THE ROOTS OF INVASION: BELOWGROUND TRAITS OF INVASIVE AND NATIVE AUSTRALIAN GRASSES

Erica Porter Bachelor of Science

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

School of Earth, Environmental, and Biological Sciences Faculty of Science and Engineering Queensland University of Technology 2019

Keywords

Ammonium, African lovegrass, Australian grasslands, belowground traits, buffel grass, Cenchrus ciliaris, Cenchrus purpurascens, Chloris gayana, Chloris truncata, Eragrostis curvula, Eragrostis sororia, functional traits, germination, grassland ecology, invasion ecology, invasion paradox, Johnson grass, leaf economic spectrum, low-resource environments, microdialysis, nitrate, nitrogen fluxes, nitrogen uptake efficiency, nitrogen use efficiency, resource economic spectrum, Rhodes grass, root economic spectrum, root traits, Sorghum halepense, Sorghum leiocladum, swamp foxtail, theory of invasibility, wild sorghum, windmill grass, woodlands lovegrass

The Roots of Invasion: Belowground traits of invasive and native Australian grasses i Abstract

Non-native grasses, originally introduced for pasture improvement, threaten Australia’s important and unique grasslands. Much of Australia’s grasslands are characterised by low resources and are an unlikely home for non-native grasses that have not evolved within these ecosystems. The mechanisms explaining this invasion remain equivocal. Ecologists use functional traits to classify species along a spectrum of resource conservation specialists and resource acquisition specialists. Previous research in Australia supports the trend that the aboveground traits of these non-native grasses follow the expected convention as resource acquisition specialists with greater biomass and height. The importance of root systems in trait-based research has recently been identified; however, few studies have concentrated on belowground traits across multiple native and non-native Australian grasses. I quantified a suite of above- and belowground traits among four congener pairs of native and non-native Australian grasses and found that some, but not all, expected acquisition specialist traits correlated with the non-native grass species. Instead, I found a mixture of resource acquisition and resource conservation traits within the belowground traits of the Australian native grasses.

Functional traits are only a surrogate measure of function. To confirm my hypothesis that non-native grasses have greater nitrogen uptake abilities, I took a further step towards measuring function. Using microdialysis as a novel technique allowed me to sample the soil nitrogen availability experimentally across all eight species in real time throughout their early stages of growth. I found that the natives and non-natives differ in their ability to absorb the available nitrogen. Non-natives were able to absorb nitrate at a faster rate within the first few weeks of growth compared with natives. The patterns found across congener pairs in the functional trait study were congruent with those of the microdialysis study. My study provides foundational knowledge regarding the life-history strategies of native and non-native grasses, which will be critical for devising effective control strategies that favour native species in Australia’s economically and ecologically valuable land.

The Roots of Invasion: Belowground traits of invasive and native Australian grasses ii

Table of Contents

Keywords ...... i Abstract ...... ii Table of Contents ...... iii List of Figures ...... v List of Tables ...... xiii Statement of Original Authorship ...... xiv Acknowledgements ...... xv Chapter 1: Introduction ...... 1 1.1 Background ...... 2 1.2 Literature review ...... 6 1.3 Purposes ...... 29 1.4 Significance and scope ...... 30 1.5 Thesis outline ...... 30 Chapter 2: Germination probability of native and non-native Australian grasses 32 2.1 Aims and objectives ...... 32 2.2 Research design ...... 32 2.3 Analysis ...... 34 2.4 Results ...... 34 2.5 Discussion and Conclusion ...... 36 Chapter 3: Functional traits of native and non-native Australian grasses ... 39 3.1 Aims and objectives ...... 39 3.2 Research design ...... 40 3.3 Analysis ...... 43 3.4 Results ...... 44 3.5 Discussion and Conclusion ...... 67 Chapter 4: Microdialysis reveals the function behind functional traits ...... 76 4.1 Aims and objectives ...... 76 4.2 Research design ...... 78 4.3 Analysis ...... 81 4.4 Results ...... 82 4.5 Discussion and Conclusion ...... 92 Chapter 5: Discussion and Conclusions ...... 99 5.1 Discussion ...... 99

