movie watchers, trivia hooligans and, most importantly, friends. To name a few: Rose Upton, Rebecca Seeto, Cassandra Bugir, Lachlan Howell, Chad Beranek, Dean Lenga, Sam Wallace, Alana Burton, Sarah Stock, Charlotte Alley, Satem Longchar, Chloe Peneaux, Emy Guilbault, Lachlan Campbell, John Gould, Cassie Maynard, Belinda Howe, Bede Moses, Cottrell Tamessar, Kim Nolan, Colin McHenry and Agnes Kovacs. I have also had the pleasure of getting to know friends from around the university: Hasintha Wijesekara, Chris Brown, Yilu Xu, Thi Kim Anh Tran, Rafiquel Islam, Elyas Karim. Thank you all for making my experience unique and amazing.

To long-term friends Isaac, Joe and Dean, and my musical support network Toronto Brass, thank you for providing me with a much-needed mental break. Particular thanks go to Josh Lowe; you have supported me not only as a friend but also mathematically and statistically.

To my family: thanks for understanding that ‘almost done’ doesn’t mean next week. My parents, Ann and Paul, have truly gifted me with the freedom and encouragement to do whatever it is that I want to do. Thanks for all of the hard work you put in to make my life so easy and wonderful. Jess and Christopher: thank you for your patience, chemical/tech support and generosity.

Finally, thank you to my wife Nicola. Every step of the way, you have been patient (with the late nights/dinners/weekends), understanding (of the emotional if not the technical), generous (with chocolate, time in the lab and backyard experiments) and most of all

vi supportive. Thank you for letting me take this opportunity. I look forward to moving on to the next stage of life with you and hopefully a little one joining us in the coming months.

Sincerely,

Rob

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Abstract

Restoration of endangered flora communities is a sensitive task that typically occurs in ecosystems where only a moderate level of disturbance has occurred. Open-cut coal mining, however, is an extreme level of disturbance, after which restoration of biodiverse, endangered ecological communities has been difficult. Regardless of the difficulties, there are constant social, economic and environmental pressures to accelerate the restoration process. This thesis examines the idea that ecosystem development (flora and microbial composition, nutrient capital and ecosystem processes) can be accelerated by various treatments without compromising the creation of an endangered flora community. The research sites are active coal mines in the Hunter Valley, New South Wales, Australia, an area where a shortage of available quality topsoil is common. This study investigates how well combinations of subsoil, coarse woodchip mulch and a commercial municipal solid waste compost, Organic Growth Medium (OGM), work as ameliorants for spoil to create an endangered flora community. On set-up, the Experimental Site received seeds from 50 targeted species comprising the endangered community and ancillary species from the local area. The site was examined 4– 6 years after establishment. Comparisons are made to an older (15 years) site that was rehabilitated by best practice methods (Ring Rd) and a best available reference (the Ravensworth State Forest (RSF)).

The flora community showed clear distinctions between treatments with OGM and treatments with subsoil. Generally, treatments with OGM had a higher proportion of members from the Chenopodiaceae family, whereas treatments without OGM and with subsoil had higher densities of the genus Acacia. As Chenopodiaceae species were typically ancillary, treatments with subsoil have the closest composition to the references. By varying the ameliorants used, a mosaic approach to the restoration would allow the positive benefits of each treatment to be included in the project and increase diversity. Further, restoration of spoil benefits from combining multiple ameliorants (for example, Subsoil + Mulch + OGM).

The microbial community composition was examined using gene sequencing with 16S and ITS primers to determine if the origin of the ameliorant had a significant effect on its community composition, and therefore the plant community composition. The study found almost no relationship between fresh OGM from the supplier and the microbial community composition on the Experimental Site. The Subsoil Mulch treatment microbial had the closest community viii composition to the references. However, there may be an overriding effect from dispersal into the site because the Spoil OGM treatment, although still trending differently, was much more like the rest of the Experimental Site than either the spoil or the OGM that it was produced from.

A nutrient inventory was developed to determine if the restored ecosystem would have sufficient nutrient capital to match the reference ecosystem. Although nitrogen had been indicated as a major limiting nutrient in previous studies, phosphorus was found to be lacking on the Subsoil Mulch treatment. The inventory suggested that even if plants were able to access all of the phosphorus in the system, Subsoil Mulch may not have enough phosphorus in the vegetation, litter and upper 30 cm of soil to match the biotic component of the reference. This suggests that although the community developing on this treatment is currently compositionally ideal, it may in the long term not develop the desired structures of a woodland or forest.

The rate of ecosystem processes was examined to determine if soil fertility accelerated the rate of decomposition, increased available nutrients and increased tree growth rates. Even though Spoil OGM had very high available nutrients compared with Subsoil Mulch, there was limited evidence for any difference in ecosystem processes. The only confirmed difference was that the negative control treatment, Spoil, performed extremely poorly. This suggests that in the short term, an unmeasured factor is having a larger effect on the functional development of the site than decomposition and soil fertility.

As part of the study, the RSF reference was identified as probably being in a retrogressive state, having low phosphorus due to the length of time since catastrophic disturbance. Hypothetically, restoring a site in retrogression would require the return of low-fertility conditions. However, this study has shown risks from that approach and suggests that the flora community does not require low fertility to be restored. Overall, of the four treatments examined in depth, the two best performers both have issues: Subsoil Mulch is deficient in phosphorus while Subsoil OGM Mulch is not attaining the highest quality outcome based on the Australian federal community assessment due to higher exotic cover. The Ring Rd, therefore, has shown the advantages of industry best practice methods because it outperformed the Experimental Site with characteristics including restoration using a high species diversity seeding list featuring the correct community, applying high-quality topsoil ix

from the local area and allowing time for development to occur. Accelerating ecosystem development as part of restoration is possible but careful management will be required to maintain the appropriate trajectory. Restoration of endangered ecological communities is still a difficult task, one that should not be attempted lightly. Given the poor restoration potential of no intervention, accelerated development is a worthy goal and, with appropriate management, many of the treatments examined in this study can provide a quality restoration outcome.

Contents

Statement of Originality ...... iii Acknowledgements ...... iv Abstract ...... viii Contents ...... xi List of Figures ...... xvi List of Tables ...... xxi List of Appendices ...... xxii Acronyms ...... xxiv Chapter 1 – Introducing Accelerated Ecosystem Development ...... 1 1.1 Restoration of Plant Communities Post-Coal Mining ...... 1 1.2 Accelerating Ecosystem Development ...... 5 1.3 Research Directions ...... 10 Chapter 2 – Study Area Background and Experimental Design ...... 12 2.1 The Mining Process ...... 12 2.1.1 Characteristics of Spoil in the Hunter Valley ...... 13 2.2 Study Sites ...... 14 2.2.1 Experimental Site and Background to Ravensworth Operations ...... 14 2.3 Reference Sites ...... 31 2.3.1 Selection of Reference Sites ...... 31 2.3.2 Local Reference Site on Ravensworth Operations – Rav Ref ...... 31 2.3.3 Mt Owen Complex – Ring Rd Reference Sites ...... 32 2.3.4 Ravensworth State Forest – RSF Reference Sites ...... 32 2.3.5 Fresh OGM ...... 33 2.4 Climate of the Central Hunter Valley ...... 33 Chapter 3 – Examining Variation in the Restoration of an Endangered Flora Community ..... 36 3.1 Introduction ...... 36 3.1.1 Importance and Benefits of Biodiversity ...... 36 3.1.2 Restoring Biodiversity in the Hunter Valley ...... 37 3.1.3 Research Direction ...... 38 3.2 Methods ...... 39 3.2.1 Additional References ...... 39 3.2.2 Flora Community ...... 40 3.2.3 Trees ...... 42 xi

3.2.4 Identifying the Treatment Most Similar to References and Targets ...... 43 3.3 Results ...... 45 3.3.1 Diversity Metrics ...... 45 3.3.2 Community Composition ...... 48 3.3.3 Tree Performance ...... 64 3.3.4 Matching References and Targets ...... 69 3.4 Discussion ...... 77 3.4.1 Did the Site Match the Target? ...... 77 3.4.2 Effect of Treatments ...... 82 3.4.3 Comparison of the Experimental Site to the References ...... 88 3.4.4 Temporal Effects ...... 93 3.5 Conclusion ...... 94 Chapter 4 – Restoring Mine Soil Microbial Diversity to Rebuild Plant Biodiversity ...... 96 4.1 Introduction ...... 96 4.1.1 Functional Guilds ...... 96 4.1.2 Restoring Microbial Communities ...... 98 4.1.3 Research Directions ...... 102 4.2 Methods ...... 103 4.2.1 Field Sampling ...... 103 4.2.2 Laboratory Extraction and Sequencing ...... 105 4.2.3 Bioinformatics ...... 106 4.2.4 Functions ...... 106 4.2.5 Statistics ...... 107 4.3 Results ...... 108 4.3.1 Culturable Microorganisms in Spoil ...... 108 4.3.2 Microbial Diversity ...... 108 4.3.3 Functional Analysis ...... 118 4.4 Discussion ...... 130 4.4.1 Spoil ...... 130 4.4.2 Subsoil and Mulch ...... 132 4.4.3 OGM ...... 133 4.4.4 References ...... 135 4.4.5 Improvements and Caveats for this Study ...... 136

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4.4.6 Implications for Practice ...... 138 4.5 Conclusion ...... 141 Chapter 5 – Restoring for the Forest of the Future – Using Nutrient Inventories in Restoration Ecology ...... 142 5.1 Introduction ...... 142 5.1.1 Assumptions in Restoration Ecology ...... 142 5.1.2 Nutrient Inventories ...... 142 5.1.3 Too Much or Too Little ...... 144 5.1.4 Question ...... 146 5.2 Methods ...... 147 5.2.1 Overview of Methodology and Treatments ...... 147 5.2.2 Field Sampling Procedure ...... 148 5.2.3 Laboratory Methods ...... 152 5.2.4 Nutrient Concentrations of Soils and Other Vegetation ...... 153 5.2.5 Estimating Biomass of Trees ...... 154 5.2.6 Nutrient Concentration of Trees and Material >10 mm ...... 154 5.2.7 Statistics ...... 155 5.3 Results ...... 157 5.3.1 Mass of Material ...... 157 5.3.2 Macronutrients ...... 159 5.3.3 Micronutrients ...... 173 5.3.4 Stoichiometry ...... 178 5.3.5 Overall Similarity to RSF ...... 181 5.4 Discussion ...... 182 5.4.1 Restoration and Total Nutrients ...... 182 5.4.2 Importance of the Mass of Material ...... 185 5.4.3 Change Between Years ...... 186 5.4.4 Unexplored Inputs ...... 187 5.4.5 Future Effects of Differences ...... 188 5.4.6 Successional Maturity and Total Nutrients ...... 189 5.4.7 Other Areas for Improvement in the Future ...... 189 5.5 Conclusion ...... 190 Chapter 6 – Are Critical Ecosystem Processes Occurring at an Accelerated Rate? ...... 191

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6.1 Introduction ...... 191 6.1.1 Acceleration Through Fertility ...... 191 6.1.2 Increasing Nutrient Cycling Rates ...... 192 6.1.3 Question and Hypotheses ...... 194 6.2 Methods ...... 195 6.2.1 Treatments Used in Study ...... 195 6.2.2 Data from Other Chapters ...... 195 6.2.3 Available Nutrient Content, pH and Cation Exchange Capacity ...... 195 6.2.4 Decomposition...... 195 6.2.5 Statistics ...... 199 6.3 Results ...... 201 6.3.1 Available Nutrients ...... 201 6.3.2 Other Soil Chemistry ...... 202 6.3.3 Decomposition...... 204 6.3.4 Growth of Trees ...... 211 6.3.5 Level of Biomass ...... 212 6.4 Discussion ...... 215 6.4.1 (Lack of Accelerated) Nutrient Cycling ...... 215 6.4.2 Potential Controlling Factors ...... 217 6.4.3 Other Improvements ...... 224 6.5 Conclusion ...... 225 Chapter 7 – Can We Accelerate Ecosystem Development, and Should We? ...... 227 7.1 Introduction ...... 227 7.2 Methods ...... 227 7.3 Results and Discussion – Did Ecosystem Development Accelerate? ...... 230 7.4 The Treatments ...... 232 7.4.1 Spoil ...... 232 7.4.2 Spoil OGM ...... 234 7.4.3 Subsoil Mulch ...... 236 7.4.4 Subsoil OGM Mulch ...... 237 7.4.5 Ring Rd ...... 238 7.5 Relationships Between Variables ...... 238 7.6 Mechanisms of Accelerating Ecosystem Development ...... 240

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7.7 Acceleration, Retrogression and Restoration ...... 242 7.8 Importance of Goals ...... 247 7.9 Future Directions ...... 250 7.10 Recommendations for Industry ...... 252 7.11 Conclusion ...... 255 References ...... 257 Appendix A – Flora Species ...... 296 Appendix B – KEGG Orthologues ...... 317 KEGG Orthology Studied ...... 317 Results of Specific KEGG Orthology ...... 323 KEGGs Related to Nitrogen ...... 323 KEGGs Related to Carbon ...... 338 KEGGs related in Phosphorus ...... 349 Appendix C – Other Total Micronutrients ...... 357 Iron ...... 357 Manganese ...... 360 Zinc ...... 362 Copper ...... 364 Boron ...... 366 Appendix D – Available Nutrients ...... 368 Sodium ...... 368 Chloride ...... 369 Nitrate ...... 370 Phosphorus ...... 371 Potassium ...... 372 Sulfur ...... 373 Calcium ...... 374 Magnesium ...... 375 Iron ...... 376 Manganese ...... 377 Copper ...... 378 Boron ...... 379 Zinc ...... 380

List of Figures

Figure 1.1. Fast-forwarding seeks to accelerate the development of an ecosystem through time towards the restoration target...... 6 Figure 2.1. The Ravensworth Region...... 15 Figure 2.2. The project area for Ravensworth Operations over an aerial photograph from 1967...... 17 Figure 2.3. Landscape of the restoration area that the experiment was situated in...... 23 Figure 2.4. Ravensworth Operations Open Cut Coal Mine, January 2018...... 23 Figure 2.5. Close-up images of the materials used...... 24 Figure 2.6. Construction of the Experimental Site on Ravensworth Operations ………………… 24 Figure 2.7. Layout of treatments...... 25 Figure 2.8. Aerial image of the site from June 2014...... 25 Figure 2.9. Aerial image of the Experimental Site on Ravensworth Operations from late 2015...... 27 Figure 2.10. Aerial image of the Experimental Site on Ravensworth Operations from January 2018...... 28 Figure 2.11. Monthly rainfall at the Bowmens Creek weather station...... 34 Figure 2.12. Mean monthly temperature at Lostock Dam ...... 35 Figure 3.1. Species richness of native and exotic flora in 2015 and 2018...... 46 Figure 3.2. Shannon–Wiener diversity index for all native species in both 2015 and 2018. .. 48 Figure 3.3. nMDS plot of all treatments on Ravensworth Operations in 2015...... 49 Figure 3.4. nMDS plot of all treatments on Ravensworth Operations in 2018 ...... 50 Figure 3.5. nMDS of square-root-transformed native and exotic species composition based on centroids of treatments from different survey years...... 53 Figure 3.6. nMDS of presence/absence-transformed native and exotic species community composition based on centroids of each treatment...... 56 Figure 3.7. Species percentage cover on plots in 2015 and 2018...... 58 Figure 3.8. Plant cover on treatments in 2015 and 2018 for natives and exotics where cover is measured for individuals ≤1m in height...... 59 Figure 3.9. nMDS based on centroids of log(x+1) transformed species cover for each Treatment in 2015 and 2018...... 61 Figure 3.10. dbRDA plot of square-root-transformed flora abundance in 2018 for Blocks 1, 2 and 5 with best matching square-root-transformed 2014 soil variables overlaid...... 63

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Figure 3.11. dbRDA plot of log(x+1) transformed species composition based on cover in 2018 for Blocks 1, 2 and 5 with best matching square-root-transformed 2014 soil variables overlaid...... 63 Figure 3.12. Number of trees growing on the Experimental Site in 2015 and 2018...... 66 Figure 3.13. Variation in tree height on the Experimental Site in 2015 and 2018...... 67 Figure 3.14. CDBH data from the Experimental Site and references in 2018...... 69 Figure 3.15. Subsoil OGM Mulch in 2015, tree heights were lower allowing many forbs and exotics to establish dense populations...... 79 Figure 3.16. Subsoil OGM Mulch in 2018, the photo is taken in approximately the same position, the same direction and the white stake in the foreground is the same as in Figure 3.15...... 80 Figure 3.17. The ground underneath two Acacia amblygona in Block 1 Forest Topsoil. The growth beneath A. amblygona is generally sparse forbs and grasses...... 89 Figure 4.1. Diagrammatic description of how topsoil application, though uncertain in what it will bring, may decrease successional distance to the microbial restoration target...... 101 Figure 4.2. Left: samples were taken 2.5m towards the centre of each plot from the corners. Right: soils were sampled to 10 cm depth...... 104 Figure 4.3. Growth of colonies on dilution ‘D’ of the E. tomentosa sample...... 108 Figure 4.4. Total Archaeal and Bacterial species richness...... 109 Figure 4.5. nMDS of all 16S treatments for Archaeal and Bacteria community composition with similarity generated using group average CLUSTER and 10% slack...... 111 Figure 4.6. nMDS of 16S community composition results generated without the reference OGM samples from the production facility and Newman (2017) Spoil Control samples. .... 111 Figure 4.7. Total species richness of ITS (fungal) sequences across treatments...... 115 Figure 4.8. Community composition of all ITS treatments displayed in an nMDS with similarity from group average CLUSTER at 10% slack...... 116 Figure 4.9. An nMDS of the ITS community composition without the reference OGM and Spoil Control samples. Similarity generated using group average CLUSTER at 10% slack...... 116 Figure 4.10. NSTI (nearest sequenced taxon index) results for PICRUSt, produced from Morgan Langille Galaxy...... 120 Figure 4.11. Count of ASVs that were normalised prior to PICRUSt procedure...... 120 Figure 4.12. A count of the number of KEGG Orthologues that were associated with each sample...... 121 Figure 4.13. Abundance of ASVs identified as probably being ectomycorrhizal...... 123 Figure 4.14. Abundance of ASVs by major guild for each treatment...... 124 Figure 4.15. Abundance of ASVs by mixed guilds for each treatment...... 125 Figure 4.16. Abundance of ASVs by mixed guilds for each treatment...... 126 xvii

Figure 4.17. Total pathotroph richness...... 127 Figure 4.18. Total pathotroph abundance...... 128 Figure 4.19. Total saprotroph richness...... 128 Figure 4.20. Total saprotroph abundance...... 129 Figure 4.21. Total symbiotroph richness...... 129 Figure 4.22. Total symbiotroph abundance...... 130 Figure 5.1. The development in total nutrients until the referenced is matched...... 145 Figure 5.2. To obtain samples from a wide range of flora types in a heterogeneous environment, samples were taken of everything in a bulk environment...... 149 Figure 5.3. As an attempt to avoid oversampling the area, three different methods of strategic sampling were used...... 150 Figure 5.4. Because the ground is variable, a wooden plank with a hole in it was used to ensure that there was minimal movement away from the sampling point...... 150 Figure 5.5. Vegetation samples were taken at randomly selected points along transects as per Figure 5.3 with a grade-316 stainless steel pole 2 m high and a ring...... 152 Figure 5.6. Mass of material in each compartment...... 158 Figure 5.7. Average total nitrogen concentration within separate compartments...... 159 Figure 5.8. Kilograms of nitrogen per hectare...... 161 Figure 5.9. Average concentration of phosphorus within separate compartments...... 162 Figure 5.10. Kilograms of phosphorus per hectare...... 164 Figure 5.11 Average concentration of potassium within separate compartments ...... 165 Figure 5.12. Kilograms of potassium per hectare ...... 166 Figure 5.13. Concentration of calcium within separate compartments...... 167 Figure 5.14. Kilograms of calcium per hectare ...... 168 Figure 5.15. Concentration of magnesium within separate compartments...... 169 Figure 5.16. Kilograms of magnesium per hectare ...... 170 Figure 5.17. Concentration of sulfur within separate compartments...... 171 Figure 5.18. Kilograms of sulfur per hectare ...... 172 Figure 5.19. Concentration of sodium within separate compartments...... 173 Figure 5.20. Kilograms of sodium per hectare...... 174 Figure 5.21. Concentration of chloride within separate compartments...... 176 Figure 5.22. Kilograms of chloride per hectare...... 177 Figure 5.23. Linear regression between organic carbon and total nitrogen from the 0–10 cm compartment...... 179

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Figure 5.24. Linear regression between organic carbon and total phosphorus from the 0– 10 cm compartment...... 179 Figure 5.25. Linear regression between total nitrogen and total phosphorus from the 0–10 cm compartment...... 180 Figure 5.26. Linear regression between total nitrogen and total phosphorus in vegetation...... 180 Figure 5.27. Linear regression between total nitrogen and total phosphorus in litter...... 181 Figure 6.1. Left: Boxes of tea used in study. Right: Soil and litter were replaced as accurately as possible when burying the tea (photo from RSF B5)...... 198 Figure 6.2. Untransformed available phosphorus...... 202 Figure 6.3. 2019 Soil pH in 1:5 water by depth...... 203 Figure 6.4. Effective CEC (sum of exchangeable Ca, Mg, K, Na and Al) for the two depths measured...... 204 Figure 6.5. Data on maximum temperature, minimum temperature and rainfall over the period of the decomposition experiment...... 205 Figure 6.6. Change in tea mass over time with moving average lines...... 206 Figure 6.7. Mass of green tea without the bag after 90 or 91 days in the ground...... 207 Figure 6.8. Mass of rooibos tea without the bag after 90 or 91 days in the ground...... 207 Figure 6.9. Percent mass remaining following loss on ignition of green tea at 550°C after 90 or 91 days in the ground...... 208 Figure 6.10. Percent mass remaining following loss on ignition of rooibos tea at 550°C after 90 or 91 days in the ground...... 208 Figure 6.11. Stabilisation factor, S, as determined by the method of Keuskamp et al. (2013)...... 209 Figure 6.12. Decomposition rate, k, after 90 or 91 days of green and rooibos tea in the ground following Keuskamp et al. (2013)...... 210 Figure 6.13. The heights of trees in the 2018 survey were deducted from the heights in 2015 to give an approximate metric of change between treatments...... 211 Figure 6.14. Biomass of litter on different treatments...... 212 Figure 6.15. Biomass of eucalypt species...... 213 Figure 6.16. Biomass of other vegetation per treatment...... 213 Figure 6.17. Linear regression between litter biomass and eucalypt biomass...... 214 Figure 6.18. Linear regression of litter biomass and other vegetation biomass...... 214 Figure 6.19. Increase in diameter at breast height on a Eucalyptus moluccana growing on Spoil OGM and Rainfall...... 218

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Figure 6.20. From Nussbaumer (2005) where it is Figure 2.4. Corymbia maculata grown in spoil as part of a nutrient omission experiment...... 219 Figure 7.1. Distance in similarity compared to the RSF reference based on flora community composition and soil chemistry...... 229 Figure 7.2. Subsoil Mulch, showing a strong shrub layer but mostly open ground layer. .... 231 Figure 7.3. Subsoil OGM Mulch treatment taken from a distance to show the development of trees...... 232 Figure 7.4. The Spoil treatment (foreground) is a negative control where all propagules were added but no ameliorant was applied...... 234 Figure 7.5. Spoil OGM, highlighting the strong cover of chenopods and grasses in the foreground with a small stand of successful trees in the middle left of the photograph. ... 235 Figure 7.6. Second stage analysis...... 240 Figure 7.7. Graphical description of the stages in long-term ecosystem development...... 243

List of Tables

Table 3.1 List of treatments and references used in Chapter 3 with the number of replicates...... 40 Table 3.2. The average similarity between and within groups following PERMANOVA on data from the Experimental Site in 2015...... 50 Table 3.3. The average similarity between and within groups following PERMANOVA on data from the Experimental Site in 2018...... 51 Table 3.4. Pairwise PERMANOVA of the difference between the 2015 and 2018 surveys. ... 54 Table 3.5. Pairwise comparisons between treatments/factors and the references...... 55 Table 3.6. Site data on the mean height and mean DBH of trees at the Ring Rd and RSF plus or minus standard error of the mean...... 64 Table 3.7. Indices of performance based on exotic species richness, native species richness, average tree height and dissimilarity to the examined reference...... 70 Table 3.8. Key factors determining the class under the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community...... 73 Table 3.9. Plant community types equivalent to or partly equivalent to the targeted EECs. . 74 Table 3.10. Similarity of treatments in each year to the targeted EECs and their best matching plant community type...... 75 Table 4.1. Average 16S similarity between and within treatments...... 112 Table 4.2. Average ITS similarity between and within treatments...... 117 Table 4.3. KEGG Orthologues were grouped according to which treatment they were highest in...... 122 Table 5.1. Pairwise differences in mass between compartments...... 158 Table 5.2. The summed amount of each micronutrient in kg/ha and its ratio to the summed RSF ...... 178 Table 5.3. Ratios of organic carbon to total nitrogen to total phosphorus from the 0–10 cm compartment...... 181 Table 5.4. Similarity of treatments to RSF aggregated over multiple mass and total nutrient characteristics...... 182 Table 6.1. Hypothetical example of how ordering was used to link trees for examination of tree height change between 2015 and 2018...... 200 Table 7.1. Linear estimates of time in years to match the RSF with reference to the oldest Spoil Treatment...... 229

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List of Appendices

Table A1. All of the species considered as part of this work and relevant information about them...... 296 Table B1. KEGG Orthology focusing on nitrogen-cycling pathways...... 317 Table B2. KEGG Orthology focusing on carbon-cycling processes...... 319 Table B3. KEGG Orthology focusing on phosphorus-cycling processes...... 321 Figure B1. Relative abundance of KEGG Orthologue K02586 from PICRUSt analysis...... 323 Figure B2. Relative abundance of KEGG Orthologue K02588 from PICRUSt analysis...... 324 Figure B3. Relative abundance of KEGG Orthologue K10535 from PICRUSt analysis ...... 325 Figure B4. Relative abundance of KEGG Orthologue K10944 from PICRUSt analysis...... 326 Figure B5. Relative abundance of KEGG Orthologue K10945 from PICRUSt analysis...... 327 Figure B6. Relative abundance of KEGG Orthologue K01428 from PICRUSt analysis...... 328 Figure B7. Relative abundance of KEGG Orthologue K00372 from PICRUSt analysis...... 329 Figure B8. Relative abundance of KEGG Orthologue K00366 from PICRUSt analysis...... 330 Figure B9. Relative abundance of KEGG Orthologue K03385 from PICRUSt analysis...... 331 Figure B10. Relative abundance of KEGG Orthologue K00368 from PICRUSt analysis...... 332 Figure B11. Relative abundance of KEGG Orthologue K00376 from PICRUSt analysis...... 333 Figure B12. Relative abundance of KEGG Orthologue K00376 from PICRUSt analysis...... 334 Figure B13. Relative abundance of KEGG Orthologue K04561 from PICRUSt analysis...... 335 Figure B14. Relative abundance of KEGG Orthologue K14660 from PICRUSt analysis...... 336 Figure B15. Relative abundance of KEGG Orthologue K12546 from PICRUSt analysis...... 337 Figure B16. Relative abundance of KEGG Orthologue K01188 from PICRUSt analysis...... 338 Figure B17. Relative abundance of KEGG Orthologue K01225 from PICRUSt analysis...... 339 Figure B18. Relative abundance of KEGG Orthologue K01179 from PICRUSt analysis...... 340 Figure B19. Relative abundance of KEGG Orthologue K01176 from PICRUSt analysis...... 341 Figure B20. Relative abundance of KEGG Orthologue K07405 from PICRUSt analysis...... 342 Figure B21. Relative abundance of KEGG Orthologue K01190 from PICRUSt analysis...... 343 Figure B22. Relative abundance of KEGG Orthologue K01181 from PICRUSt analysis...... 344 Figure B23. Relative abundance of KEGG Orthologue K01178 from PICRUSt analysis...... 345 Figure B24. Relative abundance of KEGG Orthologue K03781 from PICRUSt analysis...... 346 Figure B25. Relative abundance of KEGG Orthologue K05909 from PICRUSt analysis...... 347 Figure B26. Relative abundance of KEGG Orthologue K01183 from PICRUSt analysis...... 348

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Figure B27. Relative abundance of KEGG Orthologue K02040 from PICRUSt analysis...... 349 Figure B28. Relative abundance of KEGG Orthologue K03306 from PICRUSt analysis...... 350 Figure B29. Relative abundance of KEGG Orthologue K06162 from PICRUSt analysis...... 351 Figure B30. Relative abundance of KEGG Orthologue K05306 from PICRUSt analysis...... 352 Figure B31. Relative abundance of KEGG Orthologue K01113 from PICRUSt analysis...... 353 Figure B32. Relative abundance of KEGG Orthologue K01078 from PICRUSt analysis...... 354 Figure B33. Relative abundance of KEGG Orthologue K06135 from PICRUSt analysis...... 355 Figure B34. Relative abundance of KEGG Orthologue K06138 from PICRUSt analysis...... 356 Figure C1. Concentration of iron in separate ecosystem compartments...... 357 Figure C2. Kilograms of iron per hectare...... 358 Figure C3. The same figure as B2 without the soil values...... 359 Figure C4. Concentration of manganese in separate ecosystem compartments...... 360 Figure C5. Kilograms of manganese per hectare...... 361 Figure C6. Concentration of zinc in separate ecosystem compartments...... 362 Figure C7. Kilograms of zinc per hectare...... 363 Figure C8. Concentration of copper in separate ecosystem compartments...... 364 Figure C9. Kilograms of copper per hectare ...... 365 Figure C10. Concentration of Boron in separate ecosystem compartments...... 366 Figure C11. Kilograms of boron per hectare...... 367 Figure D1. Available sodium from soil at two depths, 0–10 cm and 20–30 cm...... 368 Figure D2. Available chloride from soil at two depths, 0–10 cm and 20–30 cm...... 369 Figure D3. Available nitrate from soil at two depths, 0–10 cm and 20–30 cm...... 370 Figure D4. Available phosphorus from soil at two depths, 0–10 cm and 20–30 cm...... 371 Figure D5. Available potassium from soil at two depths, 0–10 cm and 20–30 cm...... 372 Figure D6. Available sulfur from soil at two depths, 0–10 cm and 20–30 cm...... 373 Figure D7. Available calcium from soil at two depths, 0–10 cm and 20–30 cm...... 374 Figure D8. Available magnesium from soil at two depths, 0–10 cm and 20–30 cm...... 375 Figure D9. Available iron from soil at two depths, 0–10 cm and 20–30 cm...... 376 Figure D10. Available manganese from soil at two depths, 0–10 cm and 20–30 cm...... 377 Figure D11. Available copper from soil at two depths, 0–10 cm and 20–30 cm...... 378 Figure D12. Available boron from soil at two depths, 0–10 cm and 20–30 cm...... 379 Figure D13. Available zinc from soil at two depths, 0–10 cm and 20–30 cm...... 380

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Acronyms

AICc – Akaike information criterion, corrected for small sample size

ASV – amplicon sequence variants

CDBH – combined diameter at breast height

CSER – Centre for Sustainable Ecosystem Restoration

CFU – colony-forming unit

DBH – diameter at breast height

dbRDA – distance-based redundancy analysis

DistLM – distance-based linear model

EEC – endangered ecological community

FUNGuild – Concatenation of ‘Fungi’ + ‘Functional’ + ‘Guild’

HSD – honestly significant difference

KEGG – Kyoto Encyclopedia of Genes and Genomes

k – decomposition speed, relating to decomposition of organic matter/tea bags

LMM – linear mixed model

MWOO – mixed waste organic output

NATA – National Association of Testing Authorities

nMDS – non-metric multidimensional scaling

NSTI – nearest sequenced taxon index

OGM® – Organic Growth Medium

PERMANOVA – permutational multivariate analysis of variance

PERMDISP – permutational analysis of multivariate dispersions

PICRUSt – Phylogenetic Investigation of Communities by Reconstruction of Unobserved States

ppm – parts per million

RSF – Ravensworth State Forest

S – stabilisation factor, relating to decomposition of organic matter/tea bags

SIMPER– Concatenation of ‘Similarity’ + ‘Percentages’

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The impossible just takes a little longer

Francis Patrick Scanlon

1935–2020

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Chapter 1 – Introducing Accelerated Ecosystem Development

1.1 Restoration of Plant Communities Post-Coal Mining The field of restoration ecology has a long history in the mining industry; many of the techniques used in restoration of degraded land today were first suggested decades ago by pioneers in restoration science (Dancer, 1975; Bradshaw, 1977; Johnson et al., 1977; Sheldon and Bradshaw, 1977; Johnson et al., 1978; Marrs and Bradshaw, 1980; Marrs et al., 1980a; Marrs et al., 1980b; Roberts et al., 1980). Bradshaw and Chadwick (1980) laid a strong foundation for restoration science by not only describing a vision of what was possible at the time, but also describing many common problems with degraded landscapes. In addition to describing problems such as stability, water supply, nutrient supply, salinity, toxicity and community composition, they provided potential solutions to the issues. They created a decision structure around identifying the restoration target, characterising the site, designing the landscape, improving growing conditions with an amendment, applying an appropriate seed mixture, monitoring development and performing maintenance. These issues, solutions and structures are still discussed and used today with updates and modifications.

A major environmental consideration in coal mine rehabilitation is the properties of the mining spoil – non-target and waste rock from above and between the coal seams. Mining spoil is different from chitter and tailings, which are the residual rock and fluid respectively remaining after washing of the coal, and is often treated separately (Charnock and Grant, 2005; Kossoff et al., 2014). Spoil makes up the base substrate for most of the reconstructed mining landscape, and the layers can be more than 300 metres deep when backfilling into the pit (Kho, 2016). Because spoil comes from a variety of non-target rock strata, it can have a large diversity of properties, even in the same location. Spoils can range from acidic to basic, and have high or low levels of salt, sodium, sulfur, heavy metals and plant-available nutrients and are almost completely lacking organic matter (Nordstrom et al., 2000; Johnson, 2003; Wijesekara et al., 2016). Life in spoil, as in other rock strata, can be made up of members of the deep biosphere (bacteria, archaea and fungi in rock strata (Magnabosco et al., 2018)), although populations are greatly reduced or undetectable compared to populations in soil (Harantová et al., 2017; Kumaresan et al., 2017; Banerjee et al., 2020). The spoil will not have a seed bank; there will be no living flora or fauna. As the spoil is a dominant feature, its

characteristics influence how the post-mining landscape is designed as well as the ability of biota to colonise and grow.

Most restoration (sensu Bradshaw (1997)) globally is in secondary successional landscapes where there is still biological activity (plants, animals, microorganisms) following disturbance. Those species can assist restoration and accelerate recovery. Typically, restoration of complex and threatened communities is performed on secondary successional areas and needs targeted actions. For example, the restoration of the Eastern Suburbs Banksia Scrub Endangered Ecological Community (EEC) in Sydney, Australia, involved careful manual weeding, provision of habitat, and burning to encourage the seed bank (Perkins et al., 2012). Efforts can also target specific causes of degradation: in the Temperate Highland Peat Swamps on Sandstone EEC of the Blue Mountains and Newnes Plateau, Australia, the priority was managing water. Where flows were too high, such as at stormwater outlets, they were reduced by spreading water. Where excessive draining was occurring, the outlets were packed and infilled to reduce water loss; this created waterlogged and anaerobic soil conditions, supporting the native species to outcompete weedy grasses (Hensen and Mahony, 2010). These restoration sites are very different from a mining environment.

Comparatively, primary succession occurs when a disturbance is so severe that no biological legacy remains (Prach and Walker, 2018). Restoration in a primary successional landscape relies strongly on propagules, such as plant seeds and microbial spores, which can be supplied by the restoration practitioner and the surrounding landscape (Prach et al., 2015b). The spoil landscape after mining is primary successional, comparable in disturbance level to areas of glacial retreat and recent volcanic eruptions. Similar human-induced primary successional landscapes can include road surfaces (Walker and Powell, 1999) and demolition sites (Schröder et al., 2018). When working in a primary successional landscape, all biological material needs to be restored. Beginning restoration in a sterile environment with a primary successional landscape can be beneficial because there are no undesirable species present (Prach and Walker, 2018). However, the prolific recommendations to add topsoil and biological material to spoil (Bradshaw and Chadwick, 1980; Zipper et al., 2011; Nussbaumer et al., 2012; Department of Industry Innovation and Science and Department of Foreign Affairs and Trade, 2016) suggest that negative impacts of undesirable species are generally outweighed by the benefits of reintroducing biota. The organisms that live in soil have not

only been estimated to represent 25% of biodiversity (Bach et al., 2020), but can support the developing plant community, among other functional roles (Orgiazzi et al., 2016). This may be particularly important for threatened species and communities as well as species that have been poorly described.

One topic that was acknowledged early as being important and is still discussed today is how to ensure the restored soil can function appropriately (Bradshaw and Chadwick, 1980; Bradshaw, 1983; Feng et al., 2019). Due to expansion of spoil from decompaction after extraction (Kho, 2016), areas with thin topsoil may not have enough of this resource to cover the area required post-mining. Where topsoil is available, it may have been stockpiled. Although a sometimes necessary process, stockpiling can lead to changes in the chemistry (e.g. oxygen availability) or biology (e.g. seedbank viability) of soils (Boyer et al., 2011; Golos et al., 2016), potentially making them less suitable to the plant community and to the communities’ natural regeneration (Rokich et al., 2000a; Golos et al., 2016). Even where direct transfer of topsoil occurs, there can be a large amount of disturbance to the soil system such as changes to physicochemical, organic matter and microbial properties (Bulot et al., 2017). Regardless of the changes in topsoil qualities, use of local topsoil has been considered as the best practice for ameliorating spoil for decades (Bradshaw and Chadwick, 1980; Nussbaumer et al., 2012; Department of Industry Innovation and Science and Department of Foreign Affairs and Trade, 2016). The soil used in restoration is so important because it can have a large impact on the success of the developing plant community. The Forestry Reclamation Approach, developed in the USA, even recommends using weathered sandstone spoil as a layer between topsoil and spoil because it more closely resembles soil than other spoil materials (Zipper et al., 2011). This increases the amount of soil-like material between plants on the surface and the negative qualities of spoil at depth. The proximity of spoil to the rooting zone and low quantities of topsoil are probably a primary reason behind the poor success rates in restoring a diversity of species post-mining in some areas.

Development of strategies to best reintroduce biota and improve primary succession post- mining has been a major area of native vegetation restoration research in the Hunter Valley, New South Wales (NSW), Australia, where restoring a diversity of native flora has been an issue (Gillespie et al., 2001). Early rehabilitation attempts found poor topsoil characteristics, including low pH, low phosphorus levels and in some places high aluminium levels (Dragovich

and Patterson, 1995), as well as poor spoil characteristics with low fertility, coarse texture and high sodicity (Read, 2002). Rehabilitation goals after the NSW introduction of the Coal Mining Act 1973 were to have a similar appearance and similar productivity rate to previous land use (Dragovich and Patterson, 1995); hence, there was not a strong focus on full community composition. Most rehabilitation in the Hunter Valley during the 1980s and 1990s focused on a select range of species, predominantly exotic pasture species and small numbers of indigenous and non-indigenous species from Mimosacea, Myrtaceae and Casuarinaceae (Gillespie and Mulligan, 2003; Huxtable et al., 2005). Tree planting had been successful with specific species in the 1980s and 1990s (Burns, 1989; Briggs et al., 1995); however, these species were noted as being unusual in their ability to grow in the mine rehabilitation area,

and many species in other strata had poorer performance (Cole et al., 2006). With developing understanding that indigenous flora communities may be more suitable, have greater drought resilience and require less maintenance than alternatives such as pastural systems, research began to target how to restore the composition and structure of native systems. Research on restoration in the Hunter Valley in the late 1990s and early 2000s focused on emergence from the seed bank (Read et al., 2000), species selection (Grant et al., 2002) and surface soil characteristics (Read, 2002). These studies provided greater information on the range of species that could be applied and ways to facilitate their establishment, thereby encouraging industry to actively restore large areas of native vegetation. By the early 2000s, it became apparent that there was a looming undersupply of quality topsoil for areas that were required to be returned to native vegetation. Studies into the qualities of various soil ameliorants have been made over the years with varied results but often showing support for use of recycled organics and subsoils (Nussbaumer, 2005; Cole et al., 2006; Kelly, 2006; Kelly, 2008). Recycled organics such as mulch, biosolids and mixed waste organic output (MWOO) increased fertility, soil water-holding capacity, and tree growth (Kelly, 2006; Kelly, 2008). Subsoils can have higher water-holding capacity and may contain propagules of microorganisms and plants, potentially with reduced exotic plant species (Nussbaumer et al., 2012). Due to a concern that reconstructed soils may not provide ongoing nutrition to plants and lack local microbial communities, explorations were made into understanding the microbial associations for long-term sustainable development (Newman, 1996; Fisher, 2010; Newman, 2017). Both Fisher (2010) and Newman (2017) highlighted that there was a level of

specificity in some of the plant-microbe associations and suggested inoculation as a method of restoring microbial diversity.

1.2 Accelerating Ecosystem Development Another issue in restoration relates to business and government timelines because restoration sits within a political, social and economic context (Harris and van Diggelen, 2006). In coal mine restoration, business and government place policy, grant and lease requirements on the mining and restoration process. Once the coal supply has been exhausted from a unit of land, the land becomes a liability for the company (Mackenzie et al., 2007). Although the company can no longer make a profit from this land, it has to fund the restoration and maintain the recovering ecosystem. The maintenance cost depends on the age of the area, the restoration target of the site, the quality of the work and the style of the operator (e.g. many small corrections or a few large corrections). Often it is the more complicated targets, such as specific community compositions, that are less likely to be successful (Brudvig et al., 2017; Laughlin et al., 2017), therefore requiring more work and greater cost to achieve. The long timeframe required by a plant community to develop makes restoration a large financial risk for a mining company because the likelihood of success is uncertain. Although there has been a lot of research into improving the reliability and quality of restoration, there is still much natural variability. The mining company also has to contend with natural hazards, such as floods, fires, drought and disease, that have the potential to devastate a restoration process. If a hazard has a large impact, then the company may have to begin restoration again, an event estimated to cost $40,000 per hectare (Division of Resources and Geosciences, 2018). As such, we can assume there is an economic imperative for industry to successfully restore land as fast as possible to a level of quality approved by the regulator.

In a 2005 perspectives paper on the ‘myths’ of restoration ecology, Hilderbrand et al. (2005) warned that some common assumptions were probably unachievable by the industry at that time. One of the assumptions identified was described as ‘fast-forwarding’ – ‘the idea that one can accelerate ecosystem development’ (Hilderbrand et al., 2005). The idea of fast- forwarding is attractive to industries such as mining because they see good results in other areas and, because of the pressure to restore land quickly, assume they will be able to follow the same method and timeline (Figure 1.1). For example, a mine site may make use of ameliorants that are beneficial in some settings, such as agriculture, and assume that they

will be as useful in a restoration setting. While many ameliorant trials have been performed, an endangered native plant community is unique to an area and requires its own specific work. As another example, it may be assumed that restoration in one location will happen at the same rate and with the same level of success in another. Again, while times to restoration ‘completion’ have been estimated in some settings (Holl, 2002; Koch and Hobbs, 2007; Alday et al., 2011), the unique characteristics of an endangered flora community mean that non- specific work can only be a loose guide.

Figure 1.1. Accelerating the development of an ecosystem seeks to achieve the restoration target earlier than would otherwise occur.

Under an accelerated development scenario, particularly when working with a threatened community, there are a number of expectations. The first and largest priority is that the community composition will be recreated in a shorter time period; otherwise, the aim of restoration would not be achieved. Secondly, it is assumed that the structural and functional characteristics of the ecosystem will also develop and be reproduced faster than a negative control treatment. Finally, there is an expectation that the rest of the ecosystem will be able

to perform in a way that will support the accelerated development of the community during the acceleration process while not compromising its long-term sustainability.

In general, all restoration methods aim to create an acceleration effect. Restoration methods can be varied, such as introducing and removing species, adding topsoil and other ameliorants, and instigating targeted disturbances like fire and mowing. Operators of the Hunter Valley coal mines already know from past experience that spoil is a poor growth medium (Nussbaumer et al., 2012). Even though spontaneous natural succession can be more successful than restoration in some settings (e.g. Prach et al. (2007)), similar methods are unsuccessful in the Hunter Valley, may cause an environmental hazard and have become socially unacceptable. What is unsettled in the Hunter region is the best methodology for accelerating ecosystem development.

Previous research shows that the development of a plant community could potentially be accelerated by manipulating succession, increasing diversity, restoring the microbial community, creating nutrient cycles and removing limiting resources. Each of these methods and their interactions are briefly introduced below, but the background is explored further in each of the relevant chapters in this thesis.

Succession theory could be used to manipulate the surrounding ecosystem to best facilitate the establishment and development of the desired species. For example, Bonilla-Moheno and Holl (2010) found that restoration of mature forest species was best initiated after the initial stages of succession, 8–15 years rather than before 5 years, because this facilitated greater seedling survival.

Many studies link increasing productivity with increasing biodiversity (Reich et al., 2012; Tilman et al., 2012; Tilman et al., 2014; Zuppinger-Dingley et al., 2014; Mensah et al., 2018; Brun et al., 2019). Increasing diversity allows for niche complementarity, where the higher number of species increases the variability in function traits, lowering direct competition and increasing ecosystem function (Mensah et al., 2018). The outcome of this is that resources are better used and there is less waste, leading to increases in productivity. For example, Zuppinger-Dingley et al. (2014) showed in a glasshouse experiment that plant species in diverse communities had increased changes in height and specific leaf area to better access resources than plant species in monocultures. Further, long-term grassland experiments have

shown that improvements in productivity compound over time, feeding back to produce greater productivity (Reich et al., 2012). By increasing diversity, the associated productivity can lead to accelerated development of long-lived species, a determinant of the time required to restore a site (Jones and Schmitz, 2009).

One of the aims of sustainable restoration focuses on the microorganisms that associate with plant species. The functional characteristics of microbes, such as nitrogen fixation and improved resource acquisition, are known to increase the resources available to plants (Franche et al., 2009; Ryan et al., 2012), increasing their ability to develop in poor soil (Wang, 2017). A recent field trial using small amounts of soil as an inoculating medium has shown that the broader community of microorganisms plays a large role in the selection of a plant community (Wubs et al., 2016). Microbes could, therefore, determine the species that are supported in restored flora communities. Additionally, given the modest but positive correlation between flora and microbial diversity shown in meta-analyses (Liu et al., 2020), the diversity of microbes in the new ecosystem could influence the diversity of restored flora. It is expected, however, that the microbial community itself may change from its pre- disturbance composition following actions such as disturbance (Jasper et al., 1991), stockpiling (Harris et al., 1993), mixing with spoil (Fisher, 2010) and the addition of amendments (Ramirez et al., 2012). The microbial community that establishes during restoration could, therefore, either promote or impede the development of plant communities.

The work of Bradshaw et al. (1982) on rehabilitating china clay wastes showed that it took approximately 50 years to complete and renew the nitrogen cycle. This was based on the inability of the community to develop without reliance on nitrogen-fixing species in the early stages of rehabilitation. The authors argued that an approximately 50-year timeframe to recreate a nitrogen cycle capable of supporting the ecosystem was unacceptable for restoration and warranted acceleration. Hobbie (1992) reviewed how plant species affect nutrient cycling and concluded that the nutrient status of the ecosystem was involved in a positive feedback system. For example, the species that grow and develop on low-nutrient soils have adaptations to limit the amount of nutrients they lose to the environment. As such, their litter is of poor nutrient quality with a very high carbon to nitrogen ratio (C:N). This strategy further limits the amount of nutrients that can enter the soil environment and be

recycled back to the plant system. In high-nutrient ecosystems, resources other than nutrients (e.g. light) may be the limiting factor for plant growth. Therefore, the recovery of nutrients from leaves before abscission is a trait less likely to be selected for in high-nutrient ecosystems. These leaves with lower C:N ratios allow higher levels of nitrogen to be acquired by the soil fauna and microbes, supporting their growth and interactions with plants. This maintenance of high nutrition in the soil further supports the growth of the plant community. Since this mechanism was proposed, research has been unable to confirm how this mechanism reacts to other ecosystem factors, such as negative feedbacks from nitrogen release and variations between above-ground and below-ground decomposition (Hobbie, 2015). Although the work of Bradshaw et al. (1982) suggested that nutrient cycling was a primary area for ecological restoration research more than 40 years ago, it was conceptual developments in the 2010s that opened up new avenues for this research (Kardol and Wardle, 2010; van der Putten et al., 2016). Recent work by Horodecki and Jagodziński (2017) suggested that the plant species in use can influence the period needed for restoration on mine sites. In their field study, they examined the effect of pure stands of nine tree species on the rate of litter decomposition and found strong differences in decomposition rates. They hypothesised that selection of species that would lead to changes in the nutrient cycling, increase rate of soil development and improve the quality of soil could overall accelerate the development of the ecosystem.

The final method of accelerating ecosystem development is to provide nutrients, such as treating the soil in a way that removes the barriers for growth. For example, if nitrogen is noted to be low, then a product, such as urea, that is high in nitrogen could be applied. When applied to low-nutrient environments, such a product can increase biomass (Chiarucci et al., 1999); or if it is applied in excess, it can lead to mortality (Magill et al., 2004). Although application of nutrients is a common action, this method could be controversial because the defining characteristics of some ecosystems is that they have limited or excess resources. For example, low-fertility soils are considered one of the driving factors behind the endemic vegetation of the South African fynbos (Cowling et al., 1994; Ojeda et al., 2001). By increasing resource availability, there is a risk of changing the community and potentially losing rare species (Carter et al., 1987; Magill et al., 2004; Reinecke et al., 2014). However, by providing additional resources at the beginning of restoration, the ecosystem may establish faster,

9 which can help it develop into the desired ecosystem faster. For example, accelerated growth of trees may mean earlier establishment of shade and changes in the microclimate. This modification of the environment may facilitate selection of species better suited to shaded environments (Augspurger, 1984; Weltzin and Coughenour, 1990; Mejía-Domínguez et al., 2011), which may assist in selecting for species from the targeted community. Furthermore, the full stock of resources will be needed eventually for the ecosystem to match the reference community; otherwise, its development may be arrested and stall (Bond, 2010).

These five methods of accelerating ecosystem development are all linked and could be occurring simultaneously, making them difficult to separate. However, they each have separate underlying processes. To summarise the differences in mechanisms:

• Successional changes rely on competition and facilitation between various species as well as change in abiotic conditions to select for the desired community. • Improving productivity requires species divergence in resource use (i.e. complementarity), which probably occurs through increases in species richness. • Restoration of the microbial community can assist in the provision of functional and symbiotic services that alter the plant community. • Increases in nutrient cycling use both species selection and nutrient availability to increase the rate of internal processes regulating the ecosystem. • Restoration of soil nutrients removes one of the common limits to growth, which increases the rate at which the ecosystem develops key indicators such as height and reproduction.

1.3 Research Directions This thesis explores the concept of accelerated ecosystem development by testing the success of alternative soil amendment restoration methods that aim to accelerate restoration to the reference state. As a key part of this, the project uses interdisciplinary but related studies (the results of which are presented in Chapters 3–6) to explore and consider the implications for restoration and the fundamental mechanisms behind them.

This exploration is guided by the question:

Are there pathways, and underlying processes, that accelerate the development of an ecosystem without risking the quality of the restoration? 10

The question is explored by examining an experimental site undergoing restoration post coal mining. The site received various ameliorants (i.e. treatments) and plant seeds because of the unavailability of quality topsoil and is compared to multiple reference ecosystems. This research focuses on a study site, described in Chapter 2, where three different types of spoil ameliorants were used. The ameliorants were tested for their ability to produce the target threatened flora communities (Chapter 3) as a fundamental part of this study. Examination of the composition and function of soil microbial communities and the development of an ecosystem nutrient inventory in Chapters 4 and 5, respectively, tested the potential for the ameliorants to support the communities’ long-term development. The rate of key ecosystem processes was examined in Chapter 6, by measuring plant available soil nutrients and decomposition rates as well as measuring and estimating the growth of plants and decomposition rates as a proxy for nutrient cycling.

The null hypothesis is that no Treatment will be different from the negative control, showing no acceleration of development.

Chapter 2 – Study Area Background and Experimental Design

Throughout Chapter 2, the specific background relevant to this study is detailed. The chapter begins by describing the mining process used in the Hunter Valley, New South Wales, Australia, where the study is based, with a focus on processes that influence the outcome of rehabilitation and restoration. Then, the locations studied are detailed and the literature on the specific ameliorants used on the Experimental Site is reviewed. This provides a detailed introduction to the study area but not the experiments performed. As each of the topics examined in this thesis have very different methods, specific sampling design and methods are covered in each of the individual chapters.

2.1 The Mining Process The general mining process used in the Hunter Valley is similar to what has been described by Koch (2007) for bauxite mines of Western Australia; however, more detail is given below. After the relevant approvals have been obtained, all the trees on the surface are felled before being either chipped, sold to a timber mill and/or stockpiled for use as stags and logs in the rehabilitation process. With the vegetation removed, the soil, and sometimes the subsoil, is excavated and, for the initial period of mining at least, typically stockpiled.

Final landform planning is performed from the initial planning stages of the project (Lamb et al., 2015) because early mining typically requires moving spoil (unwanted rock layers), which will not be returned to the pit. Some reasons for not returning the spoil to pit are:

• decompaction of the spoil layers leads to an expansion in volume (bulking factor), often resulting in a 1.15–1.2 times increase in overall volume (Kho, 2016) • greater financial cost to move the spoil back to the pit • it will often be several years before the pit can be filled in, so the spoil needs to be stabilised by vegetation and this provides an opportunity to begin revegetation.

Spoil may be soft on the surface but at depth is typically broken up with targeted explosions using ammonium nitrate (NH4NO3). The spoil is then removed by using draglines where possible or, as is common in the Hunter Valley, truck and shovel operations (Mitra and Saydam, 2012). At the beginning of mining, spoil is placed in an out-of-pit dump initially to allow room for the coal be removed; this is necessary at the start of mining when there is no pit to refill. The spoil is compacted as part of the landform construction. Once the desired

12 landform has been built, ameliorants are placed on the surface followed by ripping to incorporate the material and reduce surface compaction (Bacon and Humphries, 1987).

For native vegetation restoration, habitat structures such as rock piles, stags and logs may then be placed on the surface as appropriate. Reintroduction of plant species from the target community is timed to match expected rainfall wherever possible. The target community is typically the vegetation present at a site before the mining operation or it could be based on what is advocated for by society. Both the broadcast of plant seeds and the planting of tubestock are common on mines in the region, although for trees tubestock planting has been more successful and cost-effective when there is competition from exotic species (Nussbaumer et al., 2012).

2.1.1 Characteristics of Spoil in the Hunter Valley In the Upper Hunter Valley, spoil is characterised as having:

• high pH (8–10) (only acidic when associated with sulfide minerals (Johnson, 2003)) • high levels of leachable salt • high levels of sodium with associated crusting • highly dispersive particles that make it prone to gullying • a moderate to low strength, which deteriorates rapidly on wetting • generally moderate to low nutrient levels • moderate sulfur levels (Scanlon (2015) reported values between 50 and 200ppm) • hydrophobic properties when mixed with large amounts of coal • moderate to low water holding capacity • a large proportion of particles >2 mm diameter • poor soil structure development (Fityus et al., 2008; Scanlon, 2015; Newman, 2017).

The poor characteristics of spoil are not conducive to growth of many plant species, so ideally topsoil is used as a surface substrate and ameliorant following mining. The characteristics of topsoil and its depth of spreading can have a large effect on the final landscape. Many mines in the Hunter Valley, however, have an insufficient amount of topsoil to restore their plant communities. This may be due to:

• losses in collection and transport

• poor quality soil (which could have more negative properties than spoil, such as high salinity, weeds, contamination or acid sulfate soils) • mining through already mined/disturbed areas • shallow or eroded soils due to the agricultural history and/or • the increase in surface area of the post-mining landform.

Many different ameliorants for spoil have been trialled, including the use of subsoil, fertiliser, biosolids, mixed waste organic outputs, mulch, gypsum, zeolite, vermicompost, fly ash, chitter and various microbial inoculants (Cole et al., 2006; Kelly, 2008; Nussbaumer et al., 2012; Castor et al., 2016). Each of these ameliorants has various positive and negative characteristics, with no single ameliorant being determined as optimal based on prior research. When considering ameliorants, industry has been encouraged by stakeholders to favour products that will have additional benefits for the broader environment, such as those that transform waste or prevent waste from entering landfill.

2.2 Study Sites This study was performed using three sites that are within 12 km of each other. The main experiment and one of the references were on Ravensworth Operations. Separate references were on the Mt Owen Complex and Ravensworth State Forest (Figure 2.1).

2.2.1 Experimental Site and Background to Ravensworth Operations Ravensworth Operations is a group of open-cut and underground coal mines located approximately 20km north-west of Singleton and 230km north of Sydney, NSW. The Ravensworth North mine within the Ravensworth Operations Project is relatively young, the development application being approved in 2011. The mine can produce up to 16,000,000 tonnes of run of mine (material presented for processing) coal per year, which is used for both the domestic and export markets. Ravensworth North uses a truck and shovel operation targeting the Foybrook and Burnamwood Formations within the Sydney Basin, which date to the Late Permian (259.1–251.9 million years ago) (Cohen et al., 2013:updated; Geoscience Australia and Australian Stratigraphy Commission, 2017). The spoil is therefore composed of Late Permian lithic sandstone, siltstone, shale and conglomerate from the layers between these formations.

Figure 2.1. The Ravensworth Region. There are a number of local coal mines in close proximity. Ravensworth Operations pit is directly to the south of the red square, whereas Mt Owen pit is to the south-east of the blue circles and south of the yellow triangles. Image provided by Ravensworth Operations from July 2019.

2.2.1.1 Pre-Mining Conditions Information on the pre-mining conditions of the site is provided by pre-mining reports prepared for the site (Umwelt (Australia) Pty Limited, 2010b; Umwelt (Australia) Pty Limited, 2010a)by .

Large amounts of the area mined at Ravensworth Operations was regrowth on land fit for grazing with irregular cultivation (Rural Land Class V) (Umwelt (Australia) Pty Limited, 2010b). It was estimated that 55% of the disturbance area was vegetated with woodland that was between 26 and 35 years old and only 3.5% was vegetated with woodland older than 42 years (Umwelt (Australia) Pty Limited, 2010a). The age of vegetation pre-mining highlights that the area was significantly disturbed and had a history of clearing for agricultural purposes (Figure 2.2). Despite the disturbance history, there is still considerable diversity in the mining project area with 275 native flora species and 93 exotic flora species found in pre-mining surveys

(Umwelt (Australia) Pty Limited, 2010a). Mapping prior to mining classed a large amount of the mine as Central Hunter Box–Ironbark Woodland (Umwelt (Australia) Pty Limited, 2010a), which was identified by the New South Wales (NSW) Scientific Committee as an Endangered Ecological Community (EEC) in 2010 (Major, 2010a). There was also a smaller area of Central Hunter Ironbark–Spotted Gum–Grey Box Forest (Umwelt (Australia) Pty Limited, 2010a) which was listed as an EEC in 2010 (Major, 2010b). EEC is a legally defined term of the Biodiversity Conservation Act 2016 and is described as an ecological community that ‘is facing a very high risk of extinction in Australia in the near future’. The EECs have been identified by a Threatened Species Committee to match risk criteria and may have management plans developed to reduce future impacts.

The soils are from the Liddell Soil Landscape, of which the yellow duplex soils are the most common. These broadly match to yellow natric kurosols and yellow sodosols soils based on the Australian Soil Classification (previously classified as yellow soloth and solodic soils following Northcote (1979)) (Isbell and National Committee on Soil and Terrain, 2020). Soils in the area are shallow, moderately acidic (pH 4.5–6.5), sandy clay loam to loam A horizon

and sometimes have a bleached A2 horizon (Umwelt (Australia) Pty Limited, 2010b). The B horizon is typically alkaline (though pH can range between 6–9.5), ranging in texture from sandy clays to heavy clays (Umwelt (Australia) Pty Limited, 2010b). Where these soils occur in drainage lines, they have shown a high susceptibility to erosion (Umwelt (Australia) Pty Limited, 2010b). There were also brown and red duplex soils as well as alluvium and gradational soils in some areas; however, these were less common (Umwelt (Australia) Pty Limited, 2010b).

Pre-mining surveys recorded 180 species of fauna, including 13 threatened species. Most of the species identified were birds (116), followed by mammals (32), reptiles (18) and amphibians (14) (Umwelt (Australia) Pty Limited, 2010b).

Figure 2.2. The project area for Ravensworth Operations marked on an aerial photograph from 1967 (modified from Umwelt (Australia) Pty Limited (2010b). The Experimental Site is located within the green circle. The photograph highlights the amount of land that was cleared for agriculture prior to 1967.

2.2.1.2 Review of Experimental Treatments A major summary of research performed in the restoration of mined land in the Hunter Valley recommended further trials of ameliorants for spoil (Nussbaumer et al., 2012). Suggested ameliorants included subsoils (particularly in combination with gypsum and organic ameliorants), composts (including mixed waste organic output (MWOO) and green waste), mulch and biosolids. Ravensworth Operations and members of the Centre for Sustainable Ecosystem Restoration (CSER) [then] based at the University of Newcastle decided which ameliorants would be used and their application rates. Decisions were based on availability of material, costs and additional discussions with researchers, mine environmental officers and producers of products.

The following sections review the academic literature on the ameliorants used.

Subsoil The study of subsoil as an ameliorant in mine rehabilitation has been rare because many mines mix the topsoil and subsoil layers together (Nussbaumer et al., 2012) or, rarely, lay it down as a true subsoil layer (Rokich et al., 2000b). When non-dispersive subsoil has been used locally, it has been found to greatly improve the success of rehabilitation by increasing native plant establishment, species richness, plant growth and second-generation seedling establishment (Nussbaumer et al., 2012). The positive findings from subsoils may be related to their formation process, which tends to increase clay particles. Generally, soil formation processes produce a gradient with increasing concentration of fine particles lower in the profile. This occurs through illuviation – suspension of the clay particles in soil water and their filtering through lower parts of the soil profile (Leeper and Uren, 1993; Buurman et al., 1998; Torres-Sallan et al., 2017). Soils high in clay content have higher water-holding capacity and can supply available water for longer. For example, Rhodes grass (Chloris gayana) had increased survival in a glasshouse experiment where subsoils were used, probably because of a sustained supply of water (Wehr et al., 2005). Increasing the clay content also increases the cation exchange capacity; this can increase the ability of a soil to store cations and buffer against changes.

Subsoils typically have fewer seeds than topsoils and the seeds present are older (Rydgren and Hestmark, 1997), which reduces the likelihood of exotic and native species being present and might make it necessary to reintroduce species (Nussbaumer et al., 2012). Although lower 18 in diversity and probably with different community compositions, subsoils do contain microbial communities, which are probably adapted to the conditions found away from the surface (Fritze et al., 2000; Agnelli et al., 2004).

OGM OGM® (Organic Growth Medium) is a commercial product produced by Global Renewables™ and classed as MWOO by the NSW Environment Protection Authority. OGM is classed as MWOO locally; however, this type of product is broadly classed in the scientific literature as municipal solid waste compost. The key characteristics of MWOO are that it is produced from general residential waste stream, where contaminants are sorted at the receiving facility. Products that have contaminants sorted at the source (e.g. by the household) are not considered MWOO. There are a variety of methods for the creation of MWOO compost, with each company having its own stockpiling, aeration (or lack of) and watering regime, and adaptations are made based on the qualities of the input product, such as high levels of grass clippings over summer. The key similarity is the feedstock, which is general household waste.