The Roots of Invasion: Belowground traits of invasive and native Australian grasses iii

5.2 Limitations ...... 102 5.3 Future research ...... 104 5.4 Conclusions ...... 108 Bibliography ...... 110 Appendices ...... 123 Appendix A ...... 123 Appendix B ...... 124

The Roots of Invasion: Belowground traits of invasive and native Australian grasses iv

List of Figures

Figure 1.1 Distribution maps of all eight grasses grouped into their congener pairs. Distributions across congener pairs are comparable, and all of the native grasses are commonly found across multiple ecosystems (Herbaria, 2018). Non-native grasses are on the left within each pair...... 5 Figure 1.2 Diagram of the potential effects of anthropogenic drivers such as non-native species introduction into native environments (Isbell et al., 2017, p. 65). It is important to note that the economy and human well- being are affected and entwined with ecosystem functioning and ecosystem well-being. Biodiversity loss is not only detrimental at an environmental level but has the potential to affect ecosystem services and human well-being negatively. The yellow blocks depict social components, the blue blocks refers to ecological components and the green blocks refer to socio-ecological components. The review by Isbell et al. (2017) focused on the relationships indicated by solid lines, although the relationships indicated by dashed lines are also important...... 8 Figure 1.3 This research focuses on four congener pairs of native and non- native grasses ("Atlas of Living Australia," 2017). The non-native grasses include (from the top left) African lovegrass, Rhodes grass, Johnson grass, and buffel grass. The native grasses include woodlands lovegrass, windmill grass, wild sorghum and swamp foxtail...... 10 Figure 1.4 Diagram of the ways in which a non-native species may invade new habitats as modified from (MacDougall, et al., 2018, p. 2). Invasions can occur through a single pathway, but a combination of ‘opportunity’ pathways is more likely to take place...... 14 Figure 1.5 Diagram adapted from Funk (2013) showing the interaction of disturbance and resource availability, and the effect on non-native species invasion (Funk, 2013, p. 3). It is noted here that disturbance often correlates with a temporary increase in resource availability...... 18 Figure 1.6 Belowground plant functional traits can impact various processes such as carbon cycling, nutrient cycling and structural stability (Bardgett, et al., 2014, p. 697). The belowground plant functional traits from this figure are broken down into morphological, architectural and physiological traits, which are evolved ecological strategies associated with the response of a plant to its environment, as well as the effect of a plant on its environment (Bardgett et al., 2014)...... 23 Figure 1.7 Mommer and Weemstra (2012) proposed a set of trait measurements for a ‘whole plant resource economy perspective’. Rather than focusing above or below, this framework incorporates key traits from both above- and belowground into a process-focused framework (Mommer & Weemstra, 2012, p. 725)...... 24

The Roots of Invasion: Belowground traits of invasive and native Australian grasses v