Because of the variability in the creation process and final properties of the product, this study specifically names OGM as the product of study. The general process to generate OGM is as follows. On arrival, the waste is sorted to remove products of high value (such as aluminium) and hazards (such as batteries). The remaining material is then lightly shredded to open up plastic bags, in order to release the contents, especially kitchen organics. The material is processed through a composting system that utilises aeration, turning and moisture control for 4–6 weeks (Michael Bonanno, personal communication 2018). Visually, the final product is a brown compost with some impurities such as glass and plastics. Chemically, the product is variable but typically has very high levels of organic matter, high levels of total and plant available nutrients and moderate to high levels of salts. Further details of physical and chemical properties derived from a sample of the commercial OGM product used in this study are presented in section 2.3.5.

MWOO compost generally increases the carbon to nitrogen ratio of soil compared with other agricultural fertilisers and composts, particularly with reapplications (Garcıá -Gil et al., 2004; Hargreaves et al., 2008; Chalhoub et al., 2013). Total nitrogen concentrations of the compost have been reported as between 10 and 30 g/kg, and typically about 10% will be made available in the first year after application (Hargreaves et al., 2008), although higher figures 19

(i.e. 22%) have been reported (Hadas and Portnoy, 1997). The increase in available nitrogen from MWOO compost is generally agreed to be less than that from inorganic mineral fertilisers (Hargreaves et al., 2008); however, the compost may not lose as much to leaching (Diez et al., 1997). In addition to the nitrogen being in forms less likely to be leached, increases in the microbial population may be responsible for immobilising more nitrogen with compost application (Crecchio et al., 2004). Other nutrients are comparatively less well studied. Zhang et al. (2006) reported that although nitrogen and sulfur release from compost was high in the first year, phosphorus and potassium release was steady throughout the 4-year study (although they did also apply P and K fertilisers throughout the study). Limited study has been performed on the microbial community in MWOO compost. A study by Crecchio et al. (2004) identified broad groups such as archaea, ammonia oxidisers and actinomycetes and the production of a range of enzymes such as β-glucosidase, nitrate reductase, dehydrogenase, protease, phosphatase and urease.

Initial trials of MWOO compost as an ameliorant on mine landscapes of the Hunter Valley has shown promising results, admittedly focusing on tree species that already performed well in local mine rehablitation. MWOO improved the growth of Corymbia maculata with no negative impact on photosynthesis in a pot trial (Rainsford, 2010; Nussbaumer et al., 2012). Mercuri et al. (2005) found MWOO increased tree growth in a trial aiming for forestry development but also encouraged development of weed species. Kelly (2008) examined MWOO in a study on the Narama Mine, an open-cut coal mine in Ravensworth, NSW, and found that it improved survival of Corymbia maculata, and increased soil organic matter, total nitrogen and total phosphorus. A study on Ashton open-cut coal mine by Spargo (2012) (also see Spargo and Doley (2016)) found that MWOO improved tree growth in woodland plots and ground cover; however, it was also noted that weeds were able to proliferate.

The international literature has shown known risks to both people and the environment from MWOO composts. Physically, the compost may contain sharp materials such as crushed glass and needles. Yuksel (2015) determined that although heavy metals in MWOO compost were not in excess of the allowable legal limits, they may create a risk for human and environmental health at applications higher than 10t/ha in soils with a pH>7. A review by Hargreaves et al. (2008) found levels of cadmium, molybdenum, arsenic, mercury and selenium were occasionally over the recommended levels for Canada, but not USA. A recent study by

Bourdat-Deschamps et al. (2017) concluded that the risk to soil organisms was low from pharmaceuticals and personal care products, but noted that MWOO contained anti- inflammatory compounds such as ibuprofen and diclofenac as well as carbamazepine and fluoroquinolones. However, variability in the feedstock and processing can have a large effect on the level of risk. For example, a test performed for this work of heavy metals and organic pollutants in OGM returned concentrations below national and international standards for heavy metals and negative for organic pollutants (tests by AMAL Analytical (a National Association of Testing Authorities (NATA) accredited lab) and SWEP (Australasian Soil and Plant Analysis Council member)). It should be noted though that the NSW Environment Protection Authority (EPA) has discontinued approval for the application of MWOO products (NSWEPA, 2020). Similar to in Europe, some states in Australia are moving towards source- separated organic products, such as green waste composts, following evidence of reduced physical and chemical contaminant loads (WCA, 2019).

Woodchip Mulch A broad review by Chalker-Scott (2007) found many benefits from the application of general (organic, inorganic and synthetic) mulches to the ground surface. Mulches improve soil moisture, reduce erosion, reduce compactability, reduce extreme temperature fluctuations, improve germination, increase survival, reduce weeds, reduce disease, bind heavy metals, increase resistance to stress events, and reduce the number of applications of pesticides and fertilisers required. There are also drawbacks: deep layers of mulch prevent propagules from establishing and do not distinguish between weeds or desirable species, whereas shallow mulches may provide an ideal environment for germination. Mulches from some species can be allelopathic, potentially limiting growth through the release of inhibiting substances (Schumann et al., 1995; Tabaglio et al., 2008). Poor management of mulch can also contaminate it with hazardous chemicals, diseases, weeds or pests. While the Chalker-Scott (2007) review found many benefits of mulches, it was in reference to their placement on the soil surface, while this study was performed with mulch integrated into the soil.

Locally in the Hunter Valley, woodchip mulch has been used in a number of trials because the clearing of land for mining creates a source of material. Work on Hunter Valley mines showed that mulch increases the organic matter of a soil (giving a high C:N ratio), produces microsites for seed capture and germination, increases water infiltration, reduces evaporation and

21 moderates soil temperature fluctuations (Nussbaumer et al., 2012). On Mt Owen, mulch has been shown to increase plant establishment (both in regards to plant density and species richness) from broadcast seed, especially during times of low rainfall (Read, 2002; Nussbaumer et al., 2012). Read (2002) found on Mt Owen that mulch contributed to some nitrate depletion but the gains in water penetration, plant emergence, survival as well as lower evaporation and erosion, outweighed the loss in fertility.

Incorporation of woodchip mulches into the soil horizon is not a standard restoration practice, but research on the incorporation of other types of organic matter suggests there may be benefits to the practice. For example, Layman (2010) found that complete soil profile rebuilding, which involved the incorporation of leaf litter compost into the recreated subsoil layer, gave improved results 8 months after set-up. Compared with topsoil application, roto- tilling of topsoils and a control, the incorporation of organic matter into subsoil had the greatest reduction in bulk density (at 15–20 cm depth) and led to an increase in carbon to nitrogen ratio, pH, and fertility throughout the soil profile (Layman, 2010). The same site was later found to also have higher hydraulic conductivity in subsurface layers (Chen et al., 2014).

2.2.1.3 Experimental Site Design and Construction This section describes the design and construction of the Experimental Site as it relates to this body of work; additional information can also be found in Castor et al. (2016). In September 2013, treatments for the Experimental Site were placed on an out-of-pit spoil dump that was in the process of being actively rehabilitated. The starting landscape was a spoil slope facing north-east, which rose beyond the study site above the surrounding landscape (Figures 2.3 & 2.4). The treatments studied were combinations of Spoil, Subsoil, Mulch and OGM (Figure 2.5). A local topsoil from a forested area (referred to as ‘Forest Topsoil’) was used as a positive control in each block. As Spoil is a negative control, this makes the site a 23 factorial orthogonal design, with an additional Forest Topsoil plot per replicate. Each treatment is an area 15 x 15 m, with a 10 x 10 m plot inside, which is replicated six times in a block design (Figures 2.6, 2.7 & 2.8). Although the location of the ameliorant application in a block was random, ameliorant treatments were applied next to each other because of the practicalities of working with large machinery. For example, the application of OGM to a block meant that Spoil OGM was always next to Spoil OGM Mulch (Figure 2.6). All treatments were examined as part of the flora study (Chapter 3), whereas only the Spoil, Spoil OGM, Subsoil Mulch and

Subsoil OGM Mulch treatments were examined to answer the research questions posed in the other chapters.

Figure 2.3. Landscape of the restoration area that the experiment was situated in, taken in February 2018 by Brenton Hubert.

Figure 2.4. Ravensworth Operations Open Cut Coal Mine, January 2018. The yellow box contains the Experimental Site; the red box contains the area used for the Ravensworth Offset reference.

Figure 2.5. Close-up of the materials used. From left to right the images are: Spoil, OGM and a Subsoil Mulch Treatment showing the Mulch on the surface and orange coloured subsoil.

Figure 2.6. Construction of the Experimental Site on Ravensworth Operations.

Figure 2.7. Layout of treatments. Each treatment square is 15 x 15m with a 10 x 10 m plot inside it as shown in Block 1

Figure 2.8. Aerial image of the site from June 2014. Treatments with OGM can be easily picked out because of the large amounts of green growth compared with other treatments. However, erosion lines can also be seen entering some of the plots and some areas of OGM contamination can also be seen.

Because of the time that has elapsed since the Experimental Site was constructed, there was no opportunity in the current study to directly characterise the soil ameliorants before or during their application. However, information collected 3 months after site set-up indicates their likely qualities at the time of application. The Forest Topsoils (i.e. positive control) were brown, with a sand to loam texture and low to very low levels of organic matter. The Subsoil treatment is from the Liddel formation, which is classified as yellow natric kurosols and yellow

25 sodosols and has a range of textures from light clay to sandy loam. There is no record of the definition used to distinguish topsoil from subsoil, so we can only assume it was based on the depth of soil collected. Although there is some variability in OGM due to seasonal changes in the feedstock, the material collected for this study appeared consistent with original material applied to the site. A sample collected for this study is described in section 2.3.5. Woodchip mulch (referred to as the ‘Mulch’ treatment) came from trees in the local area, predominantly Allocasuarina leuhmannii.

The quantity of material used in each treatment was based on previous studies and recommendations from suppliers. All applications of material and ripping were performed by heavy vehicles. Spoil came from the mining pit and formed the underlying material for all treatments. Subsoil and Forest Topsoil were directly transferred from areas of active mining, and they were placed as an estimated 300 mm layer over the spoil. The OGM was transported by truck from Eastern Creek in Western Sydney and applied at a rate of 100 dry solid tonnes per hectare – the equivalent of 6m3/plot. The Subsoil and OGM treatment plots were then slowly ripped four times. Ripping was performed to between 10 and 20 cm depth. The Mulch was then applied at 6m3/plot and received a separate single pass to rip into the other substrates. Some manual spreading of non-incorporated Mulch material occurred to make the application more even on the ground surface. Placement of substrates occurred in September 2013. The restoration treatments were completed in November 2013 when each plot and its surrounds were seeded with exact quantities of mixes of a selection of seeds from 50 species (see Appendix A). The species used either belonged specifically to the targeted EECs, Central Hunter Grey Box–Ironbark Woodland and Central Hunter Ironbark–Spotted Gum–Grey Box Forest, or were locally common natives found in association with those EECs. Seed selection for broadcast was based on the target flora community and the availability of local provenance seed. In addition to the 50 species in the seed mix, six herbaceous perennial species were grown to a juvenile stage and the tubestock was planted in each plot.

The intended use of the land following mining is a native woodland/forest with the same floral composition that existed before mining. Both of the state-listed Central Hunter Grey Box– Ironbark Woodland and Central Hunter Ironbark–Spotted Gum–Grey Box Forest EECs (Major, 2010a; Major, 2010b) were present before mining. These two EECs have a moderate degree of overlap in composition, with 42% of the characteristic species being shared between the

26 two communities. They are also both included in a broader federal listing, known as the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community (Threatened Species Scientific Committee, 2015). The restoration target is the recreation of either, or both, state EECs and the matching of the characteristics for the federal EEC. This is consistent with Gann et al. (2019) advocating for the creation of multiple references to inform the restoration targets. The Experimental Site was 5 years old in November 2018; the field component of this study was performed between March 2018 and June 2020.

The Experimental Site has experienced some issues that were outside the control of the researchers and reflects the nature of, and the importance of rehabilitating, spoil landscapes. The mine spoil is high in sodium, leading to it being very dispersive and eroding easily (Figure 2.9). The drain above the site was re-excavated in 2015 in an attempt to reduce erosion without having heavy machinery entering the study area, but this was not effective over the long term. Reparation works were performed in the study area in 2016 as gullies, particularly in Blocks 3 and 6, were becoming unacceptably deep (Figure 2.10). The reparation works involved placing new substrate (from an unknown source) in the major gullies. However, the only plots impacted by this were the Spoil and Spoil OGM treatments in Blocks 3 and 6 and these were excluded from the study. This means that while the initial study design had six replicates for all treatments, only four replicates were available for Spoil and Spoil OGM in the current study.

Figure 2.9. Aerial image of the Experimental Site on Ravensworth Operations from late 2015. Gullies can be seen developing through the site at this time.

Figure 2.10. Aerial image of the Experimental Site on Ravensworth Operations from January 2018. Red arrows indicate the slight change in soil colour that indicates repair. The left-most arrow shows repair in Blocks 2 and 4; however, the buffer around each plot was sufficient to prevent an impact on the plots examined in the current study. The middle arrow shows repair between Blocks 2 and 3 as well as 4 and 5 where again, there was minimal impact on the plots. However, the right arrow shows repair through Blocks 3 and 6 that was directly impacting two Spoil and Spoil OGM replicate treatments, which were excluded from the current study.

There were also incidences of localised and accidental spreading of material from the Mulch and OGM treatments into plots that they should not have been in during site set-up. This issue was thoroughly documented and, as the buffer areas surrounding the plots were sufficiently large (5m wide), the problem of treatment contamination was avoided by moving the sampling area into unimpacted buffer areas as necessary.

The Experimental Site was open to an array of native and exotic fauna, including kangaroos, lace monitors, echidnas, rabbits, bees, ants, spiders and birds. Although these fauna can perform many beneficial activities, such as pollinating flowers, they also assist in the immigration of exotic seeds, trample and eat plants and spread microbial communities, which may add complexities to this study. Finally, weeds in the ground layer were an issue just after the initiation of the site and hand weeding of invasive exotic species (predominantly Galenia pubescens) was performed between January and November 2014 to help establish the seeded species and reduce future liability.

The Experimental Site also had many positive attributes that would be difficult to find elsewhere in the Hunter Valley. The greatest advantage of the site was the diversity of native plant seeds that were applied. Projects in the Hunter Valley that have used more than

20 species are rare; this study applied 50 species. For example, Spargo and Doley (2016) only applied nine species of tree and shrub with no groundcover species to the native vegetation section of their experiment. The other important aspect is the length of time that the study ran for. Most studies only assess success after one or two years (Mercuri et al., 2005; Cole et al., 2006; Mercuri et al., 2006; Spargo, 2012; Spargo and Doley, 2016), some studies have been able to run for 3–4 years (Briggs et al., 1995; Nussbaumer, 2005; Kelly, 2008; Castor et al., 2016), but this thesis examines variables for more than five and a half years after restoration began. The only older published experiments in the Hunter Valley are a 7–8-year study by Brown and Grant (2000) on pasture areas and 8.5 years in forest restoration by Nussbaumer et al. (2012).

2.2.1.4 Findings of Scanlon (2015) and Castor et al. (2016) The Ravensworth Operations Experimental Site was studied over the first 3 years of its development as part of an Honours research project (Scanlon, 2015) and a major report (Castor et al., 2016). The inception of the experiment was based on previous research showing that forb species were disproportionately missing from rehabilitation areas and the need for additional ameliorant options (Nussbaumer et al., 2012). The work of Scanlon (2015) and Castor et al. (2016) had two points of focus: the composition of the broad plant community following application of different soil ameliorants, and barriers to life cycling of a small number of perennial herbaceous species. It was hypothesised that the use of different soil ameliorants would clarify the reasons why some species were not establishing and persisting in mine restoration, leading to off-target flora communities.

Flora community composition was examined by identifying and counting all individuals of native species and by identifying all exotic species in each plot. Subsoil and Forest Topsoil plots were found to be the most similar of the treatments to the desired EECs, after 3 years, even though they were both nutritionally deficient. Treatments containing Subsoil typically had higher species diversity, although some plots had higher exotic plant cover. OGM application to treatments had variable species richness but development of ground cover was high, which is a priority for the mining industry. Mulch application to a treatment generally improved flora diversity and reduced bare ground cover but some areas had high weed levels, probably because of poor handling of materials.

The barriers to life cycling study by Scanlon (2015) and Castor et al. (2016) targeted six species: Calotis lappulacea (Asteraceae), Chrysocephalum apiculatum (Asteraceae), Desmodium brachypodum (Fabaceae), Einadia nutans subsp. nutans (Chenopodaceae), Hypericum gramineum (Hypericaceae) and Swainsona galegifolia (Fabaceae). Individuals of each species were grown to a juvenile stage and planted into the treatments and their survival was followed for almost 2 years. Survival, based on proportional hazards regression models, was largely dependent on the soil ameliorant. Three species showed higher survival in treatments containing Subsoil (C. lappulacea, D. brachypodum and H. gramineum). Results for treatments with OGM were inconsistent, with three species having higher survival (C. lappulacea, E. nutans and S. galegifolia) and two species having lower survival (C. apiculatum and H. gramineum). All species, except E. nutans, had higher survival in treatments containing Mulch.

The ability of the six perennial herb species to form a second generation was also assessed. The only species found to be susceptible to seed predation by ants, based on seed removal from modified Petri dishes, was E. nutans. However, E. nutans, C. lappulacea and C. apiculatum were found to have produced a second generation. Although survival of the second generation was poor, probably because of competition, it was hypothesised that C. lappulaceae, C. apiculatum and E. nutans are likely to sustain populations on site because of their sizeable populations. D. brachypodum and S. galegifolia still had good survival from the initial planting but no second generation was detected, making it unclear if these species would be present on the site in the long-term future. It is not expected that H. gramineum will sustain on site because it had poor survival, and no second-generation seedlings or juveniles were found.

The dependence of vegetation on soil characteristics was linked to strong differences in soil characteristics. Three months after the site was set up, four pseudo-replicate soil cores were taken from each plot and combined. Soils were air dried at 40°C and sent for analysis at SWEP Analytical Laboratories for available plant nutrients; soil physical characteristics were analysed by standard methods in the Hancock soil laboratory at the University of Newcastle. Spoil had high bulk density, high pH, high sodicity and moderate sulfur content that created a difficult environment for plants to establish and grow in. Soils with Subsoil applied had fewer characteristics that would limit plant establishment, as the Subsoil had moderate pH and

30 generally low levels of nutrients. OGM altered soil chemistry the most, with its application increasing a range of plant available nutrients, organic matter and cation exchange capacity. There were, however, high salt levels, and in some treatments with OGM there were spikes of excessively high nitrogen and potassium. For soil chemistry, the addition of Mulch made little difference statistically, only lowering electrical conductivity and available sulfur. It was expected that the large pieces of mulch were selected against in field sampling and sieving prior to chemical analysis because treatments with Mulch had no change in organic matter.

2.3 Reference Sites

2.3.1 Selection of Reference Sites The Hunter Valley has experienced extensive clearing since colonisation and there are few records of its original condition. Ideally, multiple reference sites are chosen to represent the range of ecosystem characteristics that originally existed as a model for restoration outcomes (Gann et al. 2019), but this is not possible for this ECC in this area. Therefore, references were selected based on the availability of ecosystems that would come closest to the original condition. In this case, the best available reference was the Ravensworth State Forest (RSF), which is described below. Three other references are used throughout the current study to compare specific details that the best available reference may not be able to show. The local reference Rav Ref is extremely close to the study site and may have more similar microbial and soil properties. The Ring Rd reference provides a more realistic reference for plant development as it is on previously mined land. Additionally, Ring Rd provides an intermediate age site to compare progress towards restoration completion. Finally, as the original OGM applied to the Experimental Site utilised information provided by the supplier, a fresh sample was gathered for addition analyses.

2.3.2 Local Reference Site on Ravensworth Operations – Rav Ref We used a reference site in the native vegetation offset (area subject to improvement or protection to compensate for the degradation of another, such as a mine pit) of Ravensworth Operations, abbreviated as Rav Ref (red box in Figure 2.4). Although native vegetation, these areas lack species diversity and are of a different plant community (Central Hunter Bull Oak Forest) to the intended EEC targets. It is also not an undisturbed ecosystem, as the aerial image from 1967 shows that the site had been cleared (Figure 2.2) and it has therefore regrown over the last 50 years. This is, however, the nearest vegetation to the Experimental

Site, located only 85m away, and, as Prach et al. (2015b) argues, it is important to consider where propagules for local microorganisms and flora may migrate from (Prach et al., 2015b).

2.3.3 Mt Owen Complex – Ring Rd Reference Sites Mt Owen Open Cut Coal Mine is approximately 30km north-west of Singleton and no more than 14km from Ravensworth Operations. Rehabilitation began on Mt Owen in the late 1990s; however, as with many sites at this time, poor records were kept of the specific rehabilitation actions taken. It is believed that the site performed direct transfer of topsoil and spreading seeds of species, the list for which was modelled on the adjacent Ravensworth State Forest as part of the consent conditions and comprises mainly of Central Hunter Ironbark–Spotted Gum–Grey Box Forest EEC. The three sites examined on Mt Owen were rehabilitated in 1998, using these methods, they have had more than 20 years of development since restoration began (blue circles in Figure 2.1). Although the seed mix and soils were different from those found on Ravensworth Operations, the target plant community is partially the same as the Experimental Site and the community is developing into a mature stage of restoration. This site is used as an intermediate reference for what is likely to occur with time, when restoring similar ecosystems on spoil in the Hunter Valley.

A large amount of research has been performed at the Mt Owen Complex over the last 20 years, which has been explored in detail in Nussbaumer et al. (2012). To keep nomenclature clear between multiple sites used in various studies, the collective term used for sites on Mt Owen is based on their location adjacent to Ring Rd.

2.3.4 Ravensworth State Forest – RSF Reference Sites RSF is on the eastern edge of Mt Owen’s restoration area and is accessed through Mt Owen (yellow triangles in Figure 2.1). As one of the last remnants of native vegetation on the floor of the central Hunter Valley, it is particularly important for the conservation of the native flora and fauna of the area. It contains examples of the target EECs and will be used as a ‘best available’ reference as we are unaware of the existence of a more appropriate and less disturbed reference. The area was logged in the early 1900s as well as being grazed by horses and cattle until the early 1990s, but it is uncertain if the sites were ever completely cleared. Stray cattle are occasionally still found in the forest, particularly when fences are broken. It generally has a low level of weeds throughout, most commonly common prickly pear (Opuntia stricta) and Fire weed (Senecio madagascariensis). 32

2.3.5 Fresh OGM Because samples of the original OGM product applied to the Experimental Site in 2013 were not stored, additional samples of OGM from the same supplier were sourced directly from Global Renewables for the current study. That is, samples were collected 5 years after the original application was made to the Experimental Site from fresh compost mounds at the production facility in Eastern Creek. It is understood that there had only been minor changes in processing since 2013. Samples were used in the microbial study (Chapter 4) to indicate the microbial composition of the raw material. Samples were also combined and a single subsample was tested for fertility, heavy metals and organic contaminants. Testing for organic contaminants was by AMAL Analytical while fertility and heavy metals were tested by SWEP Analytical Laboratories (Australasian Soil and Plant Analysis Council member). All results for heavy metals and organic contaminants were substantially below Australian Certified Organic and National Association for Sustainable Agriculture Australia recommended limits. The sample was 44.2% organic matter and had total levels of: nitrogen 11.1 kg/t, phosphorus 3.6 kg/t, potassium 5.8 kg/t and sulfur 2.1 kg/t. Cations were dominated by calcium at 11,960 ppm (parts per million) (62.5% of cation exchange capacity) and sodium at 4508 ppm (20.5% of cation exchange capacity).

2.4 Climate of the Central Hunter Valley Long-term data were sourced from the nearest verified Bureau of Meterology station that had been open for at least 50 years. The nearest long-term station, Bowmans Creek (station number 061270) does not collect temperature data, so Lostock Dam (station number 061288) was also utilised.

Bowmans Creek had an average annual rainfall of 852.6 mm between 1969 and 2019 (Figure 2.11). Rainfall has been lower during the study period, however, with only 651, 626.2 and 525.6 mm falling in 2017, 2018 and 2019 respectively.

300

250

200

150

100 Monthly Rainfall (mm)

0 Jul Jul Jan Jan Jun Jun Oct Oct Apr Apr Sep Feb Sep Feb Dec Dec Aug Nov Nov Mar Mar May May 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Figure 2.11. Monthly Rainfall at the Bowmans Creek weather station (Bureau of Meteorology site number 061270), 21km north-west of the Experimental Site. Blue bars are monthly total rainfall, the orange line is a 12-month moving average of the rainfall. Bureau of Meteorology has verified this site. The Experimental Site was constructed in September 2013. Due to the frequent reference to the work of Newman (2017) in Chapter 4, this graph includes April 2011 when that site was constructed.

The highest mean monthly temperatures occur in January, and the lowest in July (Figure 2.12). Over the study period, the average maximum temperature in January at the nearby Lostock Dam was 34.3°C whereas in July it was 18.9°C. Minimum temperature ranged from an average of 18.4°C in January to 5.9°C in July. The Bureau of Meteorology (2020a) potential frost days per annum suggests the area would receive fewer than 10 frosts based on a minimum temperature less than 0°C.

40 C) ° 35

5 MeanMonthly Maximum and Minimum ( 0 Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Sep Sep Sep Sep Sep Sep Sep Sep Sep May May May May May May May May May May 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Mean Maximum Temperature Mean Minimum Temperature

Figure 2.12. Mean monthly temperature at Lostock Dam (Bureau of Meteorology site number 061288), 45.5km east-north-east of the Experimental Site. Orange line is the monthly mean maximum temperature and green line is the monthly mean minimum temperature. Bureau of Meteorology has verified this site. The Experimental Site was constructed in September 2013. Due to the frequent reference to the work of Newman (2017) in Chapter 4, this graph also includes April 2011 when that site was constructed.

Chapter 3 – Examining Variation in the Restoration of an Endangered Flora Community

3.1 Introduction

3.1.1 Importance and Benefits of Biodiversity The difficulty of restoring highly biodiverse landscapes compared with reintroducing agricultural or forestry species was first acknowledged by Bradshaw (1983) in his presidential address to the British Ecological Society. Globally, restoration of biodiversity (sensu Rands et al. (2010)) is still a challenge, with many sites improving from a degraded state but not attaining reference level biodiversity or ecosystem services (Benayas et al., 2009). In countries where both biodiversity and standards for restoration are high, achieving targets and goals that focus on taxonomic composition can be difficult (Brudvig et al., 2017). Australia is a country with two internationally recognised biodiversity hotspots: the Forests of East Australia and Southwest Australia (Mittermeier et al., 2011). It also has some of the strongest restoration standards globally. Yet, Australia has an exceedingly high mammal extinction record (Woinarski et al., 2015) and it is the only developed country in the world to be a deforestation front (WWF, 2018). When biodiversity in the surrounding landscape is in decline, restoration of high levels of biodiversity can be even more difficult but becomes increasingly urgent.

Globally, biodiversity is recognised as having a wide range of provisioning, regulating, supporting and cultural benefits (Millennium Ecosystem Assessment, 2005; WWF, 2018). Therefore, the biodiversity of an ecosystem may be able to facilitate the restoration process. While there was a broad hypothesis on how biodiversity benefited an ecosystem dating from the 1950s, it was only from the 1970s that the underlying mechanisms began to be discussed and were proven both theoretically and empirically (McNaughton, 1977). A review by Tilman et al. (2014) concluded that biodiversity allows for an increase in community productivity, above what could be achieved in a monoculture. This conclusion is also supported by a meta- analysis of 67 field studies, which were able to separate the effects of biodiversity and abiotic conditions on the production of biomass (Duffy et al., 2017). With increases in species richness, character displacement, the variation in characterisics between species, allows plant species to utilise different niches and resources, or the same resource in a different way,

36 promoting the coexistence of multiple species (Svanbäck and Bolnick, 2007; Zuppinger- Dingley et al., 2014). Higher diversity also increases functional redundancy, where multiple species perform the same functional role, which has a positive effect on community stability and resilience to disturbance events (Biggs et al., 2020). The diversity of flora communities may also influence the ability of exotic species to invade the system. At small spatial scales (<30m2), there is a strong trend for increasing native biodiversity to coincide with decreasing ability of exotic species to invade the system (Fridley et al., 2007; Oakley and Knox, 2013). While at larger spatial scales there can be a positive relationship between native and exotic diversity, it has been suggested that this may be due to environmental heterogeneity and confounding factors (Rejmánek, 2003; Fridley et al., 2007).

3.1.2 Restoring Biodiversity in the Hunter Valley The Hunter Valley is a biodiverse region because it is the southern, northern or western distribution limit for a number of species (NSW National Parks and Wildlife Service, 2003). The Great Dividing Range to the west is also of relatively low relief, which may have facilitated greater migration between inland and coastal areas over evolutionary timescales (Peake, 2006). This makes the Hunter Valley particularly floristically diverse with 22% of plant species being at the extremities of their distribution (Peake, 2006). However, it has been estimated that 76% of vegetation has been lost in the Hunter Valley since European settlement (Peake, 2006) and that there has been at least a 65% decline in the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community (Threatened Species Scientific Committee, 2015).

Similar to the challenges experienced in many coal mine restoration projects around the world, restoration of biodiversity in the Hunter Valley following mining has been difficult (Gillespie et al., 2001). For example, only 54% of the plant species in the Ravensworth State Forest (RSF) in the central Hunter Valley have been seen on Mt Owen mine rehabilitation so far, and many of those have not been able to establish a second generation (Nussbaumer et al., 2012). In particular, the understorey and ground layers have had low success rates and only a small number of species successfully have been established. These lower layers are crucial to the restoration of biodiversity because they comprise approximately 78% of the species richness in the Central Hunter Grey Box–Ironbark Woodland and Central Hunter Ironbark–Spotted Gum–Grey Box Forest EECs. Additionally, a key part of restoring the

37 community composition formally is to meet the criteria for federal- and state-listed EECs. Federally, the target is the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community. To achieve class A, B or C grading, the community must sustain an understorey of at least 12 native species as well as a ground layer of native grasses, herbs and shrubs (Threatened Species Scientific Committee, 2015). A more complex target is the NSW listings of the Central Hunter Ironbark–Spotted Gum–Grey Box Forest or the Central Hunter Grey Box–Ironbark Woodland, the latter being the preferred target due to its dominance across the site pre-mining. This target has greater requirements on the specific species present and requires a higher number of species as well as structural and functional characteristics to be met.

As has been highlighted in previous chapters, the soil conditions on restoration sites may be different from the references, which could be a factor in the poor establishment of members from the target flora communities. There is growing evidence that soil characteristics can select for community composition, and therefore influence biodiversity. In particular, soil fertility can have an effect on the community composition (DiTommaso and Aarssen, 1989; Foster et al., 2011; Kardol et al., 2013; Larios et al., 2017; Gafta and Peet, 2020). For example, Rebele (2013) found that high nutrient levels suppressed the development of tree species because of the increase and competitive ability of a tall herbaceous species. However, it is still largely unknown how soils can be used in restoration to select for the desired plant community. Previous work in the Hunter Valley has shown that some herbaceous perennials, a group needing large improvement in establishment and sustainability success, benefit from higher fertility soils whereas others do not benefit (Scanlon, 2015; Castor et al., 2016). The effect at a larger scale and for longer timeframes is still largely uncertain.

3.1.3 Research Direction With such a high level of importance placed on the soil for future outcomes, it is important to understand what the soil can produce regarding biodiversity and the correct EEC. This chapter asks:

Which treatment develops the flora community towards the reference at an increased rate?

The chapter aims to compare flora community composition, diversity and structure (tree height and ground cover) to determine which soil treatment has the most in common with the targeted references.

It was hypothesised that: (i) each soil ameliorant treatment will affect vegetation (measured by its performance compared with reference sites); and (ii) one or more of the treatments will have a greater measurable effect on the composition, diversity and structure of the vegetation than the other(s). As the application of Subsoil has been shown to benefit many species in previous mine restoration studies (Nussbaumer et al., 2012; Castor et al., 2016), it is expected to be beneficial to the growth of plants and diversity in the current study. Because of its high fertility, the Organic Growth Medium (OGM) is expected to be beneficial to vegetation growth and cover, but research (Fraser et al., 2015) suggests it may have negative effects on composition and species richness. The literature review suggests that Mulch may increase species richness but have no effect on plant growth and cover.

3.2 Methods

3.2.1 Additional References In addition to the use of the Rav Ref, Ring Rd and RSF sites as references in this study (described in Chapter 2), this chapter also uses a collection of reference species lists (Table 3.1). The ‘Reference Lists’ include formal descriptions by the NSW government for the Central Hunter Grey Box–Ironbark Woodland and Central Hunter Ironbark–Spotted Gum– Grey Box Forest as well as the federal government listing Central Hunter Valley Eucalypt Forest and Woodland Ecological Community. Both NSW communities are encompassed in the broader federal listing. The species lists for each EEC by the NSW government only highlight characteristic species; to remedy this, lists are also used that include associated species (S. Cox, 2013, personal communication). The NSW and federal Reference Lists are necessary as an additional type of reference that is consistent with the systems used by regulators to assess the successful completion of the restoration processes. Therefore, they provide an indication of how regulators may evaluate restoration outcomes.

A list of species present in the North Offset as compiled by Umwelt (Australia) Pty Limited (2010a) is also included; this is the broad area to the north of the study site. Finally, there is a list for the total area of Ravensworth Operations mine site (Umwelt (Australia) Pty Limited,

2010a). These two Reference Lists are included as a guide to the total possible species richness, therein a proxy of biodiversity, of the whole region.

As part of this study, only a single survey plot was performed in the Rav Ref because of its very different flora community (Central Hunter Bull Oak Forest).

Table 3.1. List of treatments and references used in Chapter 3 with the number of replicates. Note that there are seven Reference Lists, which were each produced using a number of replicates.

Experimental site treatments (replicates) References (replicates)

Forest Topsoil (6) Rav Ref (1)

Spoil (2015: 6, 2018: 4) Ring Rd (3)

Spoil Mulch (6) RSF (3)

Spoil OGM (2015: 6, 2018: 4) Reference lists (7)

Spoil OGM Mulch (6)

Subsoil (6)

Subsoil Mulch (6)

Subsoil OGM (6)

Subsoil OGM Mulch (6)

3.2.2 Flora Community Full site surveys were performed in November 2015 for the Experimental Site and in November 2018 for the Experimental Site and references. Within each 10 x 10 m plot, a complete search was undertaken and every adult and juvenile native plant species was identified and counted. Individuals with cotyledons were considered seedlings and were not included. Every exotic plant species was identified and noted as present in that plot, but individuals were not counted. Identification of plant species was based on the National Herbarium of NSW (Royal Botanic Gardens and Domain Trust, 2019). A complete species list can be found in Appendix A. Species that are known to be clonal or to ramet and were growing

40 in obvious clumps were counted as one individual. Species with a maximum height equal to 1 m or less had their percentage cover per plot estimated visually (Scanlon, 2015). In the text, exotic species will be followed by a ‘*’ to differentiate them from natives.

Analysis of species richness and native Shannon–Wiener diversity was performed using JMP Pro 14 (JMP®, Version Pro 14. SAS Institute Inc., Cary, NC, 1989–2019). Species richness and native Shannon–Wiener diversity were examined using a linear mixed model (LMM) (West et al., 2006; Harrison et al., 2018) with ‘block’ always as a random factor. Comparisons between treatments used ‘treatment’ as a fixed factor. Comparison between survey years used ‘year’, ‘treatment’ and the ‘interaction between year and treatment’ as fixed factors. Ring Rd and RSF were compared with the 2018 survey as the data were collected at a similar time. The application of Subsoil, OGM and Mulch (referred to as ‘factors’) were analysed as a full factorial of fixed effects. Under the full factorial model, there were no situations where the three-way interaction was significant so this factor was removed from the model for parsimony. Native Shannon–Wiener diversity was transformed by being squared and analysed using LMMs for differences between treatments and factors. All LMMs were followed by Tukey post-hoc tests if they produced a result lower than α=0.05, typically pairwise results from the Tukey test are presented.

Flora community data were analysed using PRIMER 7.0.13 with permutational multivariate analysis of variance (PERMANOVA+ 1) (PRIMER-e (Quest Research Limited), Auckland, New Zealand). Abundance data for the Experimental Site were square-root transformed and analysed using Bray–Curtis dissimilarity. Note that exotic species only have richness information, not abundances. Although exotic species have still been included, their impact on analysis based on abundance will be downweighted because their maximum value is 1. When comparing to all references, the data were transformed using presence/absence because some references were only in species list format. Cover data were transformed using log(x+1). Non-metric multidimensional scaling (nMDS) was used to display relationships between plots (Anderson et al., 2008). SIMPER (similarity percentages) (Clarke, 1993) was used to indicate the distinguishing species between treatments and factors, and PERMANOVA (Anderson et al., 2008; Anderson, 2017) was used to look for statistical differences in community composition between treatments and factors. PERMANOVA was performed using permutation of residuals under a reduced model with Type III sums of squares when

41 performed on multiple levels and using an unrestricted permutation of raw data when analysing a single level. Every analysis used 10,000 permutations. Permutational analysis of multivariate dispersions (PERMDISP) was used to ensure that assumptions for PERMANOVA were being met. Differences between years in a treatment were considered based on a Bray- Curtis dissimilarity matrix of centroids for treatments by year and PERMANOVA with year and treatment as fixed effects. The differences between years were also summarised in an nMDS based on centroids of treatments for both years. A distance-based linear model (DistLM) was developed to suggest which early soil conditions were related to the current flora community. This was performed using the ‘best’ selection criteria to examine all possible predictor variable combinations, judged using the AICc (Akaike information criterion, corrected for small sample size) (Burnham and Anderson, 2004) and these were displayed using dbRDA (distance-based redundancy analysis) (Anderson et al., 2008; Clarke et al., 2014). Soil data for this model were obtained from Scanlon (2015) for Blocks 1, 2 and 5, which constrained the flora replicates to the same blocks.

3.2.3 Trees Data on trees are necessary for comparison against the federal listing and were collected in earlier surveys. Tree heights were measured on the Experimental Site in December 2015 and March 2018. Because of the young age of the site and logistics at the time, only individuals ≥1.5 m in height were measured in the 2015 survey. All trees had their height measured in the 2018 survey. Brachychiton populneus and Callitris endlicheri were excluded from analyses of tree height because they are comparatively slow growing, still very small and skewed the data. Diameter at breast height (DBH) was also measured in 2018 for all trees at or greater than 1.3 m (Paul et al., 2013); trees shorter than breast height were given a value of 0. Because 6% of the trees had more than one major trunk, the diameters of each trunk were combined to give a more appropriate value. Combined diameter at breast height (CDBH) was determined from the formula DBH + DBH + + DBH , where n is the nth trunk of 2 2 2 an individual tree (Vaz Monteiro� et al.,1 2016; USDA2 Forest⋯ Service,𝑛𝑛 2020). For Ring Rd and RSF, data were acquired from previous work where DBH and height had been measured (C. Castor, 2015, personal communication). These data do not contain DBH data for any specimen under 5 cm DBH, as such the average of the DBHs is overestimated. Caution should be used when observing the reference data because they were sampled from a 1000 m2 area as opposed to 42 the 100 m2 plots on the Experimental Site. Trees on the Rav Ref were not studied because its canopy species composition was very different.

Preliminary analysis showed that tree species did not play a role in results, so all tree species were combined for further analysis.

Difference in number of trees was analysed using a generalised linear model with a Poisson distribution and log link function (Zuur et al., 2009; O'Hara and Kotze, 2010) in Statistical Package for the Social Sciences (IBM SPSS Statistics version 27). Pairwise comparisons were performed with corrections following the Bonferroni method (Freund et al., 2010). The Spoil treatment was excluded from the 2018 analysis; its inclusion would be inappropriate because no trees were present. Treatment and factors were analysed separately as fixed effects, and block was included in both analysis as a fixed variable. For the analysis of factors, the three- way interaction of Subsoil, Mulch and OGM was not performed for parsimony; all other crosses were examined.

Separate analyses were performed to examine height and CDBH differences between treatments in 2015 and 2018 because of differences in the data collection methods (outlined above). Height and CDBH were analysed using JMP® Pro 14. Tree height data were fourth- root transformed in 2015 and square-root transformed in 2018; CDBH was transformed using + 1. Analysis used a LMM with block as a random variable and separately analysing

√treatment𝑥𝑥 as the fixed variable, then the factors as fixed variables with crosses between each factor, but no three-way interaction due to the absence of Spoil. Following the LMM, Tukey post-hoc test was performed. The Spoil treatment was removed from the analysis of height and CDBH; its inclusion would be inappropriate because there were no trees on it in 2018 and only B. populneus in 2015.

3.2.4 Identifying the Treatment Most Similar to References and Targets Indices were created to compare the variables of a treatment as a whole to a reference. Indices were constructed after elimination of correlated variables to avoid weighting of unrelated underlying processes. The indices used were exotic species richness (square-root transformed), native species richness, average tree height (square-root transformed) and dissimilarity to the examined reference. Dissimilarity results were taken from pairwise SIMPER analysis; the dissimilarity for the reference examined was calculated by subtracting

43 its similarity from 100. Each of the indices was averaged across plots of a treatment within each year. This was then subtracted from the reference average and divided by the standard deviation of the averages to standardise the result. The absolute values for each of the indices were taken and summed for each treatment within each year to provide a metric where higher values indicate that a treatment within a year is less similar to the reference. The reference sites used were the 2018 Forest Topsoil treatment, Ring Rd and RSF. This is consistent with the International Standards for Ecological Restoration (Gann et al. 2019) recommendation to use multiple reference sites to evaluate restoration success, irrespective of whether pre-disturbance (i.e. baseline) conditions for the restoration site are known. The 2018 Forest Topsoil treatment was chosen as a reference for this comparison because the use of topsoil from the local area is considered ‘best practice’ (Department of Resources Energy and Tourism, 2009; Department of Industry Innovation and Science and Department of Foreign Affairs and Trade, 2016). Community dissimilarity comparisons for 2018 Forest Topsoil treatment, Ring Rd and RSF were all square-root transformed and analysed using Bray-Curtis dissimilarity. In addition to the reference sites, a composite reference indicator was constructed (hereafter known as ‘Ideal’). The Ideal used as indices: 0 exotic species, the maximum average native species richness value (from 2015 Subsoil Mulch, used in the absence of pre-mining plot data), the maximum average tree height (from RSF) and dissimilarity to the Reference Lists group of species (transformed using presence/absence and Bray–Curtis dissimilarity). This reference indicator was based on the ideas in Gann et al. (2019) that multiple references provide a clearer reference model.

Matching the outcome of treatments to the federal listing involved comparing data to guidelines for the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community (Threatened Species Scientific Committee, 2015). For comparisons to state listings, all plant community types in NSW were downloaded from the BioNet Vegetation Classification system (Release 3.2.4) (NSW Department of Planning Industry and Environment, 2020). Bray–Curtis dissimilarity in PRIMER 7 was performed with the total list of species from each treatment compared with the listed species in each state-defined plant community type from BioNet. This was to ensure that there was not a plant community type that better matched the treatment than the targeted EEC.

3.3 Results

3.3.1 Diversity Metrics 3.3.1.1 Species Richness Summary of findings:

• There has been a decline in species richness over time. • Subsoil Mulch has the highest flora species richness on the Experimental Site, whereas Spoil has the lowest richness. • Addition of Subsoil and Mulch ameliorants particularly increase richness.

Overall, when considering both native and exotic species, species richness declined between the 2015 and 2018 surveys of the Experimental Site (F(1,81.6)=28.7, p<0.0001).

Native species richness was highest in the Subsoil Mulch treatment for both 2015 and 2018 (Figure 3.1). Native species richness was significantly lower in Spoil than in all other treatments in both 2015 and 2018 (2015: Spoil OGM to Spoil: t=3.45, p=0.0327; 2018: Spoil Mulch to Spoil: t=4.38, p=0.0043).

In 2015, native species richness was significantly higher in treatments with Subsoil and treatments with Mulch (Subsoil: F(1,36)=181, p<0.0001; Mulch: F(1,36)=40.5, p<0.0001). There was also an interaction where application of OGM in the absence of Subsoil increased native species richness, application of both Subsoil and OGM gave higher native richness but the highest native species richness was from application of Subsoil without OGM had higher native species richness (Subsoil*OGM: F(1,36)= 32.8, p<0.0001).

In 2018, increases in native species richness were significant with the addition of Subsoil (F(1,34.89)=65.7, p<0.0001), Mulch (F(1,34.89)=26.8, p<0.0001) and OGM (F(1,32.76)=5.5, p=0.025). There was a significant interaction where addition of OGM without Subsoil increased native species richness but when Subsoil was present produced no change (F(1,32.76)=7.4, p=0.010).

Figure 3.1. Species richness of native and exotic flora in 2015 and 2018. The line in each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range. Letters represent Tukey pairwise tests within a grouping, i.e. lettering for 2015 Native is A–D, 2018 Native is J–M, 2015 Exotic is Q–T and 2018 Exotic is W–Z. Treatments that do not share a letter within their series are considered significantly different. Reference plots only have 2018 data.

Exotic species richness was significantly lower in Spoil in 2015 (F(1,81.2)=44.4, p<0.0001). The LMM gave a significant difference between treatments in 2018 (F(11,36.1)=2.52, p=0.0178); however, Tukey post-hoc tests did not meet the alpha for pairwise difference (Spoil OGM to Forest Topsoil: t=3.46, p=0.0539). In 2015, exotic species richness was significantly higher with the application of each factor (Subsoil: F(1,36)=24.9, p<0.0001; Mulch: F(1,36)=10.8, p=0.0023; OGM: F(1,36)=15.5, p=0.0004). There was a significant interaction in 2015 between Subsoil and OGM (F(1,36)=4.6, p=0.0384), where the absence of both factors resulted in significantly lower exotic richness. In 2018, the only significant difference between factors was an interaction between Mulch and OGM, where the addition of either factor on its own resulted in significantly higher richness than with both factors combined (F(1,32.2)=10.5, p=0.0028).

3.3.1.2 Native Diversity Summary of findings:

• Subsoil and Mulch ameliorants both increase the Shannon–Wiener diversity of native species.

Overall, between 2015 and 2018 there was a decrease in Shannon–Wiener diversity among the native species (F(1,83.1)=18, p<0.0001). In 2015, Subsoil Mulch, Subsoil OGM and Subsoil OGM Mulch were all significantly higher in diversity than Spoil OGM and Spoil (Subsoil OGM Mulch to Spoil: t=6.1, p<0.0001) (Figure 3.2). In 2018, Spoil continued to be low in diversity, significantly different from all treatments with Subsoil (Subsoil OGM to Spoil: t=4.16, p=0.0089). The low diversity in Spoil was due to not only low species richness but also having the lowest number of individuals on site (2015: Subsoil to Spoil: t=6.88, 2018: Spoil OGM Mulch to Spoil: t=4.3).

The diversity was strongly increased in both 2015 and 2018 by the presence of Subsoil (2015: F(1,35)=34.2, p<0.0001; 2018: F(1,32.3)=39.2, p<0.0001) and Mulch (2015: F(1,35)=17.3, p<0.0001; 2018: F(1,32.3)=37.2, p<0.0001). There were two interactions in 2015: firstly, the addition of either Subsoil or Mulch factor on its own had a large effect, but combining them did not compound effects as much as would be expected from an additive response (F(1,35)=13.4, p<0.0001). Secondly, adding OGM to Spoil produced a significant increase in diversity, but if Subsoil was also present, then OGM had a greatly reduced effect (F(1,35)=4.6, p=0.0393). There was also an interaction between OGM and Mulch in 2018, where the increase gained from adding both OGM and Mulch was less than adding just Mulch on its own (F(1,35)=7.8, p=0.0085).

Although the RSF had a high native species richness, its Shannon–Wiener diversity index was lowered because each plot had more than 280 individuals of Lomandra spp.

Figure 3.2. Shannon–Wiener diversity index for all native species in both 2015 and 2018. The line in each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range. Letters represent Tukey pairwise tests within a grouping, i.e. lettering for 2015 Natives is A–C and for 2018 natives is X–Z. Treatments that do not share a letter are considered significantly different. Reference plots only have 2018 data.

3.3.2 Community Composition 3.3.2.1 Differences Between Treatments on the Experimental Site Summary of findings:

• Subsoil and OGM ameliorants are the prime drivers of community composition on the Experimental Site. • Most treatments on the Experimental Site were significantly different from all others, particularly in 2015. • Addition of Subsoil promoted development of Acacia sp., whereas addition of OGM promoted development of members of the Chenopodiaceae family.

In both years, there was a strong difference in flora composition between treatments (2015: Pseudo-F(8)=7.5, p=0.0001; 2018: Pseudo-F(8)=6.6, p=0.0001) (Figures 3.3 and 3.4, Tables 3.2 and 3.3). Both the nMDS and PERMANOVA show a clear and significant difference between plots with the application of Subsoil (2015: Pseudo-F(1)=14.77, p(perm)=0.0018; 2018: Pseudo-F(1)=6.26, p(perm)=0.0099) and application of OGM (2015: Pseudo-F(1)=7.70,

48 p(perm)=0.0055; 2018: Pseudo-F(1)=8.58, p(perm)=0.0117). Application of Mulch had a significant effect in 2015 (Pseudo-F(1)=7.12, p(perm)=0.004) but not in 2018 (Pseudo- F(1)=1.80, p(perm)=0.1647).

There were two significant interactions in 2015:

• Subsoil and Mulch (Pseudo-F(5)=3.75, p(perm)=0.0267) • Subsoil and OGM (Pseudo-F(5)=4.35, p(perm)=0.015).

Figure 3.3. nMDS plot of all treatments on Ravensworth Operations in 2015 based on square- root transformed native and exotic species composition. Similarity ranges from 0 (no relationship) to 100 (complete similarity).

Table 3.2. The average similarity between and within groups following PERMANOVA on data from the Experimental Site in 2015. Similarity ranges from 0 (no relationship) to 100 (complete similarity). Green cells are significantly different from each other (p<0.05); grey cells are comparing within-group similarity.

Treatment Forest Spoil Spoil Spoil Spoil Subsoil Subsoil Subsoil Subsoil Topsoil Mulch OGM OGM Mulch OGM OGM Mulch Mulch Forest 65.2 Topsoil

Spoil 18.9 43.3

Spoil 41.3 35.1 57.6 Mulch Spoil 26.7 27.6 43.4 52.8 OGM Spoil OGM 39.3 26.7 52.8 51.7 60.1 Mulch

Subsoil 55.3 23.0 45.2 32.7 44.7 57.6

Subsoil 50.4 14.7 36.9 28.6 41.5 53.3 55.3 Mulch Subsoil 45.6 22.5 46.2 44.8 54.5 50.9 47.2 60.1 OGM Subsoil 45.1 18.8 44.0 38.8 53.4 51.2 52.4 57.2 58.2 OGM Mulch

Figure 3.4. nMDS plot of all treatments on Ravensworth Operations in 2018 based on square- root transformed native and exotic species. Similarity ranges from 0 (no relationship) to 100 (complete similarity).

Table 3.3. The average similarity between and within groups following PERMANOVA on data from the Experimental Site in 2018. Similarity ranges from 0 (no relationship) to 100 (complete similarity). Green cells are significantly different from each other (p<0.05); grey cells are comparing within-group similarity.

Treatment Forest Spoil Spoil Spoil Spoil Subsoil Subsoil Subsoil Subsoil Topsoil Mulch OGM OGM Mulch OGM OGM Mulch Mulch Forest 59.2 Topsoil

Spoil 21.1 49.1

Spoil 40.1 34.5 48.5 Mulch Spoil 22.7 30.6 34.2 57.5 OGM Spoil OGM 35.2 30.7 42.5 51.9 62.4 Mulch

Subsoil 51.0 28.2 45.6 27.9 39.6 52.9

Subsoil 45.5 18.1 40.2 25.3 38.5 46.7 56.2 Mulch Subsoil 36.5 26.7 39.9 50.8 58.6 42.3 42.2 60.8 OGM Subsoil 38.9 25.2 40.8 40.3 55.7 43.3 47.8 56.2 57.7 OGM Mulch

However, there were significant responses from PERMDISP for each factor in each year: Subsoil (2015: F(1,46)=12.1, p(perm)=0.005; 2018: F(1,42)=5.8, p(perm)=0.037), Mulch (2015: F(1,46)=13.0, p(perm)=0.004; 2018: F(1,42)=7.9, p(perm)=0.014) and OGM (2015: F(1,46)=16.4, p(perm)=0.004; 2018: F(1,42)=22.6, p(perm)=0.001). Anderson and Walsh (2013) found that an unbalanced design with larger dispersions from the group with a smaller number of samples was more likely to reject the null hypothesis. In 2015, all comparisons are balanced, as is the OGM comparison in 2018, so the PERMANOVA results are considered robust. In 2018, the Subsoil and Mulch comparisons are unbalanced because there are 24 plots with Subsoil compared with 20 without, and similarly there are 24 plots with Mulch compared with 20 plots without. Subsoil and Mulch also have lower deviations from their centroids. As the smaller group has larger average deviations, both Subsoil and Mulch are more likely to reject the null hypothesis (Anderson and Walsh, 2013). Therefore, interpretation of the nMDS plots in Figures 3.3 and 3.4 as well as the strength of the pseudo- F and p values should be considered in the analysis of Subsoil and Mulch factors. Given the

51 distinct difference shown in the nMDS plot and high pseudo-F value, it is highly likely that there is a significant difference with the addition of Subsoil. It is, however, unlikely that Mulch had a significant effect on the community composition in 2018 as its nMDS is unclear and did not return a significant PERMANOVA test.

The primary differences in community composition with both natives and exotic species considered was the proportion of dominant species. SIMPER analysis on the 2015 data showed high numbers of Chloris truncata and Atriplex semibraccata on treatments without Subsoil, whereas Acacia amblygona, Acacia decora and Acacia falcata were dominant on Subsoil treatments. With Mulch application, Euphorbia spp., Acacia falcata, Eucalyptus moluccana and Corymbia maculata were more common, whereas Chloris truncata, Atriplex semibraccata and Salsola australis were more common without Mulch. OGM application brought a higher abundance of Einadia polygonoides and Einadia nutans subsp. linifolia, whereas areas without OGM had higher numbers of Acacia amblygona and Acacia falcata.

In 2018, the high numbers of Enchylaena tomentosa had an overriding effect on the SIMPER analysis even though the only large difference in average abundances was an increase with application of OGM. Treatments without Subsoil application had higher numbers of Atriplex semibraccata and Chloris truncata, whereas there were higher numbers of Acacia amblygona, Einadia nutans subsp. linifolia and Acacia decora on Subsoil. OGM application was more distinctive, with higher abundance of Enchylaena tomentosa, Einadia nutans subsp. linifolia, Atriplex semibaccata, Einadia polygonoides and Einadia nutans subsp. nutans, whereas areas without OGM had higher numbers of Acacia amblygona.

3.3.2.2 Differences Between Years on the Experimental Site Summary of findings:

• With the exception of Spoil, the community composition of each treatment changed between 2015 and 2018. • Many of the changes were due to reductions in exotic species and a decline in the species that were common in 2015.

Comparing at the treatment level, there was a significant change in community composition between the 2015 and 2018 surveys (Pseudo-F(1)=21.3, p(perm)=0.0024) (Figure 3.5, Table 3.4). There was a significant interaction between year and treatment

(Pseudo-F(8)=3.62, p(perm)=0.0001). On a pairwise basis, with the exception of Spoil, every treatment in 2018 had a significantly different community composition from those in 2015 (Table 3.4). The reduction in replicates for Spoil was probably a reason for it not being significantly different between years, as the unique permutations decreased for it and Spoil OGM. By using a dissimilarity matrix, it was found that the largest change in similarity between the two survey points was for Subsoil OGM Mulch (Table 3.4). SIMPER analysis showed the large abundance changes between Subsoil OGM Mulch were from the reduction of many exotic species such as Conyza spp.*, Lysimachia arvensis* (syn. Anagallis arvensis), Cirsium vulgaris* and Gomphocarpus fruiticosa*. There were also reductions in natives such as Hardenbergia violacea and Kennedia rubicunda. Other treatments with larger changes, such as Spoil OGM Mulch, Spoil OGM and Subsoil OGM, also had similar large reductions in exotic species.

The changes in community composition over the whole site featured large declines in species that were common in 2015 and increases in other species. Notable species that declined between 2015 and 2018 were Chloris truncata, Acacia falcata, Austrostipa scabra, Salsola australis, Cynodon dactylon and Euphorbia spp. This was associated with rise of Enchylaena tomentosa, Einadia nutans subsp. linifolia, Einadia polygonoides, Einadia nutans subsp. nutans, Aristida spp. and Austrostipa verticillata. Some major species maintained their dominance over the period, including Acacia amblygona, Atriplex semibraccata, Acacia decora and Corymbia maculata.

Figure 3.5. nMDS of square-root transformed native and exotic species composition based on centroids of treatments from different survey years. Solid shapes represent treatments during the 2015 survey; hollow shapes are from the 2018 survey.

Table 3.4. Pairwise PERMANOVA of the difference between the 2015 and 2018 surveys. This was produced using a resemblance matrix from distances between centroids of each treatment in both years. Significant p(perm) values are in bold. Values in the far right column are shaded to show the degree of dissimilarity, where white indicates the least change and dark green indicates the most change.

Treatment t statistic p(perm) Unique Dissimilarity permutations Forest Topsoil 2.48 0.0062 9374 27.8

Spoil 2.09 0.0556 5153 30.8

Spoil Mulch 1.76 0.0275 9425 26.4

Spoil OGM 2.24 0.0317 5099 39.3

Spoil OGM Mulch 3.59 0.0022 9392 40.3

Subsoil 2.17 0.0117 9376 31.4

Subsoil Mulch 2.80 0.0014 9423 36.9

Subsoil OGM 3.31 0.0028 9409 39.6

Subsoil OGM Mulch 3.52 0.0018 9395 41.2

3.3.2.3 Comparing the Experimental Site to the References Summary of findings:

• Each treatment was significantly different from the references. • Differences are related to species that are present in the references but absent on the Experimental Site or absent from the references but present on the Experimental Site.

There are three groupings that can be compared with as references: the Ring Rd, RSF and the Reference Lists. Comparing these three groups to treatments and to factors by using pairwise PERMANOVA tests produced a significantly different result in almost every situation (Table 3.5). The only samples with a p(perm) greater than 0.05 were comparisons between the Ring Rd and RSF themselves as well as Rav Ref, which were heavily limited in unique permutations by the number of plots examined. The degree of separation was also clear on the nMDS, where the Ring Rd, RSF and combined Reference Lists were more separated than most treatments on the Experimental Site (Figure 3.6). Following PERMANOVA, within-site variation was examined using a two-tail t-test between the Experimental Site and the references. The references had the highest within-site similarity (RSF=65) and the lowest

54 within site similarity (Ring Rd=38), leading to a non-significant result close to the alpha (t=0.0686).

Table 3.5. Pairwise comparisons between treatments/factors and the references. Significant p(perm) values are in bold. The ‘t’ column shows the t statistic and ‘Perm’ column specifies the number of unique permutations found for each analysis from a maximum of 10,000 iterations. Experimental Site in 2015 and 2018 is a combination of all plots of all treatments compared to the reference.

Treatment Compared to Reference Compared to RSF Compared to Ring Rd Lists t p(perm) Perm t p(perm) Perm t p(perm) Perm Forest 3.54 0.0001 8979 3.62 0.0025 455 2.97 0.0022 455 Topsoil Spoil 3.88 0.0001 7756 3.73 0.0036 286 3.13 0.0043 286 Spoil Mulch 3.68 0.0001 9027 3.67 0.0025 455 3.1 0.0025 455 Spoil OGM 3.82 0.0002 7840 4.12 0.0032 286 3.2 0.0032 286 Spoil OGM 3.51 0.0002 8999 3.64 0.0021 455 2.80 0.0029 455 Mulch Subsoil 3.32 0.0001 9015 3.38 0.0016 455 2.68 0.0034 455 Subsoil 2.98 0.0001 8957 3.21 0.0019 455 2.58 0.0019 455 Mulch Subsoil OGM 3.35 0.0001 9034 3.68 0.0025 455 2.74 0.0016 455 Subsoil OGM 3.12 0.0001 9026 3.33 0.0024 454 2.43 0.0017 455 Mulch Rav Ref 1.64 0.1278 8 2.73 0.2496 4 1.73 0.248 4 Ring Rd 1.83 0.016 120 1.97 0.0965 10 RSF 1.94 0.0176 120 1.97 0.0965 10 Reference 1.94 0.0176 120 1.83 0.016 120 Lists Spoil Factor 3.81 0.0001 9923 3.15 0.0002 7420 2.62 0.0001 7383 Subsoil 3.76 0.0001 9913 3.29 0.0001 785 2.6 0.0001 7910 Factor – Mulch 3.45 0.0001 9939 2.9 0.0002 7424 2.34 0.0001 7437 Factor + Mulch 3.8 0.0001 9931 3.25 0.0003 7917 2.64 0.0002 7882 Factor – OGM 3.44 0.0001 9915 2.86 0.0001 7643 2.41 0.0002 7628 Factor

Treatment Compared to Reference Compared to RSF Compared to Ring Rd Lists t p(perm) Perm t p(perm) Perm t p(perm) Perm + OGM 4.06 0.0001 9934 3.53 0.0001 7649 2.76 0.0001 7684 Factor Experimental 3.55 0.0001 9918 3.13 0.0002 8349 2.41 0.0005 8348 Site 2015 Experimental 3.56 0.0001 9930 3.06 0.0001 8069 2.23 0.0001 8100 Site 2018

Figure 3.6. nMDS of presence/absence-transformed native and exotic species community composition based on centroids of each treatment. Samples from 2015 are solid shapes, samples from 2018 are hollow versions of 2015. Note that the Rav Ref was omitted from this figure because it was very different from all other treatments because of its different vegetation type.

Due to the presence/absence transformation, a large amount of the difference between the Experimental Site and the references (Figure 3.6) was the number of species they did not

56 share. Two species strongly highlighted the differences. Rytidosperma fulvum occurred in every Experimental Site treatment as well as the other references but was absent from each of the Reference Lists. In contrast, Brunoniella australis occurred in the Rav Ref, Ring Rd, RSF and every Reference List but was absent from the entire Experimental Site. Similarly, Allocasuarina luehmannii and Cheilanthes sieberi were absent from most of the Experimental Site but present in each list.

3.3.2.4 Plant Cover Summary of findings:

• Plant cover decreased between 2015 and 2018. • Spoil had the lowest cover of treatments on the Experimental Site. • OGM amelioration produced the highest cover in every situation.