Figure 1.8 The feedback cycles associated with root traits and community assemblage, as well as global change are shown (Bardgett, et al., 2014, p. 696)...... 26 Figure 2.1 Probability of germination rate of native and non-native germination. When all grasses were grouped together by origin, the germination rates did not differ significantly. Native grasses are depicted in blue and non-native grasses are depicted in red...... 35 Figure 2.2 Probability of germination rate by individual species. Johnson grass, woodlands lovegrass, swamp foxtail, windmill grass and African lovegrass all had a very similar percentage of germinated seeds at the end of the 3-week experiment, although there were some differences in time to germination between them. Buffel grass and wild sorghum had a lower, but still reasonably high germination rate. Rhodes grass had a germination rate around 50% and, unexpectedly, had the lowest germination rate...... 36 Figure 3.1 Diagram showing the initial experimental design for the trait quantification experiment. There were six replicates of each of the eight grasses for each of the two nutrient treatments (high and low). Pots were moved randomly each week to avoid light and temperature bias. Pots were spaced evenly apart to avoid light bias from neighbouring grasses...... 41 Figure 3.2 Linear regression model of correlation between specific root length and belowground biomass. Overall there was a significant difference between the two traits (F3 = 140.9, adjusted R-squared: 0.817, p < 2.2  10–16). The correlation between the traits is driven by the difference in traits rather than the covariate origin (specific root length p < 2  10–16; native p < 0.617)...... 47 Figure 3.3 Linear regression model of correlation between aboveground biomass and aboveground nitrogen content. Overall there was a significant difference between the traits (F3 = 50.16, adjusted R- squared: 0.6184, p < 2.2  10–16). The correlation between the traits is driven by the difference in traits as well as origin (aboveground nitrogen p < 7.07  10–8; native p < 2.12  10–9)...... 48 Figure 3.4 Linear regression model of correlation between belowground biomass and belowground nitrogen content. Overall there was a significant difference between the traits (F3 = 48.76, adjusted R- squared: 0.6064, p < 2.2  10–16). The correlation between the traits is driven by the difference in traits as well as origin (belowground nitrogen p < 5.93  10–10; native p < 3.15  10–6)...... 49 Figure 3.5 The principle components analysis of functional traits suggested a moderate level of correlation between functional traits across the native and non-native groupings, which is driven by both below and aboveground biomass. PC1 is driven by belowground biomass (0.5879), aboveground biomass (0.4954) and belowground carbon (0.4069) and explains 54.39% of the variance overall (standard deviation: 2.088). PC2 is driven by belowground biomass (0.3213)

The Roots of Invasion: Belowground traits of invasive and native Australian grasses vi

and explains 37.12% of the variance (standard deviation: 1.697). Refer to Appendix B (page 125) for PCA outputs...... 50

Figure 3.6 Trait comparison of aboveground biomass (F7 = 44.34, p < 4.21885  10–15). The only congener pairs not to have a significant difference in aboveground biomass were non-native African lovegrass and native woodlands lovegrass. All significant differences were present under both nutrient treatments as found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: Rhodes vs. windmill p < 0.00025, Johnson vs. wild sorghum p < 0.0000002, buffel vs. swamp foxtail p < 0.000004; No treatment: Rhodes vs. Windmill p < 0.00098, Johnson vs. wild sorghum p < 0.000012, buffel vs. swamp foxtail p < 0.000004)...... 51 Figure 3.7 Trait comparison of specific leaf area. The species exhibited a significant difference overall, although the Tukey HSD post hoc analysis showed that none of the congener pairs differed significantly –6 (F7 = 6.812, p < 5.69999  10 ). Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 52 Figure 3.8 Trait comparison of height showed a significant difference overall –15 (F7 = 35.04, p < 3.9968  10 ). All pairs of congeners showed a significant difference under both treatments except for non-native buffel grass and native swamp foxtail. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: African vs. woodlands p < 0.0006, Rhodes vs. windmill p < 0.0100, Johnson vs. wild sorghum p < 0.0005, No treatment: African vs. woodlands p < 0.0000017, Rhodes vs. windmill p < 0.00008, Johnson vs. wild sorghum p < 0.0020). Non-native buffel grass and native swamp foxtail only showed a significant difference between each other in height under the low- nutrient treatment (p < 0.0016)...... 52 –10 Figure 3.9 Trait comparison of tiller count (F7 = 11.99, p < 5.16  10 ). None of the congener pairs differed significantly in tiller count under either nutrient treatment. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 53 Figure 3.10 Trait comparison of tiller count by aboveground biomass. Tiller count per plant was divided by aboveground biomass (F7 = 4.155, p < 0.00055). All congener pairs except for non-native Rhodes grass and native windmill grass displayed a significant difference in tiller count by aboveground biomass under the low-nutrient treatment only. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (No treatment: African vs. woodlands p < 0.0062, Johnson vs. wild sorghum p < 0.0125, buffel vs. swamp foxtail p < 0.038)...... 54