There was a large amount of variation in cover between treatments, with 95% cover in the 2018 B5 Forest Topsoil sample and 0.003% cover in the 2015 B2 Spoil sample (Figure 3.7). Plant cover was significantly higher on the Experimental Site in 2015 than in 2018 (F(1,43.4)=18.5, p<0.0001). Three treatments had significant reductions between 2015 and 2018: Spoil OGM Mulch (t=4.2, p=0.0133), Subsoil OGM (t=3.88, p=0.0317) and Subsoil OGM Mulch (t=4.55, p=0.0048) (Figure 3.8). Spoil had significantly lower cover of native species than all other species in 2015 (Subsoil to Spoil: t=4.03, p=0.0067). The highest mean cover of natives in 2015 was Spoil OGM Mulch, significantly greater than Subsoil Mulch (t=3.32, p=0.0444), Spoil Mulch, Subsoil and Spoil. By 2018, there was an increase in cover in Forest Topsoil and a decrease on Spoil OGM Mulch to a level where they were now significantly different from each other (t=3.54, p=0.0418). In 2015, exotic cover was significantly higher in Subsoil Mulch and Subsoil OGM Mulch than in Forest Topsoil and Spoil (Subsoil OGM Mulch to Forest Topsoil: t=3.44, p=0.0329). In 2018, exotic cover was significantly higher in Spoil OGM than in all other treatments except Spoil OGM Mulch, Subsoil OGM Mulch and the reference Rav Ref (Spoil OGM to Subsoil OGM: t=3.73, 0.0266).

OGM caused the largest change in plant cover in every situation (2015 native: (F(1,35)= 131, p<0.0001; 2015 exotic: F(1,35))=7.23, p=0.0109; 2018 native: F(1,31)=24.9, p<0.0001; 2018 exotic: F(1,31)=43, p<0.0001).

Figure 3.7. Species percentage cover on plots in 2015 and 2018. Samples are colour coded by dominant plant species cover; species not named are grouped into ‘Other’.

Figure 3.8. Plant cover on treatments in 2015 and 2018 for natives and exotics where cover is measured for individuals ≤1 m in height. The line in each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range. Letters represent Tukey pairwise tests within a grouping, i.e. 2015 Natives is A–D, 2018 Native is J–N, 2015 Exotic is P–T and 2018 Exotic is V–Z. Treatments that do not share a letter are considered significantly different. Analysis was performed on fourth-root transformed data. Reference plots only have 2018 data.

2015 native cover showed significant increase from addition of Subsoil (F(1,35)=5.7, p=0.0227) and Mulch (F(1,35)=17, p=0.0002). There were also significant interactions between OGM and Subsoil (F(1,35)=10.2, p=0.0029) as well as OGM and Mulch (F(1,35)=18.3, p=0.0001). In both cases, the increase in cover from the addition of Subsoil or Mulch was eclipsed by the very large increase in cover from OGM. In 2015, exotic species also significantly increased in Subsoil (F(1,35)=7.16, p=0.0113) and Mulch (F(1,35)=6.69, p=0.014), mostly because every additional ameliorant was better than Spoil.

By 2018, the only change in exotic species cover, other than that due to OGM, was a reduction with Subsoil application (F(1,31.4):106, p=0.0026), mostly driven by the high Spoil OGM value. For natives in 2018, an interaction between OGM and Mulch (F(1,31.4)=7.8, p=0.009) was due to Mulch reducing the cover from OGM application.

There were also strong differences in the species composition of cover. Considering both native and exotic species, there were significant changes in cover composition between year (Pseudo-F(1)=22.4, p(perm)=0.0016), treatment (Pseudo-F(8)=9.97, p(perm)=0.0001) and block ((Pseudo-F(9)=4.06, p(perm)=0.0001) as well as interactions between year*treatment (Pseudo-F(8)=3.11, p(perm)=0.0001) and treatment*block (Pseudo-F(40)=1.98, p(perm)=0.0001). For the same tests, though, the PERMDISP test provided results less then p(perm)=0.05 for block (p(perm)=0.0001) and treatment (p(perm)=0.0001). As the sample sizes in 2018 are unbalanced for comparisons with Subsoil and Mulch additions, the PERMANOVA becomes less reliable (Anderson and Walsh, 2013). In this case, it is likely that treatments showing strong differences on the nMDS, such as Spoil and Subsoil, are different while more similar treatments, such as Subsoil and Subsoil Mulch, are not (Figure 3.9). Considering the factors, the addition of Subsoil (Pseudo-F(1)=9.20, p(perm)=0.004) and OGM (Pseudo-F(1)=13.08, p(perm)=0.0051) significantly changed the cover species composition, whereas Mulch had no significant effect (Pseudo-F(1)=1.97, p(perm)=0.117).

The most prominent change in cover between years was a reduction in Atriplex semibraccata and Cynodon dactylon, which declined in average cover from of 11% to 1.8% and from 8.7% to 0.04% respectively. There were increases in Enchylaena tomentosa (2.8% to 6.7%), Acacia amblygona (7.6% to 11%) and Galenia pubescens* (1.55% to 4.7%). Subsoil addition generally had higher cover of Acacia amblygona and Einadia nutans subsp. linifolia, whereas without Subsoil had higher G. pubescens*, Einadia nutans subsp. nutans and A. semibraccata (although A. semibraccata was also common on Subsoil). Addition of OGM saw large increases in the coverage of A. semibraccata, E. nutans subsp. linifolia, E. tomentosa, G. pubescens*, C. dactylon, Einadia polygonoides and E. nutans subsp. nutans. The primary species with higher cover without OGM was A. amblygona.

Figure 3.9. nMDS based on centroids of log(x+1)-transformed species cover for each treatment in 2015 and 2018. Samples from 2015 are solid shapes, samples from 2018 are hollow versions of 2015.

3.3.2.5 Effect of Propagules Summary of findings:

• Of the 50 species in the seeding mix, five species failed to establish. • A large number of species, 55 native and 33 exotic, established from other sources.

From the seeding mix, eight species successfully established on all treatments by the 2018 survey: Acacia decora, Acacia implexa, Acacia salicina, Atriplex semibracata, Chloris truncata, Dodonaea viscosa, Enchylaena tomentosa and Rytidosperma fulvum. Only one species not included in the seeding mix was able to colonise all treatments: Salsola australis.

Forty-five of the 50 species in the seeding mix were able to establish and survive on at least one of the plots to the 5-year survey in 2018. The species from the seeding mix that were not present at the 5-year survey were Acacia parvipinnula, Cassinia quinquefaria, Daviesia

61 ulicifolia, Einadia trigonos subsp. leiocarpa and Ozothamnus diosmifolius. Of these, Acacia parvipinnula and Cassinia quinquefaria had been low in numbers during the 2015 survey with only five and one individual(s) respectively. However, Daviesia ulicifolia had been much more abundant in the 2015 survey, with 33 individuals across 19 plots, so its demise is unexpected.

Fifty-five native species and 33 exotic species were not in the seeding mix and either dispersed into the site or had propagules introduced with the ameliorants such as in the Subsoil seedbank. This gives 133 species across the Experimental Site in 2018.

3.3.2.6 Relationship to Initial Soil Conditions Summary of findings:

• Moderate fits were found for associations between 2014 soil data and the 2018 flora community. • The strongest trends in community composition came from available sodium, nitrogen and manganese. Trends in community cover were most strongly related to available boron, zinc and electrical conductivity.

As has been highlighted with previous studies (see section 2.2.1.4) (Scanlon, 2015; Castor et al., 2016), soil treatment has had a significant impact on the plant communities present. Comparing the original soil dataset taken in 2014 to the 2018 flora abundances, the best model fit for associations between the flora and initial soil chemistry was based on available sodium, nitrogen and manganese (AICc=195.63; R2=0.46) (Figure 3.10). Available sodium was strongest in the direction of treatments without Subsoil, whereas the available nitrogen and available manganese were strongest in treatments with OGM. For flora species composition based on cover, the best model was based on available boron, available zinc and electrical conductivity (AICc=193.33; R2=0.67) (Figure 3.11). Available boron was lowest in Spoil, available calcium was lowest in Forest Topsoil, and available zinc and electrical conductivity were highest in OGM treatments. These comparisons to soil chemistry are not complete because there are a range of other factors that could be involved in selection, such as microbiology (Marrs, 2016), which will be considered in later chapters.

Figure 3.10. dbRDA plot of square-root-transformed flora abundance in 2018 for Blocks 1, 2 and 5 with best matching square-root-transformed 2014 soil variables overlaid.

Figure 3.11. dbRDA plot of log(x+1)-transformed species composition based on cover in 2018 for Blocks 1, 2 and 5 with best matching square-root-transformed 2014 soil variables overlaid.

3.3.3 Tree Performance 3.3.3.1 Reference Data Summary of findings:

• Tree height averaged 14.8 m on Ring Rd and 23.9 m on RSF. • Diameter at breast height (DBH) averaged 10.3 cm on Ring Rd and 18.3 cm on RSF.

Data for the references on tree DBH and height were taken from C. Castor (2015, personal communication), which were examined over an area 20 x 50 m as opposed to the 10 x 10 m used on the Experimental Site (Table 3.6). Only three tree species that were typical of the canopy were chosen to be measured from each plot. However, every tree was measured for DBH, so the data on tree DBH are more accurate, if from a larger area.

Table 3.6. Site data on the mean height and mean DBH of trees at the Ring Rd and RSF plus or minus standard error of the mean. Grey cells are averages of the three plots in each reference.

Site Mean height (m) Mean DBH (cm)

Ring Rd 9 16.5 ± 1.3 11.5 ± 0.39

Ring Rd 10 13.3 ± 0.8 9.6 ± 0.25

Ring Rd 13 14.5 ± 1.4 9.7 ± 0.26

Ring Rd average 14.8 ± 0.9 10.3 ± 0.6

RSF B3 18.7 ± 0.8 19.8 ± 1.4

RSF B5 27.2 ± 1.4 20.7 ± 2

RSF Y3 25.8 ± 1.7 14.3 ± 0.9

RSF average 23.9 ± 2.6 18.3 ± 2

3.3.3.2 Number of Trees Summary of findings:

• In 2018, there were no trees on any Spoil plots. • Mulch significantly increased the number of trees, with Subsoil Mulch having the highest number of trees at 3350 trees/ha.

There were large amounts of variation in tree numbers counted between treatments in both years (2015: Wald χ2(8)=304, p<0.001; 2018: Wald χ2(8)=296, p <0.001) (Figure 3.12). The difference was particularly noticeable in 2018, with Spoil having no trees compared to Subsoil Mulch averaging 33.5 trees per plot (equivalent to 3350 trees/ha). The strongest factor influencing the number of trees in both years was the presence of Mulch, which increased the number of trees (2015: Wald χ2(1)=184, p<0.001, 2018: χ2(1)=125, p<0.001). To a lesser, but still highly significant, extent, the addition of Subsoil and OGM also increased the number of trees in both years (2015: Subsoil: Wald χ2(1)= 116, p<0.001; OGM: Wald χ2(1)=11, p=0.001; 2018: Subsoil: Wald χ2(1)= 79, p<0.001; OGM: Wald χ2(1)=25, p<0.001). There were also significant interactions in both years, with the addition of both Subsoil and Mulch having a much larger effect than either ameliorant on its own (2015: Wald χ2(1)= 18, p<0.001; 2018: Wald χ2(1)= 12, p<0.001). Conversely, while OGM addition had a significant effect on addition to Spoil, there was no similar increase if Subsoil was also present (2015: Wald χ2(1)=24, p<0.001; 2018: Wald χ2(1)=33, p<0.001). Although there was no significant interaction between OGM and Mulch in 2015 (Wald χ2(1)= 0.08 p=0.77), in 2018 there was a significant increase in tree numbers from adding both OGM and Mulch compared with either on their own (Wald χ2(1)=5.1, p=0.024). There were also significant effects from block in this analysis with Blocks 2, 5 and 6 having significantly higher tree numbers in both years (2015: Wald χ2(5)=61, p<0.001; 2018: Wald χ2(5)=72, p<0.001). Blocks 2 and 5 both have Subsoil that was spread at the same time (see Figure 2.6); this may indicate that this soil had a seed bank of tree seeds that was not present in the other subsoil applications.

Figure 3.12. Number of trees growing on the Experimental Site in 2015 and 2018. The number of trees per plot can be worked out from dividing the y-axis by 100. The line in each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range. Letters represent Bonferonni pairwise tests within a grouping, i.e. 2015 is A–E and 2018 is V–Z. Treatments that do not share a letter are considered significantly different. Reference plots only have 2018 data.

3.3.3.3 Tree Height Summary of results:

• In 2015, each of the ameliorants significantly increased tree height. However, by 2018 the only significant increase was seen with OGM application.

Note that because of differences in collection methods (see section 3.2.3), 2015 and 2018 data were analysed separately. In both 2015 and 2018, the treatment with the tallest measured trees on average was Subsoil OGM (Figures 3.13). However, in 2015 the tallest tree was a Corymbia maculata on Subsoil OGM Mulch at 540 cm, whereas in 2018 the tallest tree was a C. maculata found on Spoil OGM Mulch at 870 cm. There were strong differences between treatments in both years but the F value was much higher in 2018 (2015: F(7, 278.1)=6.29, p<0.0001; 2018: F(7, 744.2)=47.6, p<0.0001). The difference in strength between

2015 and 2018 is probably due to the increase in data because all trees were measured in 2018 rather than only trees ≥150 cm.

Figure 3.13. Variation in tree height on the Experimental Site in 2015 and 2018. All tree heights were measured in 2018 but in 2015 only trees ≥150 cm were measured. Note that Spoil is omitted because it did not have any of the species considered for height measurements in either year. The line in the middle of each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range. Lettering represents statistical differences based on LMMs performed separately for each year, using fourth-root data in 2015 and square-root data in 2018. Tukey pairwise tests were performed within a grouping, i.e. 2015 is A–D and 2018 is W–Z. Treatments that do not share a letter are considered significantly different. Reference plots only have 2018 data.

There were strong responses in tree height to factors in 2015 with addition of Subsoil (F(1, 363.5)=12.3, p=0.0005), OGM (F(1, 363.1)=461, p<0.0001) and Mulch (F(1, 366)=7.29, p=0.0073) all significantly increasing the height of trees. There were also significant interactions between Subsoil and OGM (F(1, 364.5)=26.5, p<0.0001) where the increase made by adding Subsoil to OGM was negligible compared with the increase from adding Subsoil in the absence of OGM. Subsoil and Mulch together interacted to increase the height above what would otherwise occur (F(1, 362.3)=4.34, p=0.0379). Finally for 2015, the addition of

Mulch in the absence of OGM increased tree height (F(1, 362.4)=51.1, p<0.0001). In 2018, however, there were only two significant differences. The addition of OGM made a very large increase in the height of trees (F(1, 651.7)=137, p<0.0001); comparing untransformed means, the addition of OGM doubled the tree height from 207 cm without OGM to 417 cm with OGM. There was also an interaction between Mulch and OGM, where Mulch reduced the amount of growth in OGM plots (F(1, 651.4)=17.2, p<0.0001).

Changes in tree heights between 2015 and 2018 are examined further in Chapter 6. Although many trees grew substantially over the 5 years to the 2018 survey, there is still considerable growth required to match either the Ring Rd or the RSF (Table 3.6).

3.3.3.4 Tree Combined Diameter at Breast Height Summary of results:

• Combined diameter at breast height (CDBH) was significantly increased with OGM application.

On the Experimental Site, the effects of factors on measured CDBH were very similar to the effects on 2018 tree heights, only showing strong increase in CDBH to the addition of OGM (F(1, 654.5)=132, p<0.0001) and an interaction between OGM and Mulch (F(1, 654.2)=27.8, p<0.0001) (Figure 3.14). The addition of OGM greatly increased the CDBH; however, when Mulch was added, it reduced the effect of the OGM.

Figure 3.14. CDBH data from the Experimental Site and references in 2018. Letters represent significant differences based on an LMM with data transformed using ln(x+1). The line in the middle of each box represents the median and the ends of the box are the 25th and 75th quartiles. Whiskers extend to the furthest point within 1.5 times the interquartile range.

3.3.4 Matching References and Targets 3.3.4.1 Indices of Success Summary of results:

• Spoil was the most dissimilar treatment from any target. • The most similar treatment to the RSF was 2018 Subsoil Mulch. • The most similar treatment to the ideal reference was 2018 Subsoil OGM Mulch.

Indices were chosen from the above data that represented key differences between treatments while minimising correlated variables. Based on exotic species richness, native species richness, average tree height and dissimilarity to the reference, the indices confirm that Spoil was the most dissimilar treatment from any target (Table 3.7). The most similar treatment to the 2018 Forest Topsoil reference was the 2018 Subsoil, whereas the 2018 Subsoil OGM Mulch was most similar to Ring Rd and 2018 Subsoil Mulch was most similar to

RSF. The most similar treatment on the Experimental Site to the ‘ideal’ reference and the treatment with the lowest sum value was 2018 Subsoil OGM Mulch.

Table 3.7. Data on exotic species richness, native species richness, average tree height and dissimilarity to the examined reference were combined to create indices of performance. Indices were generated for each treatment in each year based on similarity to a reference. Low values indicate that a treatment is more similar to the reference. The ‘ideal’ used as indices: 0 exotic species, the maximum average native species richness (2015 Subsoil Mulch, 47.17), mean tree height of RSF (23.9 m) and dissimilarity to the Reference Lists group of species. Values are colour coded with lower values in darker green and higher values in stronger yellow. The Sum column is the addition of all reference comparisons together and is colour coded; darker blue indicates lower values and red indicates higher values. Lower values in the Sum column indicate strong performance compared to all references. Values in bold are the lowest in their category for their year. N/A indicates a treatment would be compared with itself as a reference.

Treatment 2018 Forest Ring Rd RSF ‘Ideal’ Sum Topsoil 2015 Forest Topsoil 2.9 5.3 9.5 9.8 27.5 2018 Forest Topsoil N/A 6.3 8.9 11.2 26.4 2015 Spoil 6.0 11.2 12.7 11.0 40.9 2018 Spoil 6.7 10.8 12.9 11.8 42.2 2015 Spoil Mulch 4.6 7.5 11.7 11.6 35.5 2018 Spoil Mulch 3.7 6.2 10.6 10.5 31.0 2015 Spoil OGM 6.0 7.7 11.8 12.2 37.8 2018 Spoil OGM 6.0 6.2 11.0 13.4 36.6 2015 Spoil OGM Mulch 4.6 5.9 10.8 11.6 32.9 2018 Spoil OGM Mulch 3.1 5.1 8.7 9.9 26.7 2015 Subsoil 5.1 6.3 10.1 11.1 32.7 2018 Subsoil 1.8 5.6 9.6 10.3 27.4 2015 Subsoil Mulch 7.0 7.1 10.0 9.3 33.3 2018 Subsoil Mulch 4.1 4.1 7.5 8.5 24.2 2015 Subsoil OGM 4.9 5.7 10.6 10.7 31.9 2018 Subsoil OGM 4.6 3.3 8.4 10.2 26.5 2015 Subsoil OGM Mulch 5.7 5.8 10.3 10.3 32.1 2018 Subsoil OGM Mulch 3.9 3.1 8.6 8.1 23.6 Reference Ring Rd 7.1 N/A 6.0 8.5 21.5 Reference RSF 8.7 4.7 N/A 4.6 17.9

3.3.4.2 Matching the Australian Federal Target Summary of results:

• Ignoring the size of the plots, all treatments except Spoil met the minimum requirement to match the federal target. • In 2015, seven of the treatments would have met class B requirements while in 2018 only five of the treatments met class B requirements.

To match the federally listed, Central Hunter Valley Eucalypt Forest and Woodland Ecological Community, the site must meet key diagnostic characteristics and the minimum condition thresholds. In particular, the site must be considered of moderate quality to be considered under the Environmental Protection and Biodiversity Conservation Act 1999 (Threatened Species Scientific Committee, 2015). The key diagnostic characteristics and the treatments performance against them (in bold font) are:

• ‘It occurs in the Hunter River catchment (typically called the Hunter Valley region); it typically occurs on lower hillslopes and low ridges, or valley floors in undulating country; on soils derived from Permian sedimentary rocks; it does not occur on alluvial flats, river terraces, aeolian sands, Triassic sediments, or escarpments.’ The location is the Hunter Valley on a reconstructed lower hill slope. Although OGM is an anthropogenic product and both Subsoil and Mulch are not necessarily from the same landscape, the landscape is built from Permian spoil which will provide all future soil development. • ‘It is woodland or forest, with a projected canopy cover of trees of 10% or more; or with a native tree density of at least 10 native tree stems per 0.5 ha (at least 20 native tree stems/ha) that are at least one metre in height’ All treatments with the exception of Spoil had sufficient tree density and by the 2018 survey all plots except Spoil had sufficient height. • ‘The canopy of the ecological community is dominated by one or more of the following four eucalypt species: Eucalyptus crebra (narrow-leaved ironbark), Corymbia maculata (spotted gum), E. dawsonii (slaty gum) and E. moluccana (grey box); Allocasuarina torulosa (forest oak/she-oak, rose she-oak/oak), Eucalyptus acmenoides (white mahogany) and E. fibrosa (red/broad-leaved ironbark) are largely

absent from the canopy of a patch.’ Canopy is dominated by Corymbia maculata and Eucalyptus moluccana. Eucalyptus fibrosa does occur on the site; however, at only 1.6% of individual trees across the site, it does not dominate any treatment. • ‘A ground layer is present (although it may vary in development and composition), as a sparse to thick layer of native grasses and other native herbs and/or native shrubs.’ All treatments have a ground layer matching these requirments.

As those requirements are satisfied, to be considered a Matter of National Environmental Significance under the Environmental Protection and Biodiversity Conservation Act 1999, the site must meet a moderate quality level. As each plot with edge effect was only 0.0225 ha, no individual plot will meet the minimum size requirement so size will be ignored for this assessment. Because of this, comparisons will be made between classes B and C because class A has the same requirements as class C, except for ≥5 ha instead of ≥0.5 ha. The area must contain at least 12 native understorey species, which includes the mid/shrub layer and ground layer. Considering this, Spoil would not meet the requirements because it averaged six species per plot (Table 3.8). All other treatments averaged greater than 12 native non-tree species. Finally, to be class B, a minimum of 70% perennial vegetative cover in each layer must be native or to meet class C 50% of cover must be native. Cover on site was quite variable; however, on average the 2015 Subsoil Mulch and the 2018 Spoil OGM, Spoil OGM Mulch and Subsoil OGM Mulch treatments would match class C as they were between 50% and 70%. All other treatments other than Spoil would be considered class B. Interestingly, if the Spoil treatments were rehabilitated, at approximately 1.75 ha the Experimental Site overall would be eligible for listing as class B because on average the native cover was 84.1%.

Table 3.8. Key factors determining the class under the Central Hunter Valley Eucalypt Forest and Woodland Ecological Community (Threatened Species Scientific Committee, 2015). Treatments need an average of 12 native non-tree species to be considered, ≥50% cover to be by native species for class C and ≥70% cover to be by native species for class B. Treatments are colour coded: green cells achieved class B, yellow cells achieved class C and orange cells did not achieve any class. Although averages are most important in the assessment criteria, minimum native non-tree species and proportion of cover by native species is given to show variability.

Year Treatment Native non-tree species Proportion of cover by native species (%) Minimum Average Minimum Average 2015 Forest Topsoil 19 26.2 99.2 99.9 2018 Forest Topsoil 13 18.3 98.2 99.8 2015 Spoil 3 6.0 66.7 97.3 2018 Spoil 4 6.0 63.6 79.8 2015 Spoil Mulch 9 15.5 43.8 90.3 2018 Spoil Mulch 11 17.5 23.1 72.2 2015 Spoil OGM 13 15.3 69.0 91.6 2018 Spoil OGM 15 17.5 23.1 50.9 2015 Spoil OGM Mulch 18 20.5 74.4 90.1 2018 Spoil OGM Mulch 13 19.3 36.5 64.4 2015 Subsoil 25 30.5 52.4 85.1 2018 Subsoil 11 19.7 77.5 91.7 2015 Subsoil Mulch 27 38.3 21.8 61.8 2018 Subsoil Mulch 25 33.3 53.5 94.6 2015 Subsoil OGM 22 26.2 69.7 89.9 2018 Subsoil OGM 21 28.0 82.3 93.8 2015 Subsoil OGM Mulch 22 28.7 53.5 81.2 2018 Subsoil OGM Mulch 21 25.7 21.0 58.0 2018 Rav Ref 12 12.0 100.0 100.0 2018 Ring Rd 22 25.3 44.2 76.3 2018 RSF 36 40.0 97.9 99.4

3.3.4.3 Matching the New South Wales State Target Summary of results:

• Using a species similarity comparison, none of the treatments was best matched to the target EECs • Some treatments were considered more similar to off-target critically endangered communities at the state and federal levels.

The state government system is more indepth than the federal system. The NSW system has 1537 plant community types, which were determined through large-scale mapping and discussions among experts in the field (NSW Department of Planning Industry and Environment, 2020). Although many of the threatened ecological community listings have been developed independently of and prior to the plant community type system, many threatened ecological communities have been associated with plant community types (Table 3.9).

Table 3.9. Plant community types equivalent to or partly equivalent to the targeted EECs.

Endangered ecological community Plant community type

Central Hunter Grey Box–Ironbark Narrow-leaved Ironbark–Bull Oak–Grey Box shrub–grass Woodland in the NSW North Coast open forest of the Central and Lower Hunter and Sydney Basin bioregions Narrow-leaved Ironbark–Native Olive shrubby open forest of the Central and Upper Hunter Central Hunter Ironbark–Spotted Narrow-leaved Ironbark–Grey Box–Spotted Gum shrub– Gum–Grey Box Forest in the NSW grass woodland of the Central and Lower Hunter North Coast and Sydney Basin Grey Ironbark–Spotted Gum–Grey Box open forest on bioregions hills of the Hunter Valley, Sydney Basin Bioregion

To ensure there was not a plant community type that better matched the targeted EEC, the presence/absence-transformed list of species from each treatment was compared to the species in the BioNet database (NSW Department of Planning Industry and Environment, 2020) for each plant community type, using a Bray–Curtis similarity in PRIMER 7 (Table 3.10).

None of the treatments was most strongly associated with the targeted communities, although many were associated with communities of the Hunter region or similar communities from within the Sydney Basin. Interestingly, six of the treatments were most associated with plant community types that are equivalent to or partly equivalent to White Box–Yellow Box–Blakely’s Red Gum Grassy Woodland and Derived Native Grassland, which is listed as critically endangered by both the state and federal governments. Four treatments associated with the Slaty Box–Grey Gum Shrubby Woodland were equivalent to the Hunter Valley Footslopes Slaty Gum Woodland and listed as vulnerable at the state level and critically endangered at the federal level. None of the references matched the targeted EECs either, although Ring Rd and RSF did have higher similarity than treatments on the Experimental Site.

Table 3.10. Similarity of treatments in each year to the targeted EECs and their best matching plant community type. Similarity is colour coded, colours changing from white to yellow then green with increasing similarity. Best matched communities that appeared more than once have been given the same colour.

Treatment Central Central Hunter Similarity Best Match Hunter Grey Ironbark — of Best Box — Spotted Gum — Match Ironbark Grey Box Forest Woodland 2015 Forest Forest Red Gum x Blakely's Red Gum - box woodland of the Yetman region Brigalow 9.2 13.5 25.3 Topsoil Belt South Bioregion 2018 Forest Slaty Box - Grey Gum shrubby woodland on footslopes of the upper Hunter Valley 17.9 17.9 30.3 Topsoil Sydney Basin Bioregion Carbeen +/- Coolabah grassy woodland on floodplain clay loam soil on north-western 2015 Spoil 0.0 5.4 18.2 NSW floodplains mainly Darling Riverine Plain Bioregion Coobah - Western Rosewood low open tall shrubland or woodland mainly on outwash 2018 Spoil 8.0 0.0 20.0 areas in the Brigalow Belt South Bioregion. 2015 Spoil Grey Box - Grey Gum - Rough-barked Apple - Blakelys Red Gum grassy open forest of 8.7 18.2 23.1 Mulch the central Hunter 2018 Spoil Slaty Box - Grey Gum shrubby woodland on footslopes of the upper Hunter Valley 13.3 14.5 28.6 Mulch Sydney Basin Bioregion 2015 Spoil Grey Box - Grey Gum - Rough-barked Apple - Blakelys Red Gum grassy open forest of 14.6 14.6 29.8 OGM the central Hunter 2018 Spoil Grey Box - Grey Gum - Rough-barked Apple - Blakelys Red Gum grassy open forest of 10.8 15.4 31.1 OGM the central Hunter 2015 Spoil Grey Box - Grey Gum - Rough-barked Apple - Blakelys Red Gum grassy open forest of 11.1 14.8 23.3 OGM Mulch the central Hunter 2018 Spoil Slaty Box - Grey Gum shrubby woodland on footslopes of the upper Hunter Valley 10.5 18.2 26.9 OGM Mulch Sydney Basin Bioregion Narrow-leaved Ironbark +/- Grey Box grassy woodland of the upper Hunter Valley 2015 Subsoil 11.6 15.4 27.8 mainly Sydney Basin Bioregion

Treatment Central Central Hunter Similarity Best Match Hunter Grey Ironbark — of Best Box — Spotted Gum — Match Ironbark Grey Box Forest Woodland Yellow Box grassy woodland on lower hillslopes and valley flats in the southern NSW 2018 Subsoil 10.2 14.7 27.8 Brigalow Belt South Bioregion 2015 Subsoil Narrow-leaved Ironbark +/- Grey Box grassy woodland of the upper Hunter Valley 11.4 15.9 25.2 Mulch mainly Sydney Basin Bioregion 2018 Subsoil Slaty Box - Grey Gum shrubby woodland on footslopes of the upper Hunter Valley 16.2 19.3 28.6 Mulch Sydney Basin Bioregion 2015 Subsoil 10.2 14.7 24.1 Narrow-leaved Ironbark - Grey Box grassy woodland of the central and upper Hunter OGM 2018 Subsoil White Cypress Pine - Buloke - White Box shrubby open forest on hills in the Liverpool 15.4 15.4 27.8 OGM Plains - Dubbo region Brigalow Belt South Bioregion 2015 Subsoil Narrow-leaved Ironbark - Forest Red Gum woodland on rocky slopes of the lower 12.5 15.6 23.2 OGM Mulch Burragorang Gorge Sydney Basin Bioregion 2018 Subsoil White Cypress Pine - Buloke - White Box shrubby open forest on hills in the Liverpool 12.9 16.1 25.0 OGM Mulch Plains - Dubbo region Brigalow Belt South Bioregion White Cypress Pine - Buloke - Grey Box grassy eolian lunette grassy woodland in the Rav Ref 0.0 12.9 17.4 southern Brigalow Belt South Bioregion Narrow-leaved Ironbark +/- Grey Box grassy woodland of the upper Hunter Valley Ring Rd 17.2 24.1 24.7 mainly Sydney Basin Bioregion Narrow-leaved Ironbark +/- Grey Box grassy woodland of the upper Hunter Valley RSF 23.9 21.1 26.4 mainly Sydney Basin Bioregion Experimental Narrow-leaved Ironbark +/- Grey Box grassy woodland of the upper Hunter Valley 10.0 14.7 27.3 Site 2015 mainly Sydney Basin Bioregion Experimental White Cypress Pine - Buloke - White Box shrubby open forest on hills in the Liverpool 16.1 16.7 27.7 Site 2018 Plains - Dubbo region Brigalow Belt South Bioregion

3.4 Discussion

3.4.1 Did the Site Match the Target? It is interesting to see that the selection of metrics and decision-making criteria makes a really big difference to the outcome and decisions on whether a treatment successfully produced the target flora community. In particular, 2018 Subsoil OGM Mulch was the highest rated treatment in the indices comparison but did not meet the preferred class according to the federal criteria. Further, although all treatments other than Spoil qualified for listing under the federal act, none of the communities best matched to their equivalent state communities. This leaves decisions as to which treatment was the ‘best’ and ‘was the restoration successful’ in doubt.

The appropriate selection of metrics in restoration ecology has been discussed for many years and a variety of methods for selecting metrics and indicators have been suggested (Seager et al., 2007; Niemeijer and de Groot, 2008; Convertino et al., 2013). When considering metrics, it is important to consider not only how they will inform success to stakeholders but also how well they characterise the restored ecosystem. The metrics used in this study were selected by a combination of ‘best professional judgement’ and ‘historical precedence’, two methods that are inexpensive and time-efficient (Convertino et al., 2013). However, these methods of selecting metrics can be biased by stakeholder values, lack transparency and lead to well- suited metrics being overlooked in favour of more familiar metrics. To accommodate these risks, this study considered a wide variety of metrics related to community composition, structure and function (Dale and Beyeler, 2001). Although this means that some metrics have overlap, it increases the ability of the study to characterise the development of the site.

There has been debate over the optimal application rate of MWOO products such as OGM in agriculture (Technical Advisory Committee, 2018) and mixed recommendations for application in mine sites (Norland and Veith, 1995; Castillejo and Castelló, 2010; Spargo, 2012; Spargo and Doley, 2016). In this study, the high cover of exotic species in Spoil OGM (49.1% exotic cover in 2018) suggests that the application rate of 100 t/ha may have been too high to optimally support the native species. However, the 2018 Subsoil OGM treatment had 93.8% native species coverage, indicating that how the OGM is applied may be the important factor. There is great variation in the recommended application rates of MWOO/municipal solid waste compost products; however, most studies recommend lower levels than were

77 applied in this experiment. Locally, Spargo and Doley (2016) also found that OGM applied at 100 t/ha could lead to excessive growth of exotic species and, following better results at 60 t/ha, recommended using 50 t/ha. Comparatively in the USA, Norland and Veith (1995) found an optimal application rate of 89.6 t/ha on a Minnesota taconite tailing dam. While results from Spain suggested rates of municipal solid waste compost between 10 t/ha and 30 t/ha were most effective in restoring community composition (Castillejo and Castelló, 2010). Although MWOO products can no longer be used in Australia, studies following application in Australia may still guide their use internationally and inform applications of other recycled organic products such as food organics and garden organics. As the 100 t/ha application produced mixed results, this study supports the use of lower application rates such as between 50 and 90 t/ha recommended by other studies.

It is important to remember that the site was changing between survey years, as was shown in the change of best matching to references and ‘ideal’ treatment between 2015 and 2018 (Table 3.7). In the 2015 survey, the treatment closest to the ‘ideal’ was Subsoil Mulch, primarily because of its high diversity and similarity in composition to the Reference Lists. Although the 2018 Subsoil Mulch treatment still had a high diversity, the Subsoil OGM Mulch treatment showed a strong successional development, mainly by reduction of exotic species abundances, towards the references, making it a higher performing treatment. The reduction in exotic species between 2015 and 2018 (Table 3.4) was probably due to the continued development of its canopy species. Shade produced by the canopy species would have dramatically changed the ground conditions over this period, as shade has a large effect on plant physiology and competitive ability (Valladares et al., 2016) (Figures 3.15 and 3.16). A review by Alpert et al. (2000) found that exotic species tend to perform best at reduced stress levels, particularly with increasing resource availability. The 2018 Subsoil OGM Mulch treatment had a reduction in soil fertility as plants accessed resources, reduction in light as the canopy develops and reduction in water because of the drought. This suggests that the reduction seen in exotic species may maintain in the long term. As these treatments continue to develop, their species compositions are likely to continue changing. Further monitoring will be needed to establish if they continue along the desired trajectory.

Figure 3.15. Subsoil OGM Mulch in 2015; tree heights were lower, allowing many forbs and exotics to establish dense populations. Photo by Carmen Castor.

A large part of the poorer characterisation for the targeted EEC communities at the state level is due to the number of species that are named on the state-level species lists. The four plant community types matching the targeted EECs averages 15.75 species, the average of the matched communities was 34.4 and the average of the treatments was 43.4 species. As the Bray–Curtis metric reduces in similarity with decreased species shared, a discrepancy in the species richness of two communities will lead to a larger difference. As the communities listed in the Plant Community Types database only feature the characteristic species of a community, ancillary species have the potential to drive the classification towards other communities. In this dataset, although the canopy species matched well to the targeted EECs and plant community types, the diverse array of other species matched well to the larger species lists of other local plant community types. The BioNet system does allow for ancillary species, with benchmarks for species richness based on the vegetation class and region. For the targeted communities, the species richness ranges from 38 to 52, a figure that much 79 better matches what occurred on site. However, the species that could be used to meet the benchmark are not named, creating a discrepancy between targeted richness and composition. At this stage, it would appear that the recovering community had not attained the plant community types associated with the target NSW EEC; however, as biodiversity was also a target of the site, it is not appropriate to call the restoration a failure. If anything, it suggests the target is not flexible enough to accept novelty, or positive but unintentional outputs. In particular, the potential unintentional creation of communities resembling the critically endangered White Box–Yellow Box–Blakely’s Red Gum Grassy Woodland and Derived Native Grassland suggests that some flexibility is required.

Figure 3.16. Subsoil OGM Mulch in 2018; the photo is taken in approximately the same position and direction as in Figure 3.15 and the white stake in the foreground is the same. Tree height has increased, spreading shade in the lower strata, and the ground is now more open but many species remain. Photo by Carmen Castor.

Novel ecosystems are a difficult topic for restoration ecology. Since discussions on the topic began, there have been strongly opposing beliefs on the correct way to approach them (Hobbs et al., 2006; Hobbs et al., 2014). One viewpoint of the novel ecosystems discussion has strongly advocated a ‘join them’ approach (Cordell et al., 2016) where benefits of a novel community to human society and the environment outweigh the disadvantages or the costs of restoration are unjustifiable. In some scenarios, this suggests restoration to a historic reference is not the ideal because it has a lower likelihood of success following changes to the abiotic and biotic conditions at a site. For example, to restore a gravel pit, Seastedt et al. (2008) used seeds from nine species that had varying adaptations to water, with 500 mm of rainfall difference between conditions preferred by the two most different species. When the site was affected by drought in the second year through to fourth year after revegetation, 50% of the plant cover on the gravel pit site consisted of salt sacaton (Sporobolus airoides), a species from outside its normal range but considered essential for revegetation given dry conditions (Seastedt et al., 2008). This example is similar to the use of ancillary species on the Experimental Site because these species are native to the area but not a key component of the EEC composition. Two notable ancillary species were Enchylaena tomentosa and Atriplex semibaccata, which are widespread in a range of habitats across Australia (Royal Botanic Gardens and Domain Trust, 2019). Given that ancillary species such as E. tomentosa and A. semibaccata were extremely common across Spoil OGM, it would seem that this site also showed benefit from receiving a large number of seeds from species adapted to a variety of conditions. With the changes to the abiotic environment following mining, it should be expected that recreation of a novel ecosystem is a more likely outcome than recreation of a historic reference or EEC. If the restoration process creates an off-target community, which was considered to be at a higher risk of extinction than the target, then it could be argued that management and regulators should support the outcome. This viewpoint is controversial but it is supported by arguments that if a novel ecosystem is manageable, cost-effective and beneficial to both society and the environment, then it would be more realistic and a better use of resources to focus on other problems (Hobbs et al., 2006; Seastedt et al., 2008; Hobbs et al., 2009; Hobbs et al., 2014). It also recognises the reality of ecosystems; policy and legistlation is often crafted by assuming species and community distributions to be static systems (Bridgewater and Yung, 2013), whereas in reality they can be exceedingly variable.

If the decision is made to actively improve the match of the site to the targeted EECs, then introduction of, or more widespread establishment of, the following species will be beneficial.

• Central Hunter Grey Box–Ironbark Woodland: Allocasuarina luehmannii, Bursaria spinosa, Breynia oblongifolia, Notelaea macrocarpa, Pittosporum undulatum, Acacia paradoxa, Pomax umbellata, Dichelachne micrantha, Daucus glochidiatus. • Central Hunter Ironbark–Spotted Gum–Grey Box Forest: Acacia parvipinnula, Breynia oblongifolia, Bursaria spinosa subsp. spinosa, Daviesia ulicifolia subsp. ulicifolia, Hakea sericea, Brunoniella australis.

Interestingly though, none of these formally defined communities include the seven most common species on the Experimental Site: Enchylaena tomentosa, Acacia amblygona, Atriplex semibraccata, Einadia nutans subsp. linifolia, Acacia decora, Einadia polygonoides and Chloris truncata. Part of this will be related to the site being a restored system that is still undergoing development and may change over time. Many species that are missing now may be better suited to the later stages of succession than the conditions of early primary succession (Zangerl and Bazzaz, 1983). The suggestion to delay reintroduction of species as the project develops and succession progresses is not uncommon in restoration (McClain et al., 2011) and may be beneficial in this scenario. Perhaps the larger part, however, is related to the species available for the project, species such as Bursaria spinosa could not be included in the mix because seed was not available. The presence of additional species that are not strictly associated with a reference community should not be seen as a negative; many of the local communities have a surplus of local species that occur in a minority of plots, or even just one plot (Somerville, 2009). These associated species contribute to biodiversity of the area and can provide functional support for the ecosystem (van der Plas, 2019).

3.4.2 Effect of Treatments 3.4.2.1 Characteristics of Spoil Compared to the other treatments in this study, Spoil had:

• the lowest species richness (for native species in 2015 and 2018, exotic species in 2015 and overall) • the lowest number of individual plants • the lowest Shannon–Wiener diversity

• a different species abundance and cover composition from all other treatments • the lowest plant cover • no trees in 2018.

These characteristics reinforce what is already known about spoil being a poor medium for plant growth (Read, 2002; Mercuri et al., 2006; Nussbaumer et al., 2012; Scanlon, 2015; Castor et al., 2016; Department of Industry Innovation and Science and Department of Foreign Affairs and Trade, 2016; Newman, 2017). The DistLM suggested plants on Spoil treatments were strongly associated with higher available sodium and calcium. Many species that did successfully establish on Spoil were from the Chenopodiaceae family, which often has a higher tolerance of saline conditions (Clarke and Lee, 2004; Flowers et al., 2010).

Community assembly processes are determined by the regional species pool, dispersal and chance of germination, environmental filters, biotic interactions, and feedbacks within these levels (HilleRisLambers et al., 2012). For this study, attempts were made to minimise differences between the species pool and their dispersal, by seeding and planting species, to further elucidate how environmental filters influence the community development. It is, however, often difficult to separate the environmental filter from biotic interactions and there have been suggestions that the effect of the environment as a filter has been overestimated (Kraft et al., 2015). Ultimately, however, Spoil is the definition of an environmental filter because very few species could survive on the treatment, and the cover metrics suggest little evidence of facilitation (Bulleri et al., 2016).

It is interesting to note that the most improved treatments were those that had the most ameliorants added, i.e. Subsoil OGM Mulch, Subsoil OGM, Subsoil Mulch and Spoil OGM Mulch. Additionally, every treatment was an improvement on the performance of the Spoil. This suggests that while there are specific benefits from different ameliorants, any ameliorant is better than none and with more ameliorant added, better outcomes can be achieved on Spoil. Increased depth between vegetation and the contaminant has been shown in other work to have a positive effect on plant development. Sydnor and Redente (2000) found that increased depths of topsoil between vegetation and shale oil increased plant biomass development of natives and introduced species. This may mean that even as a subsurface material, Spoil can have a large negative effect, similar to sodic subsoils of dryland areas in

Australia (Rengasamy, 2002). It should not be unexpected that plants need soils deeper than the 30 cm provided by subsoil in this study; a global study suggested the average maximum rooting depth for sclerophyllous shrubland and forest was 5.2 m (Canadell et al., 1996). If plants find Spoil to be an unsuitable growth medium, then the lack of deep roots could have major implications for access to water as well as soil biology, weathering rates, carbon sequestration and nutrient availability (Maeght et al., 2013). The approximately 30 cm layer of subsoil may have made a large difference because it provided more space for roots to grow before encountering this difficult material. The effect of ripping, though shallow, may have also diluted the stronger negative effects of Spoil, allowing deeper root penetration. Mixing of soils to dilute and redistribute negative effects is not always an ideal method (Pietrzak and Uren, 2011) because in this case it may not guarantee removal of the problem. Given that the subsoil was already low in almost all plant-available resources, the material may have been able to adsorb a considerable amount of the Spoil’s sodicity. Similarly, the integration of organic-rich ameliorants OGM and Mulch may have drastically improved the qualities of the spoil in the upper layers, allowing for increased growth. A technique used in the USA as part of the Forest Reclamation Approach is to supplement soil with weathered spoil, particularly sandstones, to increase the buffer between plant roots and the unweathered spoil (Zipper et al., 2013; Skousen et al., 2017). Given that the mining industry has insufficient topsoil supply for restoration in the Hunter Valley, this may be worth investigating in the future.

3.4.2.2 Effect of Adding Subsoil Compared to the other factors, adding Subsoil promoted the largest increase in species richness overall and in both years. It also increased the tree numbers and tree height in 2015. The community that developed on Subsoil was different from treatments without Subsoil in 2015 and 2018 in both abundance and cover. Particular changes were the increase in Acacia species in 2015, including A. amblygona, A. decora and A. falcata as well as the herb Dichondra repens and members of the genus Euphorbia.

One of the key characteristics of subsoils is that they typically contain low levels of plant seeds (Nussbaumer et al., 2012), although soil movements can have an effect on seed distribution (Espinar et al., 2005). Of particular interest in this study was that the highest species richness across the site was found in plots with Subsoil. The seeding mix used for this study contained 50 species and another six species were germinated and grown to juvenile state before being

84 planted. In 2015, of the 150 species found across all Subsoil plots, 105 were not included in the seeding or planting list and included many native species such as Cynodon dactylon, Lomandra multiflora and Plantago debilis. There were also a number of exotic species such as Senecio madagascariensis*, Gomphocarpus fruiticosa* and Lysimachia arvensis*, which are all locally common. These species may have been present in the seed bank, dispersed in, or a mix of these two pathways. This is in contrast to a large study in Europe that found seed application had a strong effect in reducing the number of species that colonised a site (Lepŝ et al., 2007). The rise in species richness additionally suggests that Subsoil provided a suitable microsite for germination, establishment and survival for a wide variety of species because these stages can be major barriers in restoration (James et al., 2011; Piqueray et al., 2013). This could be due to having a higher clay content, as Scanlon (2015) found treatments with Subsoil on the Experimental Site had 5% higher clay content on average (p=0.0019). Although clay can hold water tighter, making it unavailable to plants, perhaps the higher clay content provided a sustained source of soil water to the plants throughout the drought. Although much less common than on the Mulch treatment, there was some coarse hard timber integrated throughout the Subsoil ameliorant. This could have given enough texture to capture seeds early in the rehabilitation (Harper et al., 1965; Chambers, 2000). Perhaps another reason for the Subsoil being able to sustain a wide variety of species was that its physical and chemical characteristics were balanced at a point where many species’ fundamental niches overlap with minimal fitness difference.

Another option that will be explored in more detail in Chapter 4 is that the Subsoil ameliorant may have supported a diverse range of soil microorganisms that facilitated the survival of many plant species (Harris, 2009). This could be supported by the high numbers and diversity of Acacia species found on subsoil plots, which are known to form symbiotic relationships with nitrogen fixers such as Rhizobia.

3.4.2.3 Effect of Adding Mulch There were three significant positive effects from addition of Mulch: increased species richness, increased Shannon–Wiener diversity and increased height of trees in 2015. Although perhaps not a positive feature in this setting, one of the strongest results from adding Mulch was the increase in the average number of trees in each plot. This increased tree numbers on the Subsoil Mulch treatment to levels on average 1.8 times higher than on the RSF, indicating

85 seeding rates could be reduced for this treatment. There was also a potentially negative effect in 2018: combined application of Mulch and OGM produced smaller trees than application of OGM on its own. The interaction between Mulch and OGM may be related to resource availability. The coarse woodchip Mulch used is very high in carbon and can be resistant to degradation. Although allowing for higher species richness, the high carbon in the Mulch may have allowed microbial immobilisation of resources from the OGM, reducing availability of nitrogen in the short term (Hodge et al., 2000; Miyajima, 2015). This may, however, have long- term benefits because the resources are still in the system and have not necessarily been leached.

The presence of Mulch only caused a significant change to the community composition in 2015, increasing the occurrence of Euphorbia sp., Acacia falcata, Eucalyptus moluccana and Corymbia maculata while decreasing the abundance of Chloris truncata, Atriplex semibaccata and Salsola australis. Addition of carbon has also been shown to affect the community composition in other sites. For example, Cole et al. (2016) found a decline in exotic annuals in two Box Gum woodlands after carbon was applied as sugar, which was also associated with a reduction in soil available resources. Considering the effect was limited to 2015 suggests Mulch has a primary impact in the early stages of ecosystem development where it may facilitate germination and establishment of some species. Another suggestion is that the high- carbon products only have a temporary effect because some studies show an increase in available nitrogen (Prober et al., 2005; Prober and Lunt, 2009), which probably occurs after the available carbon is spent.

3.4.2.4 Effects of Subsoil plus Mulch Interestingly, it was the combination of Subsoil and Mulch that enabled the highest native species richness so perhaps this substrate supports greater niche overlap of local species. Subsoil Mulch is, however, an unexpected treatment to be most diverse; the humped-back model originally proposed by Grime (1973) suggests the maximum level of biodiversity can be found at the intermediate range of productivity. This has been attributed to high environmental stress from lack of fertility at low productivity compared with at high productivity where there is high competitive stress. Although this theory has not always been supported (Adler et al., 2011; Tredennick et al., 2016), it does have a very long history in the scientific literature (Mittelbach et al., 2001; Fraser et al., 2015; Brun et al., 2019). However, it

86 could be that this simple design does not work in all situations (Brun et al., 2019). For example, the humped-back model is inconsistent with the evidence of increased productivity with increasing diversity (Tilman et al., 2012; Tilman et al., 2014). There is also evidence from Western Australia that the highest species richness can occur at the lowest level of fertility (Zemunik et al., 2015). Subsoil Mulch is the treatment lowest in resources generally and phosphorus particularly (see Chapter 6), so this is a similar situation to the Western Australian example. Perhaps the majority of species have the functional ability to survive at low resource availability and, as the primary limiting resource is equally limiting for all members (no difference in fitness), the net outcome is coexistence or at least slower competitive exclusion (Dybzinski and Tilman, 2007). This is also supported by a study on retrogression, long-term ecosystem decline, which showed increased vascular plant richness in older sites where resources were lower (Wardle et al., 2008).

3.4.2.5 Effect of Adding OGM Consistent with previous work (see sections 2.2.1.2 and 2.3.5), the high levels of nutrients and organic matter in OGM had a large impact on the plant community. OGM is most notable for its effect on tree growth, with the strongest increase in tree height and CDBH. This increased growth is probably supported by access to nutrients required for growth, such as available nitrogen, available manganese and available zinc as is suggested in the DistLMs in Figures 3.10 and 3.11. Importantly, this growth appears to be sustained in the medium term, given the continued increase in height from 2015 to 2018. OGM also increased the cover of plants, both natives and exotics, in both survey periods. Strong plant growth has been desired for many years in the mining industry due to the erosive properties of the spoil. Some treatments were, however, limited in their performance against the Australian federal target because of exotic cover. While in some settings it has been argued that exotics are so difficult to manage that the best alternative is ‘join them’ (Cordell et al., 2016), this concept would not be consistent with the restoration of the target EEC. However, given that none of the treatments averaged below 50% cover by native species, adaptive management to control exotic species establishment may allow this ameliorant to provide higher native species cover and achieve the EEC target. It may be worth trialling lower application rates of products like OGM in the future because there is generally a trend of high-nutrient products supporting exotic species over native species (Prober and Wiehl, 2012; Larios et al., 2017).

Some OGM treatments were linked by the DistLM with an increase in available sodium in 2015, and all OGM treatments were linked with electrical conductivity in 2018, both of which are indices of salinity (Figures 3.10 and 3.11). This suggests that salt levels as well as the nutrient content was driving changes in flora characteristics, which may explain the diverse range and abundance of species from the Chenopodiaceae family. Enchylaena tomentosa averaged 116 individuals on OGM treatments compared with 31 individuals without OGM. Similar displays of dominance were seen by Atriplex semibraccata, Einadia nutans subsp. linifolia, Einadia polygonoides and Einadia nutans subsp. nutans in both abundance and cover. Although there is variability in salt tolerance (Egan and Ungar, 2001), many members of the Chenopodiaceae family are able to tolerate highly saline environments (Clarke and Lee, 2004; Flowers et al., 2010). The characteristics of Spoil may be preventing the leaching of resources from the OGM as water penetration at depth may be reduced. Perhaps, even though substrates were highly fertile on the Spoil OGM treatment, the salinity restricted growth of grasses and other forbs.

3.4.3 Comparison of the Experimental Site to the References 3.4.3.1 Forest TopsoilPositive Control The Forest Topsoil treatment on the Experimental Site was important as a positive control for the ‘best practice’ method, using a topsoil from the local area as the primary ameliorant (DIIS and DFAT, 2016). However, the topsoil utilised for the Forest Topsoil treatment was considered to be of poor quality and would probably have been buried in the new landform had it not been used for this research. The Forest Topsoil treatment had moderate native species richness with low levels of exotic species richness, moderate diversity, high cover levels, moderate numbers of trees, mid to low height of trees and low CDBH. The high cover levels, however, were misleading; the dominant species, Acacia amblygona, is a small shrub 0.5–1.5 m high, which was only counted as a ground cover because it matched the criteria of less than 1 m high. A. amblygona would be unlikely to provide quality protection for erosion, however, as the plant is not prostrate in growth form, like the members of Chenopodiaceae, and plants have large amounts of bare ground underneath them (Figure 3.17) (Gyssels and Poesen, 2003).

The moderate diversity levels observed on the Forest Topsoil treatment could have been assisted by a seed bank, although it is difficult to separate seed bank from dispersal. One

88 species that is probably from the seed bank is Acacia amblygona itself because the numbers of individuals found on this treatment were more than double those found on all other treatments. However, this species was also seeded and may just have been particularly successful on this treatment.

Figure 3.17. The ground underneath two Acacia amblygona in Block 1 Forest Topsoil. The growth beneath A. amblygona is generally sparse forbs and grasses.

3.4.3.2 Ring Rd Overall, Ring Rd had a more similar species richness to the 2018 Subsoil OGM and Subsoil OGM Mulch. Although not statistically different from the RSF, all samples were lower in richness. There may be species that had difficulty colonising the area and assisted introductions may be useful to increase the species richness. Exotic species richness was variable, though generally at moderate levels compared to the Experimental Site. Native diversity was high in Ring Rd compared to the Experimental Site in 2018, suggesting that 89 something about Ring Rd allowed for greater maintenance of plant diversity. The higher levels of diversity probably came from the soil seed bank because the soil used was a direct transfer from topsoil of the RSF in an area that was mined. It would appear though that not all topsoil is equal, because the Forest Topsoil treatment on the Experimental Site did not show this level of biodiversity from its seed bank. The Ring Rd is also much older and closer to the RSF than the Experimental Site is to species-rich vegetation so there is the chance that over time some species introduced themselves, if not to the same level as in the RSF.

Ring Rd was statistically different in community composition from all the treatments. A notable difference between Ring Rd and other areas was the presence of Acacia filicifolia, which is a common species in NSW and south-east Queensland, where it is typically found in dry sclerophyll forests on sandy soil of gullies and creeks (Royal Botanic Gardens and Domain Trust, 2019). A. filicifolia was probably an unintentional introduction to the Ring Rd because it is not present in the RSF and looks similar to a common local species Acacia parvipinnula. It is also not part of the target community so it would be undesirable on the Experimental Site.

Total cover was low in the Ring Rd, comparable with Spoil. This, however, may be more of an expression of ecosystem development because the reduced ground cover coincides with increased canopy development. The 15 years difference in growth was noticeable in tree height, with Ring Rd having much taller trees than any of the treatments on the Experimental Site. Although the Ring Rd and Experimental Site are not directly comparable as would occur in a true chronosequence, it is probable that the largest successional force during the next stage of development on the Experimental Site will be change in light conditions as canopy develops in treatments that have sufficient tree density. Light has been considered to be one of the dominating resources determining the success of individual plants to develop (Schwinning and Weiner, 1998)

The similarities and differences between Ring Rd, RSF and the Experimental Site highlight the importance of using a reference such as the Ring Rd. Reference sites and models serve to guide the restoration by providing valuable information on factors such as species composition and structure, ecological processes, change with time and change with climate (Prober et al., 2002; Gann et al., 2019). In the mining setting, the fundamental change in environmental conditions is also relevant and can complicate how the site is able to recover. The reduced soil layers and altered geomorphology in the mining environment also need to 90 be considered as a fundamental difference in how the ecosystem will develop. This is similar to discussions around how restoration in a changing climate forces anticipation of risks and therefore necessary adaptations to be considered (Harris et al., 2006). Having a reference system that matches some of the abiotic characteristics is therefore also important in this setting. Although Ring Rd had a different species mix from the outset, many of the environmental conditions could have been very similar to those at the Experimental Site, and environmental conditions can be just as, or more, important than the biotic component (Durbecq et al., 2020). Had the Ring Rd not been included in this study, it would be less clear how many of the differences, such as community composition, were attributable to the mining environment and the age of the site. Additional sites and soil samples from the references would have provided more statistical clarity. However, the Ring Rd in particular was chosen as an example of quality restoration of ecological significance that had sufficient age to both confirm desired trajectory and provide feedback on any accelerated development.

3.4.3.3 Ravensworth State Forest When indices were used to compare treatment groups to the RSF, the closest treatment was 2018 Subsoil Mulch. Compared to 2018 Subsoil OGM and 2018 Subsoil OGM Mulch, the 2018 Subsoil Mulch treatment had a much more similar native species richness and community composition, which overrode the substantially decreased tree height. The number of exotic species found in the RSF was very low compared with many treatments on the Experimental Site, suggesting that weed control may be an area that needs targeting in future management.

Although the PERMDISP suggested caution, PERMANOVA and nMDS suggested RSF was significantly different in community composition from the Reference Lists. However, at the very least RSF is more similar to the Reference Lists than the Experimental Site is. The RSF also did not match with any of the targeted communities, which, given that RSF is our best available reference, is surprising. This suggests that the method of matching to plant community types used is poorly suited and/or that an insufficient amount of RSF was examined to capture it accurately.

RSF is one of the last and largest areas of remnant vegetation on the floor of the central Hunter Valley. Although it has been exposed to disturbance through logging and cattle grazing in the past, it remains a refuge for a number of rarer species of flora and fauna. Given its size, 91 and relative stability since European settlement (compared with the surrounding region), it should be expected that it would have an overall larger diversity of species present. Within the RSF there are a number of ecotones, zones where spatial change in vegetation is more rapid than on either side of the zone (Lloyd et al., 2000). The RSF changes in soils, including red, yellow and brown podzols, valleys and ephemeral creeks with increased water availability and changes in aspect, which can all affect the community composition (Nussbaumer et al., 2012). Given the variability that can occur on this area, perhaps the RSF required additional plots to compare the full range of potential communities.

3.4.3.4 Reference Lists A difficulty with the Reference Lists is they do not provide any guidance on abundances of species. Another problem is the species on those lists are what makes the community distinctive and therefore they do not include all of the potential co-occurring species. As each of these lists are the culmination of tens to hundreds of plots, it is inappropriate to compare the synthesised and summarised output from that analysis as a single point against the 54 plots of the Experimental Site. Future studies may benefit from including all of the individual plot data from large studies (e.g. Somerville (2009)) in the same analysis to consider how this dataset compares with the full context from associated species rather than just distinctive species. This is similar to how communities were compared with the BioNet database as part of the NSW Plant Community Type comparison; however, those data are matched to already defined communities rather than the plot data that produced those community classifications.

The separation of the Experimental Site to the Reference Lists in community similarity is not unexpected. However, what was unexpected was that there was very minimal movement towards the Reference Lists with time. This is probably due to key differences in species that are present or absent, such as Brunoniella australis, as there is no comparison of dominance when comparing to a list of species. As discussed earlier, supplemental introductions of missing species may be required to further improve the community match in the future if species remain absent (Grant and Koch, 2007; Nussbaumer et al., 2012).

3.4.4 Temporal Effects Priority effects, the development of a community composition based on the order with which species arrive, are likely to have had a large effect on the Experimental Site and perhaps contributed to its diversity. It is probable that the single addition of all 50 species enabled each of these species to have an equal opportunity rather than giving individual species priority. It is also probable that the 50 species received priority over other species that dispersed on to the site from the surrounding environment. This is because the majority of species applied to the site are still present even though there has been large dispersal on to the site. As resource availability can have a large effect on community assembly (Kardol et al., 2013), it is probable that the single addition of a diverse seed mix where species had equal opportunity to establish facilitated the diversity of species seen, particularly for species in the OGM treatments with high fertility that would otherwise be expected to have low species richenss. The priority effects may only now be beginning to deteriorate (Werner et al., 2016). For example, between 2015 and 2018, the Experimental Site probably experienced a reduction in priority effects, with populations of Acacia falcata and Daviesia ulicifolia crashing (Werner et al., 2016). This suggests that at this stage, research on the Experimental Site has confirmed that species are capable of living on a substrate (fundamental niche), not that they are able to coexist with other species in the long term on this substrate (realised niche).

Caution is also needed in assuming that the restoration will proceed in the way generally depicted in these results. The results shown are an average of the communities assembled, communities that had high levels of variability (see grey cells in Tables 3.2 and 3.3). For both 2015 and 2018, within-community similarity ranged from 43.3% to 65.2%, suggesting multiple alternative states (Petraitis and Methratta, 2006). Further, there were large changes in community composition between years suggesting that the alternative states were not stable at this point. This is indicative of change caused by both environmental heterogeneity and variation in community assembly with the potential development of hysteresis (Beisner et al., 2003; Kadowaki et al., 2018). A change in equilibrium can shift the community through a transition to an alternative state; this transition is referred to as ‘hysteresis’ (Litzow and Hunsicker, 2016). Ecological theory currently suggests that changes in populations, community composition and environmental conditions can move the equilibrium of a community (Kéfi et al., 2016). As the community changes through succession, it can be

93 thought to transition through various alternative states, which based on the conditions may lead to further transitions towards or away from the target. For example, boreal stands in central Canada have been shown to have multiple successional pathways to reach a similar target based on fire, edaphic conditions and intermediate disturbance (Taylor and Chen, 2011). A key in restoration is guiding the community through the appropriate states to produce the target community. Given the variability on the Experimental Site, the suggestion could be made that subtle differences in site establishment and development can have an impact on the states that the communities progress through. Fortunately, in the case of the Subsoil Mulch and Subsoil OGM Mulch treatments, the variability between plots does not appear to be sufficient to significantly alter the trajectory away from the references. Treatments that may not be on the correct trajectory, such as Spoil OGM, suggest that the starting conditions have produced hysteresis sufficient to change the composition of the community away from the desired conditions. Even with the variability within treatments, the trend is strong enough to confirm a reliable transition away from the targeted states. The variability found on the Experimental Site was similar to that found in the RSF and is acceptable because variability is expected to occur between individuals, species, traits, communities and ecosystems (Bolnick et al., 2011; Long et al., 2011; Violle et al., 2012). It does suggest that, in order to make restoration reliable, a clear understanding of the drivers of each alternative state and the effect of community variation is necessary. It suggests that high levels of variability should be expected as part of any restoration project in the Hunter Valley.

3.5 Conclusion Which treatment develops the flora community towards the reference at an increased rate?

The results of this chapter are mixed and inconclusive. None of the treatments formally matched the state-level target EECs, although neither did any of the available references. Further, the treatment that overall appeared to best satisfy a list of indices performed poorly on one index used for the federal listing and therefore was only eligible for a lower classification than many other treatments.

Overall, all treatments made a substantial improvement compared with Spoil, and treatments with more ameliorants tended to be closer to the references. Although there were significant

94 changes in community composition and cover from adding Subsoil and OGM, there was little to suggest that any one treatment was substantially better than another in all metrics as long as multiple ameliorants were added. The application of a high diversity of seeds as part of the rehabilitation process probably played an important part in the site’s success. Even though there were many species absent from the seed mix that established on the site, the priority effects from the seeding ensured high biodiversity throughout. There is some evidence that the treatments played a role in selection of species by salt levels or fertility. Microorganisms or current resource availability could have also caused these effects and will be examined in the following chapters. Results of mechanisms for changes in composition, then, are indications rather than definitive causes. Even though this site did not reach the specified target, it has exceptional diversity for a rehabilitation site in the local area and should be considered successful at this stage of its development.

Chapter 4 – Restoring Mine Soil Microbial Diversity to Rebuild Plant Biodiversity

4.1 Introduction Below-ground biodiversity is in many ways harder to characterise than above-ground biodiversity, and there are many large questions still unanswered in soil ecology (Schmidt et al., 2014; Eisenhauer et al., 2017). Life underground is dominated by several groups: archaea, bacteria, fungi, nematodes, insects and microfauna. Most of this life is found in the upper layers of soil, typically within the top 10 cm (Eilers et al., 2012). Although small, microorganisms are incredibly abundant with estimates of 100 million to 1 billion bacteria per gram of soil (de Vrieze, 2015). This gives microbes the potential to have large impacts on their ecosystem. While there are many taxonomic discoveries that are yet to be made in soil, ecology is rapidly developing an understanding of how important soil biota is for ecosystem functioning (Sutherland et al., 2013; Wagg et al., 2014; Eisenhauer et al., 2017). Given the broad nature of microbial taxonomic diversity, functional roles, environmental adaptations and interactions with other organisms, this chapter briefly introduces the topic of microbial restoration, then focuses on literature pertaining to the broad functional roles of many microorganisms before discussing microbial reintroduction methods.

4.1.1 Functional Guilds There are many key functions played by life in the soil, often microorganisms are grouped in in to guilds or functional roles. A guild can refer to a group of species that are not necessarily related, but that expoit the same class of environmental resources in a similar way (Root, 1967; Nguyen et al., 2016b). A guild of bacteria is fixing atmospheric nitrogen, a key limiting resource in the Hunter Valley and globally (Nussbaumer, 2005; Vitousek et al., 2013). For example, groups from the order Rhizobiales are well known for their symbiotic interactions with a wide variety of plants (Lafay and Burdon, 1998; Gomes et al., 2010; Fischer et al., 2012). Although leguminous and actinorhizal plants are noted for their associations with nitrogen- fixing microbes, many other plants, including cereal crops and pastoral grasses (Franche et al., 2009) as well as lichens (Erlacher et al., 2015), can also form symbiotic relations. Perhaps even more important from a global nitrogen-fixing perspective, however, are the diverse, non-symbiotic and free-living diazotrophic bacteria such as the heterotroph Azotobacter

96 vinelandii and autotroph Cyanothece (Reed et al., 2011; Gupta et al., 2019). Diazotrophic species occur in almost all terrestrial ecosystems where atmospheric nitrogen is available (Matzek and Vitousek, 2003; Reed et al., 2011). Additionally, while they may not be associated with plants, free-living nitrogen fixers may associate with lichens and biocrusts that, through fungal partnerships, deliver nitrogen to plants (Rudgers et al., 2018). There are also many other important pathways used by microbes that are involved in nitrogen cycling, including the release of N2O to the atmosphere (Bowen et al., 2020).

Mycorrhiza, fungal symbionts, provide plants with increased surface area coverage in soil (Dickson and Kolesik, 1999). Mycorrhizal symbiotic relationships are particularly known for facilitating phosphorus, nitrogen and water uptake (Read and Perez-Moreno, 2003; Lehto and Zwiazek, 2011; Ryan et al., 2012; Veresoglou et al., 2012). There is also production of plant hormones by mycorrhiza with potentially positive and negative effects (Chanclud and Morel, 2016). A review of mycorrhiza in mined land restoration concluded they have been beneficial in almost all cases (Wang, 2017).

Soil-borne pathogens are important in an ecological sense because they contribute to determining the abundance and distribution of species (Packer and Clay, 2000). While there are certainly many diseases in soils, there is also a range of predators for many organisms, which can include other bacteria, fungi, viruses and nematodes among others.

Saprotrophs are able to degrade organic material to recycle the resources within it. Fungi are generally represented as being dominant in this role (Grinhut et al., 2007), although bacteria also play a large part in decomposition (Bani et al., 2018). Fungi and bacteria are not strictly restricted to one functional role and can for instance act as commensal symbiont, parasite or predator depending on the circumstances (Barron, 2003). An example of a species partaking in multiple functional guilds is the saprotroph Neurospora crassa, which is common in tropical/subtropical regions on burned vegetation, carbohydrate-rich foodstuffs and sugarcane processing residues. It has been found to live as an endophyte on healthy Scots pine (Pinus sylvestris) as well as a pathogen on stressed Scots pine, which shows a high level of environmental plasticity (Kuo et al., 2014).

Soil organisms are important in the development of good soil structure because they produce extracellular compounds such as polysaccharides and proteins, which, in addition to hyphae

97 and filaments wrapping around soil particles, work to aggregate particles together (Nichols and Halvorson, 2013; Lehmann et al., 2017; Chamizo et al., 2018). Soil microbes are also recognised as playing a vital role in carbon storage because of the diverse nature of the carbon transformations they control (Trivedi et al., 2013).

The microorganisms that live in soil, plants, animals, the air and other parts of the environment all play key functional roles. Given the limited number of ecosystem functions and extraordinary levels of species richness seen in microbial communities, it would be expected that there would be a degree of functional redundancy (Jia and Whalen, 2020). However, recent research suggests that increasing microbial richness promotes interkingdom associations and microbiome complexity, which drives a positive relationship between richness and multifunctionality (Wagg et al., 2019). This suggests that to establish full functional capacity, restoration of the full complexity of the microbial community is important.

4.1.2 Restoring Microbial Communities A range of factors impact on soil biodiversity at the global scale, including agricultural practices, pollution, acid rain, and nutrient overloading, as well as land degradation from sealing, compaction and erosion of soils (Orgiazzi et al., 2016). In areas that have been mined, microbes may be at low abundances or not present (Jasper et al., 1988; Reddell and Milnes, 1992; Harantová et al., 2017; Banerjee et al., 2020). While restoration of plant communities can benefit from soil microbes (Requena et al., 2001), there are still large gaps in our understanding of microbial diversity (Guerra et al., 2020) and restoration (Geisen et al., 2019).

As our understanding of the importance behind the functions performed by soil biota has developed, so has a drive to successfully restore soil communities. There has been a focus on the microbial communities that associate with plants as their absence could explain poor restoration results, slow growth and dominance of weeds. One of the early questions asked was ‘Do microbes need to be restored?’ or ‘If we build it, will they come?’ – a field of dreams hypothesis (Hilderbrand et al., 2005). This is supported by microbes’ ability to disperse into freshly disturbed environments; microbes are abundant in the atmosphere and can fall out of suspension into new environments (Cao et al., 2014; Gonzalez-Martin et al., 2014; Mayol et al., 2014). However, it has been shown that development of the community composition can take hundreds of years (Cutler et al., 2014; Uroz et al., 2014). This could be due to differences 98 in abiotic conditions, differences in plant or soil communities, dispersal limitations, disturbance regimes or a combination of the above (Peay et al., 2010; Nemergut et al., 2013; Kivlin et al., 2014).

4.1.2.1 Inoculation If microbial dispersal is an issue, then investigations need to be made into which methods are suitable for restoring microbial communities. Inoculums were an early concept and have been trialled in mine rehabilitation with mixed success (Fisher, 2010; Emam, 2016; Newman, 2017; Singh et al., 2019). For example, Emam (2016) found that a local soil inoculum increased non- native grass biomass and decreased non-native forb biomass but had no effect on native species, while a commercial inoculum had no effect overall. Trials in the Hunter Valley have shown that inoculums assisted growth of native plants but these trials have been small, and in some cases unexpected interactions with non-target members of the local soil microbiome were found (Fisher, 2010; Newman, 2017). Large concerns with using inoculums include that they may have no or only transient effects as the introduced species are outcompeted by the local community; they may also have non-target impacts, such as affects on nitrogen fixation (Trabelsi and Mhamdi, 2013). There are nevertheless many examples in the literature of restoration benefiting from inoculation (Requena et al., 2001; Middleton and Bever, 2012; Torrez et al., 2016; Chamizo et al., 2018). Experiments have shown the importance of the inoculum species being adapted for the local environmental conditions because poorly adapted communities may not survive to give beneficial outcomes (Moreira-Grez et al., 2019). With a high diversity of species in every gram of soil, it is unlikely that commercial inoculums will ever be produced that can restore the full diversity of a local area.

4.1.2.2 Topsoil Transfer A new perspective on inoculation is based on the long-standing practice of soil transfers. Although direct transfer of fresh topsoil has been considered leading practice for over a decade (Department of Resources Energy and Tourism, 2009) the full potential of this process has only recently been realised. Using a fresh surface, Wubs et al. (2016) transferred a small amount of soil from different origins, a heathland and a grassland, and applied the same plant mix to both soils. The results were that soils from the heathland developed flora community characteristics that were similar to heathlands while the grassland soils produced flora communities more similar to grassland areas. This result was related to the microbial

99 community that influenced the direction of succession. Recent research is also suggesting that there may be a conditioning effect where the act of growing plants in a soil prior to its application can encourage the growth of those plants again (Brinkman et al., 2017). This may be due to a developed familiarity between the flora communities and microbial communities. By directly transferring soil from an area of mining to an area of restoration, it is hoped that the majority of the soil community will survive the disturbance and be appropriately conditioned to support the restored vegetation. It is recognised that a number of microbes are susceptible to disturbance and may dramatically decrease in number following transfer (Jasper et al., 1991; Birnbaum et al., 2017). It is important therefore to consider the successional processes involved in microbial restoration.

Ideally, direct transfer of topsoil brings the majority of soil biota with it so that the successional distance between the now secondary successional community and the target community is shorter and the trajectory is more accurate (Figure 4.1). While models predict that as the plant community develops so too will the microbial community (Schnitzer et al., 2011), real-world data dispute that in many cases. Early microbial community restoration, particularly in heavily disturbed environments, is probably dominated by stochastic events, leading to the plant rhizosphere in particular being unpredictable (Hu, 2019). Early successional communities are more likely to be dominated by bacteria, whereas fungal dominance and diversity is likely to develop towards late successional stages (Hannula et al., 2017). It has also been suggested that early in succession, there may be a higher proportion of parasitic microbes, whereas later in succession there is an increase in broader functional roles (de Araujo et al., 2018). While the plant successional process can take a long time, microbial succession can occur relatively quickly because micoorganisms are very dispersive and may be linked tighter to the abiotic conditions of an environment than they are to the plant community. This suggests that microbial restoration should be performed prior to or concurrently with plant restoration because plant community establishment may be harmed by a lack of microbes but not vice versa. This provides a mechanism for accelerating plant community development because, although microbes will eventually disperse into an environment, their absence will hamper the ecosystem development.

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Figure 4.1. Conceptual summary of how topsoil application, although what it will bring may be uncertain, may decrease successional distance to the microbial restoration target. Inoculum application introduces a limited richness of species targeting functional characteristics, whereas topsoil application can provide both richness and functionality. Topsoil application may not match the target, which can be variable in composition. Created by R. Scanlon.

However, topsoil is often unavailable in restoration in the Hunter Valley, precluding its use in restoration. Other suitable replacements for topsoil might not be local, thereby not contain provenance species, and are unlikely to match the abiotic conditions of the historic reference. Additionally, each substrate will have its own microbial community, which may be beneficial, indifferent or detrimental to the restoration process. The Subsoil used in this study was from a native pasture area and therefore may have contained a microbial community that was conditioned to assist the performance of pasture species. Mulch was produced from trees that, although not in the soil, still had their own microbial communities. Additionally, the Mulch is suspected to have spent time on the ground, where it could have accumulated some soil microbes. However, it is unclear what, if any, microbial community will have been provided to the site with the Mulch. Organic Growth Medium (OGM) is a commercially produced product from Western Sydney, 155 km south of the Experimental Site, and a primary component of it is food and garden material from Western Sydney. The composting

101 process used to produce municipal solid waste compost/mixed waste organic output can produce a material with a unique microbial community (Herrmann and Shann, 1997). Importantly, composting can support an abundance of saprotrophs (Tuomela et al., 2000; Wang et al., 2018). The application of OGM may therefore supply a beneficial source of microorganisms to the establishing community (De Corato et al., 2018). Spoil is expected to be low in microbes because although microbes have been identified in aquifers and rock strata (Magnabosco et al., 2018), they are often low in number.

4.1.3 Research Directions While there are some useful findings from local and international studies, the literature review above raises a number of questions relevant to the current study. These questions are:

Three sources of microbial communities were introduced at the initiation of restoration; are the communities from these sources still distinct? Are they similar to the reference areas?

This chapter aimed to compare soil microbial community composition and functional capabilities to determine their origin and ability to support the developing ecosystem. This involved extraction of metagenomic DNA from field samples, 16S and ITS sequencing, bioinformatics and matching to functional association databases.

It was hypothesised that: (i) each soil ameliorant treatment would contribute a unique microbial community to the development of the Experimental Site; (ii) one or more of the treatments would produce a microbial community more similar to the references than the others; and (iii) the microbial community composition will determine how similar the functional attributes of the treatments are to the references.

Following previous work, the microbial community of Spoil was expected to be very different from the other treatments (Newman, 2017) or may have had populations of insufficient size to extract metagenomic DNA (Kumaresan et al., 2017). Subsoil was expected to have the closest microbial community to the reference because it comes from the local area; however, as the material is from lower depths, it was still expected to be significantly different from the reference (Eilers et al., 2012). Mulch, which was combined with the Subsoil for this study, was expected to provide a carbon source for the saprotrophs but was unlikely to dramatically change the community composition (Kim et al., 2017). It was recognised that in this study it is not be possible to separate effects caused by the Subsoil and Mulch. It was expected that

102 the OGM would change the microbial community (De Corato et al., 2018), particularly causing an increase in saprotrophic species (Tuomela et al., 2000; Wang et al., 2018).

4.2 Methods

4.2.1 Field Sampling A limited number of treatments were used for this experiment: Spoil, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch (see Chapter 2 for more details). The Subsoil Mulch combination was chosen because of its high plant species richness. Samples were collected from all six blocks; however, because of the loss of plots in Blocks 3 and 6, only four replicates were available for Spoil and Spoil OGM. Reference samples were taken from the Rav Ref, Ring Rd and RSF (Ravensworth State Forest), which are no more than 200 m, 8.3 km and 12 km away respectively. The RSF is thought to have had continuous native vegetation on site since before European settlement, whereas Rav Ref and Ring Rd have had native vegetation for at least 50 and 20 years respectively. Fresh OGM samples were collected from the Global Renewables production site at Eastern Creek, which is 155 km away. Three fresh OGM samples were taken from matured stockpiles, each sample being taken from between 10 and 20 cm depth from a side wall. Each sample was taken from a different section of the main stockpile. An additional sample of OGM (Immature OGM) was also taken from an underdeveloped OGM stockpile following the same procedure.

Owing to the long drought, sample collection was targeted to occur between 1 and 2 weeks after a period of significant rain. Between 30 and 50 mm of rain fell in the central Hunter Valley on 5/10/2018 and a further 15–20 mm fell on 11/10/2018. On the Experimental Site, Blocks 1, 2 and 3 were sampled on 15/10/2018, while Blocks 4, 5, 6 and the Rav Ref were sampled on 18/10/2018. Ring Rd and RSF samples were taken on 16/10/2018. OGM samples were taken from the Global Renewables production facility at Eastern Creek on 29/11/2018.

Because there is a large degree of heterogeneity within each plot, four samples were taken 2.5 m towards the centre from each of the plot’s corners (Figure 4.2 left). If an obstruction such as a tree was present, the full depth of a sample could not be taken because of rocks, or a major erosion gully occurred at a sampling point, then the nearest area of characteristic substrate was sampled. Soil was sampled using a soil auger designed for rocky soils with a diameter of 75 mm to a depth of 100 mm (Figure 4.2 right). The auger, a trowel used on loose

103 samples and nitrile gloves were thoroughly scrubbed with ISOWIPE (70% isopropyl alcohol wipe, manufactured by Kimberly Clark) between each plot. Each of the four samples per plot was placed in a ziplock bag that had been sterilised with ultraviolet light and 70% ethanol. The combined volume of all four samples was approximately 1.8 L. The soil sample in the ziplock bag was mixed to homogenise the sample and a single subsample was taken from that bag in a sterile 50 mL falcon tube. Each falcon tube was immediately placed in an ice box (esky). Samples were taken directly to the Conservation Science laboratory at the University of Newcastle and placed in a –80°C freezer for storage.

Figure 4.2. Left: samples were taken 2.5 m towards the centre of each plot from the corners. Right: soils were sampled to 10 cm depth. Note that more material has been moved away to get a clear photo of the Subsoil Mulch substrate for this image.

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4.2.2 Laboratory Extraction and Sequencing Extraction of metagenomics DNA was performed using QIAGEN DNeasy® Powersoil® Kit according to the manufacturer’s instructions with two exceptions. First, 0.5 g of soil was used for all samples as opposed to the recommended 0.25 g. Second, following 10 minutes of vortexing, samples were centrifuged for 3 minutes rather than 30 seconds. Extractions were performed by Block. Success of the extraction was checked using a NanoDrop™ 1000 spectrophotometer (Thermo Scientific) and gel electrophoresis.

Extracted samples were stored at –20°C until being sent by overnight courier to the Australian Genome Research Facility in Melbourne. Next generation sequencing was performed using Illumina MiSeq platform with paired-end chemistry, utilising Illumina’s Nextera XT Indexing. The bacterial and archaeal primer was 16S: 27F-519R (V1–V3) and fungal primer was ITS: 1F-2R.

As extraction of Spoil samples was unsuccessful by the above method, extractions were attempted using 10 g samples of Spoil with the QIAGEN DNeasy® PowerMax® Soil Kit. This procedure also failed to successfully produce any response on the NanoDrop™1000 spectrophotometer or gel electrophoresis. A series dilution was then performed to determine colony-forming units (CFUs) (JoVE, 2020). Three samples with no replicates were used: Spoil from an area with no plant growth, Spoil from underneath a large Enchylaena tomentosa (ruby saltbush), and Spoil that had been subsampled from the four corners of the plot and mixed to produce a single sample. Ten grams of field-moist Spoil was mixed with 95 mL of sterile deionised water. Serial dilution was performed by mixing 1 mL of suspended Spoil with 9 mL of sterile deionised water. Four additional dilutions were performed to produce a dilution ratio of 1:1,000,000. Dilutions were grown on R2A agar for two weeks to estimate CFUs.

To provide an estimate of potential for Spoil community composition, additional sequencing data were acquired from the control sites of Newman (2017), who had been able to extract from Spoil. The site used by Newman was established on Mt Owen between March and April 2011 and consisted of experimental treatments utilising municipal waste compost and an inoculum of many known symbiotic bacteria and fungi. The control plots used had no compost or inoculum added and organic matter was regularly removed from the areas. The key differences between the spoil samples from Newman (2017) and the Experimental Site in the 105 current study are that on the Experimental Site seeds were added and organic material was never removed from Spoil plots. Visually, Newman’s spoil appeared very similar, although chemically it averaged a lower pH (Newman 8.5 vs Experimental Site 9.4) and electrical conductivity (Newman 90 µS/cm vs Experimental Site 521 µS/cm). Spoil from Newman’s control plots was sampled in October 2015 and freshly sent under dry ice to the Australian Genome Research Facility in Adelaide for extraction and sequencing, using the same regions as samples collected for this study. Samples were treated in the same way as all other data for bioinformatics processing and analysis. These samples are referred to as Spoil Control throughout this chapter. The site used by Newman was approximately 1.4 km from the Ring Rd, 1.8 km from the RSF and 9.5 km from the Experimental Site.

4.2.3 Bioinformatics Bioinformatics was performed using QIIME 2 2019.1 (Bolyen et al., 2019). 16S and ITS had forward and reverse sequences removed by using Cutadapt (Martin, 2011). Quality control and pairing used DADA2 (Callahan et al., 2016) to produce amplicon sequence variants (ASVs); however, merge of the 16S sequences was largely unsuccessful so all subsequent steps were performed using the R1 sequence for 16S data. Sequences were truncated (at 250 nucleotides for 16S and 278 nucleotides for ITS). 16S samples were compared with the SILVA database (132 release, 97%, Quast et al. (2012) using vsearch, which resulted in 97.39% of sequences matched. ITS samples were compared with the UNITE (97, 02/02/2019, Nilsson et al. (2018) database using sklearn, which matched 65% to a level below kingdom.

4.2.4 Functions ITS sequences were assigned functional guilds using FUNGuild (Nguyen et al., 2016b). Similarly, 16S sequences were assigned KEGG Orthologues using PICRUSt (Langille et al., 2013) in the Langille Lab PICRUSt Galaxy Instance after matching to Greengenes 13_5 database. Both FUNGuild and PICRUSt assign a functional capability based on mappings from whole- genome shotgun sequencing of the same or related species and findings in the scientific literature. It should be stressed that both FUNGuild and PICRUSt make predictions of function based on taxonomic relatedness. If the taxonomic data are not well represented, then the prediction may have poor confidence or not produce a result. Further, the linking of a role to a species does not mean that this is correct or that the species is performing that role. FUNGuild results were trimmed to those that had a 93% confidence on taxonomic assignment

106 in the UNITE database and either ‘probable’ or ‘highly probable’ assignment of guild. PICRUSt data were analysed from a whole-of-system approach examining broad processes such as genetic information processing as well as targeting specific KEGG Orthologues known for roles in nutrient acquisition and cycling (see Appendix B).

4.2.5 Statistics Both 16S and ITS community compositions were analysed using PRIMER 7. Data were transformed to presence/absence and a resemblance matrix produced using Bray–Curtis similarity. Visual representation of the data were performed using nMDS and comparisons made using PERMANOVA and PERMDISP. The PERMANOVA design for treatments was treatment as a fixed effect and Block as a random effect. Factor design used presence of Spoil or Subsoil Mulch as a fixed effect, presence or absence of OGM as a fixed effect and Block as a random effect. The Block variable was removed from the design for pairwise tests. PERMANOVA’s were performed with permutation of residuals under a reduced model with Type III sum of squares and 10,000 permutations. Pairwise tests only differed by permutation method using unrestricted permutation of raw data (Anderson et al., 2008; Anderson and Walsh, 2013; Clarke et al., 2014).

Species richness was compared using JMP Pro 14 (JMP®, Version Pro 14. SAS Institute Inc., Cary, NC, 1989–2019). Linear mixed models (LMMs) were used to analyse data for treatment (with Block as a random factor) or as Factors (using only data from the Experimental Site and Spoil Control, Block as random factor). Multiple comparisons were made using Tukey–Kramer tests giving a t value and p value.

FUNGuild was analysed in JMP by summing abundances and species richness remaining after quality control and analysing for significant differences in trophic mode between treatments using LMM with Block as a random factor. As part of the FUNGuild analysis, pathotroph and saprotroph abundances were transformed using fourth root before analysis. PICRUSt was analysed by the same methods in JMP with LMMs examining differences between treatments. The KEGG Orthologues CBH1, AMY, lacZ, PHO and pqqA were fourth-root transformed and phnX was square-root transformed before analysis.

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4.3 Results

4.3.1 Culturable Microorganisms in Spoil CFU counts of Spoil sampled from an area without plants were hampered by the presence of Spoil debris on the plate; however, results ranged from 3610 to 17,000 CFU/g. The Spoil from underneath E. tomentosa ranged from 266,000 to 600,000 CFU/g (Figure 4.3). The four subsamples combined into a single sample produced 29,300 to 49,000 CFU/g. For comparison, Rengel et al. (1998) reported 13,100,000–41,200,000 CFU/g in a United Kingdom field under continuous wheat crop since 1852 although they used a different plate media. Jackson et al. (2005) obtained in the order of 106 to 107 CFU/g using R2A agar.

Figure 4.3. Growth of colonies on dilution ‘D’ of the Enchylaena tomentosa sample.

4.3.2 Microbial Diversity Across the 34 samples, 16S sequencing produced 6,808,453 sequence counts, of which 4,205,912 features passed quality control and bioinformatics processing relating to 61,857 ASVs. ITS sequencing produced 7,669,619 sequence counts, of which 4,251,377 features passed relating to 9775 ASVs.

4.3.2.1 Bacterial and Archaeal Diversity – 16S Summary of findings:

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• Richness of 16S ASVs was lowest in the Spoil Control and pure OGM samples; all other treatments were similarly high. • Spoil Control and OGM showed almost no community similarity to other treatments, which showed a cluster of samples from the Experimental Site and a cluster from the references.

Relating the 16S sequences to the SILVA database showed species from 36 phyla, most notably: Proteobacteria (15,101 amplicon sequence variants (ASVs)), Actinobacteria (11,920 ASVs), Chloroflexi (6,007 ASVs), Acidobacteria (5,191 ASVs), Bacterioidetes (4,878 ASVs) and Plactomycetes (4,378 ASVs).

Species richness of Archaea and Bacteria was heavily negatively skewed and had an unequal number of blocks, which made it unsuitable for Friedman’s non-parametric test. Results have been produced using LMMs; however, they should be interpreted cautiously. Species richness was significantly lower in the Spoil Control sample from Newman (2017) and OGM from Global Renewables’ facility than in the plots from the Experimental Site (Spoil OGM to Spoil: t=5.15, p=0.0007) (Figure 4.4).

Figure 4.4. Total Archaeal and Bacterial species richness. Letters above the figure refer to results of the Tukey post-hoc test where treatments that do not share a letter are considered significantly different. Because of poor model fit, differences should be interpreted cautiously. Spoil Control is from Newman (2017).

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There were extreme differences between plots with community similarity of zero occurring between multiple plots. Most notably, every Spoil Control plot was completely different from all other plots but had relatively moderate similarity within itself. There were also complete differences between some of the OGM from Global Renewables Facilities and plots Ring Rd 10 and Rav Ref 2. This difference is shown clearly in the nMDS, which has separated the OGM and Spoil Control treatments with all other plots straddling between them (Figure 4.5) (note that the ‘fix collapse’ feature was required for this visualisation). There are clearly two plots from the Experimental Site (Subsoil Mulch from Blocks 2 and 5) that are closer in similarity to the Rav Ref, Ring Rd and RSF grouping then the rest of the Experimental Site (Figure 4.6). Interestingly, the three left-most points of Subsoil OGM Mulch are from Blocks 5, 6 and 2 from upper to lower (Figure 4.6). Spoil OGM has significantly separated from all treatments except the Immature OGM sample with PERMANOVA (Table 4.1). The difference between Spoil OGM and Subsoil Mulch, however, is questioned by the significant PERMDISP result. Anderson and Walsh (2013) found that in situations where the group with more samples is also more dispersed, the PERMANOVA becomes increasingly conservative. In this case, Spoil OGM and Subsoil Mulch treatments are probably different. Similarly, in all other cases of PERMDISP differences, the group with the larger sample size is also more dispersed, meaning the PERMANOVA is less likely to reject the null hypothesis in these situations.

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Figure 4.5. nMDS of all 16S treatments for Archaeal and Bacteria community composition with similarity generated using group average CLUSTER and 10% slack. Note that the ‘fix collapse’ feature was required for this visualisation, which used a metric proportion of 0.05. Spoil Control is from Newman (2017).

Figure 4.6. nMDS of 16S community composition results generated without the reference OGM samples from the production facility and Newman (2017) Spoil Control samples. Similarity generated using group average CLUSTER at 10% slack.

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Table 4.1. Average 16S similarity between and within treatments where 100 is complete similarity and 0 is complete difference. Grey cells are comparisons within a treatment group. Immature OGM only has one sample and was not compared with itself. Other colours represent the results of PERMANOVA and PERMDISP analysis. Pale orange cells had p(perm)>0.05 in both PERMANOVA and PERMDISP (no difference), the deep orange cell had p(perm)> 0.05 for PERMANOVA but p(perm)<0.05 in PERMDISP, blue cells had p(perm)<0.05 in PERMANOVA but >0.05 in PERMDISP (reliable difference), and green cells had p(perm)<0.05 in both PERMANOVA and PERMDISP (possible difference). Spoil Control is from Newman (2017).

Spoil Spoil Subsoil Subsoil Rav Ref Ring RSF Immature OGM Control OGM Mulch OGM Rd OGM Mulch

Spoil 21.09 Control Spoil 0 23.63 OGM Subsoil 0 8.65 12.43 Mulch Subsoil 0 14.68 15.43 20.28 OGM Mulch Rav Ref 0 0.43 5.49 2.2 26.99 Ring Rd 0 1.45 8.71 5.65 10.4 18.79 RSF 0 0.84 7.25 3.63 13.41 17.34 23.62 Immature 0 2.63 0.49 1.5 0.22 0.16 0.24 OGM OGM 0 2.36 0.51 1.53 0.19 0.14 0.23 41.03 45.68

4.3.2.2 Notable Species – 16S Summary of findings:

• Fresh OGM from the production facility showed very high readings of species that were largely absent from the Experimental Site. • Many of the species found to be common on the Experimental Site were potentially nitrogen fixers.

Of the top 10 most widely dispersed 16S ASVs, five were identified as part of the order Rhizobiales when searched under the SILVA database. One ASV belonging to Xanthobacteraceae (k_Bacteria; p_Proteobacteria; c_Alphaproteobacteria; o_Rhizobiales; f_Xanthobacteraceae) occurred in every plot of every treatment except Spoil Control and the OGM reference. Xanthobacteraceae are aerobic chemoheterotrophs and many species are known for nitrogen fixation (Oren, 2014). Occurring within 23 plots (all but Spoil Control, OGM

112 and two of the Spoil OGM plots) was Bradyrhizobium from the Xanthobacteraceae family. Bradyrhizobium, although often thought of as being common, is an interesting find because Fisher (2010) did not find it in the RSF and Newman (2017) only had some at low readings. Occurring in 24 of the 34 samples was an ambiguous member of the Bacillus genus (k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales; f_Bacillaceae; g_Bacillus; Ambiguous_taxa). Members of the Bacillaceae family are widespread across environmental habitats and noted for forming endospores which allows their survival through adverse conditions (Mandic- Mulec et al., 2016).

By the number of reads, seven of the top eight species came from the OGM reference; these species had very high numbers within OGM but were almost completely absent from the Experimental Site. Those species included Corynebacterium 1 (k_Bacteria; p_Actinobacteria; c_Actinobacteria; o_Corynebacteriales; f_Corynebacteriaceae; g_Corynebacterium 1; s_uncultured bacterium), Fastidiosipila (k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; f_Ruminococcaceae; g_Fastidiosipila; s_uncultured bacterium), two ASVs of Anaerococcus (k_Bacteria; p_Firmicutes; c_Clostridia; o_Clostridiales; f_Family XI; g_Anaerococcus), Aerococcus (k_Bacteria; p_Firmicutes; c_Bacilli; o_Lactobacillales; f_Aerococcaceae; g_Aerococcus) and a member of Bacillales, which could only be identified to order (k_Bacteria; p_Firmicutes; c_Bacilli; o_Bacillales). Corynebacterium is comparatively well studied because some members are involved in disease and endophytes. They are well distributed in many environments, including on food products, activated sludge and the mucus of sea corals (Bernard and Funke, 2015). Fastidiosipila are members of the Ruminococcaceae, which has been described based on 16S sequences. This family is known to be morphologically diverse but all species are obligate anaerobes (Rainey, 2015). In the same order as Fastidiosipila, Anaerococcus are also anaerobes known for metabolising peptones and amino acids as well as fermenting major sugars and some carbohydrates (Ezaki and Ohkusu, 2015). Aerococcus have been found in a wide variety of environments such as air, dust, vegetation, brine, soil, marine environments, blood, urine and vaginal areas. They are infrequently associated with human disease and at least one species is a saprophyte (Collins and Falsen, 2015).

Common species on the Experimental Site and references included the above-mentioned Xanthobacteraceae, Acidothermus (k_Bacteria; p_Actinobacteria; c_Actinobacteria;

113 o_Frankiales; f_Acidothermaceae; g_Acidothermus), Pseudonocardiaceae (k_Bacteria; p_Actinobacteria; c_Actinobacteria; o_Pseudonocardiales; f_Pseudonocardiaceae) and an uncultured member of Acidobacteriales (k_Bacteria; p_Acidobacteria; c_Acidobacteria; o_Acidobacteriales; f_uncultured; g_uncultured bacterium; s_uncultured bacterium). Acidothermus is a member of Frankiales, which although well recognised within Actinobacteria are only noted as potential nitrogen fixers (Normand and Fernandez, 2020). The order Pseudonocardiales is generally poorly studied but has many traits of the actinobacteria as they are predominantly mycelial chemoorganotrophs (Franco and Labeda, 2014). Acidobacteria are poorly characterised generally but are suggested to be responsive to increased fertility (Kielak et al., 2016).

4.3.2.3 Fungal Diversity – ITS Summary of findings:

• Higher ITS ASV richness was found in the Ring Rd and RSF samples. • Spoil Control and OGM both had low ITS richness and showed distinct communities compared with the Experimental Site and the references. • The Experimental Site and References formed separate clusters based on community similarity.

Species of fungi from across 16 phyla were identified from sequences matched to the UNITE database. Most of these came from three dominant groups: members of Ascomycota (4170 ASVs) and Basidomycota (1490 ASVs) and a large proportion (35%) unassigned to any phylum (3425 ASVs).

The highest ITS species richness was in the Ring Rd and RSF samples (Figure 4.7). On the Experimental Site there was a significant effect of adding Subsoil Mulch (F(1,13.5)=29.0, p=0.0001) and adding OGM (F(1,12.8)=15.1, p=0.0019), but no interaction between the two factors.

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Figure 4.7. Total species richness of ITS (fungal) sequences across treatments. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

The nMDS of ITS data showed strong differences in community composition (Figure 4.8) and again showed Blocks 2 and 5 Subsoil Mulch to be more similar to the references than the rest of the treatments on the Experimental Site (Figure 4.9). The low sample sizes for references probably prevented any differences being significant in the PERMANOVA results (Table 4.2). While the PERMDISP results (Table 4.2) suggest that many of the PERMANOVA results may be questionable, large differences in similarity based on trends in the nMDS would also support a difference; for example, between OGM and Spoil OGM treatments.

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Figure 4.8. Community composition of all ITS treatments displayed in an nMDS with similarity from group average CLUSTER at 10% slack. Spoil Control is from Newman (2017).

Figure 4.9. An nMDS of the ITS community composition without the reference OGM and Spoil Control samples. Similarity generated using group average CLUSTER at 10% slack.

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Table 4.2. Average ITS similarity between and within treatments, where 100 is complete similarity and 0 is complete difference. Grey cells are comparisons within a treatment group. Immature OGM only has one sample and was not compared with itself. Other colours represent the results of PERMANOVA and PERMDISP analysis. Pale orange cells had p(perm)>0.05 in both PERMANOVA and PERMDISP (no difference), blue cells had p(perm)<0.05 in PERMANOVA but >0.05 in PERMDISP (reliable difference), and green cells had p(perm)<0.05 in both PERMANOVA and PERMDISP (possible difference). Spoil Control is from Newman (2017).

Spoil Spoil Subsoil Subsoil Rav Ring RSF Immature OGM Control OGM Mulch OGM Ref Rd OGM Mulch

Spoil 17.92 Control Spoil 5.01 30.06 OGM Subsoil 3.25 13.21 18.92 Mulch Subsoil 3.33 20.78 19.58 26.18 OGM Mulch Rav Ref 1.88 4.82 11.52 9.84 30.39 Ring Rd 1.72 4.85 11.23 11.32 11.86 23.16 RSF 1.43 3.16 9.61 8.74 13.24 18.64 21.96 Immature 2.54 2.06 0.60 1.08 0.64 0.23 0.19 OGM OGM 1.68 1.10 0.84 0.96 0.56 0.38 0.45 18.77 18.18

4.3.2.4 Notable Species – ITS Summary of findings:

• Many of the most widely distributed ITS ASVs were from Fusarium. • Many species noted for saprotrophic ability were found. • Batrachochytrium dendrobatidis, a species responsible for decline in amphibian populations locally and globally, was absent from all samples.

No ITS ASV was found in every one of the 34 samples; however, one was found in 32 samples, being absent in two of the OGM samples. Fusarium (k__Fungi; p__Ascomycota; c__Sordariomycetes; o__Hypocreales; f__Nectriaceae; g__Fusarium) is a widespread and complex genus of pathogens to a large variety of plant and animal groups (Summerell et al., 2010). Indeed, while the most commonly dispersed ASV could only be attributed to the genus Fusarium, there were two species that were discovered as well: Fusarium solani, which had

117 two separate ASVs attributed to it, and Fusarium proliferatum. In the same family as Fusarium was Gibberella intricans, which is also noted as a plant pathogen and occurred in all plots on the Experimental Site. Another common but unrelated pathogen was Cladosporium (k__Fungi; p__Ascomycota; c__Dothideomycetes; o__Capnodiales; f__Cladosporiaceae; g__Cladosporium; s__Cladosporium_cladosporioides), which interestingly was completely absent from all OGM and Rav Ref samples.

Saprotrophs were also common, particularly members of Sporomiaceae with two different ASVs occurring for Preussia preussia subspecies persica (k__Fungi; p__Ascomycota; c__Dothideomycetes; o__Pleosporales; f__Sporormiaceae; g__Preussia; s__Preussia_persica) as well as an unidentified species from the Preussia genus. Between the three ASVs, this species occurred in 27, 25 and 22 respective samples out of the total 34. All these ASVs were identified as being probable and possible saprotrophs of dung and plants (based on FUNGuild results detailed below). Another common saprotroph was Penicillium spinulosum (k__Fungi; p__Ascomycota; c__Eurotiomycetes; o__Eurotiales; f__Aspergillaceae; g__Penicillium; s__Penicillium_spinulosum), which was classified as a possible undefined saprotroph by FUNGuild. P. spinulosum is globally distributed and has been found in a variety of moderate and extreme environments (Hujslová et al., 2017).

Although many species were present, one notable species was missing. Batrachochytrium dendrobatidis is a significant contributing factor to the decline of amphibians both in the Hunter Valley (Stockwell et al., 2008) and globally (Fisher et al., 2009). While other members of the order Rhizophydiales were found, B. dendrobatidis was not in any samples.

4.3.3 Functional Analysis 4.3.3.1 PICRUSt Summary of findings:

• There were few KEGG Orthologues associated with OGM. • KEGG Orthologue values relating to nitrogen processes were highest on the Experimental Site. • KEGG Orthologue values relating to carbon and phosphorus processes were highest in the References.

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Under the Greengenes 13_5 database used for PICRUSt, 60,056 (97.1%) of the 16S ASVs were assigned to a match at some level.

One of the reasons that results from PICRUSt need to be viewed cautiously for these samples is the greater distance from representative genomes found in soil samples. NSTI (nearest sequenced taxon index) values are produced for each sample, which are the sum of phylogenetic distances for each organism to its nearest sequenced reference genome. As is to be expected and is common from soil samples, the levels were high in most treatments (Figure 4.10). Values across the Experimental Site, Rav Ref, Ring Rd and RSF ranged from 0.105 to 0.167, which were significantly higher than the OGM, which ranged from 0.053 to 0.06 (Rav Ref to OGM: t=5.33, p=0.0009). For comparison values, Langille et al. (2013) found that hypersaline mat microbiomes had an average NSTI of 0.23, soils averaged 0.17, mammalian guts averaged 0.14, and human microbiome samples averaged 0.03. The higher values of NSTI do not preclude the use of PICRUSt analysis; rather they provide an indication of how reliable the results will be. To improve these values, more research will be need on microbial genetics and functional attributes.

Another important aspect to consider in the interpretation of the PICRUSt data is the number of ASVs that each treatment can provide to the analysis (Figure 4.11). Spoil Control and OGM are significantly lower in ASVs than Spoil OGM, Subsoil Mulch, Subsoil OGM Mulch, Ring Rd and RSF (Ring Rd to Spoil Control: t=3.68, p=0.0331). As Spoil Control and OGM have lower ASVs contributing to the analysis, it would be expected that they have a lower number of predicted KEGG Orthologues than other treatments.

Comparing the number of KEGG Orthologues that were associated with each sample, there were significantly fewer associated with OGM (Rav Ref to OGM: t=4.31, p=0.0085) (Figure 4.12). Although to a lesser degree in some cases, the lower values of OGM held for each of the major (Level 1) categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, organismal systems and unclassified (data not shown).

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Figure 4.10. NSTI results for PICRUSt, produced from Morgan Langille Galaxy. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

Figure 4.11. Count of ASVs that were normalised prior to PICRUSt procedure. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

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Figure 4.12. A count of the number of KEGG Orthologues that were associated with each sample. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

A broad range of specific KEGG Orthologues was examined relating to nitrogen, carbon and phosphorus acquisition by microorganisms (see Appendix B for specific details). The data are summarised by placing each KEGG Orthologue into the grouped treatment that it was highest in (Table 4.3). The treatments on the Experimental Site generally performed very well at nitrogen processes. For example, Subsoil OGM Mulch had the highest response for both nifD and nifH, which are involved in nitrogen fixation. The reference treatments, particularly RSF and Rav Ref performed very well for carbon and phosphorus recycling enzymes. For example, RSF and Rav Ref had 1.5 times higher values than the next highest treatment for E3.2.1.21 (beta-glucosidase), SGA1 (glucoamylase), cpo (non-heme chloroperoxidase), E3.2.1.14 (chitinase) and PHO (acid phosphatase). OGM only performed exceptionally well for AMY alpha-amylase and was generally low in response for most KEGG Orthologues (note that E3.2.1.1A is also alpha-amylase and exhibited no response from OGM but a large response from Spoil OGM). Spoil Control was highest in narG (nitrate reductase/nitrate oxidoreductase), which transforms nitrate to nitrite. Although Spoil Control was highest for this feature, it was only significantly higher than OGM. The only two processes that did not

121 exhibit a response, with all treatments returning a value of 0, were E1.10.3.2 (laccase) and E1.11.1.7 (peroxidase), which are involved in lignin decomposition.

Table 4.3. KEGG Orthologues were grouped according to which treatment they were highest in. More specific information can be found in Appendix B. Spoil Control is from Newman (2017).

Highest in Spoil Control narG (denitrification) Highest in Spoil OGM, Subsoil Mulch, Subsoil nifD (nitrogen fixation) OGM Mulch nifH (nitrogen fixation) hao (nitrification) pmoA-amoA (nitrification) pmoB-amoB (nitrification) nasA (assimilatory nitrate reduction) nirK (denitrification) nosZ (denitrification) nodO (nodulation) E3.2.1.1A (starch/hemicellulose) phnM (organic phosphate release) phnX (organic phosphate release) pqqA (mineral phosphate solubilisation) Highest in Ring Rd, RSF, Rav Ref nirA (assimilatory nitrate reduction) nrfA (denitrification) norB (denitrification) nodE (nodulation) E3.2.1.21 (cellulose decomposition) CBH1 (cellulose decomposition) E3.2.1.4 (cellulose decomposition) lacZ (hemicellulose decomposition) xynA (hemicellulose decomposition) SGA1 (starch decomposition) cpo (lignin decomposition) E3.2.1.14 (chitin decomposition) pstS (phosphorus transport) TC.PIT (phosphorus transport) phoD (organic phosphate release) PHO (organic phosphate release) pqqD (mineral phosphate solubilisation) Highest in OGM AMY (starch/hemicellulose decomposition) Overall no difference ureC (urea hydrolysis) katE (lignin decomposition) No response E1.10.3.2 (lignin decomposition) E1.11.1.7 (lignin decomposition)

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4.3.3.2 FUNGuild Summary of findings:

• The most dominant guild was Ectomycorrhiza, which was extremely common in the RSF and Rav Ref. This guild drove the highest symbiotroph richness and abundance in these references. • Pathotroph richness was highest in the RSF but abundance was much higher in Spoil Control. • Saprotroph richness was highest in the Ring Rd, RSF and Subsoil OGM Mulch.

Of the 9775 ASVs submitted to FUNGuild, 3908 (40%) were not matched in the database; of those that were matched, only 1779 (18% of all ASVs) matched the quality controls.

Based on the abundance of ASVs, the most dominant guild was Ectomycorrhiza (45.9%) (Figure 4.13). The most species-rich group was the unidentified saprotrophs with 679 ASVs (38% of matches) (Figure 4.14). Many fungi were matched to multiple guilds (Figures 4.15 &

4.16).

Treatment

Spoil Control Spoil OGM Subsoil Mulch Subsoil OGM Mulch Rav Ref Ring Rd RSF Immature OGM OGM

0 50000 100000 150000 200000 250000 300000 350000

Abundance of ASVs

Figure 4.13. Abundance of ASVs identified as probably being ectomycorrhizal. Spoil Control is from Newman (2017).

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Lichenized Treatment

Spoil Control Endophyte Spoil OGM Arbuscular Mycorrhizal Subsoil Mulch Subsoil OGM Mulch Wood Saprotroph Rav Ref Ring Rd Undefined Saprotroph-Wood Saprotroph RSF Immature OGM Undefined Saprotroph OGM

Soil Saprotroph-Undefined Saprotroph

Soil Saprotroph

Plant Saprotroph

Leaf Saprotroph-Soil Saprotroph

Dung Saprotroph-Wood Saprotroph

Dung Saprotroph-Undefined Saprotroph

Dung Saprotroph-Soil Saprotroph

Dung Saprotroph-Plant Saprotroph-Wood Saprotroph

Dung Saprotroph-Plant Saprotroph

Dung Saprotroph

Plant Pathogen

Fungal Parasite-Plant Pathogen

Fungal Parasite

Animal Pathogen

Animal Parasite-Fungal Parasite

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

Abundance of ASVs

Figure 4.14. Abundance of ASVs by major guild for each treatment. Guilds are sorted by trophic mode. Spoil Control is from Newman (2017).

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Endophyte-Plant Pathogen-Wood Saprotroph Treatment

Spoil Control Endophyte-Plant Pathogen-Undefined Saprotroph Spoil OGM Endophyte-Lichen Parasite-Plant Pathogen-Undefined Saprotroph Subsoil Mulch Subsoil OGM Mulch Ectomycorrhizal-Fungal Parasite-Plant Saprotroph-Wood Saprotroph Rav Ref Ring Rd Dung Saprotroph-Endophyte-Plant Pathogen-Undefined Saprotroph RSF

Animal Pathogen-Dung Saprotroph-Endophyte-Plant Saprotroph-Soil Saprotroph-Wood Saprotroph Immature OGM OGM Animal Endosymbiont-Animal Pathogen-Undefined Saprotroph

Plant Pathogen-Wood Saprotroph

Plant Pathogen-Undefined Saprotroph

Plant Pathogen-Undefined Parasite-Undefined Saprotroph

Leaf Saprotroph-Plant Pathogen-Undefined Saprotroph-Wood Saprotroph

Fungal Parasite-Plant Pathogen-Plant Saprotroph

Endophyte-Plant Pathogen-Wood Saprotroph

Endophyte-Lichen Parasite-Plant Pathogen-Undefined Saprotroph

Dung Saprotroph-Plant Parasite-Soil Saprotroph-Undefined Saprotroph-Wood Saprotroph

Bryophyte Parasite-Litter Saprotroph-Wood Saprotroph

Bryophyte Parasite-Leaf Saprotroph-Soil Saprotroph-Undefined Saprotroph-Wood Saprotroph

Animal Pathogen-Undefined Saprotroph

Animal Pathogen-Plant Pathogen-Undefined Saprotroph

Animal Endosymbiont-Animal Pathogen-Endophyte-Plant Pathogen-Undefined Saprotroph

Algal Parasite--Leaf Saprotroph-Wood Saprotroph

Algal Parasite-Fungal Parasite-Undefined Saprotroph

0 5000 10000 15000 20000 25000 30000 35000

Abundance of ASVs

Figure 4.15. Abundance of ASVs by mixed guilds for each treatment. Guilds are sorted by trophic mode. Spoil Control is from Newman (2017).

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Treatment Undefined Saprotroph-Undefined Biotroph Spoil Control Spoil OGM Lichenized-Undefined Saprotroph Subsoil Mulch Subsoil OGM Mulch Epiphyte-Litter Saprotroph Rav Ref Ring Rd RSF Endophyte-Undefined Saprotroph-Wood Saprotroph Immature OGM OGM Endophyte-Undefined Saprotroph

Endophyte-Soil Saprotroph

Endophyte-Litter Saprotroph-Wood Saprotroph

Ectomycorrhizal-Undefined Saprotroph

Ectomycorrhizal-Fungal Parasite-Plant Pathogen-Wood Saprotroph

Lichen Parasite-Lichenized

Ericoid Mycorrhizal

Endophyte-Undefined Saprotroph

Endophyte-Plant Pathogen

Endophyte-Fungal Parasite-Plant Pathogen

Ectomycorrhizal-Fungal Parasite

0 10000 20000 30000 40000 50000 60000 70000 80000

Abundance of ASVs

Figure 4.16. Abundance of ASVs by mixed guilds for each treatment. Guilds are sorted by trophic mode. Spoil Control is from Newman (2017).

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Pathotroph richness was significantly higher in Ring Rd and RSF than in Spoil and OGM (RSF to Spoil: t=3.62, p=0.0357) (Figure 4.17). While pathotroph abundance was highest in the Spoil Control samples, it was only significantly different from the reference OGM (Spoil Control to OGM: t=4.82, p=0.0744) (Figure 4.18). Spoil and OGM had lower saprotroph richness than all treatments except Spoil OGM (Spoil Control to Subsoil Mulch: t=3.71, p=0.0285) (Figure 4.19). Interestingly, both Subsoil Mulch and OGM were found to have lower saprotroph abundances than Subsoil OGM Mulch and Ring Rd (Subsoil OGM Mulch to OGM: t=3.47, p=0.0482) (Figure 4.20). RSF had a vastly higher symbiotroph richness than any other treatment (RSF to Rav Ref: t=6.06, p=0.0001) (Figure 4.21). Rav Ref had significantly higher symbiotroph richness than all treatments except Ring Rd and the Immature OGM (Rav Ref to Subsoil OGM Mulch: t=11.15, p<0.0001). There were significantly more symbiotrophs by abundance in the RSF and Rav Ref than Subsoil Mulch (Rav Ref to Subsoil Mulch: t=3.12, p=0.0482) (Figure 4.22). Symbiotrophs were also more abundant in RSF than any treatment on the Experimental Site and OGM (RSF to Subsoil OGM Mulch: t=4.05, p=0.0017).

Figure 4.17. Total pathotroph richness. Trophic mode is as assigned by FUNGuild. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

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Figure 4.18. Total pathotroph abundance. Abundances within a trophic mode were summed for each plot. Note that although the graph is of untransformed data, lettering is based on Tukey post-hoc tests performed using fourth-root transformed data. Treatments that share letters are not considered statistically different. Spoil Control is from Newman (2017).

Figure 4.19. Total saprotroph richness. All trophic modes were assigned based on FUNGuild. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

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Figure 4.20. Total saprotroph abundance. All abundances within a trophic mode were summed for each plot. Note that, although the graph is of untransformed data, lettering is based on Tukey post-hoc tests performed on fourth-root transformed data. Treatments that share letters are not considered statistically different. Spoil Control is from Newman (2017).

Figure 4.21. Total symbiotroph richness. All trophic modes were assigned based on FUNGuild. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

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Figure 4.22. Total symbiotroph abundance. All abundances within a trophic mode were summed for each plot. Spoil Control is from Newman (2017). Treatments that share letters are not considered statistically different.

4.4 Discussion

4.4.1 Spoil The failure of DNA extraction to produce any response from Spoil on the Experimental Site suggests it is highly unlikely that the Spoil contributed a significant amount to the microbial communities developing on site. An inability to extract DNA from mine material has been reported before; Kumaresan et al. (2017) were unable to extract DNA from pure tailings. Studies on spoils of other coal mines have reported being able to extract DNA but at very low levels (Harantová et al., 2017; Banerjee et al., 2020). Microbes can and do live in rock strata, particularly the deep biosphere in the earth’s crust (Magnabosco et al., 2018); species composition though is probably very different from that found on the surface. While microbes within the deep biosphere are associated with the lithology, surface microbes also exhibit significant rhizosphere effects, with noted variation occurring around plants and under different abiotic conditions (Fierer, 2017; Magnabosco et al., 2018).

It is well known that plate-count techniques do not capture the full diversity of microorganisms in soil environments. Many species specialise in unique environments that

130 are difficult to replicate, grow too slowly for these methods or rely on other species that are not present on a plate (Joint et al., 2010). As such, the organisms on a plate may only account for 1% of the total soil microorganisms (Blagodatskaya and Kuzyakov, 2013). While there were evidently still microbes living in the Spoil, they were at best an order of magnitude below normal concentrations; at worst, they were four orders of magnitude lower. In practice, this means that availability of normal services delivered by soil microbes in Spoil, such as nitrogen fixation, litter decomposition, nutrient cycling and drought resilience, will be reduced. As CFUs in plate counts were higher when sampled underneath a plant, it is likely the lack of plant growth on the Spoil has an impact on the growth of microbes. The abiotic environment could also be important; high salt levels lead to a reduction in microbial respiration (Rath and Rousk, 2015), which could be an issue with the high sodium levels in Spoil.

To examine the potential species that could have occurred on Spoil, data on the control samples from Newman (2017) were reanalysed because the site is quite similar. Having samples from Newman (2017) provided an indication of what might be occurring at low levels in the Spoil as a small number of CFUs were cultured. These samples also contained very low numbers of ASVs, further supporting the suggestion that Spoil has very low numbers of microbes. The differences in composition for 16S results could not have been stronger, with no ASV overlaps. Although it is likely that this indicates a complete distinction between communities, the difference could be caused by other factors. This completely unique community could indicate that differences in procedure used by different researchers made a material difference in 16S results. It could also be a temporal difference because they were taken at different time points.

Considering the low numbers of 16S ASVs in the Spoil Control, it was expected that they would also be very low in PICRUSt responses, but this was rarely seen. As stated above, the few populations of microbes would reduce their capacity for functional performance but there appears to be potential for some roles to be undertaken, such as nitrogen fixation (see Appendix B, Figures B1 and B2) and cellulose decomposition (see Appendix B, Figure B18).

It is interesting that Spoil Control plots returned the highest fungal pathotroph abundance, although it was only significantly different from the reference OGM. One explanation is that plants living in Spoil have reduced immune systems, leading to an increase in pathogenic species, although the plots of Newman (2017) were intended as controls that had all plant life 131 and organic matter removed periodically. The control plots of Newman (2017) were also noted for frequent crossings of animals, which dropped scats on the site and could have increased pathogen load. Another explanation is that the extraction and primer used were different or biased compared with the other samples (George et al., 2019). It is also possible that the global databases used for the identification of guilds and trophic modes were biased by agricultural need to fight pathogens. However, in an examination of FUNGuild, George et al. (2019) found that pathotrophs are second to saprotrophs in the FUNGuild dataset, and in their study, wooded areas were dominated by symbiotrophs. The only area where George et al. (2019) found a different trend was fertile grassland, where pathotrophs increased with fertility, which Spoil lacks. Other studies that have noted increases in pathotrophs have associated them with invasion by plants and changed environmental conditions (Nie et al., 2018; Phillips et al., 2019; Yang et al., 2019), neither of which are conditions easily linked to Spoil. There certainly were other trophic modes present in the Spoil as well as many species that could not be identified.

4.4.2 Subsoil and Mulch The Subsoil Mulch treatments were progressing towards the reference communities more than the Spoil and OGM for both 16S and ITS. Perhaps oddly though, Blocks 2 and 5 Subsoil Mulch plots were considered more similar to the references for 16S than they were to the other Experimental Site plots (Figures 4.6 and 4.9). Blocks 2 and 5 are notable for being in the centre of the Experimental Site. The difference between blocks is probably related to the set- up of the site because the Subsoil was laid in strips with the same strip being used for Blocks 1 and 4, another strip for Blocks 2 and 5 and a final strip for Blocks 3 and 6 (Castor et al., 2016). It was noted during set-up that Blocks 1 and 4 seemed to have textures higher in clay so this difference could be due to abiotic conditions or variation in sampling location. Although not ideal for the experimental design, the variation in Subsoil characteristics reflect the natural levels of heterogeneity in soils. Another suggestion is that there may be a spatial effect whereby these two plots benefit from being in the middle of the dispersal of all other plots.

In the Hunter Valley, if topsoils are unavailable, then subsoils are often recommended as an ideal alternative if the quality is acceptable (Nussbaumer et al., 2012). Because of the increased depths of subsoils (Eilers et al., 2012), subsoils are typically assumed to have a smaller contingent of microbes. In this study, either the treatments had enough individuals

132 to respond to the restoration, dispersal on to a suitable substrate was beneficial or a combination of both options occurred. From a microbial community restoration point of view, the Subsoil Mulch was approaching the references, and ahead in some metrics. In PICRUSt, for example, there were 12 different orthologues that provided a stronger response than Ring Rd, RSF and the Rav Ref. Although they were not significantly higher in many cases, in eight of the 13 nitrogen KEGG Orthologues, the highest treatment was Subsoil OGM Mulch.

4.4.3 OGM It is clear that the pure OGM samples were very different from all other samples for both 16S and ITS community assessments and in functional composition. Within the trimmed fungal guilds, the dominant functions in the OGM were saprotrophs, particularly undefined saprotrophs. Undefined saprotrophs are a group that the designers of the database acknowledge needs some break down because it includes large groups such as litter saprotrophs (Nguyen et al., 2016b). The presence of large amounts of saprotrophs was expected because this has been seen in other composting studies (Tuomela et al., 2000; Wang et al., 2018).

There was dramatically lower ASV richness in OGM samples, which is somewhat unexpected. Composting has three phases based on temperature, initiating with a cool mesophilic phase that then heats into the thermophilic phase and cools in a maturation phase. Although it is variable, the most dynamic part of the composting procedure is at the beginning, in the mesophilic and sometimes into the thermophilic stages. During the early phase, temperatures rise and pH changes, encouraging growth and activity. As the temperatures rise over 55°C, which is important for pasteurisation and removal of pathogenic species, there is also considerable loss of other species. Numbers of fungi in particular can decrease sharply and fungi can be completely removed from the medium during the thermophilic stage (Hansgate et al., 2005). This leaves the maturing material relatively low in species richness although it is well recognised that many species can continue through all phases of composting (Villar et al., 2016). Although the literature suggests that most of the species will be bacterial (Martins et al., 2013), this was not seen in this study. Yet, even though fungi were present in the material, overall richness was lower than in the other treatments. If the material is still high in nutritional resources, then it will likely be a primary candidate for re-colonisation during maturation because this was suggested to be a limiting factor in many previous studies. The

133 lack of increased re-colonisation, however, could be related to the locality of the area. Perhaps the industrial area surrounding Global Renewables is not as well suited to distribution of a diversity of microbes as areas around the Experimental Site, which includes more native vegetation. It could also be related to disturbance events because the OGM was moved occasionally onsite and had to be relocated for application. Or perhaps seasonal variation meant that a dispersal event had not occurred by the time of sampling.

OGM generally had low or absent KEGG Orthologue results, as would be expected from the low number of ASVs used in the analysis. The strongest result was for the alpha-amylase AMY, even though the other alpha-amylase, E3.2.1.1A, showed no response. A similar variation in alpha-amylase response was found in green waste by Yu et al. (2017), who found very different results for the two alpha-amylase regardless of nitrogen treatment. This is not unexpected because more than 50 different amino acid sequences for alpha-amylase have been identified (Janeček, 1994). Alpha-amylase is important for the metabolism of starch in products such as potato, rice and wheat, which would be expected to be common in municipal waste. This example does, however, stress that even though OGM performed poorly in these tests, there are probably examples within PICRUSt where it would perform very well. The complete separation seen in ASVs suggests that very different species were present, which may have similar roles but have yet to be identified.

It is interesting that the Subsoil OGM Mulch had a significantly higher saprotroph richness than the Spoil OGM. This was predicted based on the hypothesis that the Mulch included in this treatment would provide a food source in addition to the supply of saprotrophs and continued decomposition of the OGM. However, the complete dissimilarity between OGM and Subsoil OGM Mulch suggests that the saprotrophs did not come from the OGM. With the low number of species present in the OGM perhaps the Subsoil Mulch provided the source of microorganisms while the OGM provided a food source to sustain them in the long term. Soil disturbance through transfer of soil has been identified as reducing groups involved in decomposition such as fungi (Jasper et al., 1991), perhaps the application of OGM is responsible for encouraging their growth. Another explanation could also be related to the increased litter fall and plant diversity on Subsoil OGM Mulch plots compared with Spoil OGM.

OGM produced very low pathotroph richness, which is probably related to the thermophilic phase of treatment. Although mechanisms are unclear and results can be variable, it appears 134 that composts can provide resources to support the development of the overall microbial community, thereby providing strong competition to the pathogen (Noble and Coventry, 2005; Avilés et al., 2011). For example, application of MWOO has been found to suppress Fusarium (Serra-Wittling et al., 1996; De Corato et al., 2018). There was little evidence of disease suppression in this study; Subsoil OGM Mulch had the highest pathotroph richness of all plots but there was also no obvious evidence of disease on any plots. The study was also performed in a relatively dry period, which can influence disease.

4.4.4 References Ring Rd, RSF and Rav Ref all had high species richness and responded across a broad range in the functional analysis. The predictive models FUNGuild and PICRUSt showed high levels of symbionts and a great variety of functional roles involved in nitrogen, carbon and phosphorus cycling. It was surprising to see such a strong result from the Rav Ref because from a botanical perspective it has sparser vegetation, very much dominated by bull oak (Allocasuarina luehmannii). The RSF, however, is very different; it displays a greater diversity of plants at a much greater density (see Chapter 3). It is likely then that the strong performance was not driven by plant diversity although there is evidence this can occur (Lange et al., 2015). Additionally, the Ring Rd is constructed with a lower soil horizon of spoil, just like on the Experimental Site. From the point of view of this study, perhaps the largest similarity between the Ring Rd, RSF and Rav Ref is that they are all older: Ring Rd is 20 years post-rehabilitation; Rav Ref has probably regrown since being cleared over 50 years ago; and RSF has been logged and grazed previously but is expected to have had continuous vegetation. It is possible that the strong performance in diversity and function seen in these references is due to the length of time they have had to acquire them.

The effect of time on microbial community development has been examined in some studies. Uroz et al. (2014) examined communities across the Mendocino Ecological Staircase, which has five uplifted marine terraces of the same parent material. The long gap in age of each terrace (about 100,000 years), however, limited the amount that could be shown in this study. Rather what was clearly shown was the effect of soil conditions such as pH and fertility. Examining a more reasonable time scale, Hernández et al. (2020) studied microbial communities in soils formed in Chile from eruptions at three time periods (1640, 1751, 1957). They found that microbes were probably fundamental to early soil colonisation and that a

135 successional trend developed over the periods studied. The time scale examined could also provide a reference to what could happen if spoil is not rehabilitated because there are some similarities between it and fresh lava. Most notably, the site of the 1957 eruption only had 5% coverage by lichens, suggesting a very slow natural colonisation time. In a study of primary succession on Czech mine spoil, Kolaříková et al. (2017) found that over 12, 20, 30 and 50 years of succession, ectomycorrhizal fungi and fungal plant pathogens developed as the plant community developed. Time, therefore, is probably still a great asset in microbial community development but is not the only factor to consider. The relationship between microbial community, the biotic characteristics and the abiotic characteristics of an ecosystem will be discussed later in the chapter.

4.4.5 Improvements and Caveats for this Study There are a number of ways this study could be improved:

• Drought is a major limiter of microbial activity, not only because it causes physiological stress but because water is also a critical solvent and transport medium (Schimel, 2018). It is also unclear what causes greater stress, the drought or sudden rewetting. It is clear though that there is a rapid increase in activity after rain (Landesman and Dighton, 2011). This may have been beneficial in increasing the abundance of detectable species but means that the community selected may have been different one week earlier or one month later. While the ideal timing of microbial sampling is difficult to select, we feel that a two-week wait would have been sufficient to stabilise the community. Variability should be a major consideration in microbial community research. For example, rhizosphere microbial communities are known to change over time and with plant phenology, which may have affected the results obtained (Murphy et al., 2016). Hence, this study collected all samples in a relatively short timeframe and the proximity of sites suggests they received similar rainfall amounts. • Each plot was sampled by homogenising four independent samples in a bag and mixing. Disturbance is known to be detrimental to microbes that require more stable environments and could have resulted in their loss (Jasper et al., 1991). Specific groups that could have been reduced include fungi and the filamentous bacteria Actinomycete. It was, however, thought important to reduce the amount of heterogeneity in each plot because the small samples used for analysis would be less

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representative of the overall site. Fortunately, this does not seem to have had much of an effect because some of the most common bacteria were from the class Actinobacteria. It is also not unusual for subsamples to be combined before genetic analysis and has been performed in many settings (Herrmann and Shann, 1997; Kowaljow and Mazzarino, 2007; Quadros et al., 2016; Calderón et al., 2017; Harantová et al., 2017). • 16S and ITS methods are well established and generally strong; however, there are acknowledged flaws. For example, Bacillus globisporus and B. psychrophilus are 99% similar using 16S while the standard threshold used is 97% (Fox et al., 1992; Nguyen et al., 2016a). For comparison, though, if the 97% similarity were compared with humans, then we would be grouped with chimpanzees, bonobos and the African apes such as gorillas (Staley, 1997). There are also strains with multiple copies of the 16S gene that differ by 5% in some regions, such as Escherichia coli K12 (Nguyen et al., 2016a). The primer used can also be an important cause of bias (Kim et al., 2011). While there is still much room for improvement, both 16S and ITS are considered extremely useful and largely fit for purpose (Patel, 2001; Janda and Abbott, 2007; George et al., 2019). • It should be noted that although genomic techniques can provide amazing levels of detail, there are still many improvements that need to be made in the system. Using the basic GenBank BLAST, Hofstetter et al. (2019) found that about 30% of ITS sequences were associated with the wrong taxon name. • The assignation of a species into a functional guild is still a developing area of research and is still a coarse overview of potential functions. For example, in FUNGuild a species is placed in a box of pathotroph, saprotroph or symbiotroph, whereas it could be better described as a continuum heavily dependent on context (Arnold, 2007). Trait and guild prediction methods do have an important place in the future of microbial ecology, though, because they allow the development of new ecological questions (Aguilar-Trigueros et al., 2015). • Many of the genes for enzymes and proteins studied in the PICRUSt analysis are also used for alternative processes. Similarly, just because a function is indicated in a plot does not mean that it is being performed (Nannipieri et al., 2020). Knowing that a gene

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is present is still important because it provides an indication of the minimum functional potential of a site.

4.4.6 Implications for Practice In plant ecology, there is acknowledged benefit of seed provenance; in general, many practitioners prefer to collect seed from similar, local habitats (Hamilton, 2001; Bischoff et al., 2006). Although our data agrees that there are distinct communities of microorganisms, they appear to be less distinct than plant communities. This is suggested because the community similarity within a treatment (12.4–30.4%) was not very different to the community similarity between treatments (8.7–20.8%) on the Experimental Site even though the soil conditions and flora communities on each treatment were dramatically different. Perhaps due to the large richness of species that can change from one millimetre to the next (Fierer, 2017) and the global distribution of many microbial species (Burrows et al., 2009), the importance of provenance is reduced compared with plant community ecology. This suggests that attempting to match local microorganisms with local plant species may be unnecessary. It should also be added that controlling species composition and the distribution of microbes is near-impossible (Ghosh et al., 2010). While restoring specific microbial communities may have benefit, it perhaps does not need to be the goal. However, from the results of this chapter, if local species are the focus, then restoration of the soil is important.

This does not mean that OGM is without benefits because it had the highest overall abundances of microbes for any treatment, albeit of a very different composition. As samples were not taken before and just after restoration, it is unknown if the OGM provided a beneficial initial wave of succession that, because of time and drought, cannot be detected. There is no question, however, that the Spoil OGM treatment was far superior in diversity and functional ability than the Spoil Control, so OGM can at least assist in supporting microbial development. Further, as was shown by FUNGuild, many species fall into multiple guilds. By supporting one guild, such as saprotrophs, microbes who are also members of other guilds may be supported until the environment develops favourably for their alternative roles. OGM and the resources supplied with it could therefore buy time until the ecosystem is sufficiently developed to support other roles.

For a long time, microbial biogeography was based on the concept that ‘everything is everywhere, but the environment selects’ (Rout and Callaway, 2012). This was due to the vast 138 population sizes, environmental hardiness, ability to disperse and great ancestral age of microorganisms that allowed them to disperse ubiquitously across the planet. For some species, this may well appear to be true; for example, Fusarium in this study was found in almost every sample and appears to have a global distribution (Finlay, 2002; Summerell et al., 2010). It certainly seems that from a functional perspective distributions are extremely broad, with Rout and Callaway (2012) commenting that nitrifiers and decomposers have been found in every region in which they have been sought. However, these broad roles do not determine the specific interactions at a local level. This is especially important as the interactions between individuals of different taxonomic kingdoms (e.g. plants and bacteria), which have different biogeographic limitations, may determine restoration success. For example, Fisher (2010) found that Pultenaea retusa would only form nodules with eight of the 23 identified nodulating cultures. Microbes have been shown to have a distance–decay relationship to dispersal, which is overlain by the effect of heterogeneity (Green and Bohannan, 2006). This places restrictions on microbial species by the environment it is adapted to and on potentially obligate relationships with other species.

Schmidt et al. (2014) suggested that there may be a difference in colonisation and successional processes used by bacteria and fungi. They point out that although bacteria are exceedingly small and some may be capable of intercontinental travel (Burrows et al., 2009), fungi have much larger cells that restrict the distance they can travel. This changes the way that propagule rain can occur on site, with bacteria potentially coming from all areas and fungi being limited to surrounding vegetation. Schmidt et al. (2014) also suggest that this drives a more deterministic community assembly pattern in bacteria because fungi are more limited to stochastic events. Therefore, reintroduction of bacteria such as nitrogen fixers may be unnecessary, depending on the surrounding vegetatation.

Even though neither Spoil nor OGM seemed to directly contribute much to the community composition of the overall treatments, Spoil OGM still developed to become similar to Subsoil Mulch and Subsoil OGM Mulch. This suggests that microbes were able to disperse from the surrounding area to the Spoil OGM plots. They could have come from the neighbouring Subsoil Mulch and Subsoil OGM Mulch plots or it could have been part of a broader dispersal event that seeded the entire site. Dispersal could have occurred as a result of atmospheric deposition, water flow over the site, animals bringing microbes into the site, active movement

139 and growth into the medium or surveyors bringing them onto site. Regardless of the mechanism, it stresses that dispersal is an important feature of this landscape, which should not be ignored. For wind-dispersed spores, shorter distance from the sources of fungal spores, such as mature native vegetation, may be beneficial. Many advantageous fungal species are dispersed by animals, so providing habitat for a range of mammals and insects may encourage establishment of desired fungal species, particularly endomycorrhiza. Local native animal species with evidence of mycophagy include: bush rat (Rattus fuscipes (Vernes and Dunn, 2009)), long-nosed bandicoot (Perameles nasuta) and swamp wallaby (Wallabia bicolor (Claridge et al., 2001)).

Evidence is building to suggest that abiotic conditions are a primary driver for selection of microbial community composition (Uroz et al., 2014; Yu et al., 2017; Magnabosco et al., 2018). Biotic factors can also be important, but they may be directional. For example, orchids can be obligatory dependent on orchid mycorrhiza for part or all of their life and yet none of the fungi involved are known to depend on symbiosis with orchids (Rasmussen and Rasmussen, 2009). Obligate symbionts, such as Glomus, are highly limited in ability to develop without the presence of a suitable host, but they can survive (Bécard et al., 2004; Tisserant et al., 2012). This suggests that where plant species have a high dependence on microbial associations, joint introduction of both plant and microbe would be wise. With that in mind though, Kolaříková et al. (2017) did find that development of ectomycorrhizal and fungal plant pathogens were strongly related to the development of the plant community. Plant species therefore may be more reliant on microorganisms than vice versa but both groups benefit from the other’s presence. Although not necessarily as simplistic, increases in diversity and function of one (plant or microbe) can lead to increases in the other (Schnitzer et al., 2011; Delgado-Baquerizo et al., 2020). This is exemplified by the well-known changes in microbial communities with proximity to the rhizosphere (Fierer, 2017). Testing for soil microorganisms before the plant community establishes may therefore be premature, unless testing for orchid mychorriza (Vizer, 2013), as changes with plant establishment should be expected.

This work also stresses the importance of bringing in biologically active soil, as the Subsoil (also with the Mulch) contributed to driving the most similar community to the reference. The references themselves, possibly due to time since disturbance, have the greatest relictual microbial communities. The reference topsoil is a valuable source of microbes for dispersal

140 into restoration landscapes or for application in restoration. Soil application has been seen to make marked improvements in restoration of microbial communities and still stands as good practice (Wubs et al., 2016; Ngugi et al., 2017).

4.5 Conclusion This study asked:

Three sources of microbial communities were introduced at the initiation of restoration; are the communities from these sources still distinct? Are they similar to the reference areas?

Spoil is highly unlikely to have contributed to the current microbial composition on the Experimental Site because the populations living in it are small and significantly different from the other treatments. OGM is also unlikely to have contributed significantly to the microbial composition of the site as even though the species present are in very high numbers, they are completely different from those that are found on the site. The most similar treatment on the Experimental Site was Subsoil Mulch, which had two plots considered more resemblant of the references than to the rest of the Experimental Site. It is therefore the treatment best suited to restoring microbial communities and most likely to provide an acceleration effect in restoration using microorganisms.

Two major effects were noted. First, time is likely to be a major factor in the development of communities in restoration because the Ring Rd, which had been rehabilitated 20 years prior, was noticeably closer to the RSF than the treatments on the Experimental Site. Additionally, both RSF and Rav Ref had a large number of functional guilds present that could provide ecosystem services; both have had longer time periods since disturbance. Second, dispersal of microbial species may be important in the colonisation of disturbed areas. This is suggested because Spoil OGM was compositionally more similar to Subsoil Mulch and Subsoil OGM Mulch with little contribution from Spoil or OGM.

The application of Subsoil and Mulch has led to a community of microbes increasingly closer to the reference developing. This may have reduced the need for time to reach the references.

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Chapter 5 – Restoring for the Forest of the Future – Using Nutrient Inventories in Restoration Ecology

5.1 Introduction

5.1.1 Assumptions in Restoration Ecology For more than 35 years, soils have been recognised as a fundamental part of the ecological restoration system (Marrs et al., 1980b; Bradshaw, 1983). However, it is still acknowledged that ecologists either lack an understanding of soil science or do not implement it to its full extent (Wigley et al., 2013). Soils provide structure to support plant development, protect and insulate some plant parts from environmental extremes, support a diverse range of symbionts, and function as a key reservoir for plant macro- and micro-nutrients, water and gas exchange. However, depending on the disturbance to soils, large amounts of valuable resources and structure could have been lost from the ecosystem, limiting its ability to support the vegetation that it once did.

Loss of mineral and organic resources from soils and ecosystem components, such as trees, due to disturbance can reduce an ecosystem’s capacity to perform. For example, it is a well- documented risk in the forestry industry that stocks of nitrogen, phosphorus and cations can be lost from the ecosystem through harvesting whole trees and the high disturbance processes during harvest (Likens et al., 1978; Cleavitt et al., 2018). While losses of nutrients can be reduced, if loss does occur, then the process to restore those resources is slow and difficult (Vadeboncoeur et al., 2014). Although examination of element resources has been performed in mine rehabilitation settings (Marrs et al., 1980b; Ward and Koch, 1996; Mercuri et al., 2006), it is a much rarer activity, so much of the background for this chapter is based on related fields. In restoring a forest environment, the expectation is that the ecosystem will be there indefinitely. As such, the nutrient content needs to be sufficient to support the development of a mature ecosystem.

5.1.2 Nutrient Inventories Extensive and detailed experiments on the balance of nutrients have already been done in some ecosystems where they continue to be important for showing the sources of environmental issues. For example, they are frequently used to study the process of eutrophication of water bodies (Waters and Webster-Brown, 2016) and as part of ensuring

142 sustainability in logging operations (Likens et al., 1978). They have also been used in mine rehabilitation (Marrs et al., 1980b), including locally in the Hunter Valley to determine leaching of nutrients (Mercuri et al., 2006). Perhaps the most famous nutrient balance is the Hubbard Brook Experimental Forest, where multiple catchments were used to test the effect of a range of manipulations (Bormann and Likens, 1967; Whittaker et al., 1979; Campbell et al., 2007). The works within Hubbard Brook highlighted effects of acid rain, whole tree harvesting, mineral weathering, succession and hydrology, among other topics. Although the forestry environment is different from mine restoration in terms of the degree of ecosystem damage, they both involve removal of plant cover and initiation of plant succession, and studies have shown that nutrient inventories are useful for testing the effects of this disturbance on an ecosystem. For example, by performing multiple harvests, forestry experiments or surveys have been able to demonstrate the consequences of over withdrawal of the soil resources.

Where nutrient inventories have been performed for forestry, they have typically inquired on the length of a suitable rotation or the sustainability of the current rotation. The findings and implications of these nutrient inventory studies are necessarily localised because the conditions in each part of the world are different. In the case of timber harvesting at the Hubbard Brook Experimental Forest, it was found that there was little change to cations even though substantial amounts were removed with harvest (Johnson et al., 1997). This suggests that in the Hubbard Brook Experimental Forest scenario, a restoration project would have enough resources to repopulate the forest. In Belgium, Vangansbeke et al. (2015) found that while there were enough resources to complete a whole harvest cycle, each harvest removed so many resources from the system that a 48-year rotation was insufficient to restore calcium, potassium and phosphorus levels. In this case, restoration may fail if multiple harvests are made at that rate of rotation. For an Australian example, Turner and Lambert (1986) working on the NSW south coast found that with an 80-year rotation, the soils were more than fertile enough to support 320 years of harvesting. This prediction also did not take into account mineral weathering, which could extend the sustainability substantially. Although these examples are limited to forestry, they show that with substantial disturbance, there is the risk of producing a landscape that cannot support the desired ecosystem in the future.

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5.1.3 Too Much or Too Little The richness of the soil shown in the study by Turner and Lambert (1986), however, is not considered the norm for Australian soils (Attiwill and Leeper, 1987; Orians and Milewski, 2007). In particular, the mining setting of the Hunter Valley often only has shallow soils available, sometimes of poor quality, which need to cover an increased surface area of spoil. By their nature, sedimentary rocks are made from sediments that have already weathered (Buol et al., 2011) and there is much less change in primary and secondary minerals during soil development because they are already more thermodynamically stable (Velde and Meunier, 2008). As the weathering process has already occurred, and some of the plant macro- and micro-nutrients have been released, the parent material may be less able to provide as many resources. The mining environment in the Hunter Valley is situated in a sedimentary basin and most of the rocks forming the local spoil are sedimentary in origin, including siltstones, shales and sandstones. Although some shales can still have moderately high macro- and micro-nutrient levels, sandstones are often very low in resources (Buol et al., 2011). With both the topsoil and spoil of the new system potentially low in resources, it would be expected that additional nutrients need to be added either through fertiliser or an organic amendment. However, this could lead to another issue. As has been discussed in Chapter 3, the flora characteristics of a site can change with different nutrient inputs to a system. If the nutritional properties of a site are ove-r or under-estimated, then the desired final ecosystem may not be able to be reached (Figure 5.1).

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Figure 5.1. The development in total nutrients until the reference is matched. There are various pathways that could be taken, indicated by the red, yellow and blue pathways. The pale red, yellow and blue pathways are what could occur if the pathway moved in a direction other than the reference ecosystem.

As an example of what could occur if there are insufficient supplies of resources to achieve the reference ecosystem, consider the South American savannah. Although the climatic conditions in central Brazil’s savannah would largely support the development of forest (Lehmann et al., 2011), there is an interaction between fire and soil fertility that prevents its development. While fires prevent the dispersal of forest species into the savannah, preventing further succession, the lack of calcium and phosphorus also requires plants to be adapted to low-nutrient conditions, further deterring development and selecting species (Silva et al., 2013b).

The theory behind this chapter is based on the law of the conservation of mass, Antoine Lavoisier’s 1789 discovery that mass is neither created nor destroyed in a reaction. Importantly, the total amount of an element before a reaction is the same as the total amount of an element after a reaction. What can change is where that element is in the ecosystem after the reaction. By determining the total amount of an element in a plot, we can compare it to the total amount of an element in a reference. If the reference has much more of an element than a treatment does, it suggests that accelerating ecosystem development is futile as the restoration process is unlikely to ever achieve its goal. An important aspect of this is that if a single element is lacking, then a limit can be placed on the ecosystem due to 145 stoichiometric requirements. In ecology, stoichiometry can be defined as ‘the balance of multiple chemical substances in ecological interactions and processes’ (Sterner and Elser, 2002). Modern ecological stoichiometry is based on the law of definite proportions, where ‘the relative amount of each element in a particular compound is always the same’ (Sterner and Elser, 2002). Because a plant needs a defined ratio of carbon to nitrogen to phosphorus etc. to form each part of its body, if any one element is missing, then the creation of the whole cannot proceed, referred to as the law of the minimum (further discussed in Chapter 6). Due to stoichiometric ratios being required to grow, all macro- and micro-nutrients required by plants must be considered in any analysis because they each have the potential to inhibit the full development of the ecosystem if any one of them are in short supply.

In a natural setting, undersupply of a nutrient may be apparent as lack of growth and nutritional stress or as a change in community composition. Oversupply can also be represented by a change in community composition, particularly the invasion of weed species. Given the importance of restoring an EEC to this project, the consequences of poor growth, nutritional stress, changed community or weed invasion are serious risks to restoration success and the environmental management of the site.

5.1.4 Question This chapter produces a nutrient inventory as a method for determining the potential of a site to restore to the target ecosystem. Therefore, the question asked is:

Does the reconstructed ecosystem have sufficient nutritional capital to match the reference community?

The chapter aims to compare the capital of macro- and micro-nutrients measured in two compartments of the soil: the leaf litter and both the non-tree and tree vegetation. It then compares the capital to the reference to determine if the restoring ecosystems have sufficient capital to match the reference conditions without being stoichiometrically limited by any one nutrient.

It is hypothesised that: (i) each soil ameliorant will produce a variation in soil chemistry; (ii) there will be variation between treatments in how much nutritional capital each compartment contains; and (iii) treatments will vary in nutritional capital from the amount present in the references. Prior testing of the site shortly after establishment ((Scanlon, 2015;

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Castor et al., 2016) also see section 2.2.1.4) showed that Subsoil was generally depleted in resources, OGM (Organic Growth Medium) was very resource-rich and Spoil was low to moderate in most macronutrients. As OGM, a high-nutrient ameliorant, was applied at moderate to high rates compared with other sites (Norland and Veith, 1995; Spargo, 2012), treatments with OGM may have total nutrients similar to or higher overall than the RSF (Ravensworth State Forest). OGM treatments are expected to have less evidence of stoichiometric limitation. As the Subsoil Mulch treatments previously produced low-fertility results, nutritional capital may be similar to, or lower than, what is present in the references. With 5 years of development, it is also hypothesised that a transfer of nutrients from the soil to the vegetation will have occurred.

This study is performed acknowledging that a complete understanding of the system and its development is impossible, given the lack of pre-existing conditions and the lack of measurments of the ameliorants when they were deposited. The RSF itself is also not a perfect reference, having had disturbances in the past. However, these deficiencies do not negate attempts to address important questions in mine restoration science, as the implications of insufficient nutritional capital is long-term failure of the restoration. The study does not attempt to specify what the optimal nutritional compliment of the restoration ecosystem should be, just that consistent with restoration theory it should at least match the RSF as this is the reference of a mature ecosystem.

5.2 Methods

5.2.1 Overview of Methodology and Treatments In this chapter, comparisons between treatments and the reference are made to ensure there are enough resources in the recovering ecosystem to one day emulate the reference. To do this, a nutrient inventory was developed similar to but simpler than Whittaker et al. (1979), which like the work of Marrs et al. (1980b) shows change in element balances over time and deficiencies present. Within each treatment, five ‘compartments’ were examined: trees, other (non-tree) vegetation <2 m high, leaf litter, 0–10 cm deep soil and 20–30 cm deep soil. These compartments were necessary to gather the data, but they also provide an indication of where elements are moving within the ecosystems. The compartments were measured for all macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur) and many micronutrients of plants (sodium, chloride, iron, manganese, copper, zinc and boron).

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Elements required by plants for growth (nutrients) were the focus for this study but total nutrients is not the same as plant-available nutrients. Specifically, the total amount of an element considers the amount that is available to plants as well as the amount which is unavailable. This simplifies the analysis, as factors that could prevent an element from becoming available are not considered, but still provides valuable information on the potential of the ecosystem. Total nutrient analysis also allows for direct comparison of resources in soils with those in plants. Although this does not describe what will be available for a plant as an ongoing supply, plants will fail to acquire sufficient resources if the ecosystem is lacking an element based on its total analysis. An inventory is created for each element, comparing how much of that element is present in each compartment of each treatment. To transform the data from raw concentrations in each compartment to a more meaningful amount, the concentrations are multiplied by the biomass for litter, other vegetation and trees, or the volume and bulk density for soils. This presents two sets of data for each element: the concentrations of the total nutrient test, and the kg/ha analysis, which is found by multiplying the concentration by the mass of material.

This study will focus on a subset of the possible treatments. From the Experimental Site, Spoil, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch will be examined (see Chapter 2 for details of each treatment). Ring Rd is used as a reference for time because it is 15 years older; however, there is little information on the soils used to construct the site. RSF is the ultimate reference; it is the condition that restoration aims to achieve.

5.2.2 Field Sampling Procedure Because the site is very heterogeneous, with a wide diversity of flora species, a sampling protocol was designed that ensured an adequate volume of sample was collected while limiting oversampling, balancing time constraints, and avoiding biased sampling. The final design for each sample consisted of harvesting an above-ground cylinder of height 2 m, diameter 15 cm and volume 35,341 cm3, and two below-ground cylinders each of depth 10 cm, diameter 7.5 cm and volume 442 cm3 (Figure 5.2). The below-ground cylinders were taken at depths of 0–10 cm and 20–30 cm. All material inside each cylinder was sampled. Because of the mass required for analysis, a different number of samples were required for above- and below-ground procedures.

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Soils were sampled from three points in each plot, each 3.5 m towards the centre of the plot from its corners (Figure 5.3, left). Although four points could be sampled with this procedure, it was decided that three provided an optimal balance between understanding heterogeneity, time requirements and a desire to not oversample the site in order to allow for future studies. The point not to be sampled in each plot was randomly selected by a random number generator in Microsoft® Excel® 2016. The spoil had many large boulders, which makes sampling difficult even with a rocky auger. This produces a disturbed sample, so a large amount of care was taken to ensure that consistent samples were collected with regards to depth and horizontal movement (Figure 5.4). Accurate volumes of the actual sample were estimated by lining the hole with a thin bag and refilling it with sand. The sand was then extracted and its volume measured. Samples were kept cool while being transported to the Conservation Biology Research Groups laboratory at the University of Newcastle and placed in the refrigerator at 5°C until processing.

Figure 5.2. To obtain samples from a wide range of flora types in a heterogeneous environment, samples were taken of everything in a bulk environment.

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Figure 5.3. As an attempt to avoid oversampling the area, three different methods of strategic sampling were used. Soil only required three samples (left). Most plant and litter plots only needed, in addition to three locations sampled for soil, an additional six samples, which were performed randomly across an ‘X’ (middle). Spoil plots had drastically lower vegetation and litter levels and so, in addition to the three points taken for soils, points were taken at 1 m increments along the ‘X’ and when needed an additional 20 m transect was targeted on the plot edge with most vegetation (right).

Figure 5.4. Because the ground is variable, a wooden plank with a hole in it was used to ensure that there was minimal movement away from the sampling point. Consistency of depth was checked with a tape measure. Soil samples were taken after vegetation and litter had been sampled and were placed in a ziplock bag.

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The above-ground samples were split into two categories. If the material was alive or being completely supported above the ground by a live plant, then it was grouped with plant biomass. This meant that any dieback on a plant was also included as live plant material. All other material was included in the litter with the distinction between litter and mineral soil being that litter contained >30% organic material. In most plots, vegetation and litter samples required additional sampling to obtain enough material for chemical analysis compared with the soil sampling, so, in addition to the three points at 3.5 m from the corners, six points were randomly chosen at 1 m increments between diagonally opposite corners of the plot; this gave nine points (Figure 5.3, middle). Because of the lack of growth on Spoil plots, additional sampling was required. As well as the normal samples for plants and litter, 20 points along the edge of the plot with the most vegetation development were sampled, giving 49 points in total (Figure 5.3, right). To ensure a vertical sample was taken from the correct area, a pole made from grade-316 stainless steel was put next to where soil samples were taken, and checked against a spirit level, and samples were taken in a 15 cm diameter around the pole with sharp secateurs (Figure 5.5). All the samples for vegetation within a plot were combined as were all litter samples. On rare occasions, a minimum 20 g vegetation or litter sample could not be captured by these methods. In this scenario, a separate collection was taken by walking from the plant or litter patch to the next nearest patch, taking a representative sample from each until 20 g was obtained. The additional material from the walks was kept separate and only used to supplement the amount of material sent for nutrient analysis. Plant and litter samples were kept cool while being transported to a refrigerator. Samples were only taken of non-tree vegetation because individual trees (in all cases, these were eucalypts) were often taller than the 2 m sampling procedure; they will be accounted for by allometric equations (see section 5.2.5).

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Figure 5.5. Vegetation samples were taken at randomly selected points along transects as per Figure 5.3 with a grade-316 stainless steel pole 2 m high and a ring (inside square highlight) 15 cm in diameter.

5.2.3 Laboratory Methods In the laboratory, soil fragments greater than 10 mm in diameter were removed. The >10 mm fractions for both plants and litter were separated from the rest of the material, dried at 70°C and weighed to determine dry biomass. Litter and plant samples <10 mm were weighed and divided in half; one half was sent for total nutrient analysis at SESL Australia, a NATA- accredited laboratory, and the other was kept to estimate biomass. Biomass was estimated

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by weighing the remaining half sample, air-drying it at 70°C, weighing it and then applying the equation:

Dry biomass of half sample Dry biomass of full sample = Wet biomass of full sample × Wet biomass of half sample

For the 0–10 cm compartment, the three soil samples taken in the field were combined and homogenised by hand; similarly for the 20–30 cm compartment. From each sample, 1 kg of field-moist soil, about half, was sent for laboratory analysis at SESL Australia. For the remaining sample, bulk density was estimated based on the measured volume in the field and the mass of a sample dried at 105°C (Donahue et al., 1971; Sheppard and Addison, 2007).

5.2.4 Nutrient Concentrations of Soils and Other Vegetation Total nitrogen content in soils was analysed at SESL Australia by Dumas high-temperature combustion as in method 7A5 (Rayment and Lyons, 2011). All other soil elements were analysed by Aqua-Regia digestion and atomic absorption spectroscopy according to US EPA methods 3050B and 6010. For plants and litter, the samples were digested with nitric acid

and hydrogen peroxide, and nutrient analysis was performed by inductively coupled plasma

optical emission spectroscopy. Tests are for the total nutrient contents and data are presented as the average concentration of an element in a compartment per treatment. For stoichiometry (detailed below), organic carbon was determined using a LECO Corporation furnace.

The area of each sample was multiplied by the number of samples taken on each plot and transformed into number of hectares sampled. The sum of the full dry biomass in kilograms from a plot was then divided by the area to determine the kg/ha. Plant and litter material collected on random walks was not included in biomass determination; it was only used as a supplement to provide a suitably sized sample for total nutrient tests. Similarly, the bulk density of each plot’s 0–10 cm and 20–30 cm soil compartments was converted from tonnes per cubic metre to kg/ha. The kg/ha of biomass and soil mass were then multiplied by the concentration of nutrients to determine the number of kilograms of total nutrients per hectare (Wigley et al., 2013).

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This study also used soil data in the 0–10 cm compartment collected in 2014 for Scanlon (2015). These samples were taken four months after soils were added to the site, which means that they are not a true sample of the time of set-up and is a weakness in the study design. Rather, they are closer to the initial stabilised system. At this time, seeding had been completed two months earlier. It is assumed that vegetative growth and interaction with soil nutrient supply was minimal at this point. These soil data used bulk density also measured for Scanlon (2015) to transform to kg/ha.

5.2.5 Estimating Biomass of Trees Data on tree combined diameter at breast height (CDBH) from Chapter 3 were transformed to biomass using formulas adapted from Paul et al. (2013). Specific formulas were available for Corymbia maculata, Eucalyptus cladocalyx, Eucalyptus crebra, Eucalyptus moluccana and Eucalyptus tereticomis. The generic formula for Eucalyptus was used for Angophora floribunda, Eucalyptus punctata, Eucalyptus fibrosa, Eucalyptus canaliculata as well as any unidentified Eucalyptus species. The general formula for Casuarinaceae was used for Casuarina cunninghamiana. A single Exocarpus cupressiformis from Ring Rd 10 was excluded because a formula was not available for it. The allometric formulas developed by Paul et al. (2013) are typically based on larger trees than occur on the Experimental Site and may be a poor fit; they are probably more representative of the trees found on the Ring Rd and RSF.

5.2.6 Nutrient Concentration of Trees and Material >10 mm Nutrient concentrations were not able to be determined for trees or vegetation/litter that was >10 mm because the external laboratory could not handle the coarse material, so estimates were sought from the literature. For trees, Table 18 ‘Forest’ in Judd et al. (1996) provided average values for a eucalypt forest based on a database constructed from 245 literature articles. Data are for whole trees and probably for trees that are significantly older/larger than what were studied here. Additionally, they are a from a variety of species with a focus on those used commercially. As such, they may be a poor fit for the trees on the Experimental Site but will probably perform well for those on the RSF. The same data source was also used to estimate the values for material >10 mm in the litter and vegetation only; instead of using whole trees, the data for branches were used. Unfortunately, this source was unable to provide values for iron, so they were not included in the analysis.

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5.2.7 Statistics 5.2.7.1 Comparisons Among All Treatments With the exception of mass of material analysis, statistical comparisons do not include the tree layer. This is because the data used for allometric scaling and nutrient concentrations were both developed on larger trees than occur on the Experimental Site, which is expected to increase the uncertainty beyond a reasonable level. Comparisons between treatments on trees within the same element may not have provided different results than have already been shown for CDBH in Chapter 3. The tree data are still shown in the figures, so an indication of its contribution can be understood. The estimated data on litter and vegetation >10 mm were included in the kg/ha analysis. Its inclusion is due to the biomass being directly measured rather than using a potentially poor fitting model as occurred with the trees. Additionally, the >10 mm fraction averaged 46% and 28% of the other vegetation and litter compartments respectively.

All statistical analyses were performed in JMP Pro 14 (JMP®, Version Pro 14. SAS Institute Inc., Cary, NC, 1989–2019).

Mass of material was transformed using log(x+1) and analysed using a linear mixed model (LMM) (Harrison et al., 2018). Effects in the model were a factorial between Treatment and Compartment, with Block as a random effect. Model tests deliver an F test and p values for each factor. Further comparisons were performed using Tukey post-hoc test, where the outputs are pairwise comparisons with t-test and p values. Where discussing multiple comparisons, pairwise tests discussing significant differences in broad groups of data will specify the result for the least significant difference (i.e. the pairwise comparison below but closest to the p=0.05).

All elements, except boron, for both unit concentration and kg/ha measures were tested by the same LMM approach. For consistency, both unit concentration and kg/ha values were fourth-root transformed to produce a normal distribution for analysis. The exception to this was the concentration (%) of sodium, which following a Box–Cox procedure, used the following transformation (Box and Cox, 1964).

Total sodium (%) . 1

9.5495−0 364 − − 155

Boron had many values that were below the level of detection at 3 mg/kg. So that an analysis could be run, all values below the detection level were assumed to be 0 and a generalised linear model on a zero-inflated Poisson distribution (Zuur et al., 2009) was performed with a factorial of Treatment and Compartment as model effects.

Testing between year sampled for the comparison between 2014 and 2019 was performed using a LMM with full factorial of Treatment and Year and random effect of Block. This was only performed on nitrogen and phosphorus because data for other elements were not collected in Scanlon (2015).

5.2.7.2 Comparisons to the Reference Results were compared with the references in multiple ways. Within the above comparisons, a specific note was made of a difference between the treatments and compartments with respect to the RSF. Additionally, a specific analysis was performed using the kg/ha data to compare the RSF Biomass (trees + other vegetation + litter) to the complete resources (trees + other vegetation + litter + 0–10 cm + 20–30 cm) of each treatment. This was performed because the restored ecosystems need to be capable of producing the same level of vegetation. Litter was also included in the biomass because it is a by-product of community development.

A separate analysis using a similarity matrix was designed to compare the overall effects of all elements to the RSF. Initially, the results were standardised on a compartment basis using the following formula:

RSF treatment

Standard deviation of all treatments and references − The standardised values of compartments in each treatment were then summed to give an overall value for each treatment on the mass of material, element concentration and kg/ha.

5.2.7.3 Stoichiometry Ecological stoichiometry in soil was considered because relationships have been seen between fertility and elemental balance. The relationships between organic carbon and total nitrogen and total phosphorus in the 0–10 cm soil were examined based on their concentrations, using regression analysis (Moore et al., 2012). Similarly, regression analysis of total nitrogen and total phosphorus was used for both other vegetation and litter.

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Relationships between the variables were examined by regression analysis. Significant differences between treatments were compared by using LMM of the ratios between organic carbon and total nitrogen, organic carbon and total phosphorus, as well as total nitrogen and total phosphorus.

5.3 Results This section first detail the mass of material representing each compartment of the ecosystem. Each macro- and micro-nutrient is then examined independently to first compare its concentration in the compartments. The material mass is then combined with concentration data to provide an abundance (kg/ha) measure of each element in the ecosystem. For nitrogen and phosphorus, differences in abundances (kg/ha) in the 0–10 cm compartment between 2014 and 2019 are considered. Finally, for each element a comparison is made of the RSF Biomass and the abundance (kg/ha) of total elements in each treatment. As the primary consideration of success is that the ecosystem has at least matched the resource capacity of the RSF Biomass, this final comparison is considered the most important analyses for each element.

5.3.1 Mass of Material The mass used for the study was mostly from the soil environment (Figure 5.6). The mass in the 20–30 cm layer was significantly higher than all others (20–30 cm compared with 0–10 cm: t=3.75, p=0.006) and 0–10 cm was higher than all vegetation and litter (0–10 cm compared with Trees: t=22.36, p<0.0001) (Table 5.1).

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Soil 0-10cm Soil 20-30cm Litter >10mm Litter <10mm Other Vegetation >10mm Other Vegetation <10mm Tree

Figure 5.6. Mass of material in each compartment. Note that mass values <0 represent amounts below the soil surface, not negative values. The data have been square-root transformed to show the fine layers in the vegetation and litter.

Table 5.1. Pairwise differences in mass between compartments following a Tukey–Kramer test on log+1 transformed data. Treatments that share a letter in the Tukey pairwise test are not considered to be different.

Compartment Tukey pairwise Untransformed mean differences value (kg/ha) Soil 20–30 cm A 2,494,898 Soil 0–10 cm B 1,138,006 Tree C 45,649 Litter <10 mm C 10,475 Litter >10 mm D 4113 Other vegetation <10 mm E 1065 Other vegetation >10 mm E 923

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5.3.2 Macronutrients 5.3.2.1 Nitrogen Comparing broad compartments (other vegetation, litter, 0–10 cm and 20–30 cm), the concentration of nitrogen was significantly different between all compartments. The smallest difference was between the 0–10 and 20–30 cm layer (t=3.01, p=0.0218) (Figure 5.7).

Comparing treatments, there was only a significant difference between the higher concentration in Spoil OGM and lower concentration in Subsoil Mulch (t=3.62, p=0.015).

There were no significant interactions between treatments and compartments. Although Block was controlled for as a random variable in this analysis, it is worth noting that samples in Block 5 had higher nitrogen concentrations than samples in Block 1.

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0.5 Average Total Nitrogen Concentration (%) Concentration Nitrogen Total Average 0 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch Treatment

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.7. Average total nitrogen concentration in each compartment. The values 20–30 cm and 0–10 cm refer to the depths from which the soil samples were taken. Each compartment is significantly different from all others; however, there are no significant differences between treatments within a compartment. Error bars show standard deviation.

Between 2014 and 2019, there was no significant change in nitrogen abundance (kg/ha) in the 0–10 cm soil layer on the Experimental Site (F(1,14)=0.68, p=0.42) (Figure 5.8).

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There were significant differences between compartments, with soil samples being higher in nitrogen than the litter (0–10 cm to litter: t=6.46, p<0.0001) and the vegetation lower than all others (litter to other vegetation: t=5.31, p<0.0001).

In the 2019 samples, Spoil overall had less abundance (kg/ha) of nitrogen than did Spoil OGM and Subsoil OGM Mulch (Spoil OGM to Spoil: t=3.09, p=0.0459).

There were no significant differences in pairwise tests between any treatment in the 0–10 cm or 20–30 cm compartments. There were, however, significant differences in litter nitrogen, with the RSF, Ring Rd and Subsoil Mulch treatments all having significantly higher abundance (kg/ha) than Spoil (Subsoil Mulch and Spoil: t=4.16, p=0.023). There was so much nitrogen in the Ring Rd and RSF litter that nitrogen abundance (kg/ha) was significantly greater than that found in its other vegetation compartment (Ring Rd litter to Ring Rd other vegetation: t=4.53, p=0.008).

When compared with the nitrogen concentration in the RSF pool of biomass (litter + other vegetation + trees), none of the treatments were significantly different during 2014 (2014 Spoil OGM 0–10 cm to RSF Biomass: t=2.51, p=0.0978). By 2019, however, the total combined pool (20–30 cm + 0–10 cm + biomass) for the treatments on the Experimental Site had increased and both Spoil OGM and Subsoil OGM Mulch were significantly larger than the biomass pool in the RSF (Subsoil OGM Mulch to RSF Biomass: t=3.36, p=0.016). Ring Rd also had a larger pool of nitrogen in total than the RSF Biomass (Ring Rd to RSF Biomass: t=4.22, p=0.0011).

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0-10cm 20-30cm Litter Other Vegetation Trees

Figure 5.8. Mass (kg) of nitrogen per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis. For 2014, only the 0–10 cm compartment was analysed.

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5.3.2.2 Phosphorus Among broad compartments, the concentration of phosphorus was similar between the 0– 10 cm and 20–30 cm compartments, higher in the litter (litter to 0–10: t=5.42, p<0.0001) and highest in other vegetation (other vegetation to litter: t=4.53, p=0.0002) (Figure 5.9).

Among treatments, Spoil OGM had the highest concentrations of total phosphorus (Spoil OGM to Subsoil OGM Mulch: t=4.36, p=0.001). Subsoil Mulch had the lowest concentrations, and was significantly lower than Ring Rd and Subsoil OGM Mulch (Subsoil OGM Mulch to Subsoil Mulch: t=3.58, p=0.0101).

The interaction between compartment and treatment had significant results. In the 0–10 cm compartment, Spoil OGM was significantly higher in phosphorus than RSF, Ring Rd and Subsoil Mulch were (Spoil OGM to RSF: t-4.05, p=0.0308). In the 20–30 cm compartment, the Spoil OGM was again significantly higher in phosphorus concentration than RSF, but Ring went against the trend and was also higher than RSF (Spoil OGM to RSF: t= 3.89, p=0.0471). In the other vegetation, the Spoil OGM had a significantly higher phosphorus concentration than Spoil (t=3.88, p=0.0477).

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Average Total Phosphorus Concentration (%) Concentration Phosphorus Total Average Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch -0.05

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.9. Average concentration of phosphorus in each compartment. The values 20–30 cm and 0–10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation. Subsoil Mulch 0–10 cm and RSF 20–30 cm both have a value of 0.001%. 162

Between 2014 and 2019, there was a significant reduction in abundance (kg/ha) phosphorus in the 0–10 cm soil layer on the Experimental Site (F(1,16)=21.2, p=0.0003) (Figure 5.10). Both Subsoil Mulch and Subsoil OGM Mulch had significant reductions from 2014 to 2019 (Subsoil OGM Mulch 2014 to Subsoil OGM Mulch 2019: t=3.59, p=0.039).

Between compartments in 2019, the vegetation was significantly lower in phosphorus (kg/ha) than all others (litter to vegetation: t=4.36, p=0.0004).

There was a significant difference in overall treatment abundances (kg/ha), with Spoil OGM having higher phosphorus abundance than either Spoil or Subsoil Mulch (Spoil OGM to Spoil: t=3.2, p=0.0337).

There was a significant interaction between compartment and treatment in abundances (kg/ha). In the 0–10 cm compartment, phosphorus abundances were significantly higher in Spoil OGM than in Subsoil Mulch (t=6.14, p<0.0001), whereas in the 20–30 cm compartment the difference between Spoil OGM and Subsoil Mulch was reduced (Figure 5.10). In the 20– 30 cm compartment, RSF had significantly lower phosphorus abudance than Ring Rd (t=5.2, p=0.001). Both RSF litter and Ring Rd litter were higher than Spoil litter (Ring Rd litter to Spoil litter: t= 4.14, p=0.0243).

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0-10cm 20-30cm Litter Other Vegetation Trees

Figure 5.10. Mass (kg) of phosphorus per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis. For 2014, only the 0–10 cm compartment was analysed.

Comparing the 2014 0–10 cm soil samples to the RSF Biomass, the Spoil OGM treatment had significantly more phosphorus in it (Spoil OGM to RSF Biomass: t=4.44, p=0.0141). This trend remained in 2019, with the combined pool of phosphorus in Spoil OGM significantly higher than in RSF Biomass (Spoil OGM to RSF Biomass: t=4.49, p=0.0107). In 2019, the RSF Biomass averaged 385 kg/ha of phosphorus. While it was not significantly different, the average total phosphorus in the sum of the 0–10 cm, 20–30 cm, litter, other vegetation and tree compartments for Subsoil Mulch was 295 kg/ha, a shortfall of 90 kg/ha on RSF Biomass. The 10–20 cm compartment was not measured and is not accounted for in this calculation, so if the soil compartment with the largest pool of phosphorus is doubled, then the shortfall is still 6 kg/ha (Subsoil Mulch 20–30 cm averages 84 kg/ha of phosphorus). Such a calculation assumes that the biomass will be able to access all the phosphorus in the upper 30 cm of soil, which, even without the shortfall, is unrealistic. A similar shortfall was also present in 2014 for Subsoil Mulch. All other treatments had more phosphorus on average than RSF Biomass.

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5.3.2.3 Potassium Total potassium concentrations were significantly higher in the sampled vegetation in every case (vegetation to litter: t=27.01, p<0.0001) (Figure 5.11).

At the treatment level, Spoil OGM had significantly higher potassium concentrations than Subsoil Mulch, Subsoil OGM and RSF (Spoil OGM to Subsoil OGM Mulch: t=3.36, p=0.0359).

The significant interaction between treatment and compartment showed that RSF and Subsoil Mulch had significantly less potassium in vegetation than Ring Rd and Spoil OGM did (Ring Rd other vegetation to Subsoil Mulch other vegetation: t=4.25, 9=0.0196). In the litter compartment, RSF and Ring Rd had a significantly lower concentration than Spoil OGM (Spoil OGM litter to RSF litter: t=4.19, p=0.0228).

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0 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.11. Concentration of potassium in each compartment. The values 20–30 cm and 0– 10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation.

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The abundance (kg/ha) potassium was significantly different among compartments, the 20– 30 cm compartment being highest, then 0–10 cm, litter and sampled vegetation (0–10 cm to litter: t=4.02, p=0.0012) (Figure 5.12).

There was no significant difference between treatments in abundance (kg/ha) (F(5,43.1)=1.1, p=0.39).

There were strong interactions between compartment and treatment (F(15,45.3)=4.3, p<0.0001), most notably in the litter compartment where RSF was significantly higher than Spoil, a trend that did not occur in any other compartment (RSF litter to Spoil litter: t=6.32, p<0.0001).

Compared with the RSF Biomass, each treatment had a significantly larger combined pool of potassium (Subsoil OGM Mulch to RSF Biomass: t=3.11, p=0.0232).

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Figure 5.12. Mass (kg)of potassium per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

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5.3.2.4 Calcium In general, there was a significant difference in calcium concentration between compartments (F(3,33.9)=5.6, p=0.0029), with litter being significantly higher than both the 0–10 cm and 20–30 cm compartments (litter to 0–10 cm: t=3.27, p=0.0124) (Figure 5.13).

There was also a difference between treatments (F(5,19.1)=4.8, p=0.0054), with RSF being significantly lower than Spoil and Spoil OGM (Spoil OGM to RSF: t=3.7, p=0.016).

The model did not find a significant interaction between compartment and treatment (F(15,33.9)=1.7, p=0.09).

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Average Total Calcium Concentration (%) Concentration Calcium Total Average -0.5

-1 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.13. Concentration of calcium in each compartment. The values 20–30 cm and 0– 10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation.

The model for abundance (kg/ha) of calcium showed a significant random effect (Wald p=0.0003), with Block 1 having a higher intercept (0.21) and Block 5 having a lower intercept (–0.28).

There was a very strong difference between compartments in abundance (kg/ha) (F(3,45.8)=69.3, p<0.0001). Each compartment was significantly different from the others,

167 with 20–30 cm the highest, then 0–10 cm, litter and vegetation (20–30 cm to 0–10: t=3.66, p=0.0036).

There was a significant difference in abundance (kg/ha) between treatments for calcium (F(5,47)=3.2, p=0.0149) (Figure 5.14), which was probably influenced by the strong transformation. However, in pairwise tests, this was only evident between higher abundance (kg/ha) in Spoil OGM and lower abundances in RSF (Spoil OGM to RSF: t=3.44, p=0.0146).

In a similar way to potassium, there was a strong interaction between treatment and compartment for calcium (F(15,45.8)=6.9, p=<0.0001). The interaction was between Spoil and RSF, where in both the 0–10 cm and 20–30 cm compartments Spoil had more calcium (Spoil 0–10 cm to RSF 0–10 cm: t=4.23, p=0.0108). In the litter, RSF had more calcium (RSF litter to Spoil litter: t=5.23, p=0.0009).

The RSF Biomass had significantly less calcium than the combined pool of Spoil and Spoil OGM (Spoil OGM to RSF Biomass: t=3.91, p=0.0013).

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Figure 5.14. Mass (kg) of calcium per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

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5.3.2.5 Magnesium There was no significant difference between compartments in the concentration of magnesium, although the p value was close to the alpha (F(3,43.4)=2.6, p=0.06) (Figure 5.15).

There was a strong treatment effect (F(5, 25.8)=10.3, p<0.0001), however; Spoil and Spoil OGM were higher in magnesium concentration than Subsoil Mulch, Subsoil OGM Mulch, Ring Rd and RSF were (Spoil OGM to Ring Rd: t=3.51, p=0.0184).

There was no interaction found between compartment and treatment (F(15,43.3)=0.4, p=0.96).

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-0.5 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.15. Concentration of magnesium in each compartment. The values 20–30 cm and 0– 10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation.

Considering the abundance (kg/ha) of magnesium, there was a significant random effect (Wald p<0.0001), with Block 1 having a lower intercept (–0.24) and Block 5 having a higher intercept (0.22).

There was a very strong difference in abundance (kg/ha) between compartments (F(3,45.9)=155.3, p<0.0001). Each compartment was significantly different from each other,

169 with 20–30 cm being the highest, then 0–10 cm, litter and vegetation (litter to vegetation: t=4.57, p=0.0002).

There was no significant difference in magnesium abundance (kg/ha) between treatments (F(5,47)=1.6, p=0.19) (Figure 5.16).

As for calcium, Spoil and RSF drove an interaction between treatment and compartment, where in the 0–10 cm and 20–30 cm compartments there was more magnesium in the Spoil (Spoil 20–30 cm to RSF 20–30 cm: 4.52, p=0.008), while in the litter the RSF had significantly more than Spoil (RSF litter to Spoil litter: t=5.2, p=0.001).

Magnesium abundance (kg/ha) in the combined pools were significantly higher on all of the treatments except Subsoil OGM Mulch when compared with the RSF Biomass (Subsoil Mulch to RSF Biomass: t=3.34, p=0.0143). Subsoil OGM Mulch was still higher than RSF Biomass, but not significantly different.

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Figure 5.16. Mass (kg) of magnesium per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

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5.3.2.6 Sulfur There were large differences in sulfur concentration between compartments, with significantly higher concentrations in the vegetation (vegetation to litter: t=6.21, p<0.0001) (Figure 5.17). Litter was also significantly higher than both 0–10 cm and 20–30 cm (litter to 0– 10 cm: t=10.99, p<0.0001).

There were significantly higher concentrations in the Spoil and Spoil OGM than at the Ring Rd and RSF (Spoil to Ring Rd: t=3.68, p=0.0077), and Spoil OGM was also significantly higher than Subsoil Mulch and Subsoil OGM Mulch (Spoil OGM to Subsoil OGM Mulch: t=3.09, p=0.0378).

There was a significant interaction between compartment and treatment (F(15,45.9)=3.84, p=0.0002). The main feature of this was the very high concentration of sulfur in vegetation on Spoil, which was significantly higher than every other combination except Spoil OGM (Spoil vegetation to Subsoil Mulch vegetation: t=3.93, p=0.0422).

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Figure 5.17. Concentration of sulfur in each compartment. The values 20–30 cm and 0–10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation.

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In the compartments at the per hectare scale, the soil layers, 0–10 cm and 20–30 cm, had significantly higher abundances (kg/ha) of sulfur than the litter or the vegetation (0–10 cm to litter: t=6.97, p<0.0001). The vegetation was significantly lower in sulfur than the litter was (litter to vegetation: t=4.97, p<0.0001).

There was no significant difference in abundance of sulfur between treatments (F(5,29)=2.6, p=0.0559) (Figure 5.18).

The significant interaction between treatment and compartment was partly due to Ring Rd having higher sulfur abundance in the 20–30 cm layer than RSF did (Ring Rd 20–30 cm to RSF 20–30 cm: t=4.26, p=0.0178). In the litter compartment, RSF, Ring Rd and Subsoil Mulch all had higher sulfur abundance than Spoil did (Subsoil Mulch litter to Spoil litter: t= 4.11, p=0.027).

Both Spoil OGM and Ring Rd had significantly higher abundance of sulfur in their combined pools than RSF Biomass did (Spoil OGM to RSF Biomass: t=5.4, p=0.0044). No treatment was lower in sulfur than RSF Biomass was.

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Figure 5.18. Mass (kg) of sulfur per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis. 172

5.3.3 Micronutrients 5.3.3.1 Sodium Other vegetation showed significantly higher sodium concentrations than all other compartments (other vegetation to litter: t=11.69, p<0.0001) (Figure 5.19). The 0–10 cm compartment was significantly lower in sodium than the other compartments were (20– 30 cm to 0–10 cm: t=3.3, p=0.01).

Subsoil Mulch, Ring Rd and RSF all had significantly lower sodium concentrations than Spoil, Spoil OGM and Subsoil OGM Mulch did (Subsoil OGM Mulch to Subsoil Mulch: t=4.32, p=0.0013).

There was a significant interaction between treatment and compartment, the strongest being in the litter compartment, where Spoil, Spoil OGM and Subsoil OGM had drastically higher sodium concentrations than the other treatments did (Subsoil OGM Mulch to RSF: t=4.12, p=0.0243).

Average Total Sodium Concentration (%) Concentration Sodium Total Average 0

-1 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Litter <10mm Other Vegetation <10mm

Figure 5.19. Concentration of sodium in each compartment. The values 20–30 cm and 0–10 cm refer to the depth at which the soil sample was taken. Error bars show standard deviation.

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There was, however, a difference in sodium abundance (kg/ha) between every compartment, with 20–30 cm the highest, then 0–10 cm, litter and vegetation (0–10 cm to litter: t=2.69, p=0.048).

There was no significant difference between treatments in the abundance (kg/ha) of sodium (F(5,33.1)=1.2, p=0.32) (Figure 5.20).

In the 0–10 cm compartment, Spoil was significantly higher in abundance (kg/ha) sodium than Subsoil Mulch, Subsoil OGM Mulch, Ring Rd and RSF were (Spoil 0–10 cm to Subsoil OGM Mulch 0–10 cm: t=3.98, p=0.0372). In the 20–30 cm, Spoil was significantly higher than Ring Rd was (Spoil 20–30 cm to Ring Rd 20–30 cm: t=4.04, p=0.0316). The Spoil was lower in sodium abundance in the litter compartment than Ring Rd, RSF and Subsoil Mulch were (Subsoil Mulch litter to Spoil litter: t=4.22, p=0.0192).

As a combined pool, Spoil had significantly more abundance (kg/ha) of sodium than RSF Biomass did (Spoil to RSF Biomass: t=6.01, p=0.0042). No treatment had less sodium than RSF Biomass did.

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Figure 5.20. Mass (kg) of sodium per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis. 174

5.3.3.2 Chloride The model for chloride is a poor fit, with 12 of the 72 samples returning values of 0, the random effects parameter returning a significant result (Wald p=0.0036) and transformation having a strong right skew. LMMs are typically robust to violations of assumptions (Schielzeth et al., 2020); therefore, the results should be interpreted cautiously. This model is still a better fit for the data than a generalised regression on a zero-inflated negative binomial distribution.

The model showed a strong difference in chloride concentration between compartments (F(3,45.7)=158.2, p<0.0001) and treatments (F(5,46.4)=10.1, p<0.0001). There was a lesser but still significant result for an interaction between compartment and treatment (F(15,45.7)=3.4, p=0.0006) (Figure 5.21).

The strong effect of compartment was due to vegetation, which was significantly higher than all others (other vegetation to litter: t=14.93, p<0.0001). There was also a significant difference between litter and 0–10 cm, with litter being higher in chloride concentration (litter to 0–10 cm: t=4.67, p=0.0002).

Between treatments, Spoil and Subsoil OGM Mulch had significantly higher chloride concentrations than Subsoil Mulch and Ring Rd did (Subsoil OGM Mulch to Subsoil Mulch: t=3.37, p=0.0177).

There were stand-out treatments within the 0–10 cm compartments; for example, Spoil in the 0–10 cm was significantly higher in chloride than Spoil OGM, Subsoil Mulch and Ring Rd were (Spoil to Subsoil Mulch: t=3.91, p=0.0448).

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Figure 5.21. Concentration of chloride in each compartment. The values 20–30 cm and 0– 10 cm refer to the depth which the soil sample was taken. Error bars show standard deviation.

Between compartments, there was a significant difference in abundance (kg/ha) between litter, which had the highest value, and all others (litter to 20–30 cm: t=3.2, p=0.014). The 0– 10 cm compartment was significantly lower in chloride than all others (vegetation to 0–10 cm: t=3.18, p=0.0144).

There was a significant difference in abundance (kg/ha) between treatments with Ring Rd being lower in chloride than RSF, Spoil and Subsoil OGM Mulch (Subsoil OGM Mulch to Ring Rd: t=3.39, p=0.0313) (Figure 5.22).

Within compartments, there was a strong difference in abundance (kg/ha) in 0–10 cm with Spoil being much higher in chloride than all other treatments on the Experimental Site and Ring Rd (Spoil 0–10 cm to Subsoil OGM Mulch 0–10 cm: t=4.41, p=0.013). Within the 20– 30 cm compartment, Ring Rd was significantly lower in chloride than all others (RSF 20–30 cm to Ring Rd 20–30 cm: t=4.54, p=0.0091). The Spoil litter combination was significantly lower

176 in chloride abundance than litter in Ring Rd or RSF (Ring Rd litter to Spoil litter: t=4.27, p=0.0193).

There was no significant difference in abundance (kg/ha) between the combined pool of chloride in any treatment and RSF Biomass (RSF Biomass to Spoil OGM: t=2.53, p=0.14). However, the very high abundance of chloride in the RSF litter mean that all treatments had at least one plot that was lower in total pools than the average RSF Biomass.

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-1500 Mass (kg) -2000

-2500 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure 5.22. Mass (kg) of chloride per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

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5.3.3.3 Other Micronutrients Details of the results of micronutrient analysis are presented in Appendix C. A summary of their values and relation to the RSF was produced (Table 5.2). Although boron levels were lower than the total of RSF in some treatments, no micronutrient was below the abundance (kg/ha) values of RSF Biomass.

Table 5.2. The summed amount of each micronutrient (kg/ha) and its ratio to the summed RSF (in brackets). Note that this includes the values for trees and material >10 mm. Cells are colour coded based on which compartment had the highest amount of each element: brown cells are the 20–30 cm compartment, yellow cells are the 0–10 cm compartment and purple cells are the litter. Note that iron does not contain an estimate for the amount in trees or other vegetation >10 mm. Numbers are to the nearest whole number while ratios are to the nearest 0.1. Full details of results are in Appendix C.

Element Summed Summed Summed Summed Summed Summed amount in amount in amount in amount in amount in amount in RSF Spoil (ratio Spoil OGM Subsoil Subsoil Ring Rd (kg/ha) to RSF) (ratio to Mulch OGM Mulch (ratio to (kg/ha) RSF) (ratio to (ratio to RSF) (kg/ha) RSF) (kg/ha) RSF) (kg/ha) (kg/ha) Iron 44,219 144,783 107,819 144,792 155,799 107,673 (3.3) (2.4) (3.3) (3.5) (2.4) Manganese 641 2309 (3.6) 1686 (2.6) 1368 (2.1) 1696 (2.6) 1639 (2.5) Zinc 79 304 (3.8) 345 (4.4) 158 (2) 174 (2.2) 214 (2.7) Copper 21 135 (6.3) 164 (7.6) 69 (3.2) 74 (3.5) 81 (3.8) Boron 19 10 (0.5) 18 (1) 17 (0.9) 24 (1.2) 16 (0.9)

5.3.4 Stoichiometry A very strong relationship was found between organic carbon and total nitrogen in the 0– 10 cm soil compartment (F(1,16)=35, p<0.0001) (Figure 5.22). Although significant values were also found for comparisons between organic carbon and total phosphorus (F(1,16)=9, p=0.0079), and total nitrogen and phosphorus (F(1,16)=18, p=0.0006,) these both had poorer fits (Figures 5.23 and 5.24). No significant relationship was found between total nitrogen and total phosphorus for the vegetation or the litter (Figures 5.25 and 5.26).

Between treatments, there was no significant difference in C:N ratio in the 0–10 cm compartment (F(5,9.7)=0.63, p=0.68). Total phosphorus levels in the Subsoil Mulch treatment were an order of magnitude lower than other treatments, however, which gave a significantly higher N:P ratio (Subsoil Mulch to Subsoil OGM Mulch: t=4.45, p=0.009). The C:P ratio was close to significance, with levels higher in the Subsoil Mulch treatment, but did not meet the

178 alpha (F(5,10.5)=3.23, p=0.0512). Mean C:N:P ratios ranged from 103:5:1 in Spoil OGM to 3233:157:1 in Subsoil Mulch (Table 5.3). For comparison, Cleveland and Liptzin (2007) found a global average atomic C:N:P ratio in the soil of 186:13:1.

Figure 5.23. Linear regression between organic carbon and total nitrogen from the 0–10 cm compartment.

Figure 5.24. Linear regression between organic carbon and total phosphorus from the 0–10 cm compartment.

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Figure 5.25. Linear regression between total nitrogen and total phosphorus from the 0–10 cm compartment.

Figure 5.26. Linear regression between total nitrogen and total phosphorus in vegetation.

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Figure 5.27. Linear regression between total nitrogen and total phosphorus in litter.

Table 5.3. Ratios of organic carbon to total nitrogen to total phosphorus from the 0–10 cm compartment. The value of C in C:N:P was determined by multiplying the average C:N by the average N:P rather than using the C:P value. Mean values are presented for C:N, C:P and N:P plus or minus one standard error.

Treatment C:N:P C:N C:P N:P Spoil 320:12:1 26.5 ± 5 282.1 ± 683 12.1 ± 16.6 Spoil OGM 103:5:1 21.1 ± 5 115.6 ± 683 4.9 ± 16.6 Subsoil Mulch 3231:157:1 20.6 ± 5 3233.3 ± 683 156.6 ± 16.6 Subsoil OGM 1385:50:1 27.8 ± 5 1876.3 ± 683 49.8 ± 16.6 Mulch Ring Rd 497:26:1 19 ± 5 491.3 ± 683 26 ± 16.6 RSF 481:23:1 20.6 ± 5 478.5 ± 683 23.3 ± 16.6

5.3.5 Overall Similarity to RSF By constructing and summing together a similarity matrix for differences to the reference, RSF, we can consider which treatment is most similar overall (Table 5.4). Ring Rd overall had the most characteristics similar to RSF, with Subsoil Mulch close behind. Overall, the greatest difference for Ring Rd in unit concentration was calcium, which was low in the sampled 181 vegetation, while in the abundance (kg/ha) assessment the 20–30 cm compartment was higher in sulfur. The largest difference in unit concerntration from RSF for Subsoil Mulch was the high iron levels in all compartments, particularly sampled litter and vegetation. The most difference in Subsoil Mulch abundance (kg/ha) was found in the lower boron levels in the sampled vegetation. Subsoil OGM Mulch is moderately dissimilar overall with lower potassium and chloride than RSF.

Spoil had very different masses of material in soil and litter and a complete lack of trees. This also contributed strongly to its difference from RSF in the abundance (kg/ha) metric. The top three elements contributing to the difference in unit concentration for Spoil OGM (given in decreasing strength) were zinc, copper then phosphorus, which were all higher in every compartment examined.

Table 5.4. Similarity of treatments to RSF aggregated over multiple mass and total nutrient characteristics. Colours indicate how a value compares with other treatments within a characteristic, and range from green (low and more similar to RSF), to yellow, orange and red (high and more dissimilar. to RSF).

Spoil Spoil OGM Subsoil Subsoil Ring Rd Mulch OGM Mulch Based on mass of material 12.8 10.2 6.6 8.2 5.9 Based on unit concentration 75.5 97.4 47.7 68.4 46.0 Based on abundance (kg/ha) 122.5 107.4 62.2 90.7 63.7

5.4 Discussion

5.4.1 Restoration and Total Nutrients An important part of this study was to determine if the soil had the resource capital to provide the plants with what they needed to match the characteristics of the reference ecosystem. The theoretical grounding is the law of conservation of mass, which states that matter cannot be created or destroyed. In a restoration ecosystem, although carbon, oxygen and nitrogen can be sourced from the atmosphere, most micro- and macro-nutrients may not accumulate if they are not present in sufficient amounts. If the elements are not present in sufficient

182 quantities, then they must be added or the community will be unable to develop into the desired ecosystem. This study examined total nutrients because they provide a representation of the complete amount of an element in the system (Hazelton and Murphy, 2007; Eash et al., 2016). Although the available resources detailed in Chapter 6 are more commonly examined, those tests are designed to give an indication of what resource is available to plants at the time of sampling. However, this study aimed to understand what could be available over the life of the ecosystem, considering both material that is available now and material that could become available in the future.

From that perspective, the results are mixed, particularly around phosphorus and chloride. While no treatment was significantly lower in phosphorus than the RSF Biomass, Subsoil Mulch did have a lower average phosphorus abundance (kg/ha). This is not a definitive negative, but it is a cause for concern because it is highly unlikely that the plant community will be able to use the whole phosphorus inventory of the soil to support itself. A very large proportion of the total phosphorus in a soil is typically in an occuluded state (Smeck, 1985; Olander and Vitousek, 2004; Turner et al., 2007; Devau et al., 2011; Gérard, 2016); plants in deficient ecosystems rely heavily on microbial relationships to access this occluded material (Bolan, 1991; Cairney, 2011; Richardson and Simpson, 2011). If the plant community did use the entire soil inventory, no phosphorus would be left for the soil fauna and microbiota.

The particularly high C:P ratios with generally optimal C:N ratios in Subsoil Mulch also suggests phosphorus unavailability. A fundamental concept in marine sciences is the Redfield ratio, which is the ratio of carbon to nitrogen to phosphorus of 106:16:1 found in both seawater and plankton. While terrestrial ecological stoichiometry does not necessarily follow the Redfield ratio (McGroddy et al., 2004), it can provide an indication of the scarcity and availability of a resource. Where carbon to phosphorus ratios are higher then 300, it has been suggested that phosphorus will be immobilised in soil structures (Bui and Henderson, 2013). With carbon to phosphorus ratios higher than 3000, the phosphorus in the Subsoil Mulch treatment is probably in very low abundance, immobilised and unlikely to become available. This carbon to phosphorus ratio may be unusually high, even in the Australian environment. Bui and Henderson (2013) report a median carbon to phosphorus ratio of Australian eucalypt woodlands as 56 and the only values higher than 3000 were the 90th percentile in heathlands. While there were certainly layers of the soil that were not taken into account (for example,

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Wigley et al. (2013) sampled to 50 cm deep), it is likely that deeper soil layers will be primarily spoil. The spoil has additional phosphorus but is a poor medium for plant and microbial growth, and based on the depauperate flora communities of the Spoil treatment it is unlikely that the roots will explore the material for resources. Concern that the Subsoil Mulch treatment may not have the resources to achieve the stature of the RSF is therefore valid and warranted.

The phosphorus levels of the Spoil OGM treatment are also a concern for the opposite reason. The average total kg/ha of phosphorus for the compartments measured in the RSF was 494 kg/ha, while Spoil OGM is 1437 kg/ha, almost three times the amount of phosphorus. As the link between total phosphorus and available phosphorus is context dependent, these comparatively high total levels may not strictly indicate an excess, but they do suggest investigation is warranted (see Chapter 6). Increases in the concentration of a macronutrient at that scale could have ramifications for the outcome of the restoration, although whether that trend is positive or negative depends on the community composition as the minimum requirements of the ecosystem have been met. There are examples of where excessive phosphorus use has reduced species richness and led to an unintended community composition of a restoration site (Dorrough and Scroggie, 2008; Prober and Wiehl, 2012; Daws et al., 2015; Daws et al., 2019; Tibbett et al., 2019). For example, Daws et al. (2019) found that 17 years after a single application of 80 kg/ha of phosphorus, there were significantly fewer native legume species and phosphorus levels were still elevated above reference conditions. As a positive though, the low stoichiometry in Spoil OGM is theoretically considered more likely to contribute to a higher plant growth rate (Ågren, 2008) and therefore may provide an acceleration effect in growth (further examined in Chapter 6). High levels of a macronutrient would, however, probably encourage weed growth in this environment. Spoil is generally undesirable, with higher bulk densities and cations than RSF.

Chloride was also found to be a potentially limiting factor, with almost all treatments in the analysis having lower chloride levels than the RSF. As with the macronutrients, a certain amount of chloride is required to produce healthy vegetation and if the concentration of chloride is too low, then the site may in theory reduce fitness. As a micronutrient, chloride is only needed in relatively small amounts by most plants but is very important for regulation of osmotic and turgor pressures, changing the size of plant cells and can affect photosynthesis

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(White and Broadley, 2001; Franco-Navarro et al., 2016; Raven, 2017). Chloride binds poorly in soils and is primarily deposited naturally in rainfall and sea spray (White and Broadley, 2001), so it is less surprising that the RSF and Ring Rd, older sites, have higher levels in the litter and other vegetation layers. Further, chloride levels are well above the critical values presented in the literature (Schulze et al., 2019). With this mode of accumulation in ecosystems and its micronutrient status, it is likely that chloride will not be an issue in the future.

Subsoil OGM Mulch was moderately dissimilar to RSF overall, but on the Experimental Site it is the next closest treatment to RSF after Subsoil Mulch. The Subsoil OGM Mulch treatment also had more of every element than RSF Biomass, suggesting it has the minimum capabilities without being excessive. With this considered, Subsoil OGM Mulch is likely the best restoration treatment from a total nutrient perspective because, although moderately different, it has sufficient elements. Ring Rd, however, looks to be the best match to RSF, matching closely in many properties. This is most likely due to the quality of the soil applied at initiation of restoration, although time since restoration may have also assisted with chloride levels.

5.4.2 Importance of the Mass of Material Of the six macronutrients examined, four (nitrogen, phosphorus, potassium and sulfur) had significantly higher concentrations of nutrients within the vegetation components (sodium and chloride are not typically considered macronutrients). Yet when this was translated into an abundance (kg/ha), of the compartments examined, vegetation was significantly lower in macronutrients for every case. More than anything else, this strong dichotomy shows the importance of the size of compartments chosen. The 20–30 cm compartment had a mass of 2,494,900 kg/ha, whereas the vegetation that could be sampled and tested (other vegetation <10 mm) had an average biomass of 1060 kg/ha. With measured vegetation at just 0.04% of the largest soil compartment, the concentration levels would have to have been exceedingly high to overcome the mass of material. With that in mind though, the litter layer has come out of this study displaying an ability to compete with the soil in the content of nutrients. The litter <10 mm compartment had approximately 10 times more mass (average 10,480 kg/ha) than the other vegetation <10 mm compartment. Similarly, there was 4.4 times the mass of the >10 mm litter compartment than the other vegetation >10 mm compartment. This meant

185 that, for phosphorus, in a comparison with treatments grouped together, the litter was not different from either of the soil layers.

The sampling strategy involved sampling everything in an area, which has been done by some (Wilhelm et al., 2013) but is not necessarily the norm. Many other published works target specific species or parts of plants (e.g. tree leaves) (Whittaker et al., 1979; Turner and Lambert, 1986; Turner and Lambert, 1996; Paul et al., 2013), making it difficult to compare directly. As has been noted in the literature, concentrations can vary widely depending on where nutrient values are obtained (Augusto et al., 2009). In addition, with distinctly different species and climates, it is not sensible to compare actual nutrient concentrations with other published works. However, the trends seen in these data certainly follow those seen in the literature. Judd (1996) presents data supporting that the highest concentration of nitrogen, phosphorus and potassium are in the foliage of plants rather than the rest of the tree, and wood in particular is quite low in concentrations. Similarly, Whittaker et al. (1979) found that the highest concentrations of nutrients of nitrogen and potassium were in the leaves. Turner and Lambert (1996) present data for forests of eastern Victoria, showing that on a kg/ha scale biomass has 15% of the nitrogen, 3.2% of the phosphorus and 1.3% of the potassium present in the soil. This helps demonstrate why the larger volumes of trees did not contribute as much overall to the estimate of kg/ha.

5.4.3 Change Between Years It is interesting that within the 0–10 cm compartment, there was no change in total nitrogen between the years but that the Subsoil Mulch and Subsoil OGM Mulch had decreased in total

– phosphorus. Depending on its form, nitrogen can be quite mobile in the soil, with NO3 being easily leached in water (Eash et al., 2016). As mentioned in earlier chapters, nitorgen can also be fixed from the atmosphere. It is quite interesting that there is still a difference between the treatments in total nitrogen, with OGM treatments having significantly higher levels. Application of OGM may have not only delivered initially high levels of available nitrogen but also delivered a stable nitrogen source to the ecosystem. In the case of phosphorus, however, there has been a strong reduction in the total reserves over the last 5 years. This reduction was most significant in plots with Subsoil and Mulch applied.

Loss of phosphorus is most likely to occur as a result of erosive forces rather than through leaching because phosphorus binds tightly to particles and organic matter (Heathwaite and 186

Dils, 2000; Eash et al., 2016). However, previous work has shown the Spoil treatment to be the most erosive, with very few gullies forming in Subsoil treatments. As part of the hypothesis, it was thought that the loss of phosphorus from soil could represent a reallocation from the soil to the plant material. But there was no strong evidence for this in the results; the size of the phosphorus pools was generally unrelated to reduction from the 0–10 cm compartment, although the sample size was small. Although there is no statistical significance, the 0–10 cm compartment of Subsoil Mulch treatment did appear to severely deplete in phosphorus between 2014 and 2019 (203 kg/ha to 10 kg/ha based on modelled estimate of the mean). With consideration of the vegetation, however, it matches closely to the 2014 estimate (211 kg/ha). Subsoil Mulch was the only treatment to display this pattern clearly, though. Subsoil OGM Mulch displayed a similar strong drop in soil phosphorus content, but the addition of vegetation only increased the values halfway to their 2014 levels. Conversely, both the Spoil and Spoil OGM showed very little change over the two periods. With lack of consistency, it is difficult to make firm conclusions on what was really occurring. With small plots, however, it could be hypothesised that plants in low-fertility treatments are putting roots into fertile plots to access resources. Eucalypts, in particular, are known to modify root architecture to target nutrient requirements (Graciano et al., 2009). If an overall level of loss is then assumed, this could explain why Subsoil OGM Mulch has lost more phosphorus while Subsoil Mulch has maintained its levels. That, however, does not explain the results for Spoil and Spoil OGM.

5.4.4 Unexplored Inputs While this study put a lot of emphasis on the inability to lose or gain elements according to the law of conservation of mass, a field study is not a closed system. The mining environment can be extremely dusty under certain climate conditions, with dust levels occasionally reaching over national limits and being a concern as a health risk (Higginbotham et al., 2010). There was no significant change between 2014 and 2019 in nitrogen or phosphorus, but that does not mean that other elements were not being added. The majority of dust probably consists of spoil because spoil is the dominant material used for building mine roads; spoil dust is likely to contribute high levels of sodium, magnesium, manganese and calcium. There was, however, a notable, if non-significant, increase in nitrogen on the Subsoil Mulch treatment. This treatment generally has more nitrogen-fixing plant species, which may have

187 contributed to the rise. A question raised by community members about the mine sites is whether the explosives used, ammonium nitrate, are leading to regular low doses of nitrogen deposition. However, this is unlikely because the reaction (2NH4NO3 2N2 + O2 + 4H2O) typically produces stable nitrogen and oxygen gases as well as water (Housec→ roft and Sharpe, 2012). Atmospheric deposition following explosions would then only occur through incomplete combustion. There are many other sources of nitrogen in the surrounding landscape; vehicles, power stations and agriculture can all have a large impact (Vitousek et al., 1997). Significant sources of nitrogen have been identified as part of air pollution in the region (Higginbotham et al., 2010); however, there is minimal evidence of large-scale nitrogen deposition on this site.

The potential widespread deficiency of chloride on the site compared with the RSF would be a key feature examined in any future depositional study. As chloride does not bind well to most soil components, its atmospheric transport from sources such as rainwater and sea spray will be important.

5.4.5 Future Effects of Differences As was seen in Chapter 3, changes in amendments can affect the response of the vegetation to the soil. The Spoil OGM treatment seemed to have more phosphorus, calcium, magnesium, zinc and copper than the reference RSF, when compared as abundance (kg/ha). The Spoil treatment had more calcium, magnesium, sodium, iron, manganese, zinc and copper; as is to be expected, there are many similarities between the Spoil and Spoil OGM. Many of those elements (sodium, iron, manganese, zinc and copper) are required in relatively small quantities by plants and therefore are unlikely to be a key selection factor behind the species composition, although at high concentrations they may become toxic. Calcium can be a limiting resource in some situations, particularly in forests exposed to long periods of acid deposition (Battles et al., 2014). Calcium can also drastically improve the physical structure of sodic soils by reducing the amount of dispersion (Emerson and Bakker, 1973; Rengasamy and Marchuk, 2011). As such, higher long-term concerntration calcium is unlikely to have a significant impact on the community. It is more likely that higher phosphorus levels will have a larger effect on the ecosystem. Although previous studies have shown that nitrogen has a greater limiting capacity in the local mining industry (Nussbaumer, 2005), this ecosystem was most likely be limited by phosphorus. It is still possible that the additional resources in OGM

188 may allow the development of exotic species over the long term. This is a risk that Spoil OGM may have compared with all other treatments.

5.4.6 Successional Maturity and Total Nutrients A common trend in the data was that where Spoil had high levels of an element in the 0– 10 cm layer, RSF comparatively had high levels in the litter. This occurred for the cations calcium, magnesium and sodium as well as the anion chloride. Generally, all other treatments followed a trend where those more similar to the RSF were progressing in a similar manner towards increasing levels in the litter. The strong presence of nutrients within litter could be seen as an indication of the maturity of the ecosystem because the elements are more significant in biotic parts of the ecosystem. Once this occurs, the ecosystem must successfully recycle the resources. Indeed, estimates of global terrestrial nutrient supply suggest that 90% of nitrogen and >98% of phosphorus used for net primary productivity are from recycled sources (Cleveland et al., 2013). Litter therefore could be an important part in the accelerated development of an ecosystem; if use of recycled resources is a dominant method of nutrient acquisition, then treatments with more litter may be better placed to develop faster. Litter becoming a dominant nutrient store is not an idea published widely and does not occur in many ecosystems (Whittaker et al., 1979). The more common view of the dichotomy between early and late succession is that in a late system, the litter is of poor quality with low rates of decomposition and mineralisation (Walker et al., 2007). The RSF does not fit that category compared with the treatments on the Experimental Site because generally there were no differences in the nutrient concentrations of the litter pool between treatments. This may be due to the species contributing the largest biomass being more common in late successional environments within Australia. This does not take away from the importance of litter, which because of its fertility and the potential abundance of seeds, microbes and soil fauna, may be worth further investigation to improve restoration outcomes.

5.4.7 Other Areas for Improvement in the Future It is important to remember that, in this thesis, even though all comparisons are made with RSF, it does not represent a perfect reference. RSF has a history of logging and cattle grazing, which will limit the quality of the results. Logging in particular can remove large amounts of resources when a system is already low in nutrients (Likens et al., 1978). The reference is also only composed of three points spread across an area with diverse flora, fauna and at least

189 two soil types. Further, the RSF had one of the highest levels of trees, >10 mm litter and >10 mm vegetation, which used estimates to produce results. There is a great deal of variation in the nutrient concentrations provided for branches and twigs used in the literature, probably based on the differences in definitions and sampling locations. Although the values chosen were considered reasonable, this is an area where variation could occur depending on the source used. Overall, there are many factors that could be improved.

Although it is widely acknowledged that it is important to sample at depth (Wigley et al., 2013), the difficulties in sampling in spoil prevented successful access to the B horizon in many cases. Based on a visual analysis of the sample, it was obvious that in subsoil plots, the underlying spoil was rarely reached. Conversely, in the Ring Rd, at times it was near- impossible to take the 20–30 cm samples with a manual auger because the spoil sublayers have a high volume of large intact boulders. It was comparable to hitting bedrock. This is probably a leading cause of the high bulk density in the Ring Rd 20–30 cm compartment. This diversity in samples makes interpretation of the sample difficult because different layers were being examined at different sites. However, heterogeneity is part of working in the environment. Ward (2000) found that the effect of ripping, which occurred on all our sites except RSF, is to mix the soil layers together. This can introduce significant variation into the results. Although performing a test in the 10–20 cm compartment may have provided the ability to better detect the difference in horizons, it would not have provided much more information on the Experimental Site. Ideally, a 2 m deep intact core would have been taken and subsampled based on horizon.

5.5 Conclusion This study aimed to use nutrient inventories to determine if the treatments had sufficient resources to attain the community seen on the reference RSF. Although Subsoil Mulch was very similar to the RSF in many respects, it may not have had enough phosphorus to match the reference biomass in the future. This will especially be the case if both plants and microbes avoid deeper layers of spoil where additional phosphorus may be available. Conversely, Spoil OGM had almost three times as much phosphorus per hectare than the reference ecosystem. The best balance could be found in the Subsoil OGM Mulch, which although moderately dissimilar, had the elementary capital to match the reference while not being excessive.

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Chapter 6 – Are Critical Ecosystem Processes Occurring at an Accelerated Rate?

6.1 Introduction

6.1.1 Acceleration Through Fertility Many important characteristics and processes in a restored ecosystem take time to develop. For example, Lindenmayer and Laurance (2017) identified that large old trees can perform many functions and processes that young and small trees cannot, including:

• redistributing nutrients and water vertically and spatially through their deep and extensive root systems (Attiwill and Leeper, 1987; Ludwig et al., 2004) • supporting epiphytic bryophytes and cyanobacteria that contribute to nitrogen budgets (Lindo and Whiteley, 2011) • altering water availability and micro-/meso-climatic environments with their canopy and evapotranspiration (Nepstad et al., 1994; Rigg et al., 2002; Ludwig et al., 2004; Chen et al., 2019) • providing habitat to various forms of life (Dean et al., 1999; Watson, 2001; Thor et al., 2010; Kartzinel et al., 2013) • having both suppressive and promoting effects on other plants (Tewksbury and Lloyd, 2001; Lutz et al., 2013) • contributing to carbon stocks (Stephenson et al., 2014) • providing large pieces of coarse woody debris (Killey et al., 2010) • providing key food sources for animals (Law and Chidel, 2009; Felton et al., 2010) • producing increased numbers of propagules (Wenk and Falster, 2015).

By their nature, large old trees have been growing for a long time. Although trees require a long time to develop, their size and rate of growth can be influence by environmental conditions, which has a large effect on productivity and mortality (Healy et al., 2008). As slow development of plant characteristics, such as reproductive maturity, is a key factor explaining the long time frames involved in forest restoration (Jones and Schmitz, 2009), influencing plant growth and development could accelerate the restoration process. By accelerating plant development, other processes missing in young restoration forests such as decaying coarse timber and tree hollows (Koch and Hobbs, 2007) will occur sooner. The benefits that

191 accelerated development could bring, such as the characteristics of large old trees, could be beneficial to the restored ecosystem. One of the most common ways of trying to accelerate growth, particularly in agriculture, is to ensure optimum soil fertility by minimising nutrient limitation. This allows plants to achieve optimum growth of shoots, fruits and seeds throughout their lives. As the theories of mineral nutrition developed through the 1800s, the Sprengel–Liebig law of the minimum became understood as a guiding principle for what would improve the growth of a plant (van der Ploeg et al., 1999). Where the concentration of an element was low enough to constrain growth, then the addition of a supplement containing that element could restore optimal growth conditions. In Chapter 5, the total nutrients of the sites were assessed. This only provides an indication of what is present; there is no information on how much of that element is in an accessible form. Comparatively, this chapter examines the resources available to the plants, a form which has been assessed based on the continued growth of plants (Holford et al., 1985; Holford and Doyle, 1992). Nutrient limitation is not just an agricultural problem; it is a global condition reducing plant productivity by 16–28% when averaged across all terrestrial ecosystems (Fisher et al., 2012). Sustained plant productivity and soil fertility are positively correlated (Delgado-Baquerizo et al., 2017) and experiments have shown that fertilisation of natural areas increases plant productivity similarly to agricultural areas (DiTommaso and Aarssen, 1989). This not only occurs in plants but also occurs in soil microbes, with fertilisation spurring growth and carbon- use efficiency (Poeplau et al., 2019). Further, the removal of a single limiting factor, such as provision of nitrogen where nitrogen is the limiting resource, can have large flow-on effects overall. Johnson and Turner (2019) surmised, based on fertilisation in forestry, that adding limiting nutrients can increase productivity, which can lead to greater cycling of other elements in the system. If the soil can continually provide the resources needed by plants, then their growth and development will be able to proceed at its maximum rate. However, encouraging growth to accelerate native restoration by adding fertiliser is not necessarily a long-term solution in many settings, practically, ecologically or economically, so methods of manipulating natural processes should be investigated.

6.1.2 Increasing Nutrient Cycling Rates Natural processes that produce available nutrients include weathering of minerals in soil and parent material, atmospheric deposition, and decomposition of organic matter. To a large

192 degree, the parent material determines a soil’s natural fertility (Augusto et al., 2017). Unfortunately, in the Hunter Valley’s mining environment, the spoil, which becomes the parent material, is known to be a poor growth medium (Nussbaumer et al., 2012). By adding amendments to the spoil, however, reliance on the parent material is reduced (Larney and Angers, 2012). These amendments could be targeted to allow for greater facilitation of other nutrient-releasing processes. For example, a product high in nutrients and organic matter may increase the rate of nutrient release from processes such as decomposition.

The use of organic ameliorants has a long history in agriculture and their desirability has increased recently because of their contribution to the circular economy as a waste resource (Razza et al., 2018). Products such as OGM (Organic Growth Medium) decompose and mineralise the organic matter to release available nutrients (Quilty and Cattle, 2011). An organic-rich product such as OGM, therefore, may continue to decay after application, creating the potential for a medium-term supply of available nutrients. Indeed, even short- term increases in resources can provide a developing ecosystem with longer-term benefits. For example, From et al. (2015) found that nitrogen applied prior to forest harvest and regeneration was associated with increased soil nitrogen mineralisation rates and availability, increased plant nitrogen concentration and increased growth rates 10 years after treatment.

Decomposition breaks down large structures of plants, animals and microorganisms into smaller molecules that can be used by other organisms, bind to soil particles or leach from the system. Release of nutrients can be variable from freshly deceased material. Percolating water can leach considerable amounts of dissolved organic carbon and nitrogen from material, which is available for both plant and microbial uptake (Neff et al., 2003). However, the larger volume of material in a dead branch or leaf is not readily available, because it is often locked in complex macromolecules and structures. The material can have a high ratio of carbon to nutrients (Thomas and Packham, 2007) as plants withdraw resources from the material for internal recycling before abscission (Freschet et al., 2010; See et al., 2019). Soil fauna have an important role in the early stage of decomposition where they mechanically degrade material and change surface area, pH, redox reactions, types of polysaccharides and polyphenols, abundance of lignin, and carbon to nitrogen ratios of material (Frouz, 2018). Because of the low nutritional output from eating raw materials, many soil fauna either have a long gut or perform coprophagy, the consumption of their own faeces, which is thought to

193 provide additional opportunities to scavenge resources from the degrading material (Frouz, 2018). As the material degrades, it provides a greater opportunity for microorganisms that, because of their wide variety of enzymes, can better degrade many macromolecules (Bani et al., 2018). Throughout the degradation process, the organic material becomes simpler in structure (Thomas and Packham, 2007) and, because of respiration by the consuming organisms, the relative amount of nutrients increases compared with carbon (Manzoni et al., 2008). It is the finer organic matter from degraded plant matter, as well as dead soil fauna and microorganisms, that supports plant growth.

The amount of resources released from litter depends on the concentration of nutrients in freshly senesced leaves, which is often related to the nutrient concentration of leaves on the plant (Osborne et al., 2020). Increasing the concentrations of nutrients in leaves is often related to the fertility of the soil. This strongly suggests the presence of positive feedbacks within the ecosystem (Hobbie, 1992; Hobbie, 2015) that can promote a higher fertility cycle (Thomas and Packham, 2007). Specifically, increasing soil fertility may increase plant nutrient concentrations and productivity, leading to increased quality and quantity of organic matter for decomposition, thereby resupplying the available resources in the soil and increasing plant growth. The benefit of accelerating the nutrient cycle would be in the increased availability of resources, which could not only support the flora but also support the diverse range of fauna and microorganisms. Conversely, the rate of nutrient flow within a system can also lead to impoverishment of soils if the processes do not cycle enough material, as occurs with exotic species in the Columbian Andes (Ramírez et al., 2014).

6.1.3 Question and Hypotheses As OGM is a high-nutrient product, which could allow continued decomposition and release of nutrients as the ecosystem develops (see sections 2.2.1.2 and 2.3.5), it may have a significant impact on the rate of ecosystem processes. This chapter asks:

Are critical ecosystem processes (i.e. cycling of nutrients, decomposition and plant growth) occurring at a greater rate in a treatment with high organics and nutrients?

Specifically, to assess nutrient cycling rates, this chapter aims to investigate the following ecosystem characteristics and processes: nutrient availability, organic matter decomposition, tree growth rates and ecosystem biomass development.

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It is hypothesised that: (i) each soil ameliorant treatment will influence the ecosystem processes occurring on the site; and (ii) one or more of the treatments will have a greater measurable effect on the examined ecosystem processes than the others. As OGM has been shown in previous work to have very high nutrient contents, it is expected to result in higher levels of available nutrients, faster decomposition rates and higher growth rates. Conversely, as Subsoil Mulch has been shown in previous work to be nutrient poor, it is expected to have lower levels of available nutrients, decomposition, growth rates and biomass.

6.2 Methods

6.2.1 Treatments Used in Study Similar to Chapters 4 and 5, this study was performed on a reduced number of treatments: Spoil, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch. The Ring Rd and RSF (Ravensworth State Forest) references were used in all sections of this study. Although Rav Ref is the closest native vegetation to the site, it has a very different plant community and was only used to examine decomposition.

6.2.2 Data from Other Chapters Data presented on biomass are the same as presented in Chapter 5. However, the data have been redrawn to be appropriate for this chapter. The statistics and methods relevant to this analysis have also been previously described and will only be mentioned in this chapter.

6.2.3 Available Nutrient Content, pH and Cation Exchange Capacity The sampling and processing of soil and plant material was as described in Chapter 5, see methods sections for details.

pH was determined by SESL Australia in 1:5 water by Rayment and Lyons (2011) method 4A1. Water-soluble nitrate was measured by Rayment and Lyons (2011) method 7B1a. Effective cation exchange capacity (CEC) and exchangeable bases were determined by Rayment and Lyons (2011) method 15A1. All other available soil nutrients were measured by Rayment and Lyons (2011) method 18F1, Mehlich 3 extractable elements.

6.2.4 Decomposition To investigate the decomposition process, we used the tea bag index (Keuskamp et al., 2013). This new method has many advantages, as previous techniques were affected by considerable variation in source species, enclosure, depth of burial (or lack of), incubation times, pre-

195 treatment and post-treatment. Unlike previous methods, the tea bag index is standardised and can be used anywhere in the world independent of vegetation type, habitat or climate and with minimal financial constraints. Green tea consists of a high proportion of water- soluble material and has a low carbon to nitrogen ratio (12:1), making it decompose very quickly. Rooibos tea consists of a large amount of acid in soluble material and has a moderately high carbon to nitrogen ratio (43:1); this makes it harder to break down and it decomposes very slowly. Using the two teas provides a picture of an ecosystem’s ability to degrade material (green tea) and the rate at which decomposition occurs (rooibos tea).

Lipton green tea (EAN 8714100770542) and rooibos tea (EAN 87 22700 18843 8) as per the Teabag Index website (www.teatime4science.org) were ordered for this study. The ordering period coincided with a change in branding of green tea by Lipton (see Figure 6.1, left); Lipton (Unilever) were contacted to ensure that there was no change to leaf composition. Green and rooibos tea were separated into randomised pools and 11 bags of each tea type were randomly assigned to a plot. Bag 0 was used as a time 0 reference and not buried in the ground. On site, separate trenches were dug for the green and rooibos teas, separated by at least 1 m. Trenches were used to keep the samples in a confined space, homogenise conditions within each plot and easily locate each bag. Each trench was 8 cm deep and approximately 60 cm long. Green tea was arbitrarily placed in the uphill trench and rooibos placed in the downhill trench. Trenches were oriented along rather than across the contour. Ten bags were placed in the trenches so that they were separated by at least 1 cm between each bag. Replacement of the covering soil and litter was performed to mimic the original characteristics of the environment (Figure 6.1, right). A single bag was carefully extracted after 1, 2, 3, 4, 6, 8 and 10 weeks and the remaining three bags were removed after 90 days on the Ring Rd and RSF, or 91 days on the Experimental Site. On removal, bags were transported to the Hancock Soil Laboratories at the University of Newcastle, where they were dried at 70°C and carefully cleaned of soil, roots and obvious microbial masses. Tea was buried on 20/08/2018 and collection was completed by 19/11/2018, which largely coincided with the southern hemisphere spring.

In total, 638 bags were used for this study, of which 626 bags were fully recovered. Four bags were unable to be recovered (potentially taken by animals) and eight bags had large holes

196 with an expected loss of material. Where smaller holes were found in the bags, extreme care was taken to prevent loss and to capture all material.

Before the experiment began, every bag was weighed and numbered to track loss over time. Unfortunately, the numbered tags deteriorated, making that system unusable. For this reason, results of weights are presented as mass after loss rather than percentage of mass lost. To calculate the fraction decomposed, all masses were subtracted from the grand mean of all tea bags initial mass within their type, green or rooibos. The mean initial mass of 666 green tea bags was 2.051 g with a standard deviation of 0.062 g. The mean initial mass of 680 rooibos tea bags was 2.235 g with a standard deviation of 0.054 g.

Every bag was weighed, including the string and tag, to the nearest 0.001 g. To determine S and k variables, only the tea (not including the bag or string) from the 90 or 91 day samples were examined. Keuskamp et al. (2013) describes S, the stabilisation factor, as:

= 1 𝑎𝑎g 𝑆𝑆 − 𝐻𝐻g where ag is the fraction of green tea that decomposed, and Hg is the hydrolysable fraction of tea that has been predefined as 0.842. The decomposition speed, k, is found following:

(1 ) = r 𝑎𝑎 𝑙𝑙𝑙𝑙 � �𝑊𝑊t − − 𝑎𝑎r � 𝑘𝑘 where ar is the fraction of rooibos tea predicted𝑡𝑡 to be labile found by multiplying the predetermined hydrolysable fraction of rooibos tea (0.552) by 1 –S, Wt is the fraction of rooibos tea remaining after decomposition, t is the incubation time in days (90 or 91 days) and ln is the natural logarithm.

As recommended by Keuskamp et al. (2013), the tea then underwent loss on ignition at 550°C for 16 hours (Rayment and Lyons, 2011). It was then reweighed to the nearest 0.001 g.

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Figure 6.1. Left: Boxes of tea used in study. Right: soil and litter were replaced as accurately as possible when burying the tea (photo from RSF B5). Red boxes highlight the location of tea trenches, with green at the top of the image and rooibos at the bottom.

A similar but larger tea decomposition experiment using soils from the Experimental Site and the references was also performed in a glasshouse setting. Treatments included Spoil, Spoil OGM, Subsoil Mulch, Subsoil OGM Mulch, Rav Ref, Ring Rd, RSF, pure OGM and twice- autoclaved sand. There were three replicate pots containing soil of each treatment. The experiment placed nine bags in a pot, each separated by a minimum of 1 cm of soil from a treatment. Pots were covered by a fine mesh designed for frost protection to prevent fungus gnats entering the pots. Pots were randomly placed within the glasshouse and every fortnight

198 they were moved to another random location. Bags were progressively removed at random following similar timing to field samples, dried and weighed. However, the irrigation system did not deliver water equally to all pots. Additionally, the mesh did not allow equal application of water to the soil, often resulting in the soil on one side of the pot being wet while the other side was dry. Attempts to rectify the issues by alternate placement of pots and alternating the way pots were covered with the mesh did not appear to change the consistency of water availability in the pots. This issue was not found in pre-experiment trials. As water availability is a major factor in weight loss, particularly for green tea, this meant that some replicates within treatments would unpredictably show dramatic changes in mass while others would show negligible change. This resulted in extreme variations in mass both within and between replicates and treatments. As samples were moved regularly and the position of the mesh was changed after sampling, the treatment and replicate affected could not be predicted with enough certainty to control or allow exclusion of affected samples. These data are not reported or considered further.

6.2.5 Statistics 6.2.5.1 General Statistics As in Chapter 5, linear mixed models (LMM) were performed in JMP Pro 14 (JMP®, Version Pro 14. SAS Institute Inc., Cary, NC, 1989–2019). Sodium, chloride, phosphorus, sulfur, magnesium, iron, copper, boron and zinc all received fourth-root transformation before analysis occurred. Nitrate, potassium, calcium, manganese, pH and effective CEC did not require transformation to meet LMM assumptions. Pairwise comparisons were performed with Tukey–Kramer test; this is typically the value reported if the LMM produced a significant result. Treatment and Compartment were considered fixed effects whereas Block was a random effect. Comparisons between years had Year and Treatment as a fixed effect and Block as a random effect.

Many samples had extractable phosphorus levels below the limit of detection. A conservative approach was taken and all of these data points used 5 mg/kg, their maximum possible value.

Although tea bags were sampled regularly throughout the experiment, decomposition was examined at 4 weeks and at 90 or 91 days because green tea showed a strong levelling off after 4 weeks. Loss on ignition was transformed with the natural logarithm; the other

199 decomposition variables did not require transformation to meet LMM assumptions. LMM and Tukey post-hoc test were used to determine differences between treatments for loss of bag weight, loss on ignition, S and k.

6.2.5.2 Tree Growth As was discussed in Chapter 3, OGM treatments had significantly taller trees during both the 2015 and 2018 surveys. Detailing whether the trees of a treatment continued to increase in height during the intervening period, however, is more difficult because trees were not measured if they were less than 150 cm height in 2015. Trees were also not tracked between surveys; therefore, it was uncertain which tree is which. This information is important for determining if there was sustained tree growth several years after restoration began. To estimate differences in height increase after the 2015 survey, trees in both surveys were ordered from highest to lowest. Following the assumption that the tallest individual of a species in a plot in 2015 was still the tallest individual in the 2018 survey, individuals between the two surveys were linked to determine the change in height. The lack of detail on measurements below 150 cm in 2015 means that trees were not considered if they were less than 150 cm in 2015. Fore example, in 2015 a plot had four trees: A, B, C and D with respective heights of 260, 180, 160 and <150 cm (Table 6.1). The same plot in 2018 had trees W, X, Y and Z with respective heights 260, 170, 420 and 160 cm. It would be assumed that tree A linked with tree Y as they were both the tallest in the plot, tree B linked with W and tree C with X as that is the order of height. Trees D and Z would be excluded as D has no known height in 2015.

Table 6.1. Hypothetical example of how ordering was used to link trees for examination of tree height change between 2015 and 2018.

Tree label Height Tree label Height Assumed change in 2015 2015 (cm) 2018 2018 (cm) height (cm) A 260 Y 420 160 B 180 W 260 80 C 160 X 170 10 D <150 Z 160 Excluded

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Performing the LMM with Treatment as a fixed factor while both Block and Species of tree as random factors showed very poor fit because of the high level of variation explained by tree species. The final model used had both Treatment and Species of tree as fixed effects and Block as a random variable. These data provide a reasonable estimate of height change, unless an undocumented event, such as high death rates in taller trees occurred. Despite its limitations, the information provided gives an indication of whether increased growth is still occurring in the absence of any other available dataset.

6.3 Results

6.3.1 Available Nutrients Full results for the available nutrients can be found in Appendix D. In summary:

• Spoil had significantly higher levels of available sodium and chloride on the Experimental Site. Spoil was also significantly higher in magnesium than Subsoil Mulch and Subsoil OGM Mulch. • Spoil OGM had significantly higher levels of phosphorus (Figure 6.2) and potassium than Spoil and Subsoil Mulch. Spoil OGM was significantly higher in copper, zinc and boron than all other treatments except Subsoil OGM Mulch. Available calcium was elevated in the 0–10 cm compartment of Spoil OGM but not significantly different. • Subsoil Mulch was comparatively low in all available nutrients. • Subsoil OGM Mulch showed significantly higher phosphorus levels than Spoil and Subsoil Mulch. Boron levels were significantly higher in Subsoil OGM Mulch than all other treatments except Spoil OGM. Zinc levels were also significantly higher than in Subsoil Mulch and RSF. • Ring Rd had significantly higher available iron levels than all treatments except RSF, which was significantly higher than Spoil OGM and Spoil. Both Ring Rd and RSF had elevated but not significantly different available potassium. RSF also had elevated available magnesium at depth. • Nitrate, sulfur and calcium levels showed no significant difference between treatments. • The findings for extractable phosphorus were slightly different from the results of the total phosphorus. While both total and extractable phosphorus were high in the Spoil

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OGM treatment, the moderate total phosphorus treatment effect shown for Spoil and Ring Rd (Figure 5.9) was not observed for extractable phosphorus. Subsoil OGM Mulch, which had moderate total phosphorus values, had relatively high extractable phosphorus. There was a significant correlation between total and extractable phosphorus in the 0–10 cm soils (F(1,16)=26.53, p<0.0001, R2=0.62) but no relationship at the 20–30 cm depth (F1,16)=0.006, p=0.94, R2=0.0). The lack of relationship at the greater soil depth was due to all but three samples being below the limit of detection.

Figure 6.2. Untransformed extractable phosphorus. Analysis was performed on fourth-root transformed data. In this graph, data below the limit of detection have been given the minimum value of detection (5 mg/kg indicated on the y-axis). Letters indicate significant differences following pairwise Tukey tests with interaction between depth and treatments., where treatments that do not share the same letter are considered significantly different.

6.3.2 Other Soil Chemistry Summary of findings:

• Spoil had a high pH (pH of 1:5 water was 9–10). • Subsoil Mulch and Subsoil OGM Mulch pH ranged from 6.6 to 9.2 (1:5 water).

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• Soil from RSF was moderately acidic (pH of 1:5 water was 5–6). • CEC was variable and did not show a statistical difference.

Although CaCl2 tests for pH are more reliable (Miller and Kissel, 2010), tests on water are more common in the local area and therefore data from both tests are presented here. However, the findings derived from the two analyses are essentially the same, so only the pH of 1:5 water results are discussed. There was no difference between year in soil pH measured in water (F(1,14)=3.6, p=0.0784). Soil pH in water was significantly higher in Spoil and Spoil OGM than in all other treatments (Spoil OGM to Subsoil OGM Mulch: t=5.5, p=0.0004) (Figure 6.3). RSF was significantly lower in pH than all treatments (Ring Rd to RSF: t=4.43, p=0.0038). However, the 0–10 cm sample of Ring Rd was significantly lower than the 20–30 cm sample (t=6.3, p=0.0002) and, when compared as an interaction, not different from RSF (Ring Rd 0– 10 to RSF 0–10: t=0.48, p=1).

Figure 6.3. 2019 Soil pH in 1:5 water and pH 1:5 in calcium chloride by depth. Letters indicate significant differences following Tukey pairwise tests with interaction between depth and treatments, where treatments that do not share the same letter are considered significantly different. Separate Tukey pairwise analyses were run for pH of 1:5 water and pH of 1:5 calcium chloride.

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Soil effective CEC was variable and did not show a statistical change between year (F(1,14)=0.03, p=0.8595). There was no significant difference in effective CEC between treatments (F(5,16)=2.24, p=0.0995), depths (F(1,19.3)=0.09, p=0.765) or interactions (F(5,19.3)=0.26, p=0.93) (Figure 6.4).

Figure 6.4. Effective CEC (sum of exchangeable Ca, Mg, K, Na and Al) for the two depths measured. Letters indicate significant differences following Tukey pairwise tests with interaction between depth and treatments where treatments that do not share the same letter are considered significantly different.

6.3.3 Decomposition Summary of findings:

• Green tea degraded rapidly over 4 weeks, while rooibos tea continued to degrade over the length of the experiment. • Green tea degraded more in Rav Ref than in almost all other treatments, suggesting some characteristic of the Rav Ref ecosystem was facilitating decomposition.

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• Spoil showed an extremely low rate of decomposition.

The study was performed over a relatively normal period of climate for the area (Figure 6.5). Mean cumulative rainfall for the months of August, September, October and November over 60 years at the Bulga Down Town station is 194.5 mm. During the same period as the decomposition study, the nearby Bureau of Meteorology measuring station in Bulga received 172.2 mm of rain. Comparing monthly means to other stations in the area, including Lostock and Tocal, showed minimal differences from long-term averages.

Climate During Decomposition Study 40 40 35 35 C) 30 30 25 25 20 20 15 15 10 10 Daily Temperature ( ° 5 5 Daily Precipitation (mm) 0 0

Date

Maximum Temperature (°C) Minimum Temperature (°C) Daily Rainfall (mm)

Figure 6.5. Maximum temperature, minimum temperature and rainfall over the period of the decomposition experiment. Temperature data come from Singleton Army Base, approximately 33 km south-south-east of the study site. Rainfall data come from Bulga Down Town 25 km south of the Experimental Site. Both stations are verified by the Bureau of Meteorology.

As is to be expected, the two tea types behaved in very different ways; green tea degraded quickly over the first 4 weeks, whereas rooibos degraded slowly over time (Figure 6.6). Because of the change in loss over time, the data were analysed in two parts: loss over the first 4 weeks and loss by the end of the study.

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During the first 4 weeks, the green tea in the RSF lost significantly more mass than green tea in all other treatments except Spoil and the Ring Rd (Subsoil Mulch to RSF: t=3.58, p=0.0119). The rooibos had significantly less loss in Spoil over the first 4 weeks than in all other treatments (Spoil to Subsoil Mulch: t=3.18, p=0.0353). Additionally, in the rooibos tea, Ring Rd and RSF lost significantly more mass than all other treatments except Rav Ref during the first 4 weeks (Subsoil OGM Mulch to RSF: t=3.19, p=0.0345).

Figure 6.6. Change in mass of tea over time with moving average lines.

At the end of the experiment (90 days on Ring Rd and RSF and 91 days on the Experimental Site and Rav Ref), there was significantly less green tea present in the Rav Ref than all sites except Spoil OGM (RSF to Rav Ref: t=4.01, p=0.0029) (Figure 6.7). Rooibos tea was similar among all treatments except Spoil, which lost minimal weight (Spoil to Spoil OGM: t=6.46, p<0.0001) (Figure 6.8). The mean mass of bagged rooibos tea prior to the experiment was 2.23 g while mean mass of bagged tea in Spoil following the experiment was 2.02 g.

Loss on ignition tests following decomposition for 90 or 91 days showed that Spoil had a significantly higher percentage of ash in both the green and rooibos tea (green, Spoil to Spoil OGM: t=3.81, p=0.0085; rooibos, Spoil to Subsoil Mulch: t=5.61, p<0.0001) (Figures 6.9 and

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6.10). RSF had lower levels of ash in the green tea than any treatment on the Experimental Site (Subsoil OGM Mulch to RSF: t=3.15, p=0.0465). Rooibos tea from the Ring Rd showed lower ash levels than in Subsoil Mulch or Spoil (Subsoil Mulch to Ring Rd: t=3.31, p=0.0286).

Figure 6.7. Mass of green tea without the bag after 90 or 91 days in the ground. Letters above the graphs show statistical differences following Tukey post-hoc test with treatments considered significantly different if they do not share a letter.

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Figure 6.8. Mass of rooibos tea without the bag after 90 or 91 days in the ground. Letters above the graphs show statistical differences following Tukey post-hoc test with treatments being different if they do not share a letter.

Figure 6.9. Percent mass remaining following loss on ignition of green tea at 550°C after 90 or 91 days in the ground. The letters above the graph indicate Tukey post-hoc test on the LMM, using data transformed with the natural logarithm. The rest of the figure is based on untransformed; therefore, 95% confidence of the mean is a poor fit.

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Figure 6.10. Percent mass remaining following loss on ignition of rooibos tea at 550°C after 90 or 91 days in the ground. The letters above the graph indicate Tukey post-hoc test on the LMM, using data transformed with the natural logarithm. The rest of the figure is based on untransformed; therefore, 95% confidence of the mean is a poor fit.

Following from Keuskamp et al. (2013), variables S, the stabilisation factor, and k, the decomposition rate, were determined for each plot. Rav Ref had an extremely low stabilisation factor, lower than all other treatments except Spoil OGM (RSF to Rav Ref: t=3.87, p=0.0052) (Figure 6.11). Lower values of S indicate that more material can be degraded overall. Spoil had significantly lower k values than all treatments except Rav Ref (Spoil OGM to Spoil: t=3.28, p=0.0332) (Figure 6.12). Higher k values indicate that decomposition is occurring at a faster rate. It should be noted that one sample from RSF and another from Subsoil OGM Mulch had very high levels of rooibos decomposition compared to green tea. This led to a failure in the computation of k for these replicates, so they were removed from the analysis; had they been included, they would have probably strengthened the difference between these treatments and Spoil.

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Figure 6.11. Stabilisation factor, S, as determined by the method of Keuskamp et al. (2013). Letters above the graphs show statistical differences following Tukey post-hoc test with treatments being different if they do not share a letter.

Figure 6.12. Decomposition rate, k, after 90 or 91 days of green and rooibos tea in the ground following Keuskamp et al. (2013). Letters above the graphs show statistical differences following Tukey post-hoc test with treatments being different if they do not share a letter.

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6.3.4 Growth of Trees Summary of findings:

• No significant change in estimated growth of trees between 2015 and 2018 was found between treatments.

The dominant feature of the change in height data was the difference between species; surveyed; E. moluccana had the greatest increase, followed by C. maculata, with no difference between A. floribunda or E. crebra (C. maculata to E. crebra: t=3.46, p=0.0042) (Figure 6.13). There was no significant difference between treatments (F(2,111.3)=0.13, p=0.8707). Interestingly, the least-square mean estimates for Spoil OGM is an increase of 195.5 cm between 2015 and 2018, for Subsoil Mulch is 195.8 cm and for Subsoil OGM Mulch is 204 cm.

500 Species

A. floribunda Sample A. floribunda Mean 400 A. floribunda 95% Confidence C. maculata Sample C. maculata Mean 300 C. maculata 95% Confidence E. crebra Sample E. crebra Mean E. crebra 95% Confidence 200 E. moluccana Sample E. moluccana Mean E. moluccana 95% Confidence

100

-100

Spoil OGM Subsoil Mulch Subsoil OGM Mulch

Treatment / Species

Figure 6.13. The heights of trees in the 2018 survey were deducted from the heights in 2015 to give an approximate metric of change between treatments. Overall, the strongest effect was a difference between species, Eucalyptus moluccana having the greatest change in height, followed by Corymbia maculata with no difference between Eucalyptus crebra and Angophora floribunda.

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6.3.5 Level of Biomass Summary of findings:

• RSF and Ring Rd had the highest litter biomass and estimated tree biomass. • There was a strong association between biomass of litter and trees. • No difference was found between treatments of biomass from other (non-tree) vegetation.

Litter biomass was highest in the RSF and Ring Rd, where it was significantly different from Spoil (Ring Rd to Spoil: t=3.49, p=0.0254) (Figure 6.14). RSF and Ring Rd also had the highest tree biomass (Ring Rd to Subsoil OGM Mulch: t=5.35, 0.0032) (Figure 6.15). There was no significant difference between other (non-tree) vegetation (F(5,9.9)=1.6, p=0.2556) although it is interesting that Spoil OGM and Subsoil OGM Mulch generally had a higher average biomass (Figure 6.16).

There was a strong association between the level of biomass in the litter and the biomass of trees (F(1,16)=32, p<0.0001) (Figure 6.17). There was no similar relationship between the litter biomass and other vegetation biomass (F(1,16)=1.12, p=0.3049) (Figure 6.18).

Figure 6.14. Biomass of litter on different treatments. Letters above the graph indicate significant differences between treatments where treatments that do not share the same letter are considered significantly different.

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Figure 6.15. Biomass of tree species. Letters above the graph show significant differences, with analysis performed on fourth-root transformed data.

Figure 6.16. Biomass of other vegetation per treatment. No significant differences were found between treatments.

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Figure 6.17. Linear regression between litter biomass and tree biomass.

Figure 6.18. Linear regression of litter biomass and other vegetation biomass.

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6.4 Discussion

6.4.1 (Lack of Accelerated) Nutrient Cycling It had been previously identified that shortly after placement, the treatments with OGM were very high in available nutrients (Scanlon, 2015; Castor et al., 2016). In Chapter 5, it was seen that the concentration of total phosphorus, in particular, was high in the Spoil OGM treatment for both the vegetation and litter compartments. Similarly, in this chapter, the Spoil OGM had very high extractable phosphorus in soils closely followed by Subsoil OGM Mulch, again attributed to the OGM application. Conversely, levels of extractable phosphorus were below detection limits for Spoil and Subsoil Mulch plots. Although low phosphorus conditions are common throughout NSW (Charman and Murphy, 2007), it is unusual for them to be completely deficient. Another finding from Chapter 5 was that both the vegetation and litter of Spoil OGM had elevated phosphorus. This shows that, when available, increased levels of phosphorus can be transported through the different compartments. This would be expected to improve productivity. However, there were few signs of an associated increase in decomposition or growth rates, as was hypothesised to occur with high resource availability.

While Spoil OGM appeared to present a lower S value in the tea decomposition study, the high variability meant it was not statistically different from the other treatments. The S value suggests how much tea could be degraded (Keuskamp et al., 2013) and these results suggest that future studies should test whether more samples would reduce the variability among treatment replicates. The k value indicates how fast the tea degrades. There was a difference in k values but that was for Spoil, which is so dramatically different from all other treatments that deducing a specific reason for the difference is difficult. Compared with k values of 15 sites from a range of ecosystems by Keuskamp et al. (2013), S values were generally moderate, though some values were greater than 0.35 in Spoil and Subsoil Mulch, which is very high, and many of the Rav Ref values were very low at less than 0.1. Similarly, the k values were typically moderate, but Spoil was generally low, less than 0.01, while the outliers above 0.03 in Subsoil Mulch were extremely high. The generally moderate results highlight that decomposition was unrelated to the nutrient limitation of some treatments. The lower levels of percent remaining after loss on ignition in Ring Rd and RSF suggest that perhaps these older, more stable sites had mineralised more of the organic matter. This suggests that the different microbial communities are having a difference in organic matter transformations

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(Garcia-Pausas and Paterson, 2011). However, with only 5–10% difference between treatments (excluding Spoil), the effects of this may be minimal in the short to medium term.

Growth of trees over time on the Experimental Site also showed no significant differences between treatments. This suggests that although OGM once provided very strong growth (Castor et al., 2016), the effect has disappeared. This is unexpected because although growth efficiencies of trees do decline with age (Mencuccini et al., 2005), the leaf mass increases (Bartelink, 1997; Forrester et al., 2017), leading to a continual increase in growth (Stephenson et al., 2014). Generally the larger a tree is, the more potential it has to grow (Stephenson et al., 2014), so it would have been expected that the trees on OGM treatments continued at higher growth rates. The effect of most nutrient boosts are lessened over time because of uptake, immobilisation, leaching and erosion. However, high availability of resources, which is typically related to high cycling rates, has been shown to be linked to high rates of plant growth (Miller, 1981). Perplexingly, the high extractable phosphorus in soils, high vegetation concentrations and high litter concentrations in Spoil OGM could not be linked with high plant growth in this study.

Biomass was examined as another long-term indication of productivity, but showed minimal evidence of accelerated productivity. The only significant difference in biomass on site was the higher levels of tree biomass in the Subsoil OGM Mulch plot. Biomass is not as neat a metric as tree growth because plants die and decompose, losing biomass. Regardless of this, it is one of the most common measures of productivity (Jenkins, 2015). With the exception of the category ‘other vegetation’, Spoil OGM was lower in biomass than Subsoil Mulch in every biomass metric. Given that this study only examined above-ground biomass, and plants divert resources to the root system when nutrients are scarce (Hermans et al., 2006), as is the case for Subsoil Mulch, it would have been expected that Subsoil Mulch would display lower levels of above-ground biomass than Spoil OGM did. The increased biomass of other vegetation in Spoil OGM and Subsoil OGM Mulch suggests that there may be an effect of the OGM increasing biomass of shrubs, grasses and herbs. However, overall there was not any real change. Therefore, it can be stated that none of the data collected for this chapter definitively show a difference in cycling due to the changed plant community or the difference in nutrients between Spoil OGM, Subsoil Mulch or Subsoil OGM Mulch.

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What can be shown, however, is the poor ability of Spoil to cycle nutrients with a lower rate of decomposition (k) than all treatments on the Experimental Site as well as the Ring Rd and RSF. This could be due to the presence of actively inhibitory compounds in the Spoil, but, because of the lack of active response by the tea, it may be more likely due to the lack of an effective microbial community.

6.4.2 Potential Controlling Factors Explanations as to why ecosystem processes were not observed to occur at an accelerated rate in this study, even though there were large differences in fertility, need to consider the potential factors controlling the underlying mechanisms. The following sections explore reasons why ecosystem process may not have been accelerated.

6.4.2.1 Water Availability Moisture has potential to be a major factor, as has been shown for other mine substrates (Bateman et al., 2019). The drought has been mentioned already in this thesis. For the majority of this PhD, Australia has been in the midst of one of the worst droughts in recorded history (Bureau of Meteorology, 2020b). Water availability is known to be a major factor for decomposition (Prescott, 2010; Marley et al., 2019; Fanin et al., 2020) and growth of plants (Yang et al., 2011; Nitschke et al., 2017). To demonstrate the level of effect it could have had on the experiment, consider a single dendrometer log from a Eucalyptus moluccana on a Spoil OGM plot (Figure 6.19). Since January 2020 (mid-summer), there has been a general increase in the amount of rainfall on the site, few large events, but many small and frequent events. This illustrates that from mid-2019 to early 2020, the site was probably in a state of persistence with little active growth. It is likely that the site was in a similar condition for much of the previous 2 years.

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Dendrometer Growth and Rainfall 20 100

18 90

16 80

14 70

12 60

10 50

8 40

6 30 Daily Rainfall (mm)

4 20

Tree Circumference Increment (mm) Circumference Tree 2 10

0 0 16/05/2019 16/08/2019 16/11/2019 16/02/2020 16/05/2020

Dendrometer Increment Daily Rainfall

Figure 6.19. Increase in circumference at breast height on a Eucalyptus moluccana growing on Spoil OGM and rainfall. The dendrometer measured change in circumference at breast height between 16/05/19 and 11/06/20; the starting number is not indicative of the size of the plant. The daily rainfall is from Bulga (South Wambo), a station verified by the Bureau of Meteorology.

Although rain fell during the decomposition study and all treatments were subject to the same rainfall, if the landscape was already extremely dry, these rain events may have not been able to negate the effect of prior longer-term low water availability conditions. This may show itself by smaller differences in decomposition rates between treatments if they have similar soil water absorption and availability as well as if the microbial communities were similarly affected in each treatment. Similarly, the growth of plants over the time period would have been slowed, possibly making it harder to detect significant differences among treatments. It is quite likely then that water availability has been a limiting factor for this study.

6.4.2.2 Nitrogen The importance of nitrogen to ecosystems has been very well documented in many major reviews (Barton et al., 1999; Vitousek et al., 2002; Galloway et al., 2004; Templer et al., 2012; Fowler et al., 2013). Nitrogen is an important limiting resource early in primary successional landscapes, such as following recent volcanic eruptions and glacial till, as it is mostly delivered

218 by atmospheric deposition or fixed by microbes (Vitousek et al., 1997; Walker et al., 2003). As the mining landscape is constructed with sedimentary material, there may be some relictual nitrogen; however, previous work locally has shown nitrogen to be a highly limiting element for restoration of spoil. Nussbaumer (2005) demonstrated through a nutrient omission trial in Spoil, that nitrogen was the most limiting nutrient on the Mt Owen mine (Figure 6.20). Although spoil can be variable, it is likely that the spoil is highly limited by nitrogen initially. That, however, is the purpose behind amelioration and inclusion of nitrogen-fixing species in

– restoration (Fisher, 2010). In this study, the levels of soil nitrate (NO3 ) were generally moderate with the exception of the RSF, which was very depleted. Soil nitrate did not seem to relate to the differences in nitrogen-fixing species, which were common in Subsoil Mulch, Subsoil, Forest Topsoil and the RSF. With little difference in soil nitrate, it is unlikely that nitrogen availability was a driving factor in the results of this study.

Figure 6.20. From Nussbaumer (2005), where it is Figure 2.4. Corymbia maculata grown in spoil as part of a nutrient omission experiment. C is the complete range of nutrients provided in a modified Hoagland’s solution at 17 weeks growth. The remaining treatments received the same Hoagland’s solution but with an element, or combination of elements, missing: –N, –P and –K are missing nitrogen, phosphorus and potassium respectively; –Fe, S, Mg, Ca is missing iron, sulfur, magnesium and calcium; –Micro is missing manganese, boron, chloride, zinc, copper and molybdenum; O was provided distilled water only. 219

6.4.2.3 Community Composition The community of plants, particularly canopy species, can be a major factor in the degradation process because they determine the structural properties of the leaf litter. There is a trend for plants from lower fertility locations to produce structural and defensive compounds that can inhibit decomposition (Scott and Binkley, 1997; Hobbie, 2015). Many species in the studied community produce sclerophyllous leaves, which has been related to leaf longevity and sites that are nutrient poor (Aerts, 1999; Aerts and Chapin, 1999). Eucalypt species in particular are known to produce a wide range of defensive compounds to reduce herbivore attack (Eschler et al., 2000). Perhaps, due to adaptations to herbivory, low fertility and low water environments, all the species in this study produce some level of structural and defensive compounds, which would have a major impact on the degradation potential of the area (Freschet et al., 2012).

The biomass of litter on plots showed a strong positive relationship to the biomass of trees on plots, suggesting trees were a major contributor to the litter characteristics. Fanin et al. (2020) found that the identity of dominant tree species could have a large effect on the decomposition of low-quality litter. The lack of variation in decomposition may therefore be due to the similarity in litter characteristics produced by the dominant tree species, which are all eucalypts. However, as shown in Chapter 3, eucalypts were in low numbers on Spoil OGM plots; instead, members of the Chenopodiaceae family were much more common. Chenopods are commonly associated with saline and arid environments, which can have high levels of water stress (Clarke and Lee, 2004). As such, chenopods tend to have small fleshy leaves with a higher leaf mass per area, a trait typically associated with longer living and less nutritious leaves with lower photosynthetic capability (Wright et al., 2004). This would also align with lower production of litter that has a higher recalcitrance to decomposition. The slightly lower S value on Spoil OGM suggests a reduction in recalcitrance to decomposition compared with other treatments but the effect was not strong enough to show an overall change in the ecosystem’s ability to cycle nutrients.

An alternative explanation for the similar levels of biomass in Spoil OGM and Subsoil Mulch is that there is much higher biodiversity in Subsoil Mulch. Increased biodiversity has been shown to increase productivity and biomass (Cardinale et al., 2013; Tilman et al., 2014). The significance of biodiversity increase in productivity should not be ignored, as Tilman et al.

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(2012) found that increasing plant richness from four to 16 species produced an increase in productivity equivalent to the addition of 54 kg/ha/year of nitrogen. Perhaps the biodiversity levels were high enough in Subsoil Mulch to match the increased fertility of Spoil OGM. Further, the increased biomass in Subsoil OGM Mulch may be the result of both increased fertility and increased biodiversity.

Perhaps in future studies of litter decomposition in the context of accelerated ecosystem development, the community distribution on the plant economic spectrum should be considered. The plant economic spectrum describes a wide range of plant functional traits in relation to how they support a species’ interaction with its environment, conspecifics and other species (Wright et al., 2004; Santiago, 2007; Freschet et al., 2012; Donovan et al., 2014; Reich, 2014). When selecting from a specific community, particularly one as restricted as an EEC, the reintroduced species pool may not include all functional trait. For example, the functional trait to associate with nitrogen fixers is known to benefit restoration (Nezomba et al., 2008; Chaer et al., 2011). There could also be considerable benefit from increasing variation in, for example, stem hydraulic conductivity or specific leaf area traits. The species pool has been identified as a potential limiting factor to the development of the ecosystem (Foster et al., 2011).This is a further reason why including ancillary species in restoration, not only to increase biodiversity, may be beneficial.

6.4.2.4 Phosphorus As stated above, phosphorus is probably not controlling the rate of decomposition, even though it displays the trademarks of a limiting nutrient. It is worthwhile exploring why this is.

The concentrations of phosphorus in spoil of coal mines is varied and probably related to the depositional landscape of the strata. Kent (1982) reviewed impediments to plant growth on British colliery spoil heaps and found that low phosphorus levels was a widespread issue, exacerbated when conditions became acidic. Miller et al. (2012) found variability in phosphorus based on spoil material of the Appalachian region, with brown sandstone having much higher phosphorus levels than grey sandstone or shale. The shale and mudstones of the Hunter Valley also show variability. Previous work at Mt Owen showed low levels of available phosphorus (Nussbaumer et al., 2012), and at Drayton Colliery the average baseline kg/ha phosphorus (Mercuri et al., 2006) was the same as the Spoil in 2014 at the study site. Conversely, values for Spoil at Howick Coal mine (now Hunter Valley Operations) were much 221 higher in both total and available phosphorus (Brown and Grant, 2000), which is particularly interesting because this site is next to Ravensworth Operations. This suggests that low phosphorus conditions may be representative of some of the depositionary environments associated with coal formation, but highlights the variability that can occur.

Phosphorus in soil is involved in multiple high-strength interactions with the various soil components (Hinsinger et al., 2011). The most common primary mineral forms of phosphorus are apatite minerals. Release of the phosphorus from apatite can occur through weathering, enzyme action and acidic attack, which converts the phosphorus into labile and soluble inorganic forms (Smeck, 1985). Microbes and plants take up soluble inorganic phosphate, making it unavailable until exudation in another form or death of the organism. Bioavailability of phosphorus in soil is varied, depending on the mineralogy of the soil and pH (Hinsinger, 2001). With decreasing pH, soluble and labile phosphorus can be released from clays, although they can also be adsorbed and precipitate secondary minerals with aluminium and iron (Gérard, 2016). Adsorption and secondary mineral precipitation with calcium also occurs at pH greater than 7.5 (Devau et al., 2011). Aluminium and iron secondary minerals can develop occluded phosphorus due to phosphorus encapsulation by aluminium and iron oxides (Smeck, 1985). Once occluded, phosphorus is unable to be solubilised, making it unavailable in the long term. As was seen by the strong but inconsistent relationship between total and extractable phosphorus, the nature of phosphorus can determine how accessible it is likely to be in the ecosystem.

The Subsoil Mulch plots with very low phosphorus probably did not have much occluded phosphorus because, although the extractable phosphorus was below detection limits, the total level was also quite low. In this case, it is likely that there simply was not enough phosphorus present to produce suitable availability. Subsoil OGM Mulch has much higher phosphorus levels due to the OGM although not as high as the Spoil OGM treatment. The reduction compared with Spoil OGM could be due to greater uptake by the plant community.

The Spoil and Spoil OGM plots both had water pH around 9.3, which may have limited availability because of precipitation with calcium (Weng et al., 2011). The low availability of both phosphorus and calcium in Spoil suggests this may have been occurring. However, Spoil OGM showed high availability of both phosphorus and calcium. It is possible that the high organic matter content in Spoil OGM was competing for adsorption sites, increasing 222 availability (Weng et al., 2011). Low molecular weight organic acids such as citrate can also adsorb to iron and aluminium hydrous oxide surfaces, reducing the amount of phosphorus adsorbed to surfaces and thereby increasing phosphorus in solution (Earl et al., 1979; Bolan et al., 1994; Haynes and Mokolobate, 2001). This suggests that the organic nature of OGM may be a key part of why it maintained high levels of phosphorus availability in plots over time.

The Ring Rd showed quite high available iron levels that may have been interacting with phosphorus and could have potentially occluded phosphorus had the pH dropped. RSF had a much lower pH and very low total phosphorus in the soil yet phosphorus availability was moderate. This is probably related to high phosphorus volumes in the litter layer (Nash et al., 2014). Organic phosphorus in soils is commonly composed of phosphomonoesters (most commonly inositols), phosphodiesters (nucleic acids) and organic polyphosphates (e.g. adenosine triphosphate) (Turner et al., 2007; Nash et al., 2014). Plants and microorganisms in phosphorus-deficient ecosystems commonly produce phosphatase, protons/hydroxyls and organic acids to encourage phosphorus availability (Richardson and Simpson, 2011). Additionally, in a phosphorus-limited environment, many flora species will associate with mycorrhiza to achieve higher rates of phosphorus uptake (Johnson et al., 2015). Notably, in Chapter 4, exceedingly high numbers of ectomycorrhiza were found in the RSF. Ectomycorrhiza can produce a range of phosphatase enzymes to access the various forms of organic phosphorus (Cairney, 2011). It is therefore likely that high availability in RSF came from the organic phosphorus contained in litter. Were the organic phosphorus in the litter layers to mix deeper into the mineral layers of soil, it is quite possible that it will become occluded with iron and aluminium.

Overall, there are plausible explanations for the levels of extractable phosphorus in each of the treatments examined. Explanations for the similarity between Subsoil Mulch and Spoil OGM treatments in nitrogen, decomposition and growth response are still mere hypotheses though. While phosphorus is a vital macronutrient, it is needed in much lower quantities than nitrogen and water. Under a hierarchical system of importance, then, its influence may be quite low.

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6.4.3 Other Improvements The ideal way to identify the establishment of nutrient cycles would be to trace isotopes through the ecosystem. This can be done by examining changes in 13C and 15N (Silva et al., 2013a) as well as the addition of short-lived 32P (Olander and Vitousek, 2004). However, this would have required samples to be taken at set-up so that changes can be seen over time. The alternative is to add isotopes to the site; however, the large scale makes this prohibitive. With the benefit of hindsight, isotope analysis could have also been used in conjunction with the tea bag study to provide greater clarity on transformations and rates of loss.

The tea bag method has great potential in the variety of uses it has, but, as seen from this study, high number of replicates may be needed to clarify results because of high variability. One of the major flaws of traditional decomposition methods is that studies in the literature have rarely used the same methods. Often, local materials and species have been used, which makes sense because microbes may be better adapted to local conditions (i.e. home-field advantage (Di Lonardo et al., 2018)) but this means results can not be directly translated between different flora communities or ecosystems. The tea bag method, however, has the potential to improve variety, regularity and size of studies because it is applicable to terrestrial and aquatic studies (Becker and Kuzyakov, 2018; Marley et al., 2019; Seelen et al., 2019; Teo et al., 2020). Tea is produced to an industrial precision, large volumes of tea can be purchased at relatively low cost, and the method is simple enough that it has been performed by school children and volunteers all over the world (Ogden, 2017; Seelen et al., 2019). With higher replication, the method will enable a consistent approach to a topic important not only for forest dynamics, river health and agriculture but also global climate (Keuskamp et al., 2013).

An important next step in the analysis of what tea can be used for will be clarifying what the results mean in the real world. Even though a greater degradation potential (S) was found in Rav Ref, it cannot be related to any specific factor, just the ‘black box’ that is the soil. Unfortunately, it is not clear what additional material could be degraded by soils with a lower S value. Although it has been suggested that all material except the acid-insoluble compounds (e.g. lignin) should be degraded (Keuskamp et al., 2013), there is evidence that waxes and fats may also be preserved (Duddigan et al., 2020) – an important factor in the sclerophyllous Australian ecosystems. Performing tea bag studies in conjunction with nutrient-use methods such as Biolog (Stefanowicz, 2006) would provide greater detail on the true ability of tea bags.

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Potentially important to plots with low canopy cover such as Spoil, and in parts Spoil OGM, is photodegradation of organic matter, which can be more important than microbial decay in some settings (Austin and Vivanco, 2006; Pieristè et al., 2019). There is a strong difference between surface and buried conditions, with decomposition rates on the surface being slower (Fanin et al., 2020). It would have been worthwhile for this study, in addition to tea bags, to have made litter bags from local species and placed them on the surface in a similar way to natural litter fall. This would have provided evidence for how the specific conditions of each plot, such as light availability, litter fall rate, species-specific senescent leaf chemistry and macroinvertebrates, would interact with the material.

Two important biotic factors not accounted for in this thesis are the level of microbial biomass and effects of soil fauna. In Chapter 4, microbial community composition was measured but not its actual biomass levels. Microbial biomass has been related to rates of decomposition at regional scales (Bradford et al., 2017). It could be that the community was diverse but overall very low in numbers, so low that they were unable to utilise the desirable stoichiometry presented in the Spoil OGM. Anecdotally, while ants are common across the site, soil samples did not reveal many soil invertebrates. Soil fauna are still largely understudied but are thought to have large indirect effects on soil processes through their consumption of microbes (Briones, 2014). While direct soil transfer was used to set up the site, perhaps only a minimal amount of fauna have been able to colonise the site successfully.

There is also a chance that plant roots were leaving the 10 x 10 m plots in search of resources and found them in other plots. This will be worth examining if future study is performed.

6.5 Conclusion Are critical ecosystem processes (i.e. cycling of nutrients, decomposition and plant growth) occurring at a greater rate in a treatment with high organics and nutrients?

Strong differences in extractable phosphorus and potassium were found between Spoil OGM and Subsoil Mulch. Yet Spoil OGM and Subsoil Mulch failed to show similar differences between decomposition, tree growth and biomass. Instead, strong differences were found between the Spoil treatment and the Experimental Site. Spoil displayed high sodium, chloride and magnesium availability, high pH, very low rate of decomposition and an inability to support trees. This suggests that the differences in ecosystem processes between Spoil OGM,

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Subsoil Mulch and Subsoil OGM Mulch were minimal compared with the difference between these treatments and Spoil.

The main difference between the Experimental Site and the references was the estimated biomass of trees, which continues to develop on the Experimental Site and may match the references in the future. The tea bag decomposition experiment showed that Rav Ref had a significantly lower stabilisation factor than all treatments except Spoil OGM, but this cannot be explained with the available data.

The lack of evidence that ecosystem processes were occurring at a greater rate in treatments with higher organics and nutrients may be due to factors such as water availability, plant species composition limiting the use of resources provided by the OGM, or the methods not being sensitive enough to detect a response. To accelerate ecosystem development, the limiting factor would need to be identified. Alternatively, the lack of a treatment effect may have not been influenced by these factors and the model of a self-reinforcing cycle targeted in this chapter may need modification.

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Chapter 7 – Can We Accelerate Ecosystem Development, and Should We?

7.1 Introduction This chapter initially asks if the restoration treatments accelerated the ecosystem’s development, and considers the performance of the four major treatments examined throughout this thesis as well as the references Ring Rd and RSF (Ravensworth State Forest). The discussion then focuses on what may be the mechanisms behind the differences and asks the question:

Are there pathways, and underlying processes, that accelerate the development of an ecosystem without risking the quality of the restoration?

The chapter concludes with comments on other features noticed through the study, future research directions and recommendations for industry.

7.2 Methods The data obtained through this study allow the question to be answered two ways. First, two variables were collected at two time points: the flora community composition and soil chemistry data. These can be used to examine the trajectory of the ecosystem in comparison with the best available reference, RSF. Accelerated change was measured by comparing treatments with each other, as well as comparing treatments with the 20-year-old Ring Rd site and with the RSF. To produce concise points for each treatment, both the 2015 and 2018 flora community composition datasets were square-root transformed and a matrix was constructed based on Bray–Curtis similarity between plots. Transformations are specified for clarity. Similarly, both the 2014 and 2019 soil chemistry datasets were square-root transformed and a matrix was produced using Euclidean distances. Both of these matrices were then converted to a centroids matrix based on year and treatment. The flora community composition and soil chemistry were then plotted in reference to distance from the RSF (Figure 7.1). Given that a true starting point for the treatments is unavailable, using two time points can suggest a rate of progression towards the targeted goal.

The second method of examining if ecosystem development was accelerated is based on comparing rates of progression and attempts to quantify time to reach the reference in years.

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This involved comparing the progress made by a treatment’s most recent (2018 and 2019) samples towards the RSF, assuming a linear trajectory from the initial conditions. This could be performed with flora composition and soil chemistry but also with microbial composition and biomass. Like the above methodology, flora community composition and soil chemistry were transformed to centroids; similarly, the microbial community data were examined by using presence–absence transformation, Bray–Curtis similarity and transformation to centroids. As there are no direct measurements of starting conditions, analogues were used and assumptions were made. Although it could be assumed that there was an absence of flora and microbes at the initiation of restoration, Bray–Curtis is undefined where no species are shared, making the statistics unworkable. It was therefore decided that the origin would be defined as the inaugural data from the Spoil treatment because it had been the negative control and baseline throughout this study. For the flora community, 2015 Spoil samples were used. Microbial community composition was performed based on beginning at the Spoil Control samples from Newman (2017), obtained as described in Chapter 4. Soil chemistry was based on 2014 Spoil samples. An estimate of time to RSF was developed and implemented using the equation:

Oldest spoil distance to RSF Time to match RSF = (Oldest spoil distance to RSF treatment distance to RSF)

Years since restoration initiated � � − � � This provides a rate of progression towards the RSF, measured from the oldest Spoil as a starting point, to give an output in years to match the RSF (Table 7.1). As biomass is an accumulation over time (assuming that there are no losses over time), it can be assumed that at the initiation of restoration there was no biomass. The rate of progression towards the RSF was also estimated using the formula:

Summed biomass of RSF Time to match RSF = Summed biomass of treatment Years since restoration initiated � � � �

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Change in Distance to the RSF 10 14/15 Spoil OGM 9

8 14/15 Subsoil OGM Mulch 7

6 14/15 Spoil

5 18/19 Spoil 18/19 Subsoil OGM Mulch 18/19 Spoil OGM 4 14/15 Subsoil Mulch 3 18/19 Subsoil Mulch Soil Similarity Distance

2 Ring Rd 1 RSF 0 0 10 20 30 40 50 60 70 80 90 100 Flora Community Similarity Distance

Spoil Spoil OGM Subsoil Mulch Subsoil OGM Mulch Ring Rd RSF

Figure 7.1. Distance in similarity compared with the RSF reference based on flora community composition and soil chemistry. As these calculations were performed using centroids, a distance of zero is complete similarity with the RSF and values of distance can range up to infinity. Note that the x- and y-axes are based on different distance metrics, Bray–Curtis for flora and Euclidean for soils, and are based on a very different number of variables. Although both are distance metrics, a 10 on the x-axis is not equivalent to a 10 on the y-axis.

Table 7.1. Linear estimates of time in years to match the RSF with reference to the oldest Spoil treatment. No estimate was produced for the microbial community in Spoil as no microbes could be extracted from it. Note that, without absolute values, the microbial community of Spoil OGM would produce a value of –308. Within column, cells are colour coded and range from red (high value), orange, yellow (moderate value), light green to dark green (low value)..

Treatment Flora Combined Microbial Soil Average community Tree, community chemistry years to Other Veg and match RSF Litter Biomass Restoration initiated in 2013 Spoil 244 631 69 315 Spoil OGM 69 56 308 46 120 Subsoil Mulch 15 32 25 14 22 Subsoil OGM Mulch 20 16 59 18 28 Restoration initiated in 1998 Ring Rd 46 25 66 27 41

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7.3 Results and Discussion – Did Ecosystem Development Accelerate? The simple interpretation of these two modelled datasets is that, of the treatments on the Experimental Site, Subsoil Mulch was accelerated the most. Subsoil Mulch is on the most direct trajectory towards and has the lowest time to reach the RSF (Figure 7.1, Table 7.1) with a linear projection of on average matching the RSF by the year 2035 (Table 7.1).

It should be stressed that this simple formulation is based on an assumption of linear progression from Spoil conditions to the RSF, which is unrealistic (Rydgren et al., 2019; Rydgren et al., 2020). However, with only two time points, an asymptotic fit is not possible (Rydgren et al., 2019). As a factor gets closer in similarity, its progression towards the RSF is likely to slow (Myster and Pickett, 1994; Foster and Tilman, 2000). Although Ring Rd is closer in every metric to the RSF (shown most clearly in Figure 7.1), when the rate of progress is assessed on the basis of years since restoration was initiated, its rate of progress is divided by values around 20 rather than values around 5, as occurs in Table 7.1. This explains the higher average value in time for the Ring Rd to match the RSF. If time is not factored into the metric as in Figure 7.1, then 20 years of development, a moderate number of species seeded and quality topsoil used on the Ring Rd produces a better result than 5 or 6 years with a high diversity of species and any non-topsoil ameliorant. This is consistent with prior recommendations that quality local topsoil should be the first preference for restoration to native vegetation post-mining (Department of Resources Energy and Tourism, 2009; Zipper et al., 2011; Nussbaumer et al., 2012; Department of Industry Innovation and Science and Department of Foreign Affairs and Trade, 2016)

Predicting years to completion also assumes that the treatments will continue to change in ways that will produce a greater similarity to the reference. Propagule pressure over time can lead to changes in the community composition (Simberloff, 2009), especially if there are changes to the environmental conditions (Long et al., 2014; Schantz et al., 2015; Turley et al., 2017). However, as the Experimental Site has already had 5 years of dispersal, many species in the local pool will have probably had the opportunity to establish. If species have not been able to establish yet, then perhaps the conditions on site are unsuitable for them and they will be unable to establish in the future. The 50-year average yearly rainfall at Bowmans Creek is 853 mm; rainfall since site establishment in 2013 has only been above average in one year (2015, 1154 mm), which was followed by a year of average rainfall (2016, 854 mm). The below

230 average in rainfall in 2014, 2017, 2018 and 2019 (range: 526–651 mm) may be temporarily limiting establishment of some species on the site. Alternatively, as the site develops, it may change in a way that will better facilitate the establishment of currently missing species. In this situation, the surrounding vegetation is very important (Prach et al., 2015a; Prach et al., 2015b). This is particularly a concern on the Experimental Site because the surrounding vegetation, Rav Ref, is a different community. If a change in species composition towards the reference is to occur in the future, it is likely to be related to changes in the environmental filter, which is still developing. Given the slower structural changes occurring on Subsoil Mulch from smaller trees and lower groundcover levels, factors such as ground-level light availability may change at a reduced rate in the future (Figures 7.2 and 7.3).

Figure 7.2. Subsoil Mulch treatment, showing a strong shrub layer but mostly open ground layer. Photo taken in June 2020, 6 years 7 months after restoration was initiated.

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Figure 7.3. Subsoil OGM Mulch treatment taken from a distance to show the development of trees. Photo taken in June 2020, 6 years and 7 months after restoration was initiated.

7.4 The Treatments This section considers the outcomes of each of the treatments and how they will influence the future development of the community.

7.4.1 Spoil The development in Spoil was so little, and the estimated time until it would be rehabilitated is so long, that it is worth wondering if the change shown in Figure 7.1 is only natural variability. Spoil is a naturally variable product, being a conglomeration of multiple rock types. However, on this site the variability was never strong enough to show false positive results. For example, pH 1:5 in water of spoil may vary from 8 to 10 but is consistently higher than all references where the targets typically have soil pH 6–7.

As a negative control, the Spoil Treatment really highlighted the importance of applying an ameliorant. Spoil had sparse vegetation, very low levels of microorganisms, very high bulk density and minimal evidence of nutrient cycling (Figure 7.4). The Spoil plots in blocks 3 and 6 were lost after 2015 due to severe erosion, suggesting a high chance of fundamental

232 landform failure with this substrate. Detracting physical and chemical characteristics of the Spoil both probably influenced success. The large amount of boulders and gravel, mixed with fine particles that fill pore spaces, probably contributed to a dense, compacted substrate (Haigh and Sansom, 1999) and limited the areas that roots could penetrate the ground (Zipper et al., 2011). High sodicity can be a major issue for establishment of the plant community (Schuman et al., 1994). Exchangeable sodium percentage greater than 10 is considered strongly sodic in NSW (Hazelton and Murphy, 2007); however, Spoil exchangeable sodium percentage ranges from 12.8 to 18.5. This was probably responsible for the severe surface crusting and gully erosion seen on site. The high pH (9–10) of local spoil may also have been important; international work in coal mining has shown improvements in restoration characteristics with pH below 7 (Alday et al., 2011; Zipper et al., 2011), although these are different environments with different species compositions.

The linear estimate suggests 315 years for Spoil to match the RSF (Table 7.1), a potential completion date of 2328. Predicting ecosystem development so far into the future is fraught with uncertainty. As a comparison with the other treatments it is useful, but should remain tentative. Essentially, restoration of this Spoil with only the addition of propagules is not possible; it requires amelioration to transform it into soil. Without active restoration, this substrate will provide a hazard from erosion and dust creation into the future and may take centuries of development to match the reference.

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Figure 7.4. The Spoil treatment (foreground) is a negative control where all propagules were added but no ameliorant was applied. It showed extremely poor colonisation and development towards the reference. Photo taken in June 2020, 6 years and 7 months after restoration was initiated.

7.4.2 Spoil OGM The Spoil OGM treatment has been an interesting comparison; from this study, it appears to be progressing on a separate trajectory (Figure 7.1), not necessarily moving away from the RSF but certainly not moving towards it. The Spoil OGM was drastically different from Spoil, with very high levels of organic carbon, high levels of phosphorus, a substantial biomass of other vegetation (non-trees) and an identified microbial community (Figure 7.5). In terms of flora community composition, the treatment has become slightly more dissimilar over the two survey points. Although the OGM application made a big difference to the Spoil, it did not have the same accelerating effect as the Subsoil Mulch addition. While there were large amounts of growth, the community composition is probably developing in the wrong direction. This was unexpected because the high nutrient and organic matter contribution of the OGM was hypothesised to accelerate the development of the correct ecosystem by facilitating growth from the diverse seed mix. Rather, the Spoil OGM has selected for a

234 minority of species, which are performing well on that substrate. Most of the species on Spoil OGM are ancillary to the target EECs rather than being characteristic of the EECs.

Figure 7.5. Spoil OGM treatment, highlighting the strong cover of chenopods and grasses in the foreground with a small stand of successful trees in the middle left of the photograph. Photo taken in June 2020, 6 years and 7 months after restoration was initiated.

Previous work showed that some species had lower survival rates on the Spoil OGM treatment (Scanlon, 2015; Castor et al., 2016); however, the trees in particular provide another picture of the system. Trees on Spoil OGM were among the tallest and healthiest on the site, yet they were very few in number, averaging 183 trees/ha in 2018, whereas Subsoil OGM Mulch averaged 3017 trees/ha. Perhaps this an example where early fertility was not beneficial to the system, promoting growth of herbaceous species, including weeds, which outcompeted many of the tree individuals for light early in restoration (Horsley, 1993; Skinner et al., 2010; Gafta and Peet, 2020). In 2018, Spoil OGM had the highest cover of exotic species, which makes long-term development of this treatment without intervention a concern. Strong differences in microbial composition (Callaway et al., 2004; Dawson and Schrama,

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2016) and soil chemistry (Larios et al., 2017) may also continue to encourage differences in plant community. The overall interpretation of this is that Spoil OGM will probably produce a novel community and ecosystem compared with the reference. The dominant native species on the Spoil OGM were chenopods such as Enchylaena tomentosa, Atriplex semibraccata, Einadia nutans subsp. nutans and Einadia nutans subsp. linifolia, with a significant density of grasses such as Chloris truncata and Austrostipa verticillata. Given this species mix, perhaps it will develop into a mixed grassland or as was suggested by the plant community type, a grassy open forest. Although this would not be a loss for biodiversity because it increases the overall richness of species (Seastedt et al., 2008), and grasslands provide high levels of ecosystem services (Lamarque et al., 2011), it was not the intended outcome. As an overall application, this treatment is not desirable, but in a biodiverse mosaic it could be a valuable addition if weeds are managed appropriately.

The effects of organic amendments have been debated recently, with a review suggesting that the effect of organic amendment on productivity is equal to the effect of applying inorganic fertiliser with the same nutrient content (Celestina et al., 2019). Prior research has suggested that the organic component has specific potential to improve other important characteristics of a soil. For example, they may improve soil structure, water-holding capacity, organic carbon, microbial biomass, pest/disease control and immobilisation of contaminants, and from these improvements, increase the resilience of a site (Quilty and Cattle, 2011; Larney and Angers, 2012; Chen et al., 2018; Shrestha et al., 2018; Palansooriya et al., 2020). On balance, it is likely that there are still major gains that can be made from including organics in work on heavily degraded landscapes as prior comparisons have shown strong benefits from organic ameliorants compared with fertiliser (Kelly, 2008).

7.4.3 Subsoil Mulch In almost all respects, of the treatments examined in depth on the Experimental Site, the Subsoil Mulch treatment appears to be the best treatment to apply based on the modelled prediction. Comparatively, the Subsoil Mulch contributed very little to soil nutrients and organic matter, yet it had the highest biodiversity and a microbial community that in some plots was more similar to the references than the rest of the Experimental Site (Figure 7.2). Further, the soils generally had a high degree of similarity with the RSF. The high degree of similarity with the reference is one of the key reasons it performed so well; it matched the

236 conditions that the targeted species are already adapted too. However, the results demonstrated in Chapter 5 raised concerns that the Subsoil Mulch treatment lacked enough phosphorus to reproduce the reference system. If strict similarity to the reference was required, then Subsoil Mulch would on average need an additional 203 kg/ha of phosphorus across all pools measured, including the trees, other vegetation, litter, 0–10 cm soil and 20– 30 cm soil. If it does lack phosphorus, which has occurred before in other settings (Vangansbeke et al., 2015), it could be predicted that the final ecosystem is more likely to develop into an open woodland or shrubland. Unfortunately, arrested successional development (Acácio et al., 2007) or stalling is not often planned for as part of successional models or many metrics (Suding, 2011) and must be considered in this case. Perhaps in this case the similarity to the reference, particularly the low-fertility soils that supported the targeted community so well in the metrics, provided a false sense of acceleration.

7.4.4 Subsoil OGM Mulch In most comparisons of the Experimental Site treatments, Subsoil OGM Mulch is second best or the best. The issues raised in Chapter 3 around development of community composition are valid, as this treatment had a moderate cover of exotic species, which needs to be considered. The Subsoil OGM Mulch does provide, however, a lot of opportunity for the community to continue developing if managed well. Having a strong developing canopy (Figure 7.3), it is likely to increase shade, which can be a strong driver of the direction of succession (Lienard et al., 2015). The treatment also still has relatively fertile soils, a characteristic associated with higher soil activity (Marinari et al., 2006; Chen et al., 2017). The stronger soil activity is one of the key ways that the spoil underlying the treatments may begin to slowly improve. For example, both plants and microorganisms are involved in a range of processes that can lead to long-term acidification of soils. Leguminous plants in particular can produce acidification of the rhizosphere following nitrogen fixation (Tang et al., 1999). Organic acid production by plant roots and microorganisms can also change the pH of the soil (Adeleke et al., 2017). If roots grow into the spoil, then over time the production of acids may change the pH. Progressive subsoil acidification may also increase availability of calcium from carbonates, which can displace sodium and allow it to leach (Qadir et al., 2005). This may slowly lead to improvement in the qualities of the spoil; however, formation of a subsoil crust, as occurred on the surface of the Spoil treatment, would reduce or negate any effect.

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7.4.5 Ring Rd Perhaps most important for this study is that Ring Rd was more similar to the RSF than any treatment on the Experimental Site. Ring Rd had higher flora and microbial similarity to RSF, more similar soil chemistry, closer biomass and similar decomposition characteristics. This suggests that the best restoration method is high-quality topsoil from the local area, a high species diversity seeding list featuring the correct community and time. Had any treatment on the Experimental Site matched the Ring Rd, it would have been extremely impressive because Ring Rd had topsoil applied and was approximately four times as old. To determine acceleration of ecosystem development, it is necessary to have references for comparison that show advanced stages of development. That was the main purpose for the use of Ring Rd in this study. Although the years since restoration analysis suggests that the Ring Rd will take longer to restore to RSF conditions than the Subsoil Mulch treatment (Table 7.1), this is probably an artefact of the site’s moderate age and the asymptotic nature of restoration (Rydgren et al., 2019).

7.5 Relationships Between Variables Sections 7.5–7.8 consider the variables and mechanisms that could be influencing ecosystem development.

Although various methods of accelerated ecosystem development were suggested in Chapter 1 (more detail is in section 7.6), considerations of the variables measured may shed light on mechanisms behind them. For example, even though there were large differences in nutrient availability between treatments, the similarity in flora species composition between the treatments with Subsoil suggests that there may have been other effects linking the community composition. A second-stage analysis (Clarke et al., 2006) was performed to explore similarities in plot relationships between ecosystem components (Figure 7.6). Data were selected from Chapters 3–6 of this work and Scanlon (2015) where the greatest number of datasets was available; this restricted data to Blocks 1, 2 and 5 of Spoil, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch. Variables examined were chosen based on their ability to represent other similar variables and ability to assess relationships between components of the ecosystem (Figure 7.6). Non-metric multidimensional scaling of the second-stage analysis suggested the nutrient composition of litter and the 20–30 cm total nutrients were poorly related to other variables. Cluster analysis suggested stronger similarities between the

238 available nutrients in the 0–10 cm and 20–30 cm as well as 0–10 cm total nutrients. The bacterial and fungal community compositions were highly correlated (Kendall’s Tau 0.77). Both the flora community and tea decomposition rates were closely linked to the bacterial and fungal community composition (bacteria to flora: Kendall’s Tau 0.55; fungi to flora: Kendall’s Tau 0.54; bacteria to tea: Kendall’s Tau 0.52; fungi to tea: Kendall’s Tau 0.54).

Given how closely related the microbial and flora variables are by the second-stage analysis and Kendall’s Tau, there is probably a strong relationship between the two variables. One suggestion is that there was a microbial conditioning effect in the Subsoil driving this relationship, encouraging the establishment of particular flora communities. This is supported by studies showing changes in plant communities due to the composition of soil communities (Wubs et al., 2016; Brinkman et al., 2017; Bauer et al., 2020). Interestingly, the support of a desirable species richness in the current study occurred even though the Subsoil was from a pasture environment created by European activities over the last 200 years, suggesting the biotic legacy could still be supportive of the previous native communities. Alternatively, the correlation of variables from the second-stage analysis could also be strongly driven by the relationships between Spoil and Subsoil Mulch treatments. The tea decomposition had little response from Spoil, and in the analysis of the flora and microbial communities, Spoil was depauperate. Comparatively, the chemical analysis showed that Spoil was moderate or high in elements and Subsoil Mulch was lacking in phosphorus. These results are, however, based on correlation, which is distinct from causation and a mechanism has yet to be confirmed.

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Figure 7.6. Non-metric multidimensional scaling analysis of second-stage analysis. All variables received appropriate transformation followed by standardising (normalise variables) and analysis performed on resemblance matrix. Analysis was only performed using Spoil, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch treatments in Blocks 1, 2 and 5 because this is where there were overlapping data. Flora community data were based on abundances for natives and presence of exotics. The Spoil Control plots used for Newman (2017) were substituted for Spoil in the bacteria and fungi communities. ‘Available’ and ‘Total’ refer to available soil nutrients and total soil nutrients respectively. Data for decomposition were the mass of green and rooibos tea after drying at 70°C and loss on ignition.

7.6 Mechanisms of Accelerating Ecosystem Development In Chapter 1, several potential methods were presented by which accelerated ecosystem development could occur. These were through manipulation of succession, increase in diversity, influences of the microbial community, creation of nutrient cycles and removal of limiting resources. Although these effects were not separated in any of the chapters, there are hints as to which may be effective in the restoration of the Hunter Valley’s coal mines and similar degraded areas.

Successional processes were not directly manipulated as part of this study; however, the different ameliorants have caused several different successional patterns to develop. One noticeable pattern was the reduction in trees seen on Spoil OGM, probably due to the strong performance of herbaceous and grass species (Skinner et al., 2010; Rebele, 2013). As was described above, this is a common feature in restoration projects. In this setting, planting of

240 trees may have been more successful because they would have been taller at the point where other species became competitive.

Increasing diversity has been shown elsewhere to increase productivity (Tilman et al., 2012; Tilman et al., 2014) and could be a prime explanation as to why the Subsoil Mulch had slightly higher levels of biomass than Spoil OGM. Subsoil Mulch was significantly higher in Shannon– Wiener diversity index than Spoil OGM (see Figure 3.2 in Chapter 3). Even though no OGM was applied to provide fertility and enable accelerated growth, Subsoil Mulch had an average of 23 t/ha of biomass compared with 13 t/ha in Spoil OGM. Both of these figures were dwarfed by Subsoil OGM Mulch at 47 t/ha and RSF at 160 t/ha; however, it does suggest that diversity can have as much of an effect on productivity as soil fertility can (Tilman et al., 2012). The effect of diversity on productivity, however, is still controversial in the literature (Schmid, 2002; Thompson and Starzomski, 2007; Whittaker, 2010) even though there are some explanations (Zuppinger-Dingley et al., 2014). A large part of the difficulty in determining a mechanism for diversity is that there is no single mechanism that changes how species interact (Shade, 2017). Increased productivity could be caused by multiple processes and change with every species. This suggests that there is still a large body of work to be done in understanding the effects of diversity. For future restoration projects, though, the suggestion of a beneficial effect from increasing biodiversity should be seen as a positive (not a necessity), especially when many projects aim to restore the maximum amount of biodiversity.

The microbial community has been suggested to have a large effect on the developing plant community (Wubs et al., 2016; Brinkman et al., 2017; Bauer et al., 2020) and there is a strong positive relationship between the beta diversity of plant and microbial communities (Prober et al., 2015). Other than showing a strong relationship between the plant and microbial communities (Figure 7.6), this study was unable to separate the variables. What has been seen in this work is that the initial characteristics of the microbial community can be overridden by dispersal from the surrounding landscape. This may be due to a poor ability of species from OGM to survive in the field environment. It could also be an indication that priority effects are minimal in microbial communities (Calderón et al., 2017), perhaps because of rapid turnover rates. While reintroduction of beneficial microbes may be beneficial in

241 restoration systems, if microbes in the surrounding landscape have the potential to colonise the site, then they may become a dominant feature with time.

Nutrient cycles, particularly rates of decomposition, have a long history of study but there are still many questions about their mechanisms (Hobbie, 2015). This study found very little evidence of differences in nutrient cycles even though nutrient availability varied substantially. This may have been confounded with other variables such as the availability of water or the composition and diversity of plants. While it seems logical that an increasing rate of nutrient cycling would support stronger growth and development of the ecosystem (Horodecki and Jagodziński, 2017), this study did not find evidence to support such an effect.

The effect of limiting resources is likely to have a strong impact on the development of the Subsoil Mulch treatment in the future; however, it has yet to fully manifest itself. In many ecosystems where limiting resources have been tested, no deficiencies have been found (Turner and Lambert, 1986; Johnson et al., 1997; Bond, 2010; Wigley et al., 2013), so this is not likely a common issue across systems. Nutrient deficiency in restoration is probably only an issue in work performed on areas that are particularly poor in resources; for example, in this study it occurred on the Subsoil Mulch treatment. It could occur in the sandy soils examined by Vangansbeke et al. (2015) in north-western Europe and potentially it could occur in ecosystems undergoing retrogression.

7.7 Acceleration, Retrogression and Restoration An interesting development throughout this thesis has been the growing evidence to suggest that the reference ecosystem, the RSF, may be in the early stages of ecosystem retrogression. Retrogression is a relatively recent concept, having developed out of work showing that ecosystems can decline in many characteristics in the long-term absence of catastrophic disturbance (Figure 7.7). Wardle et al. (2004) found that ecosystems in retrogression had a strong reduction in tree basal area, an increase in substrate nitrogen to phosphorus ratio and a decrease in phosphorus over time. Litter decomposition was also affected, with reductions in decomposition rates, phosphorus release, biomass of decomposer microbes and activity of decomposer microbes. One of the most common descriptions of retrogression is the increasing limitation by phosphorus, whereas in the progressive stage limitation is primarily by nitrogen (Vitousek, 2004; Peltzer et al., 2010; Newman and Hart, 2015). This is due to the

242 chemical composition and weathering of most soils. Nitrogen, as a predominantly gaseous element, is not often present in unweathered volcanic materials and in a natural system is primarily introduced by atmospheric deposition and nitrogen-fixing species (Walker et al., 2003; Vitousek, 2004). In early development, or the progressive stage, the development of the ecosystem is probably limited by the availability of nitrogen. Phosphorus, however, is present in volcanic material and progressively weathers to provide nutrition as the system develops. As a macronutrient, phosphorus is required in large quantities by plants and its erosion over thousands to millions of years produces landscapes low in phosphorus (Walker and Syers, 1976; Vitousek, 2004; Vitousek et al., 2010). At older stages of development, retrogression, recycling of nutrients within plants becomes more important, as do the feedbacks within an ecosystem that can help release nutrients. The stage between progression and retrogression is referred to as maximal and features co-limitation of both resources. This model of ecosystem development has been examined in many parts of the world by using chronosequences, and is becoming accepted as a reliable feature in the evolution of ecosystems (Vitousek, 2004; Peltzer et al., 2010; Newman and Hart, 2015).

Figure 7.7. Graphical description of the stages in long-term ecosystem development. Immediately following catastrophic disturbance there are few ecosystem processes. Processes develop over time through a progression phase to eventually reach the maximal phase. As available soil resources begin to deplete, the processes reduce and the system becomes retrogressive. Eventually the system stabilises based on inputs such as atmospheric deposition.

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Most of the phosphorus in the RSF is stored in the litter layer with the 0–10 cm part of the soil averaging 0.01% total phosphorus and the 20–30 cm layer being <0.001% (i.e. below the detection limit for total phosphorus). These values are similar to those used to demonstrate retrogression in the oldest soil chronosequences following deglaciation of the Franz Josef (22,000 years since deglaciation) and Reefton (70,000 and 130,000 years since deglaciation) sites in New Zealand (Walker and Syers, 1976). Compared with other examples of retrogression, the RSF is similar to the older values of the 3,000,000-year chronosequence following volcanism in Northern Arizona (Selmants and Hart, 2010). Compared with a dune sequence with phosphorus-poor parent material in south-western Australia, the phosphorus levels in the RSF are similar to those in sites between 7000 and 125,000 years old (Laliberté et al., 2012). As the RSF matches phosphorus levels from sites that in chronosequences display retrogression, it could be hypothesised that it is in a similar stage. Unfortunately, as one of the last remaining patches of vegetation on the floor of the Hunter Valley, there are few other sites to compare with for other symptoms of retrogression, such as tree basal area, responses of above-ground herbivores, nutrient-use efficiency and decomposition rates (Wardle et al., 2004; Peltzer et al., 2010).

One concern with retrogressive ecosystems is that, as the plant community progressively reduces in resources and primary production decreases, the rest of the ecosystem processes may also contract in size. The contraction could be in stature and in population size but also affect species richness due to loss of rare species as populations shrink. This could mean a reduced supply of flowers for pollinators, reduced supply of decomposable material for saprotrophs, and reduced biomass for herbivores; these are a few examples of groups that may suffer losses. The implication of this in restoration is that it would be possible to overpopulate the community, leading to a slower development of long-lived individuals as they struggle to gain resources. Evidence for this, however, is mixed. McNaughton et al. (1989) found that herbivore biomass and consumption are generally related to the productivity of an area, suggesting a reduction in herbivore biomass with retrogression. Similarly, Metcalfe et al. (2016) found that an insect herbivore caused nutrient fluxes approximately four times greater in progressive ecosystems than in retrogressive ecosystems. Conversely, while soil organisms show some evidence of increases in abundance and biomass during early successional stages, their response has been inconsistent and poorly linked to

244 retrogression (Decaëns, 2010; Bokhorst et al., 2017). It would therefore appear that some components of the ecosystem may be more affected by the restoration of a retrogressive state than others.

It is also worth asking: Does it matter if the retrogressive state does not get restored? The key target of the restoration is the plant community composition. Arguably, in this setting, if the correct species are present, the restoration is considered successful; the system could be in a progressive phase of development and the goal will still be met. Understanding the state of an ecosystem’s development through progression, maximal and retrogression stages is important because it provides a layer of context on what the long-term characteristics of the ecosystem should be. Although state and transition models are developing at a rapid pace, consequences of retrogression, such as stalling in development, is not often planned for as part of successional models or many metrics (Suding, 2011) and must be considered in a retrogressive system. The concern with a progressive or maximal phase of development is that many species of this particular community may be adapted to the characteristics of a retrogressive ecosystem and only able to successfully compete under those conditions.

The following examples are not given to suggest that these ecosystems are in retrogression, rather to suggest how aspects correlated with retrogression can impact on vegetation development. There are many examples where soil fertility has influenced community composition in other settings, such as the soil nitrogen levels of Californian grasslands. Modelling by Larios et al. (2017) indicated that the native Stipa species could dominate in low- nitrogen conditions, but that at high levels Stipa was reliably outcompeted by the exotic Avena species. This has interesting implications for management because at low levels of nitrogen a ‘do nothing’ approach was viable, at intermediate levels active restoration was required and at high levels restoration was unlikely to be successful unless nitrogen levels were lowered. In Australia, Cole et al. (2016) found that suppression of available nitrogen and phosphorus by sugar application had a dramatic effect on the community; most notably, it decreased the abundance and biomass of annual grasses and broadleaf exotics. This suggests that the low-fertility conditions of a retrogressive state are more likely to support native perennial vegetation. The community may also be poorly adapted to disturbance events, given the long time between catastrophic change. Were the community adapted to the retrogressive state, it may be considered a ‘humpty-dumpty community’; once it tips over the

245 point of resilience, it is very difficult to put back together again (Tielke et al., 2020). In this study, adding additional nutrients came with risks of introducing exotic species, such as in the Subsoil OGM Mulch treatment. However, these risks were not beyond what could be managed as part of the restoration process.

The long-term implications of any restoration action are also important. Although restoring the species composition of a community was a fundamental aim of this project, the other expectations of a restored ecosystem such as its structure and function are also important. There is still the expectation that the forest will be restored, which may not be achieved with the Subsoil Mulch treatment because of its low phosphorus levels. If the structure of a system was not restored, then an off-target community may develop, jeopardising the restoration of the targeted EEC.

One of the challenges a retrogression ecosystem raises, however, is how best to restore an ecosystem whose defining feature is its time since catastrophic disturbance. The catastrophic action needs to be sufficient to provide a ‘rejuvenating’ effect (Peltzer et al., 2010). This can be by events that characteristically produce primary succession, such as volcanism, glaciation and mining (Wardle et al., 2004). There are also forms of disturbance that are not catastrophic enough in some ecosystems. For example, fire in regularly burnt areas of Australia may only produce secondary succession whereas fire on the forest islands in Sweden removes substantial amounts of humus, drastically changing the soil (Wardle et al., 1997; DeLuca et al., 2002). By disturbing the landscape, fresh minerals may be brought into the rooting zone, allowing ecosystem development to begin again and re-establishing the progressive phase. This is particularly important for many Australian landscapes because much of Australia is dominated by late successional ecosystems (Walker et al., 2007) and many of its ecosystems are phosphorus limited (Attiwill and Leeper, 1987). This suggests that questions around the ideal methods for restoring retrogressive ecosystems may be highly applicable to many other places. Additionally, Laliberté et al. (2013) confirmed that there is a trend of increasing species diversity with increasing soil age, even though productivity peaks mid-sequence. This suggests that ecosystems under retrogression may be highly diverse and successful restoration of retrogressive ecosystems may be particularly important due to potential loss in biodiversity in the event of failure.

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Currently, there is little evidence from this study that the restoration process specifically compromises the community composition. There is a general trend for nitrogen-fixing species to be less successful on the OGM treatment (data not shown) and chenopods to perform better, so there is evidence that communities can vary with soil characteristics. These differences, however, although significant, are not enough to prevent species from living on these treatments. Rather, this is an example of competitive exclusion that may be able to be managed by trained bush regenerators until the ecosystem stabilises.

The low levels of resources, such as phosphorus, typical of retrogressive ecosystems can be replicated in restoration projects, as was done by applying Subsoil in this study, but that does not recreate the ecosystem. The target system of this project was old-growth vegetation that has developed over the long term, allowing acquisition of resources through slower processes of atmospheric deposition and mineral weathering. This may explain why Subsoil Mulch performed well at replicating the community; it replicated what naturally occurs during low level disturbance of this reference ecosystem. Perhaps in a natural setting the Subsoil Mulch would be equivalent to a canopy gap after tree death, where large amounts of the nutritional capital are already locked away in the mature biota. In this setting, it would be expected that an individual plant could eventually receive more resources through litter fall and decomposition; availability of resources just might take time. Theoretically, if mature and fully developed old-growth vegetation could be successfully placed on the Subsoil Mulch, then restoration of the retrogressive ecosystem would be achievable. Perhaps the only way this could be plausible is if all mature vegetation could be successfully transplanted during rehabilitation. Although some innovative methods of tree transplanting are being developed (Arnold, 2005; Watson and Hewitt, 2020), this method would need considerable development and standard methods of increasing soil nutrient levels are likely to be more practical.

7.8 Importance of Goals Deciding on appropriate goals still remains an issue in restoration (Perring et al., 2015). Using EECs as a target for restoration may be too unrealistic without extensive management because variability in results is extremely common. Brudvig et al. (2017) argue that when the requirement for success is specific to taxonomic characteristics, the susceptibility to variation by stochastic events is high; therefore, success is less predictable. Conversely, other metrics such as biomass or species richness can be produced by a variety of functionally comparable

247 species, making these metrics more reliably achievable (Laughlin et al., 2017). This means that achieving a specific species composition is possibly the hardest task, and then that task is made even more difficult by the communities endangered nature. Additionally, even relatively small variations in the initial landscape or in restoration action can lead to divergence in a stable state (Petraitis and Dudgeon, 2005; Houseman et al., 2008), as was seen with the variability within treatments in this study. For example, Houseman et al. (2008) found that even small variations in environmental starting conditions lead to substantial changes following the same fertilisation treatment. Ideally, multiple metrics are used to judge success (Ruiz-Jaen and Aide, 2005; Marrs, 2016). However, as can be seen from the many metrics examined in this work, there is considerable variability between metrics that will not be detected when studying only the flora community (Holl, 2002) or the flora community and soil characteristics (Alday et al., 2011). Perhaps the larger consideration should be that restoration is a human endeavour (Burke and Mitchell, 2007; Suding, 2011). Without advocating for a ‘free for all’ approach, perhaps there is merit in allowing flexibility where unintentional success does occur such as in the creation of an EEC community that was not the original target.

There has been variable success in meeting goals in coal mining rehabilitation. Alday et al. (2011) suggested Spanish coal mines progressively approached native composition over 32 years. Although Holl (2002) found that 35 years was not long enough to match the reference conditions for restoration in eastern USA coal mines, they were still progressing towards the reference. In the Western Australian bauxite mines, Koch and Hobbs (2007) calculated that over approximately 30 years the restoration had achieved a score of 90–92% successful. What was lacking in the Western Australian bauxite mines were processes that potentially take hundreds of years, such as wood rotting and development of tree hollows. The results of the current work suggest that an outcome similar to Holl (2002) or Koch and Hobbs (2007), rather than Alday et al. (2011), is more likely for the Experimental Site; Ring Rd is showing that after 20 years there is still room for improvement, with a linear estimate of 40 years to match the reference (Table 7.1). Jones and Schmitz (2009) found that 34.6% of restoration studies across all ecosystems, disturbance levels and reported variables were able to successfully match the reference target within 10–40 years. Comparatively, 28% of studies did not record any kind of recovery for any variable, although this may have been due to a

248 short period of study. Jones and Schmitz (2009) also found that of all ecosystems, forests took longest to restore, probably because of the long lives of individuals and long time to change conditions with canopy development. Oddly, they reported that although more than half of the articles on mining did not show any success, where mining was successfully restored, it took less than 10 years on average. This may have been due to the variation in the level of degradation of the mining ecosystems and the type of target ecosystem. For example, sites that experienced only minimal disturbance in comparatively simple ecosystems were restored quickly compared with sites that experienced extreme disturbance in more complex ecosystems and which showed minimal development. Restoration is generally more successful when ecosystems are more intact and because many systems are degraded, incomplete restoration may actually be the most likely outcome following restoration interventions (Suding, 2011).

The references used in this study are not perfect but they do provide an appropriate goal for the outcome of restoration. Even though the RSF has undergone disturbance events in the past, it is the closest matching vegetation to the restoration target. As more landscapes around the world become degraded, ‘pristine’ landscapes dwindle (e.g. Prober et al. (2002); it is extremely unlikely that ‘perfect’ analogues exist for many ecosystem types. Rather, the native vegetation that remains is a realistic expectation of what success may look like. For example, it is unlikely that all exotic species will remain absent from a ‘restored’ ecosystem. This makes RSF a good reference because it is showing how a mature ecosystem is responding to current stress events rather than referring to the static conditions of a historic reference (Gann et al., 2019). Similarly, the use of the Ring Rd as a reference is valid because the reduced soil layers and altered geomorphology in the mining environment make it questionable that the full flora and microbial composition, structure and function of the RSF will be restored. When substantial change has occurred to an ecosystem, such as open cut mining, it is appropriate to consider alternative references that are more realistic (Gann et al., 2019). In this setting, successful restoration to a realistic level is difficult but probably not impossible, hence the selection of feasible references. The two references have limitations, but they are both reasonable and some treatments with positive metrics (species richness, relationship to EEC lists) are trending towards the references. This confirms that the selection of references was appropriate and that restoration goals may be met in the future.

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7.9 Future Directions There is a lot of development in the plant economic spectrum classifications (Wright et al., 2004; Chave et al., 2009; Díaz et al., 2016), which has the potential to provide greater functional and evolutionary understanding to the current state of ecosystems. Given the relationship between plant community composition and decomposition, widespread characterisation of all species could enable the further development of ecological theory. In particular, for this study it would have been very interesting to see if there was a relationship between the species selected for by the OGM application and their leaf, stem, root and decomposition characteristics. If the functional attributes can be further mapped and applied to global ecosystems, they could also assist the targeting of restoration processes. Particularly by studying areas with high species diversity, characterising how and where different members of a community use a landscape based on where they are on the economic spectrum could guide improvements in restoring community composition. This could produce functional specific restoration techniques rather than species or community specific restoration, which may be more successful.

Soil ecology is still a ‘black box’, even after decades of work to unravel its effects (Eisenhauer et al., 2017). The financial cost of performing analyses such as those used in this thesis has dropped dramatically and the bioinformatics software are developing to a level where they may become standardised over the next few years. As metagenomics, transcriptomics and proteomics develop, they will also bring a new world of understanding to the processes performed by the diverse range of life in soil (Renella et al., 2014; Prosser, 2015). Critical, though, for relevance to an industry such as restoration, is quantifying the importance of the full diversity of microbial species. In this study, 35% of ITS sequences could not even be assigned to a phylum; with such poor characterisation, it is important to understand that this represents a large known unknown. So far nitrogen fixers such as rhizobia varieties and the many mycorrhiza species are well identified as performing important roles in restoration (Herrera et al., 1993; Jasper, 2007; Teng et al., 2015; Koziol et al., 2018). This study was able to show differences in ectomycorrhiza between the Experimental Site and the references, but, given that so little is characterised, it can only be imagined what other differences there may be. There is much work to be done but the state-of-the-art methods are approaching a level where microbiology can regularly become integrated with restoration ecology research.

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This study has also highlighted the importance of water in the recovery of an ecosystem. Although it was not a focus of this study, water availability probably influenced the flora community composition, the microbial composition and the ability of the ecosystem to cycle nutrients. Ngugi et al. (2015) found that water dynamics improve with age of the site and, as young sites are most at risk of failure, this seems like a primary area to target. Future studies should focus on not only the water-holding capacity of the topsoils, but also the ability of water to infiltrate into the deeper spoil layers. The Spoil treatment demonstrated very minimal loss of sodium and chloride through leaching. Probably, the crusting caused by high sodium levels resulted in an inability to leach soluble salts, which, depending on the circumstances, can be both a positive and a negative.

This study confirmed that Spoil is a very poor medium for growth. An area of future study could be the use of soils that have unfavourable characteristics for landform stability but which could provide an additional buffer between the Spoil and the topdressing (or capping). Currently, unfavourable soils are buried in the landform. Their use would effectively involve the creation of a B horizon rather than an A horizon applied directly over a C horizon of spoil, which is standard in the industry. It may also be worth separating distinct parts of the spoil for placement on the surface, as is performed in the Forestry Reclamation Approach of the USA: weathered rock is placed above unweathered rock layers, particularly sandstones, so that there is at least 1.2 m of suitable rooting material (Zipper et al., 2013; Skousen et al., 2017). However, this should be tested because the extra depth of soil may not provide enough of a benefit to justify the extra cost of coordination to appropriately store and deposit the material.

We know that fauna restoration research following mining is lacking compared with flora research (Cristescu et al., 2012). Fauna play important roles in pollination and seed dispersal that can have a large impact on the development of the flora community of a site, yet they were not considered as part of this study. Similarly, soil fauna are suspected to play important roles in ecosystem processes, particularly decomposition (Briones, 2014). However, as for soil microbiology, research is lacking. Given the studies suggesting the importance of bacteria and fungi to restoration, research on the species that consume bacteria and fungi, among many other roles, could increase the performance of restoration and deserves more attention (Decaëns et al., 2006; Boyer and Wratten, 2010).

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One of the great challenges in restoration ecology is that variation between sites is not well explained. This becomes increasingly noticeable when international comparisons are made. Particularly strong differences are seen between the coal mines of the Hunter Valley and those of Central Europe, where spontaneous natural succession is a preferred method of restoration (Prach and Pyšek, 2001; Prach et al., 2007). Detailed study of spoil characteristics, distances to vegetation and plant characteristics may be able to determine key differences between the regions that could allow for an increase in natural succession strategies. Were it successful, natural succession would be vastly more cost-effective; Hodačová and Prach (2003) estimated a saving of approximately US$40,000.

7.10 Recommendations for Industry The experiment examined alternative soil ameliorants for when topsoil was unavailable as a resource for restoration. Given the 5 years since restoration began, Subsoil Mulch and Subsoil OGM Mulch treatments were the most successful treatments for achieving the EEC, although better results for EEC composition can still be gained through long-term development of a quality topsoil, as shown by the Ring Rd. If no topsoil or subsoil is available, then the combined application of Mulch and OGM to spoil is still a valuable treatment for establishment of vegetation and creation of EEC community composition. Spoil OGM Mulch was a good performer overall; the indices of performance examined in Chapter 3 showed floristic results comparable to many of the Subsoil treatments.

Subsoils can be highly useful as a substrate if they do not have undesirable qualities such as being dispersive or having a large weed seed bank. This suggests that the benefits of using a local subsoil, such as relictual microbial community and seedbank, are more important than the soil type and fertility. This conclusion can assist in the development of restoration priorities. Although OGM can no longer be used, products like it, such as food organics and garden organics, can enhance restoration and would be best used as part of a mosaic landscape. In addition to the nutritional benefits focused on in this study, the organic fraction of OGM is probably a key part of its success, as previous studies show it alters competition for exchangeable elements. Woodchip mulch was generally beneficial to flora community development; however, it generally had less of an effect on community composition or soil fertility than the addition of Subsoil or OGM. The reduced effect of Mulch may have been due

252 to its coarse size, which could have been beneficial for increasing soil heterogeneity but reduced its influence on soil carbon dynamics.

Planning to include variation in the characteristics of a landscape and soils can increase biodiversity and improve the development of the overall site (Matonis and Binkley, 2018). This study found that the different soil types encouraged the development of variation in flora communities. Flora biodiversity could be best reintroduced by varying fertility from moderate to relatively low levels as part of a mosaic. Higher fertility areas can assist certain species; for example, Plantago debilis was only found on the Experimental Site in OGM treatments. However, regular maintenance for exotic species would be necessary. Developing mosaics of varying characteristics across landscapes could also insure against unsuitable restoration methods or implementation as the variation may provide reduced risk of failure. For example, on the Experimental Site the Subsoil Mulch treatment had low phosphorus, but, as it was only one treatment in a mosaic landscape, the whole site is not considered to have failed. Similarly, if one area reduces in species richness because of strong competition in a part of the mosaic where fertility was accidentally made too high, a low-fertility section may have increased richness and produce community composition closer to the restoration target, which reduces the impact at the landscape scale. This explanation is not intended to be given as an opportunity to perform lower quality restoration because intentional poor restoration in the post-mining setting can lead to low success, such as the Spoil treatment in this work. Rather, it is intended as an opportunity to improve restoration and reduce risk. Reduction in risk for restoration would enable more certainty for major corporations, governments and not-for- profit organisations to commit to restoration.

High biodiversity seeding may have been a factor in increasing productivity and development towards the reference. There was little difference in biomass between Spoil OGM and Subsoil Mulch treatments even though soil conditions were very different. This suggests species richness and composition may have made up for low nutritional characteristics in Subsoil Mulch. Although research on relationships between species richness and productivity remains a developing field, the potential benefits to ecosystem development from implementing higher diversity restoration outweigh the additional economic costs. Selection of species is important because species composition is particularly important to achieving EEC targets and the species richness of a plot can be a factor in determining plant community

253 type. Where diagnostic species to the community do not establish in restoration areas, late introductions may be performed as the ecosystem matures. Based on the current study, it would be recommended that late introductions are performed at least 5 years after restoration initiation because this period of time has seen large changes in canopy height, percentage ground cover and community composition.

This study supports the already recognised importance of monitoring and maintenance to rectify off-target restoration trajectories; for example, in species composition, soil fertility and landscape stability. As the variability within treatments can be substantial, it should not be expected that all areas of restoration will develop identically. This highlights the importance of comparing data with multiple modern reference ecosystems, including those that are known to have received similar disturbance and are recovering, to determine how successful the restored system is. Considering that the flora community is the main target of many restoration projects in the mining industry, and unassisted dispersal and the soil ameliorants resulted in a soil bacterial community with similar species richness, the costs of monitoring the trajectory of the soil bacterial community (as a standard procedure) may outweigh the benefits. There are, however, situations where soil bacteria monitoring would be worthwhile, such as trialling new restoration methods, restoration in areas with minimal surrounding native vegetation, and if the restoration is not performing to an adequate standard. The recommendations for the soil fungal community are different. The low abundance and richness of ectomycorrhiza and other fungal symbionts in this study suggests further examination of fungal communities would be valuable if there were concerns about poor establishment of a keystone plant species or poor development of a diverse or specific flora community. Soil chemical analyses are already performed as part of many restoration projects. However, given the deficiency seen in Subsoil Mulch, total phosphorus is extremely important to the development of a forest ecosystem. Where the intention is to restore to a native forest similar to the RSF, the ecosystem will ideally average more than 385kg/ha of phosphorus because this is the average amount required for biomass. The average of the summed components (including both biomass and soils) of RSF was 498 kg/ha. Theoretically, 385 kg/ha is the equivalent of 107 ppm total phosphorus 0–0.3 m deep across one hectare with 1.2 t/m3 bulk density. Given that large amounts of phosphorus are unavailable to plants

254 from soil, professional assistance is recommended to gain the correct amount of phosphorus for each system.

7.11 Conclusion Restoration of forested lands is a process that takes years, decades and perhaps even centuries. There are, however, actions that can accelerate restoration post coal mining and improve the quality of the final ecosystem. Addition of a quality topsoil from the target community is still the ideal method of restoration, but if quality topsoil is unavailable, subsoil and coarse woodchip mulch can be used effectively. Products that are high in organics and nutrients can also provide great benefits to restoration sites but may be best used as part of mosaic landscapes to facilitate species that have differing preferences for soil fertility. A high- diversity species mix was very beneficial to the study site, assisting the development of a community similar to the reference.

This study has both found potential pitfalls and allayed some concerns in the methodologies and pathways of coal mine restoration. Understanding that the community is from a retrogressive ecosystem suggests that the restoration should be performed with very low- fertility soils and that restoration of the microbial community composition will be important to the long-term community composition. However, this study has shown that restoring some level of soil fertility, particularly of phosphorus, is important to match the biomass of the reference. The research has also shown that, except for fungal symbionts, similar soil microbial communities have the ability to establish in restoration areas within 6 years if there is nearby vegetation. Further, biodiverse areas can be restored if diverse seed mixes are used from the outset. This study also reinforced many of the benefits behind current best practice methods.

Restoration of endangered ecological communities is still a difficult task, one that should not be attempted lightly. Even the Experimental Site did not match all of the criteria as an endangered ecological community recognised by NSW. Part of this is the fundamental variability inherent in the restoration process, due not only to the broad range of species, substrates, locations and times of restoration, but also to the high degree of stochasticity inherent in the system. Another part is that ecosystems change with time and there are multiple alternative states which the same restoration actions can produce. This does not

255 make attempts to restore endangered ecological communities less important, with declines in biodiversity across the globe, restoration needs to be performed even if success is not guaranteed.

This study provided further understanding on how ecosystem development will differ with alternative soil ameliorants and the underlying processes that affect restoration. This information will not only benefit the coal mining industry of the Hunter Valley but restoration of degraded sites in a variety of contexts.

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Appendix A – Flora Species

All species authority citations were based on the NSW Herbarium (PlantNET), the NSW Plant information network system by the Royal Botanic Gardens and Domain Trust, Sydney (Royal Botanic Gardens and Domain Trust, 2019) (Table A1).

Table A1. All the species considered as part of this work and relevant information about them. Species were identified based the PlantNET system (Royal Botanic Gardens and Domain Trust, 2019) and synonyms are given where a recent name change is noted. Growth form group is as defined by Oliver et al. (2019) and is a functional categorisation (rather than taxonomic) for use with the NSW regulatory tool, the Biodiversity Assessment Method. The ‘Seeded/Planted’ column specifies which species were introduced to the Experimental Site as part of its set-up. A ‘1’ is used to indicate that the species was present on the Experimental Site in 2015 and 2018, on the Ring Rd and/or RSF in 2018 and in at least one of the Reference Lists used. The ‘Reference Lists’ include formal descriptions by the NSW government for the Central Hunter Grey Box–Ironbark Woodland and Central Hunter Ironbark–Spotted Gum–Grey Box Forest as well as the federal government listing Central Hunter Valley Eucalypt Forest and Woodland Ecological Community. As the species lists for the state government-listed communities only contain characteristic species, lists are also used that include associated species (S. Cox, 2013, personal communication). The ‘Reference Lists’ also includes a species list for the North Offset (Umwelt (Australia) Pty Limited, 2010a), which is the broad area to the north of the Experimental Site, and a list for the total area of Ravensworth Operations mine site (Umwelt (Australia) Pty Limited, 2010a).

Species Family Synonym

lists

(subfamily) form

2015

oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Acacia amblygona A.Cunn. ex Benth. Fabaceae Native Shrub 1 1 1 1 1 (Mimosoideae) Acacia bulgaensis Tindale & S.J.Davies Fabaceae Native Tree 1 (Mimosoideae) Acacia cultriformis A.Cunn. ex G.Don Fabaceae Native Shrub 1 (Mimosoideae) 296

Species Family Synonym

lists

(subfamily) form

2015

oup

Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Acacia concurrens Pedley Fabaceae Native Tree 1 (Mimosoideae) Acacia decora Rchb. Fabaceae Native Shrub 1 1 1 1 (Mimosoideae) Acacia doratoxylon A.Cunn. Fabaceae Native Tree 1 (Mimosoideae) Acacia falcata Willd. Fabaceae Native Shrub 1 1 1 1 1 (Mimosoideae) Acacia filicifolia Cheel & M.B.Welch Fabaceae Native Tree 1 (Mimosoideae) Acacia implexa Benth. Fabaceae Native Tree 1 1 1 1 1 (Mimosoideae) Acacia irrorata Sieber ex Spreng. Fabaceae Native Tree 1 1 (Mimosoideae) Acacia melanoxylon R.Br. Fabaceae Native Tree 1 (Mimosoideae) Acacia parvipinnula Tindale Fabaceae Native Tree 1 1 1 1 (Mimosoideae) Acacia pendula A.Cunn. & G.Don Fabaceae Native Tree 1 (Mimosoideae) Acacia salicina Lindl. Fabaceae Native Tree 1 1 1 1 (Mimosoideae) Acacia saligna (Labill.) H.L.Wendl. Fabaceae Exotic Tree 1 1 (Mimosoideae) Ajuga australis R.Br. Lamiaceae Native Forb 1

297

Species Family Synonym

lists

(subfamily) form

2015

oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Allocasuarina luehmannii (R.T.Baker) Casuarinaceae Native Tree 1 1 1 1 1 L.A.S.Johnson Amyema cambagei (Blakely) Danser Loranthaceae Native Other 1 Alternanthera denticulata R.Br. Amaranthaceae Native Forb 1 1 Alternanthera sp. A. Flora of New South Amaranthaceae Native Forb 1 Wales (M.Gray 5187) J. Palmer Andropogon virginicus L. Poaceae Exotic Grass 1 & like Angophora floribunda (Sm.) Sweet Myrtaceae Native Tree 1 1 1 1 Anthosachne scabra (R.Br.) Nevski Poaceae Elymus scaber Native Grass 1 & like Aristida spp. Poaceae Native Grass 1 1 1 1 & like Aristida benthamii Henrard var. benthamii Poaceae Native Grass 1 & like Aristida calycina R.Br. var. calycina Poaceae Native Grass 1 & like Aristida personata Henrard Poaceae Native Grass 1 & like Aristida ramosa R.Br. Poaceae Native Grass 1 1 & like Aristida vagans Cav. Poaceae Native Grass 1 1 & like Asperula conferta Hook.f. Rubiaceae Native Forb 1 Arthropodium sp. B sensu Harden (1993) Anthericaceae Native Forb 1 1 298

Species Family Synonym

lists

(subfamily) form

2015

oup

Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Aster subulatus Michx. Asteraceae Exotic Forb 1 1 Asteraceae spp. Asteraceae Exotic Forb 1 Atriplex semibaccata R.Br. Chenopodiaceae Native Forb 1 1 1 1 1 Austrostipa scabra (Lindl.) S.W.L.Jacobs & Poaceae Native Grass 1 1 1 1 1 J.Everett & like Austrostipa verticillata (Nees ex Spreng.) Poaceae Native Grass 1 1 1 1 1 S.W.L.Jacobs & J.Everett & like Bidens spp. Asteraceae Exotic Forb 1 1 1 1 Boerhavia dominii Meikle & Hewson Nyctaginaceae Native Forb 1 Bothriochloa biloba S.T.Blake Poaceae Native Grass 1 & like Bothriochloa decipiens (Hack.) C.E.Hubb. Poaceae Native Grass 1 1 1 & like Bothriochloa macra (Steud.) S.T.Blake Poaceae Native Grass 1 & like Brachychiton populneus (Schott & Endl.) Malvaceae Native Tree 1 1 1 1 R.Br. Brachyscome ciliaris (Labill.) Less. Asteraceae Native Forb 1 Brachyscome multifida DC. Asteraceae Native Forb 1 Breynia oblongifolia Müll.Arg. Phyllanthaceae Native Shrub 1 1 Briza minor L. Poaceae Exotic Grass 1 & like Brunoniella australis (Cav.) Bremek. Acanthaceae Native Forb 1 1 Bursaria spinosa Cav. Pittosporaceae Native Shrub 1 1 1 1 1

299

Species Family Synonym

lists

(subfamily) form

2015

oup

Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Callitris endlicheri (Parl.) F.M.Bailey Cupressaceae Native Tree 1 1 1 1 Calocephalus citreus Less. Asteraceae Native Forb 1 1 Calotis cuneifolia R.Br. Asteraceae Native Forb 1 Calotis lappulacea Benth. Asteraceae Native Forb 2 1 1 1 1 Carex inversa R.Br. Cyperaceae Native Grass 1 1 1 & Like Carthamus lanatus L. Asteraceae Exotic Forb 1 1 Cassinia sifton Orchard Asteraceae Native Shrub 1 Cassinia quinquefaria R.Br. Asteraceae Native Shrub 1 1 1 Casuarina spp. Casuarinaceae Native Tree 1 Casuarina glauca Sieber ex Spreng. Casuarinaceae Native Tree 1 Cayratia clematidea (F.Muell.) Domin Vitaceae Native Other 1 Cenchrus clandestinus (Hochst. ex Chiov.) Poaceae Pennisetum Exotic Grass 1 1 1 Morrone clandestinum & like Centaurea calcitrapa L. Asteraceae Exotic Forb 1 1 Centaurium erythraea Rafn Gentianaceae Exotic Forb 1 Centella asiatica (L.) Urb. Apiaceae Native Forb 1 1 Cheilanthes distans (R.Br.) Mett. Pteridaceae Native Fern 1 1 1 Cheilanthes sieberi Kunze Pteridaceae Native Fern 1 1 1 Chenopodiaceae spp. Chenopodiaceae Native Forb 1 Chloris divaricata R.Br. var. divaricata Poaceae Native Grass 1 1 1 1 & like Chloris gayana Kunth Poaceae Exotic Grass 1 1 1 1 & like

300

Species Family Synonym

lists

(subfamily) form

2015

oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Chloris truncata R.Br. Poaceae Native Grass 1 1 1 1 1 & like Chloris ventricosa R.Br. Poaceae Native Grass 1 1 & like Chorizema parviflorum Benth. Fabaceae Native Forb 1 1 (Faboideae) Chrysocephalum apiculatum (Labill.) Asteraceae Native Forb 2 1 Steetz Chrysocephalum semipapposum (Labill.) Asteraceae Native Forb 1 Steetz Cirsium vulgare (Savi) Ten. Asteraceae Exotic Forb 1 1 1 Clematis aristata R.Br. ex Ker Gawl. Ranunculaceae Native Other 1 Commelina cyanea R.Br. Commelinaceae Native Forb 1 1 1 Convolvulus angustissimus R.Br. Convolvulaceae Native Forb 1 1 1 Convolvulus arvensis L. Convolvulaceae Exotic Forb 1 Convolvulus erubescens Sims Convolvulaceae Native Forb 1 1 1 Conyza spp. Asteraceae Exotic Forb 1 1 1 Corymbia maculata (Hook.) K.D.Hill & Myrtaceae Eucalyptus Native Tree 1 1 1 1 1 L.A.S.Johnson maculata Cotula australis (Sieber ex Spreng.) Hook.f. Asteraceae Native Forb 1 Crassula sieberiana (Schult. & Schult.f.) Crassulaceae Native Forb 1 Druce Cryptandra amara Sm. Rhamnaceae Native Shrub 1

301

Species Family Synonym

lists

(subfamily) form

2015

oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Cyanthillium cinereum (L.) H.Rob. Asteraceae Vernonia Native Forb 1 1 cinerea Cyclospermum leptophyllum (Pers.) Apiaceae Exotic Forb 1 1 Sprague ex Britton & P.Wilson Cymbonotus lawsonianus Gaudich. Asteraceae Native Forb 1 Cymbopogon refractus (R.Br.) A.Camus Poaceae Native Grass 1 1 1 1 1 & like Cynodon dactylon (L.) Pers. Poaceae Native Grass 1 1 1 1 & like Cynoglossum australe R.Br. Boraginaceae Native Forb 1 Cyperus gracilis R.Br. Cyperaceae Native Grass 1 1 1 & like Cyperus imbecillis R.Br. Cyperaceae Native Grass 1 1 1 & like Daucus glochidiatus (Labill.) Fisch., Apiaceae Native Forb 1 C.A.Mey. & Avé-Lall. Daviesia genistifolia A.Cunn. ex Benth. Fabaceae Native Shrub 1 1 1 1 1 (Faboideae) Daviesia ulicifolia Andrews Fabaceae Native Shrub 1 1 1 (Faboideae) Desmodium rhytidophyllum (F.Muell.) Fabaceae Native Forb 1 1 Benth. (Faboideae) Desmodium varians (Labill.) G.Don Fabaceae Native Forb 1 1 1 (Faboideae) Dianella caerulea Sims var. caerulea Phormiaceae Native Forb 1 1

302

Species Family Synonym

lists

(subfamily) form

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Dianella caerulea var. producta Phormiaceae Native Forb 1 R.J.F.Hend. Dianella longifolia R.Br. var. longifolia Phormiaceae Native Forb 1 1 Dianella revoluta R.Br. var. revoluta Phormiaceae Native Forb 1 1 Dichanthium sericeum (R.Br.) A.Camus Poaceae Native Grass 1 1 1 1 & like Dichelachne inaequiglumis (Hack. ex Poaceae Native Grass 1 Cheeseman) Edgar & Connor & like Dichelachne micrantha (Cav.) Domin Poaceae Native Grass 1 1 1 & like Dichondra repens J.R.Forst. & G.Forst. Convolvulaceae Native Forb 1 1 1 1 1 Digitaria breviglumis (Domin) Henrard Poaceae Native Grass 1 & like Digitaria ramularis (Trin.) Henrard Poaceae Native Grass 1 1 1 & like Dodonaea viscosa Jacq. Sapindaceae Native Shrub 1 1 1 1 1 Dodonaea viscosa subsp. cuneata (Sm.) Sapindaceae Native Shrub 1 J.G.West Dysphania carinata (R.Br.) Mosyakin & Chenopodiaceae Native Forb 1 1 1 Clemants Echinochloa colona (L.) Link Poaceae Native Grass 1 & like Echinopogon caespitosus C.E.Hubb. var. Poaceae Native Grass 1 caespitosus & like

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Echinopogon ovatus (G.Forst.) P.Beauv. Poaceae Native Grass 1 1 & like Einadia hastata (R.Br.) A.J.Scott Chenopodiaceae Native Forb 1 1 Einadia nutans subsp. linifolia (R.Br.) Paul Chenopodiaceae Native Forb 1 1 1 1 G.Wilson Einadia nutans (R.Br.) A.J.Scott subsp. Chenopodiaceae Native Forb 2 1 1 1 1 nutans Einadia polygonoides (Murr) Paul G.Wilson Chenopodiaceae Native Forb 1 1 1 Einadia trigonos subsp. leiocarpa Paul Chenopodiaceae Native Forb 1 G.Wilson Eleusine tristachya (Lam.) Lam. Poaceae Exotic Grass 1 & like Enchylaena tomentosa R.Br. Chenopodiaceae Native Forb 1 1 1 1 1 Enteropogon acicularis (Lindl.) Lazarides Poaceae Native Grass 1 1 & like Entolasia stricta (R.Br.) Hughes Poaceae Native Grass 1 & like Eragrostis brownii (Kunth) Nees Poaceae Native Grass 1 1 1 1 & like Eragrostis curvula (Schrad.) Nees Poaceae Exotic Grass 1 1 & like Eragrostis leptostachya (R.Br.) Steud. Poaceae Native Grass 1 1 1 1 1 & like Eragrostis spp. Poaceae Native Grass 1 1 1 & like

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oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Eremophila debilis (Andrews) Chinnock Scrophulariaceae Native Forb 1 1 1 1 1 Eriochloa procera (Retz.) C.E.Hubb. Poaceae Native Grass 1 1 1 & like Eriochloa pseudoacrotricha (Stapf ex Poaceae Native Grass 1 Thell.) J.M.Black & like Erodium crinitum Carolin Geraniaceae Native Forb 1 1 1 Eucalyptus albens Benth. Myrtaceae Native Tree 1 Eucalyptus blakelyi Maiden Myrtaceae Native Tree 1 Eucalyptus crebra F.Muell. Myrtaceae Native Tree 1 1 1 1 1 Eucalyptus dawsonii R.T.Baker Myrtaceae Native Tree 1 Eucalyptus fibrosa F.Muell. (red variant) Myrtaceae Native Tree 1 1 1 1 Eucalyptus fibrosa (blue-green variant) Myrtaceae Native Tree 1 1 Eucalyptus glaucina (Blakely) Myrtaceae Native Tree 1 L.A.S.Johnson Eucalyptus melliodora A.Cunn. ex Schauer Myrtaceae Native Tree 1 Eucalyptus moluccana Roxb. Myrtaceae Native Tree 1 1 1 1 1 Eucalyptus punctata DC. Myrtaceae Native Tree 1 1 Eucalyptus spp. Myrtaceae Native Tree 1 1 1 Eucalyptus tereticornis Sm. Myrtaceae Native Tree 1 1 1 1 1 Euchiton involucratus (G.Forst.) Holub Asteraceae Native Forb 1 1 Euchiton japonicus (Thunb.) Holub Asteraceae Euchiton Native Forb 1 gymnocephalus Euphorbia drummondii Boiss. Euphorbiaceae Chamaesyce Native Forb 1 drummondii

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oup Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference

Euphorbia spp. Euphorbiaceae Native Forb 1 1 Evolvulus alsinoides (L.) L. Convolvulaceae Native Forb 1 Exocarpos cupressiformis Labill. Santalaceae Native Tree 1 1 Facelis retusa (Lam.) Sch.Bip. Asteraceae Exotic Forb 1 1 Fimbristylis dichotoma (L.) Vahl Cyperaceae Native Grass 1 & like Galenia pubescens (Eckl. & Zeyh.) Druce Aizoaceae Exotic Forb 1 1 1 1 Galium divaricatum Lam. Rubiaceae Exotic Forb 1 Galium spp. Rubiaceae Native Forb 1 1 Gamochaeta americana (Mill.) Wedd. Asteraceae Exotic Forb 1 Gamochaeta calviceps (Fernald) Cabrera Asteraceae Exotic Forb 1 Geijera parviflora Lindl. Rutaceae Native Tree 1 Geranium molle L. Geraniaceae Exotic Forb 1 Geranium solanderi Carolin Geraniaceae Native Forb 1 1 Glossocardia bidens (Retz.) Veldkamp Asteraceae Native Forb 1 1 Glycine clandestina J.C.Wendl. Fabaceae Native Forb 1 1 1 1 1 (Faboideae) Glycine latifolia (Benth.) C.A.Newell & Fabaceae Native Forb 1 1 1 1 Hymowitz (Faboideae) Glycine tabacina (Labill.) Benth. Fabaceae Native Forb 1 1 1 1 1 (Faboideae) Gomphocarpus fruticosus (L.) W.T.Aiton Apocynaceae Exotic Forb 1 1 1 Goodenia hederacea Sm. Goodeniaceae Native Forb 1 Goodenia rotundifolia R.Br. Goodeniaceae Native Forb 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Hakea sericea Schrad. & J.C.Wendl. Proteaceae Native Shrub 1 Hardenbergia violacea (Schneev.) Stearn Fabaceae Native Other 1 1 1 1 1 (Faboideae) Heliotropium amplexicaule Vahl Boraginaceae Exotic Forb 1 1 1 Hibbertia spp. Dilleniaceae Native Shrub 1 Hovea spp. Fabaceae Native Shrub 1 (Faboideae) Hyparrhenia hirta (L.) Stapf Poaceae Exotic Grass 1 1 & like Hypericum gramineum G.Forst. Hypericaceae Native Forb 2 1 1 Hypochaeris radicata L. Asteraceae Exotic Forb 1 1 Indigofera australis Willd. Fabaceae Native Shrub 1 1 1 1 (Faboideae) Kennedia rubicunda Vent. Fabaceae Native Other 1 1 1 1 (Faboideae) Kunzea ambigua (Sm.) Druce Myrtaceae Native Shrub 1 1 Lactuca serriola L. Asteraceae Exotic Forb 1 1 1 Lagenophora stipitata (Labill.) Druce Asteraceae Native Forb 1 Laxmannia gracilis R.Br. Anthericaceae Native Forb 1 1 1 1

Lepidium africanum (Burm.f.) DC. Brassicaceae Exotic Forb 1 1 1 Lepidium bonariense L. Brassicaceae Exotic Forb 1 1 Lepidium didymum L. Brassicaceae Exotic Forb 1 Lepidosperma laterale R.Br. Cyperaceae Native Grass 1 & Like

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Linum marginale A.Cunn. ex Planch. Linaceae Native Forb 1 1 1 Linum trigynum L. Linaceae Exotic Forb 1 1 1 Lissanthe strigosa (Sm.) R.Br. Ericaceae Native Shrub 1 1 (Epacridoideae) Lobelia purpurascens R.Br. Campanulaceae Native Forb 1 1 Lomandra confertifolia subsp. pallida Lomandraceae Native Grass 1 A.T.Lee & like Lomandra filiformis (Thunb.) Britten Lomandraceae Native Grass 1 subsp. filiformis & like Lomandra spp. Lomandraceae Native Grass 1 1 & like Lomandra glauca (R.Br.) Ewart Lomandraceae Native Grass 1 & like Lomandra longifolia Labill. Lomandraceae Native Grass 1 1 & like Lomandra multiflora (R.Br.) Britten Lomandraceae Native Grass 1 1 1 1 & like Lycium ferocissimum Miers Solanaceae Exotic Shrub 1 Lysiana exocarpi subsp. tenuis (Blakely) Loranthaceae Native Other 1 Barlow Lysiana linearifolia Tiegh. Loranthaceae Native Other 1 Lysimachia arvensis (L.) U.Manns & Primulaceae Anagallis Exotic Forb 1 1 1 1 Anderb. arvensis Maireana microphylla (Moq.) Paul Chenopodiaceae Native Shrub 1 1 1 G.Wilson 308

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Marsdenia viridiflora R.Br. Apocynaceae Native Other 1 Medicago sativa L. Fabaceae Exotic Forb 1 (Faboideae) Megathyrsus maximus (Jacq.) B.K.Simon & Poaceae Exotic Grass 1 1 1 S.W.L.Jacobs var. maximus & like Melaleuca uncinata R.Br. Myrtaceae Native Shrub 1 Melia azedarach L. Meliaceae Native Tree 1 Melichrus urceolatus R.Br. Ericaceae Native Shrub 1 (Epacridoideae) Melilotus indicus (L.) All. Fabaceae Exotic Forb 1 (Faboideae) Melinis repens (Willd.) Zizka Poaceae Exotic Grass 1 1 1 & like Mentha diemenica Spreng. Lamiaceae Native Forb 1 Microlaena stipoides (Labill.) R.Br. Poaceae Native Grass 1 1 1 1 & like Modiola caroliniana (L.) G.Don Malvaceae Exotic Forb 1 1 1 Myoporum montanum R.Br. Scrophulariaceae Native Tree 1 Notelaea microcarpa R.Br. var. microcarpa Oleaceae Native Tree 1 1 Olearia elliptica DC. Asteraceae Native Shrub 1 1 1 1 1 Olea spp. Oleaceae Exotic Tree 1 1 Opercularia aspera Gaertn. Rubiaceae Native Forb 1 Opercularia diphylla Gaertn. Rubiaceae Native Forb 1 Opercularia hispida Spreng. Rubiaceae Native Forb 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Opuntia aurantiaca Lindl. Cactaceae Exotic Forb 1 Opuntia humifusa (Raf.) Raf. Cactaceae Exotic Forb 1 1 1 Opuntia spp. Cactaceae Exotic Forb 1 1 Opuntia stricta (Haw.) Haw. Cactaceae Exotic Forb 1 1 Oxalis spp. Oxalidaceae Native Forb 1 1 1 1 Oxytes brachypoda (A.Gray) H.Ohashi & Fabaceae Desmodium Native Forb 2 1 1 1 1 K.Ohashi (Faboideae) brachypodum Ozothamnus diosmifolius (Vent.) DC. Asteraceae Native Shrub 1 1 Panicum effusum R.Br. Poaceae Native Grass 1 1 1 & like Panicum simile Domin Poaceae Native Grass 1 & like Paronychia brasiliana DC. Caryophyllaceae Exotic Forb 1 Paspalidium distans (Trin.) Hughes Poaceae Native Grass 1 1 1 1 & like Paspalidium gracile (R.Br.) Hughes Poaceae Native Grass 1 & like Paspalum dilatatum Poir. Poaceae Exotic Grass 1 1 & like Paspalum spp. Poaceae Exotic Grass 1 & like Passiflora herbertiana Ker Gawl. Passifloraceae Native Other 1 Pavonia hastata Cav. Malvaceae Exotic Shrub 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Petrorhagia nanteuilii (Burnat) P.W.Ball & Caryophyllaceae Exotic Forb 1 Heywood Phyllanthus hirtellus F.Muell. ex Müll.Arg. Phyllanthaceae Native Shrub 1 Phyllanthus virgatus G.Forst. Phyllanthaceae Native Forb 1 1 1 1 Phytolacca octandra L. Phytolaccaceae Exotic Forb 1 1 Plantago debilis R.Br. Plantaginaceae Native Forb 1 1 1 1 Plantago gaudichaudii Barnéoud Plantaginaceae Native Forb 1 1 1 Plantago lanceolata L. Plantaginaceae Exotic Forb 1 1 Plantago myosuros Lam. Plantaginaceae Exotic Forb 1 1 Polycarpaea corymbosa var. minor Pedley Caryophyllaceae Native Forb 1 Polymeria calycina R.Br. Convolvulaceae Native Forb 1 Pomax umbellata (Gaertn.) Sol. ex A.Rich. Rubiaceae Native Forb 1 1 Psydrax odorata (G.Forst.) A.C.Sm. & Rubiaceae Native Tree 1 S.P.Darwin Pullenia gunnii (Benth. ex Hook.f.) Fabaceae Desmodium Native Forb 1 1 1 H.Ohashi & K.Ohashi (Faboideae) gunnii Pultenaea microphylla Sieber ex DC. Fabaceae Native Shrub 1 1 1 (Faboideae) Pultenaea retusa Sm. Fabaceae Native Shrub 1 (Faboideae) Pultenaea spp. Fabaceae Native Shrub 1 (Faboideae) Pultenaea spinosa (DC.) H.B.Will. Fabaceae Native Shrub 1 (Faboideae)

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Richardia stellaris (Cham. & Schltdl.) Rubiaceae Exotic Forb 1 1 1 Steud. Rostellularia adscendens (R.Br.) Acanthaceae Native Forb 1 1 R.M.Barker Rumex brownii Campd. Polygonaceae Native Forb 1 Rytidosperma auriculatum (J.M.Black) Poaceae Native Grass 1 Connor & Edgar & like Rytidosperma caespitosum (Gaudich.) Poaceae Native Grass 1 Connor & Edgar & like Rytidosperma fulvum (Vickery) A.M. Poaceae Native Grass 1 1 1 1 Humphreys & H.P.Linder & like Rytidosperma setaceum (R.Br.) Connor & Poaceae Native Grass 1 Edgar & like Rytidosperma tenuius (Steud.) Connor & Poaceae Native Grass 1 Edgar & like Rytidosperma spp. Poaceae Native Grass 1 1 & like Salsola australis R.Br. Chenopodiaceae Native Shrub 1 1 1 Schenkia australis (R.Br.) Mansion Gentianaceae Native Forb 1 Schkuhria pinnata (Lam.) Thell. Asteraceae Exotic Forb 1 Schoenus apogon Roem. & Schult. Cyperaceae Native Grass 1 & like Sclerolaena birchii (F.Muell.) Domin Chenopodiaceae Native Shrub 1 Scleria mackaviensis Boeckeler Cyperaceae Native Grass 1 1 1 & like 312

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Senecio linearifolius A.Rich. Asteraceae Native Forb 1 Senecio madagascariensis Poir. Asteraceae Exotic Forb 1 1 1 1 Senecio quadridentatus Labill. Asteraceae Native Forb 1 1 Senna artemisioides subsp. zygophylla Fabaceae Native Shrub 1 1 1 (Benth.) Randell (Caesalpinioideae) Setaria parviflora (Poir.) Kerguelen Poaceae Exotic Grass 1 1 & like Setaria sphacelata (Schumach.) Stapf & Poaceae Exotic Grass 1 1 1 C.E.Hubb. & like Sida corrugata Lindl. Malvaceae Native Forb 1 1 1 1 Sida cunninghamii C.T.White Malvaceae Native Shrub 1 Sida filiformis A.Cunn. Malvaceae Native Shrub 1 Sida hackettiana W.Fitzg. Malvaceae Native Shrub 1 1 Sida rhombifolia L. Malvaceae Exotic Shrub 1 1 1 1 Sida spinosa L. Malvaceae Exotic Shrub 1 Sida trichopoda F.Muell. Malvaceae Native Forb 1 Sida spp. Malvaceae Native Forb 1 1 Sisymbrium orientale L. Brassicaceae Exotic Forb 1 Sisyrinchium rosulatum E.P.Bicknell Iridaceae Exotic Forb 1 Solanum americanum Mill. Solanaceae Native Forb 1 1 1 Solanum brownii Dunal Solanaceae Native Shrub 1 Solanum cinereum R.Br. Solanaceae Native Shrub 1 1 1 1 Solanum nigrum L. Solanaceae Exotic Forb 1 1 1 1 Solanum opacum A.Braun & Bouché Solanaceae Native Forb 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Solanum prinophyllum Dunal Solanaceae Native Forb 1 1 1 Solanum pungetium R.Br. Solanaceae Native Forb 1 Solanum stelligerum Sm. Solanaceae Native Shrub 1 1 Solenogyne bellioides Cass. Asteraceae Native Forb 1 1 Sonchus asper (L.) Hill Asteraceae Exotic Forb 1 1 Sonchus oleraceus L. Asteraceae Exotic Forb 1 1 1 1 Sporobolus creber De Nardi Poaceae Native Grass 1 1 1 & like Sporobolus caroli Mez Poaceae Native Grass 1 & like Stackhousia muricata Lindl. Stackhousiaceae Native Forb 1 1 1 Stackhousia viminea Sm. Stackhousiaceae Native Forb 1 Swainsona galegifolia (Andrews) R.Br. Fabaceae Native Forb 2 1 1 1 (Faboideae) Tagetes minuta L. Asteraceae Exotic Forb 1 1 1 1 Taraxacum officinale Weber Asteraceae Exotic Forb 1 Templetonia stenophylla (F.Muell.) Fabaceae Native Shrub 1 1 1 J.M.Black (Faboideae) Teucrium junceum (A.Cunn. ex Walp.) Lamiaceae Native Shrub 1 1 1 Kattari & Heubl Themeda triandra Forssk. Poaceae Themeda Native Grass 1 1 1 1 1 australis & like Tribulus terrestris L. Zygophyllaceae Exotic Forb 1 Tricoryne elatior R.Br. Anthericaceae Native Forb 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Trifolium arvense L. Fabaceae Exotic Forb 1 1 (Faboideae) Trifolium campestre Schreb. Fabaceae Exotic Forb 1 1 (Faboideae) Trifolium dubium Sibth. Fabaceae Exotic Forb 1 (Faboideae) Trifolium glomeratum L. Fabaceae Exotic Forb 1 (Faboideae) Trifolium repens L. Fabaceae Exotic Forb 1 (Faboideae) Verbena bonariensis L. Verbenaceae Exotic Forb 1 1 1 1 Verbena quadrangularis Vell. Verbenaceae Exotic Forb 1 Verbena rigida Spreng. Verbenaceae Exotic Forb 1 Verbesina encelioides (Cav.) A.Gray Asteraceae Exotic Forb 1 Veronica plebeia R.Br. Plantaginaceae Native Forb 1 1 Vittadinia condyloides N.T.Burb. Asteraceae Native Forb 1 Vittadinia cuneata DC. Asteraceae Native Forb 1 Vittadinia cuneata var. hirsuta N.T.Burb. Asteraceae Native Forb 1 Vittadinia muelleri N.T.Burb. Asteraceae Native Forb 1 1 Vittadinia pterochaeta (F.Muell. ex Benth.) Asteraceae Native Forb 1 1 1 1 J.M.Black Vittadinia pustulata N.T.Burb. Asteraceae Native Forb 1 1 1 Vittadinia sulcata N.T.Burb. Asteraceae Native Forb 1 1 Vittadinia spp. Asteraceae Native Forb 1 1 1

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Native/Exotic Growth gr (1)/ Seeded (2) Planted Experimental Site Experimental 2018 Site & Rd Ring RSF 2018 Reference Vulpia muralis (Kunth) Nees Poaceae Exotic Grass 1 & like Wahlenbergia communis Carolin Campanulaceae Native Forb 1 1 1 Wahlenbergia gracilis (G.Forst.) A.DC. Campanulaceae Native Forb 1 1 1 Wahlenbergia luteola P.J.Sm. Campanulaceae Native Forb 1 1 1 Wahlenbergia spp. Campanulaceae Native Forb 1 1 1 1 1 Withania somnifera (L.) Dunal Solanaceae Exotic Shrub 1 Zornia dyctiocarpa DC. Fabaceae Native Forb 1 1 (Faboideae)

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Appendix B – KEGG Orthologues

KEGG Orthology Studied To examine specific functional roles, 36 KEGG Orthologues (Kanehisa et al., 2016) were selected from the literature (Tables B1, B2 and B3).

Table B1. KEGG Orthology focusing on nitrogen-cycling pathways. Definition is as presented on the KEGG Orthology. Enzyme Commission (EC) and reaction refer to the parent enzyme. Reference refers to other studies that have used the orthologue.

KEGG Name Definition Process Reference E.C. Reaction Orthologue [EC] + K02586 nifD Nitrogenase molybdenum-iron Nitrogen fixation Fani et al. (2000); 8 reduced ferredoxin + 8H + N2 + [EC:1.18.6.1] protein alpha chain Zhu et al. (2016); 16ATP + 16H2O  8 oxidised Mickan et al. (2018) ferredoxin + H2 + 2NH3 + 16ADP + 16 phosphate K02588 nifH Nitrogenase iron protein Nitrogen fixation Fani et al. (2000); Zhu et al. (2016)

K10535 hao Hydroxylamine dehydrogenase Nitrification Zhu et al. (2016); hydroxylamine + H2O + 4 [EC:1.7.2.6] Mickan et al. (2018) ferricytochrome c  nitrite + 4 ferrocytochrome c + 5H+; hydroxylamine + 3 ferricytochrome c  nitric oxide + 3 ferrocytochrome c + 3H+ K10944 pmoA- Methane/ ammonia Nitrification Zhu et al. (2016); methane + quinol + O2 methanol [EC:1.14.18.3; amoA monooxygenase subunit A Mickan et al. (2018) + quinone + H2O; 1.14.99.39]

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KEGG Name Definition Process Reference E.C. Reaction Orthologue [EC] NH3 + a reduced acceptor + O2 NH2OH + an acceptor + H2O K10945 pmoB- pmoB-amoB; methane/ Nitrification Mickan et al. (2018) amoB ammonia monooxygenase subunit B K01428 ureC Urease subunit alpha Urea hydrolysis Zhu et al. (2016) urea + H2O CO2 + 2NH3 [EC:3.5.1.5] K00372 nasA Assimilatory nitrate reductase Assimilatory Zhu et al. (2016) [EC:1.7.99.-] catalytic subunit nitrate reduction K00366 nirA Ferredoxin-nitrite reductase Assimilatory Zhu et al. (2016) NH3 + 2H2O + 6 oxidised ferredoxin [EC:1.7.7.1] nitrate reduction nitrite + 6 reduced ferredoxin + 7H+ K00370 narG, Nitrate reductase/ nitrite Denitrification Mickan et al. (2018) nitrate + a quinol nitrite + a [EC:1.7.5.1; narZ, oxidoreductase quinone + H2O 1.7.99.-] nxrA K03385 nrfA Nitrite reductase (cytochrome c- Denitrification Mickan et al. (2018) NH3 + 2H2O + 6 ferricytochrome c [EC:1.7.2.2] 552) nitrite + 6 ferrocytochrome c + 7H+ K00368 nirK Nitrite reductase (NO-forming) Denitrification Zhu et al. (2016); nitric oxide + H2O + ferricytochrome [EC:1.7.2.1] Mickan et al. c nitrite + ferrocytochrome c + (2018); Bowen et al. 2H+ (2020) K00376 nosZ Nitrous oxide reductase Denitrification Zhu et al. (2016); nitrogen + H2O + 2 ferricytochrome [EC:1.7.2.4] Mickan et al. c nitrous oxide + 2 (2018); Bowen et al. ferrocytochrome c + 2H+ (2020)

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KEGG Name Definition Process Reference E.C. Reaction Orthologue [EC] K04561 norB Nitric oxide reductase subunit B Denitrification Zhu et al. (2016) nitrous oxide + 2 ferricytochrome c [EC:1.7.2.5] + H2O 2 nitric oxide + 2 ferrocytochrome c + 2H+ K14660 nodE Nodulation protein E Nodulation Yan et al. (2017) [EC:2.3.1.-] K12546 nodO Putative nodulation protein nodulation Yan et al. (2017)

Table B2. KEGG Orthology focusing on carbon-cycling processes. Definition is as presented on the KEGG Orthology. Enzyme Commission (EC) and reaction refer to the parent enzyme. Reference refers to other studies that have used the orthologue.

KEGG Name Definition Process Reference EC Reaction Orthologue [Enzyme Commission] K01188 E3.2.1.21 Beta-glucosidase Cellulose Vries (2018); Fernández- Hydrolysis of terminal, non-reducing beta- [EC:3.2.1.21] decomposition Bayo et al. (2019) D-glucosyl residues with release of beta- D-glucose K01225 CBH1 Cellulose 1,4-beta- Cellulose Vries (2018); Fernández- Hydrolysis of (1 →4)-beta-D-glucosidic [EC:3.2.1.91] cellobiosidase decomposition Bayo et al. (2019) linkages in cellulose and cellotetraose, releasing cellobiose from the non- reducing ends of the chains K01179 E3.2.1.4 Endoglucanase Cellulose Mickan et al. (2018); Endohydrolysis of (1 →4)-beta-D- [EC:3.2.1.4] decomposition Fernández-Bayo et al. glucosidic linkages in cellulose, lichenin (2019) and cereal beta-D-glucans

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KEGG Name Definition Process Reference EC Reaction Orthologue [Enzyme Commission] K01176 AMY, Alpha-amylase Starch/ Mickan et al. (2018); Endohydrolysis of (1 →4)-alpha-D- [EC:3.2.1.1] amyA, hemicellulose Fernández-Bayo et al. glucosidic linkages in polysaccharides malS; decomposition (2019) containing three or more (1->4)-alpha- linked D-glucose units K07405 E3.2.1.1A Alpha-amylase Starch/ Mickan et al. (2018); Endohydrolysis of (1 →4)-alpha-D- E3.2.1.1A hemicellulose Fernández-Bayo et al. glucosidic linkages in polysaccharides decomposition (2019) containing three or more (1->4)-alpha- linked D-glucose units K01190 lacZ Beta-galactosidase Hemicellulose Mickan et al. (2018); Hydrolysis of terminal non-reducing beta- [EC:3.2.1.23] decomposition Fernández-Bayo et al. D-galactose residues in beta-D- (2019) galactosides K01181 xynA Endo-1,4-beta- Hemicellulose Fernández-Bayo et al. Endohydrolysis of (1 →4)-beta-D-xylosidic [EC:3.2.1.8] xylanase decomposition (2019) linkages in xylans K01178 SGA1 Glucoamylase Starch Mickan et al. (2018) Hydrolysis of terminal (1 →4)-linked [EC:3.2.1.3] decomposition alpha-D-glucose residues successively from non-reducing ends of the chains with release of beta-D-glucose K03781 katE, CAT, Catalase Lignin Fernández-Bayo et al. 2H2O2  O2 + 2H2O [EC:1.11.1.6] catB, srpA decomposition (2019) K05909 E1.10.3.2 Laccase Lignin Janusz et al. (2017) 4 benzenediol + O2  4 [EC:1.10.3.2] decomposition benzosemiquinone + 2H2O K00430 E1.11.1.7 Peroxidase Lignin Fernández-Bayo et al. 2 phenolic donor + H2O2  2 phenoxyl EC:1.11.1.7] decomposition (2019) radical of the donor + 2H2O K00433 cpo Non-haem Lignin Fernández-Bayo et al. RH + chloride + H2O2  RCl + 2H2O [EC:1.11.1.10] chloroperoxidase decomposition (2019)

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KEGG Name Definition Process Reference EC Reaction Orthologue [Enzyme Commission] K01183 E3.2.1.14 Chitinase Chitin Mickan et al. (2018) Random endo-hydrolysis of N-acetyl-beta- [EC:3.2.1.14] decomposition D-glucosaminide (1 →4)-beta-linkages in chitin and chitodextrins

Table B3. KEGG Orthology focusing on phosphorus-cycling processes. Definition is as presented on the KEGG Orthology. Enzyme Commission (EC) and reaction refer to the parent enzyme. Reference refers to other studies that have used the orthologue.

KEGG Name Definition Process Reference EC Reaction Orthologue [Enzyme Commission] K02040 pstS Phosphate transport system Transporter Bergkemper et substrate-binding protein al. (2016) K03306 TC.PIT Inorganic phosphate transporter Transporter Bergkemper et [PiT family] al. (2016) K06162 phnM C-P Lyase Subunit; alpha-D-ribose Organic Bergkemper et alpha-D-ribose 1-methylphosphonate 5- [EC:3.6.1.63] 1-methylphosphonate 5- phosphorus al. (2016) triphosphate + H2O alpha-D-ribose 1- triphosphate diphosphatase release methylphosphonate 5-phosphate + diphosphate K05306 phnX Phosphonoacetaldehyde Organic Bergkemper et phosphonoacetaldehyde + H2O [EC:3.11.1.1] hydrolase phosphorus al. (2016) acetaldehyde + phosphate release K01113 phoD Alkaline Phosphatase D Organic Bergkemper et a phosphate monoester + H2O an [EC:3.1.3.1] phosphorus al. (2016) alcohol + phosphate release

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KEGG Name Definition Process Reference EC Reaction Orthologue [Enzyme Commission] K01078 PHO Acid Phosphatase Organic Bergkemper et a phosphate monoester + H2O an [EC:3.1.3.2] phosphorus al. (2016) alcohol + phosphate release K06135 pqqA Pyrroloquinoline quinone Cofactor for Liu et al. biosynthesis protein A mineral (1992); Farhat phosphate et al. (2009) solubilise K06138 pqqD Pyrroloquinoline quinone Cofactor for Liu et al. biosynthesis protein D mineral (1992); Farhat phosphate et al. (2009) solubilise

322

Results of Specific KEGG Orthology

KEGGs Related to Nitrogen The nifD nitrogenase gene (K02586) showed a much lower response in the OGM where the highest relative abundance was 332 in the normalised ASVs (Figure B1). OGM was significantly lower than Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch (Spoil OGM to OGM: t=3.5, p=0.02).

Figure B1. Relative abundance of KEGG Orthologue K02586 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where Treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

323

The gene for nitrogenase protein nifH (K02588) was also higher in the Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch treatments than in OGM (Spoil OGM to OGM: t=3.41, p=0.0262) (Figure B2).

Figure B2. Relative abundance of KEGG Orthologue K02588 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where Treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

324

The hao gene (K10535) was significantly lower in the OGM than in Subsoil OGM Mulch but there was no difference between any other Treatment (Subsoil OGM Mulch to OGM: t=4.11, p=0.0132) (Figure B3).

Figure B3. Relative abundance of KEGG Orthologue K10535 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

325

The pmoA-amoA gene (K10944) was significantly more common in the Experimental Site treatments Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch than in OGM and Spoil Control (Subsoil Mulch to OGM: t=3.73, p=0.027) (Figure B4). Subsoil OGM Mulch was also significantly higher in pmoA-amoA than RSF and Rav Off (Subsoil OGM Mulch to RSF: t=4.14, p=0.013).

Figure B4. Relative abundance of KEGG Orthologue K10944 from PICRUSt analysis. Note that the data were analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

326

The pmoB-amoB gene (K10945) results were very similar to the pmoA-amoA results, with just slight differences in two of the Subsoil Mulch plots and one of the Spoil OGM plots (Figure B5). The Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch treatments were higher in pmoB- amoB than Spoil Control and OGM (Subsoil Mulch to OGM: t=3.92, p=0.0204).

Figure B5. Relative abundance of KEGG Orthologue K10945 from PICRUSt analysis. Note that the data were analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

327

There was no significant difference found between treatments in the amount of ureC gene (K01428) present (F(8,92.7)=1.86, p=0.0744) (Figure B6).

Figure B6. Relative abundance of KEGG Orthologue K01428 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

328

The nasA gene (K00372) was significantly higher in the Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch treatments than in OGM (Spoil OGM to OGM: t=4.29, p=0.0013) (Figure B7).

Figure B7. Relative abundance of KEGG Orthologue K00372 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

329

In a change from the previous distributions, the nirA gene (K00366) was significantly higher in RSF and Rav Ref than in the Experimental Site, Spoil Control and OGM (RSF to Subsoil Mulch: t=3.4, p=0.0244) (Figure B8).

Figure B8. Relative abundance of KEGG Orthologue K00366 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

330

The narG gene (K00370) was significantly higher in Spoil Control, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch than in the OGM (Spoil OGM to OGM: t=3.38, p=0.0275) (Figure B9).

Figure B9. Relative abundance of KEGG Orthologue K03385 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data. Where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

331

The nrfA gene (K03385) was significantly more abundant in the Spoil Control, Spoil OGM and RSF than in the OGM (Spoil Control to OGM: t=3.23, p=0.0401) (Figure B10).

Figure B10. Relative abundance of KEGG Orthologue K00368 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

332

The nirK gene (K00368) is significantly higher in Spoil Control, Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch than in OGM (Spoil Control to OGM: t= 3.8, p=0.0071) (Figure B11).

Figure B11. Relative abundance of KEGG Orthologue K00376 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

333

The gene nosZ (K00376) was significantly higher in all Experimental Site treatments than in the OGM (Subsoil Mulch to OGM: t=3.24, p=0.0389) (Figure B12).

Figure B12. Relative abundance of KEGG Orthologue K00376 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

334

With the exception of Spoil Control, gene norB (K04561) was lower in OGM than in all other treatments (Subsoil Mulch to OGM: t=4.01, p=0.0031) (Figure B13).

Figure B13. Relative abundance of KEGG Orthologue K04561 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

335

Gene nodE was significantly more common in Subsoil OGM Mulch and Subsoil Mulch than in Spoil Control and OGM (Subsoil Mulch to Spoil Control: t=3.27, p=0.0365) (Figure B14). Rav Ref was also significantly higher in nodE than OGM (t=3.89, p=0.0052).

Figure B14. Relative abundance of KEGG Orthologue K14660 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

336

Gene nodO was significantly higher in Subsoil Mulch than in Spoil Control and OGM (Subsoil Mulch to OGM: t=3.62, p=0.0493) (Figure B15).

Figure B15. Relative abundance of KEGG Orthologue K12546 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

337

KEGGs Related to Carbon There was no response to E1.10.3.2 laccase (K05909) or E1.11.1.7 peroxidase (K00430); all treatments returned values of 0.

The gene E3.2.1.21 beta-glucosidase (K01188) was significantly higher in the RSF and Rav Ref than in the Experimental Site and OGM (RSF to Subsoil Mulch: t= 3.51, p=0.0172) (Figure B16).

Figure B16. Relative abundance of KEGG Orthologue K01188 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

338

The CBH1 gene (K01225) was significantly higher in Ring Rd than in OGM (t=3.61, p=0.0376) (Figure B17).

Figure B17. Relative abundance of KEGG Orthologue K01225 from PICRUSt analysis. Note that the data was analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

339

With the exception of Spoil Control and Ring Rd, OGM was significantly lower than all other treatments for gene E3.2.1.4 (K01179) (Spoil OGM to OGM: t=3.43, p=0.0232) (Figure B18).

Figure B18. Relative abundance of KEGG Orthologue K01179 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

340

AMY gene (K01176) had significantly higher values in OGM than all other treatments (OGM to Spoil OGM: t=4.16, p=0.0122) (Figure B19). Spoil OGM was also significantly higher than Spoil Control (t=4.26, p=0.0097).

Figure B19. Relative abundance of KEGG Orthologue K01176 from PICRUSt analysis. Note that the data was analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

341

In contrast to AMY, the E3.2.1.1A gene (K07405) was significantly higher in Spoil Control, Spoil OGM and Subsoil OGM Mulch than in OGM (Spoil Control to OGM: t=4.26, p=0.0101) (Figure B20).

Figure B20. Relative abundance of KEGG Orthologue K07405 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

342

The lacZ gene (K01190) was significantly higher in the Rav Ref than on the Experimental Site (Rav Ref to Subsoil Mulch: t=3.72, p=0.0299) (Figure B21). The OGM was significantly lower than all treatments except the Spoil Control (Spoil OGM to OGM: t=4.87, p=0.0025).

Figure B21. Relative abundance of KEGG Orthologue K01190 from PICRUSt analysis. Note that the data was analysed using a fourth-root transformation, which the letters above the figure describe. The figure, however, displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

343

For the xynA gene (K01181), OGM was significantly lower than Rav Ref, RSF, Subsoil OGM Mulch and Subsoil Mulch (Subsoil Mulch to OGM: t=3.57, p=0.0139) (Figure B22).

Figure B22. Relative abundance of KEGG Orthologue K01181 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

344

RSF and Rav Ref were significantly higher in SGA1 gene (K01178) than all other treatments, except Ring Rd (RSF to Subsoil Mulch: t=3.91, p=0.0044) (Figure B23).

Figure B23. Relative abundance of KEGG Orthologue K01178 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

345

There was no difference in katE gene (K03781) between the treatments (F(8,98.1)=1.42, p=0.1958) (Figure B24).

Figure B24. Relative abundance of KEGG Orthologue K03781 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where no difference was found between treatments. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

346

The cpo gene (K00433) was highest in Rav Ref and RSF (Figure B25). Rav Ref was significantly higher than all treatments on the Experimental Site, Spoil Control and OGM (Rav Ref to Subsoil Mulch: t=3.5, p=0.018). OGM was significantly lower than all treatments except the Spoil Control and Spoil OGM (Subsoil OGM Mulch to OGM: t=3.67, p=0.0106).

Figure B25. Relative abundance of KEGG Orthologue K05909 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

347

E3.2.1.14 gene (K01183) was significantly higher in RSF and Rav Ref than all treatments except the Ring Rd (RSF to Subsoil Mulch: t=4.2, p=0.0016) (Figure B26).

Figure B26. Relative abundance of KEGG Orthologue K01183 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

348

KEGGs related in Phosphorus The pstS gene (K02040) was significantly higher in Rav Ref than in Spoil Control or OGM (Rav Ref to Spoil Control: t=4.11, p=0.0023) (Figure B27).

Figure B27. Relative abundance of KEGG Orthologue K02040 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

349

TC.PIT (K03306) was significantly higher in RSF and Rav Ref than OGM (RSF to OGM: t=3.34, p=0.0302) (Figure B28).

Figure B28. Relative abundance of KEGG Orthologue K03306 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

350

The phnM gene (K06162) was significantly lower in OGM than all treatments except Spoil Control (Rav Ref to OGM: t=3.35, p=0.0309) (Figure B29).

Figure B29. Relative abundance of KEGG Orthologue K06162 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

351

The gene phnX (K05306) was significantly higher in Spoil OGM, Subsoil Mulch and Subsoil OGM Mulch than in Spoil Control and OGM (Subsoil Mulch to Spoil Control: t=3.95, p=0.0186) (Figure B30).

Figure B30. Relative abundance of KEGG Orthologue K05306 from PICRUSt analysis. Note that the data were analysed using a square-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

352

OGM was significantly lower in phoD gene (K01113) than Subsoil OGM Mulch and RSF were (Subsoil OGM Mulch to OGM: t=3.61, p=0.0131) (Figure B31).

Figure B31. Relative abundance of KEGG Orthologue K01113 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

353

PHO gene (K01078) was significantly higher in RSF and Rav Ref than on the Experimental Site, Spoil Control and OGM (Rav Ref to Subsoil Mulch: t=3.61, p=0.038) (Figure B32). Subsoil Mulch, Subsoil OGM Mulch and Ring Rd were also significantly higher than OGM (Subsoil OGM Mulch to OGM: t=3.9, p=0.0208).

Figure B32. Relative abundance of KEGG Orthologue K01078 from PICRUSt analysis. Note that the data were analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

354

The gene pqqA (K06135) was significantly higher in Spoil OGM than in Spoil Control, Ring Rd, RSF, Rav Ref and OGM (Spoil OGM to Spoil Control: t=5.07, p=0.0019) (Figure B33).

Figure B33. Relative abundance of KEGG Orthologue K06135 from PICRUSt analysis. Note that the data were analysed using a fourth-root transformation, which the letters above the figure describe. The main graph displays the untransformed data. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

355

Gene pqqD (K06138) was significantly lower in OGM than in all treatments except Spoil Control (Spoil OGM to OGM: t=3.33, p=0.0341) (Figure B34). Both Subsoil OGM Mulch and RSF were significantly higher in pqqD than Spoil Control (RSF to Spoil Control: t=3.23, p=0.0449).

Figure B34. Relative abundance of KEGG Orthologue K06138 from PICRUSt analysis. The letters above the figure describe results of Tukey tests on untransformed data, where treatments that do not share a letter are considered significantly different. Spoil Control is from Newman (2017); Immature OGM and OGM are from the production facility.

356

Appendix C – Other Total Micronutrients

This section contains the full results on micronutrients that were summarised in Table 5.2 of Chapter 5.

Iron It is important to remember when analysing the iron data that there is no estimate for trees or for material >10 mm in diameter.

The most notable feature of iron was the drastically low levels present in vegetation compared to the soils (Figure C1). While there was no significant difference between the 0– 10 cm and 20–30 cm compartments, litter was significantly lower than both of those and sampled vegetation was significantly lower than all other compartments (20–30 cm to litter: t=9.45, p<0.0001). There was also a strong difference between treatments, with RSF significantly lower than Spoil, Subsoil Mulch and Subsoil OGM Mulch (Spoil to RSF: t=3.39, p=0.0255). There was no significant interaction between the compartment and treatment (F(15,43.8)=1.4, p=0.19).

90000

80000

70000

60000

50000

40000

30000

20000

10000

Average Total Iron Concentration (mg/kg) Concentration Iron Total Average 0

-10000 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Sampled Litter Sampled Vegetation

Figure C1. Concentration of iron within separate ecosystem compartments. The values 20– 30 cm and 0–10 cm refer to the depth at which the soil was sampled. Error bars show standard deviations.

357

Iron abundance (kg/ha) was significantly lower in the RSF than in Ring Rd, Spoil, Subsoil Mulch and Subsoil OGM Mulch (Ring Rd to RSF: t=3.52, p=0.019) (Figure C2 and Figure C3). There were highly significant differences between compartments, with every compartment different from all others (F(3,43.4)=1166.5, p<0.0001). The 20–30 cm compartment was highest, followed by 0–10 cm, litter and vegetation was lowest (litter to vegetation: t=5.75, p<0.0001). Within the 0–10 cm compartment, RSF was lower than Ring Rd, Spoil, Subsoil Mulch and Subsoil OGM Mulch (Ring Rd to RSF: t=4.18, p=0.0222). In the 20–30 cm compartment, the RSF was significantly lower in iron than Spoil, Subsoil Mulch and Subsoil OGM Mulch (Subsoil Mulch to RSF: t=4.16, p=0.0233).

20000

-20000

-40000

-60000

-80000

-100000

-120000

Kilograms Kilograms of Iron per Hectare -140000

-160000

-180000 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure C2. Kilograms of iron per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Negative values refer to those below the ground surface. Note that there are no data for trees of vegetation >10 mm. Vegetation and litter were still modelled; however, their small values make them practically invisible on this figure and they have been redrawn in Figure B3. Note that trees were not estimated.

358

140

120

100

60 Kilograms Kilograms of Iron per Hectare

0 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

Litter Other Vegetation Trees

Figure C3. The same figure as B2 without the soil values. Note that trees were not estimated.

359

Manganese There was no significant differences due to treatment in manganese (F(5,15.3)=1.2, p=0.37) (Figure C4). There was a strong difference in compartments, with vegetation significantly lower than all other treatments (litter to sampled vegetation: t=7.12, p<0.0001). The litter was also significantly different from the 0–10 cm compartment (0–10 cm to litter: t=2.78, p=0.0395). A significant interaction between compartment and treatment occurred (F(15,40.1)=3.2, p=0.0015), with RSF 20–30 cm layer significantly lower than the 20–30 cm layers of Spoil and Subsoil OGM Mulch (Subsoil OGM Mulch 20–30 cm to RSF 20–30 cm: t=3.9, p=0.0492). Comparatively, in all other compartments, RSF showed minimal difference from the other treatments.

1200

1000

800

600

400

200

0 Average Total Manganese Concentration (%) Concentration Manganese Total Average Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch -200

20-30cm 0-10cm Sampled Litter Sampled Vegetation

Figure C4. Concentration of manganese within separate ecosystem compartments. The values 20–30 cm and 0–10 cm refer to the depth at which the soil was sampled. Error bars show standard deviations.

360

There was no significant difference between treatments in manganese abundance (kg/ha) (F(5,35.7)=1.9, p=0.1144) (Figure C5). There was, however, a significant difference between compartment,s with each compartment different from all others (F(3,45.1)=127.9, p<0.0001). The 20–30 cm compartment was the largest, followed by the 0–10 cm, litter and vegetation compartments (20–30 cm to 0–10 cm: t=3.9, p=0.0015). Within the 20–30 cm compartment, the RSF was significantly lower than Ring Rd, Spoil, Spoil OGM and Subsoil OGM Mulch (Subsoil OGM Mulch to RSF: t=4.1, p=0.0288). The litter compartment also contained an interaction with both RSF and Ring Rd having more manganese than Spoil (Ring Rd to Spoil: t=4.42, p=0.0107).

400

200

-200

-400

-600

-800

-1000 Kilograms of per Kilograms Hectare Manganese -1200

-1400 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure C5. Kilograms of manganese per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

361

Zinc There was a strong difference between treatments in zinc concentrations, with Spoil OGM having significantly higher levels than all other treatments (Spoil OGM to Spoil: t=3.4, p=0.0195) (Figure C6). The RSF was also significantly lower in zinc than Subsoil OGM Mulch and Spoil (Subsoil OGM Mulch to RSF: t=3.95, p=0.0044). There was only a weak difference between compartments, with the vegetation lower in zinc than 0–10 cm (0–10 cm to sampled vegetation: t=2.77, p=0.0393). There was no significant interaction between the compartment and treatment.

250

200

150

100

50 Average Total Zinc Concentration (mg/kg) Concentration Zinc Total Average

0 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Sampled Litter Sampled Vegetation

Figure C6. Concentration of zinc within separate ecosystem compartments. The values 20– 30 cm and 0–10 cm refer to the depth at which the soil was sampled. Error bars show standard deviations.

There was a significant difference in zinc abundance (kg/ha) between treatments with RSF being significantly lower than Spoil OGM and Subsoil OGM Mulch (Subsoil OGM Mulch to RSF: t=3.05, p=0.0457) (Figure C7). Subsoil Mulch was also significantly lower than Spoil OGM (Spoil OGM to Subsoil Mulch: t=2.28, p=0.0176). Every compartment was significantly different from each other, with zinc being highest in the 20–30 cm compartment, followed by the 0–10 cm, litter and then vegetation compartments (litter to vegetation: t=4.98,

362 p<0.0001). There was a strongly significant interaction between treatment and compartment (F(15,44.9)=8.6, p<0.0001). In the 0–10 cm compartment, the Ring Rd and RSF were significantly lower than the Spoil and Spoil OGM (Spoil to Ring Rd: t=4.36, p=0.0131). In the 20–30 cm compartment, the RSF was significantly lower than the Ring Rd, Spoil and Spoil OGM (Ring Rd to RSF: t=5.24, p=0.0009). Conversely, in the litter compartment, Spoil was significantly lower than RSF and Ring Rd (Ring Rd to Spoil; t=4.4, p=0.0117).

-50

-100

-150

-200

-250

Kilograms Hectare per of Zinc Kilograms -300

-350

-400 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure C7. Kilograms of zinc per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Note that both the >10 mm and <10 mm components of the litter and other vegetation are included in the graph and analysis.

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Copper Copper showed a significant difference between treatments, with Spoil OGM higher than Subsoil Mulch, Ring Rd and RSF (Spoil OGM to Subsoil Mulch: t=4.91, p=0.0005) (Figure C8). RSF was significantly lower than Spoil OGM, Spoil, Subsoil OGM Mulch and Subsoil Mulch (Subsoil Mulch to RSF: t=3.57, p=0.0153). There were significant differences between compartments, with litter lower than 0–10 cm and 20–30 cm, while sampled vegetation was lower than all other compartments (litter to sampled vegetation: t=2.73, p=0.043). The significant interaction between treatment and compartment was partly due to much higher variation in the 0–10 cm compartment, with RSF significantly lower than Spoil OGM, Subsoil OGM Mulch and Spoil (Spoil 0–10 cm to RSF 0–10 cm: t=4.47, p=0.0096). Spoil OGM was also significantly higher than Subsoil Mulch and Ring Rd (Spoil OGM 0–10 cm to Subsoil Mulch 0– 10 cm: t=5.75, p=0.0002). There was significant variation in the 20–30 cm compartment, with RSF significantly lower than Spoil OGM, Spoil and Subsoil OGM Mulch (Subsoil OGM Mulch 20–30 cm to RSF 20–30 cm: t=3.88, p=0.0489) but Spoil OGM was not significantly different from other treatments.

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20 Average Total Copper Concentration (mg/kg) Concentration Copper Total Average

0 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Sampled Litter Sampled Vegetation

Figure C8. Concentration of copper within separate ecosystem compartments. The values 20– 30 cm and 0–10 cm refer to the depth at which the soil was sampled. Error bars show standard deviations.

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There was a significant difference between treatments in copper abundance (kg/ha), with RSF being significantly lower than Spoil OGM, Subsoil OGM Mulch and Spoil (Spoil to RSF: t=3.19, p=0.0336) (Figure C9). There was a significant difference between every compartment, with 20–30 cm being the highest in copper, followed by 0–10 cm, litter and vegetation the lowest (litter to vegetation: t=4.4, p=0.0004). There was a significant interaction between treatments and compartments. Within the 0–10 cm, there were significantly higher values in Spoil OGM and Spoil than in Ring Rd and RSF (Spoil 0–10 cm to Ring Rd 0–10 cm: t=3.99, p=0.037). In the 20–30 cm compartment the Ring Rd was not different from the other treatments while RSF was significantly lower than all treatments except Subsoil OGM Mulch (Subsoil Mulch 20– 30 cm to RSF 20–30 cm: t=4.28, p=0.0163). Conversely, in the litter compartment Spoil was significantly lower than Ring Rd and RSF (Ring Rd litter to Spoil litter: t=4.09, p=0.0281).

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Kilograms Hectare per of Copper Kilograms -140

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-180 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure C9. Kilograms of copper per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface.

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Boron Boron showed a significant difference between treatments, with RSF significantly higher than Subsoil Mulch and Subsoil OGM Mulch (RSF to Subsoil OGM Mulch: t= 2.97, p=0.0497). There was a very strong difference in boron concentration between the biotic and the abiotic compartments (litter to 20–30 cm: t=5.03, p<0.0001) (Figure C10). There was no interaction between compartment and treatment (Wald (df=15, sample size=72)=9.9, p=0.8225)

0 Average Total Boron Concentration (mg/kg) Concentration Boron Total Average

-10 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch

20-30cm 0-10cm Sampled Litter Sampled Vegetation

Figure C10. Concentration of boron in separate ecosystem compartments. The values 20– 30 cm and 0–10 cm refer to the depth at which the soil was sampled. Error bars show standard deviations. Some values were below the range of detection; where this occurred, 0s have been used.

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There was no significant difference between the treatments (Wald(df=5, sample size=72)=0.08, p=1), compartments (Wald(df=3, sample size=72)=0.07,p=0.99), or interaction (Wald(df=15, sample size=72)=0.44, p=1) for abundance (kg/ha) of boron (Figure C11).

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Kilograms per Hectare of Boron Kilograms -10

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-20 Spoil Spoil OGM Subsoil Mulch Subsoil OGM Ring Rd RSF Mulch 2019 Reference

0-10cm 20-30cm Litter Other Vegetation Trees

Figure C11. Kilograms of boron per hectare; on the y-axis, 0 represents the boundary between the mineral soil and organic litter. Specifically, negative values refer to those below the ground surface. Some values were below the level of detection, these have had a 0 substituted.

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Appendix D – Available Nutrients

Sodium Available sodium was significantly higher at 20–30 cm depths (t=7.26, p<0.0001). Compared between treatments, Spoil was significantly higher than all others except RSF (Spoil to Spoil OGM: t=6.15, p=0.0002) (Figure D1). Subsoil Mulch was significantly lower than Spoil and Spoil OGM (Spoil OGM to Subsoil Mulch: t=3.5, p=0.0314).

Figure D1. Available sodium from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Chloride Available chloride is significantly higher at 20–30 cm depth (t=3.27, p=0.0035). Spoil had significantly more chloride than all other treatments (Spoil to Subsoil OGM Mulch: t=6.77, p<0.0001) and Ring Rd was significant lower than all treatments except RSF (Spoil OGM to Ring Rd: t=3.25, p=0.0371) (Figure D2).

Figure D2. Available chloride from soil at two depths, 0–10 cm and 20–30 cm. Lettering is the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Nitrate Nitrate was significantly higher in the 20–30 cm depth (t=2.36, p=0.0285). No treatment was significantly different from any other (Treatment: F(5,17.5)=1.4, p=0.2563; Depth*Treatment: F(5,20.1)=1.9, p=0.1262) (Figure D3).

Figure D3. Available nitrate from soil at two depths, 0–10 cm and 20–30 cm. Lettering is the result of Tukey tests of the interaction between treatment and depth for untransformed data. No difference was found between treatments.

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Phosphorus Note that where phosphorus availability was below the detection limit (5 mg/kg), the value 5 was used for statistics. Extractable phosphorus was significantly more available in the 0– 10 cm layer (t=3.25, p=0.0038) (Figure D4). Extractable phosphorus levels were significantly higher in Spoil OGM than in Ring Rd, Spoil and Subsoil Mulch (Spoil OGM to Ring Rd: t=3.3, p=0.0379). Subsoil OGM Mulch was significantly higher than Spoil and Subsoil Mulch (Subsoil OGM Mulch to Spoil: t=3.17, p=0.0487).

Figure D4. Extractable phosphorus from soil at two depths, 0–10 cm and 20–30 cm. Where phosphorus availability was below the detection limit (5 mg/kg) the value 5 was used for graphing and statistics. Lettering is the result of Tukey tests of the interaction between Treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Potassium Available potassium was significantly higher in the 0–10 cm soil layer (t=4.84, p=0.0001) (Figure D5). Spoil OGM was significantly higher in potassium than in Spoil and Subsoil Mulch were (Spoil OGM to Spoil: t=4.49, p=0.0043).

Figure D5. Available potassium from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for untransformed data. Treatments that share a letter are considered not significantly different.

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Sulfur There were no significant differences between any variable for available sulfur (Depth: F(1,20.1)=2.7, p-=0.1143; Treatment: F(5,17.4)=2.7, p=0.0563; Depth*Treatment: F(5,20.1)=0.6, p=0.7226) (Figure D6).

Figure D6. Available sulfur from soil at two depths, 0–10 cm and 20–30 cm. Lettering is the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. No significant differences were found between treatments.

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Calcium There was no significant difference between depths for calcium availability (F(1,19.8)=3.5, p=0.0774). The model found a significant difference between treatments (F(5,16.9)=3.2, p=0.031) but no significant pairwise difference. Similarly, there were no significant differences in pairwise comparisons in the Depth*Treatment comparison even though there was a significant result of the model (F(5,19.8)=2.8, p=0.0472) (Figure D7).

Figure D7. Available calcium from soil at two depths, 0–10 cm and 20–30 cm. Lettering is the result of Tukey tests of the interaction between treatment and depth for untransformed data. No significant differences were found between treatments.

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Magnesium There was significantly more magnesium at the 20–30 cm depth (t=3.56, p=0.002) (Figure D8). The Spoil treatment was significantly higher in magnesium than Subsoil Mulch and Subsoil OGM Mulch were (Spoil to Subsoil Mulch: t=3.89, p=0.0133). No interaction was found between depth and treatment (F(5,19.4)=1.4, p=0.2668).

Figure D8. Available magnesium from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. No significant difference was found between treatments.

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Iron There was no change in iron with depth (F(1,19.5)=1, p=0.3232). Ring Rd was significantly higher than all treatments except RSF (Ring Rd to Subsoil OGM Mulch: t=4.3, p=0.0058), whereas RSF was higher than Spoil OGM and Spoil (RSF to Spoil OGM: t=4, p=0.0105). There was no significance in the interaction between depth and treatment (F(5,19.5)=0.9, p=0.4742) (Figure D9).

Figure D9. Available iron from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Manganese There was no significant difference between depths (F(1,19)=1, p=0.3358) or treatments (F(5,15.7)=2.5, p=0.0748) for manganese availability. There was, however, a significant interaction between depth and treatments, with Ring Rd being significantly higher in 20– 30 cm than it was in the 0–10 cm compartment (Ring Rd 20–30 cm to Ring Rd 0–10 cm: t=5.23, p=0.0021) (Figure D10).

Figure D10. Available manganese from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for untransformed data. Treatments that share a letter are considered not significantly different.

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Copper There was no significant difference between depths in copper availability (F(1,19.8)=0.0.4, p=0.5227). There was a strong difference between treatments, with Spoil OGM higher in available copper than all other treatments except Subsoil OGM Mulch (Spoil OGM to Spoil: t=3.38, p=0.0356). Both Subsoil OGM Mulch and Spoil were significantly higher than Subsoil Mulch and RSF were (Spoil to Subsoil Mulch: t=3.38, p=0.0355). The interaction between depth and treatment was significant (F(5,19.8)=5, p=0.004) but related to multiple not significant changes (Figure D11). The 0–10 cm compartment of both Spoil OGM and Subsoil OGM Mulch were higher than their respective 20–30 cm compartments. The opposite occurred for Spoil, Subsoil Mulch and Ring Rd, with higher values in the 20–30 cm compartments than in the 0–10 cm compartments.

Figure D11. Available copper from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Boron Available boron was significantly higher in the 0–10 cm layer than in the 20–30 cm layer (t=4.14, p=0.0006). Subsoil OGM Mulch and Spoil OGM were significantly higher than all other treatments (Spoil OGM to Subsoil Mulch: t=3.3, p=0.0428). There was no significant interaction between depth and treatment (F(5,18.4)=0.8, p=0.5765) (Figure D12).

Figure D12. Available boron from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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Zinc There was a significant difference in depth for available zinc with 0–10 cm higher than the 20–30 cm compartment (t=2.6, p=0.0172). The Spoil OGM treatment was significantly higher than all treatments except Subsoil OGM Mulch (Spoil OGM to Spoil: t=4.3, p=0.0052). Subsoil OGM Mulch was significantly higher than Subsoil Mulch and RSF (Subsoil OGM Mulch to Subsoil Much: t=5.18, p=0.0009). There was a significant interaction between depth and treatment (F(5,20)=4.7, p=0.0051) (Figure D13). The interaction was related to variation in zinc availability at depth, with Spoil OGM having significantly high concentrations in the 0– 10 cm compartment compared with the 20–30 cm compartment (t=4.26, p=0.0147).

Figure D13. Available zinc from soil at two depths, 0–10 cm and 20–30 cm. Letters are the result of Tukey tests of the interaction between treatment and depth for fourth-root transformed data. Treatments that share a letter are considered not significantly different.

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