The Roots of Invasion: Belowground traits of invasive and native Australian grasses vii

Figure 3.11 Trait comparison of inflorescence count (F7 = 22.72, p < 3.77476  10–15). Non-native Rhodes grass and native windmill grass were the only pair to have a significant difference in inflorescence count (Nutrient addition: p < 0.0001, no treatment: p <0.000001). However, this reflected the observation that very few of the grasses matured to produce flowers within the time span of this experiment. Only species that produced flowers were included in this figure. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 55

Figure 3.12 Trait comparison of belowground biomass (F7 = 84.03, p < 3.55271  10–15). All congener pairs differed significantly in belowground biomass except for non-native African lovegrass and native woodlands lovegrass under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: Rhodes vs. windmill p < 0.00022, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.00001, No treatment: Rhodes vs. windmill p < 0.0100, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.0023)...... 56 Figure 3.13 Trait comparison of tiller count by belowground biomass. For this trait measure, tiller count per plant was divided by aboveground –14 biomass (F7 = 18.81, p < 3.73035  10 ). Non-native African lovegrass and native woodlands lovegrass had a significant difference under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: p < 0.0011; No treatment: p < 0.0014). Johnson grass and wild sorghum had a significant difference under the high nutrient treatment only (p < 0.0012)...... 57

Figure 3.14 Trait comparison of specific root length (F7 = 38.72, p < 3.33067  10–15). Non-native African lovegrass and native woodlands lovegrass had a significant difference under both nutrient treatments (Nutrient addition: p < 0.00001; No treatment: p < 0.0008). The other three congener pairs had a significant difference in specific root length under the high-nutrient treatment only (Rhodes vs. windmill p < 0.0018, Johnson vs. wild sorghum p < 0.0023, buffel vs. swamp foxtail p < 0.0016). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 58 Figure 3.15 Trait comparison of root surface area was overall significantly –15 different (F7 = 41.67, p < 3.10862  10 ). All congener pairs had a significant difference in root surface area under both treatments except for non-native African lovegrass and native woodlands lovegrass. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient treatment: Rhodes

The Roots of Invasion: Belowground traits of invasive and native Australian grasses viii vs. windmill p < 0.0055, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.00004, No treatment: Rhodes vs. windmill p < 0.0124, Johnson vs. wild sorghum p < 0.000001, buffel vs. swamp foxtail p < 0.0127)...... 59 Figure 3.16 Trait comparison of root length had a significant difference overall –6 (F7 = 8.352, p < 6.61999  10 ). The only pairs with a significant difference in root length under both treatments were non-native Johnson grass and native wild sorghum (Nutrient addition: p < 0.00008, No treatment: p < 0.0308). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 60 Figure 3.17 Trait comparison of root diameter was overall significantly –15 different (F7 = 26.76, p < 2.88658  10 ). Non-native Johnson grass and native wild sorghum had a significant difference under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: p < 0.0000002, No treatment: p < 0.0010), as did buffel grass and swamp foxtail (Nutrient addition: p <0.00001, No treatment: p <0.0015)...... 60

Figure 3.18 Trait comparison of root mass fraction (F7 = 18.72, p < 3.65263  10–14). Non-native Johnson grass and native wild sorghum had a significant difference under both nutrient treatments (Nutrient addition: p < 0.0117; No treatment: p < 0.00002), and none of the other congener pairs displayed a difference under either nutrient treatment. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 61

Figure 3.19 Trait comparison of root-to-shoot ratio (F7 = 14.37, p < 1.68598  10–11). Non-native Johnson grass and native wild sorghum were the only congener pairs to have a significant difference and it was only under the low-nutrient treatment (p < 0.0000003). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 62

Figure 3.20 Trait comparison of aboveground nitrogen content (F7 = 13.43, p < 8.10002  10–11). Significant differences are between non-native Johnson grass and native wild sorghum (Nutrient addition: p < 0.0365; No treatment p < 0.00028) and buffel grass and swamp foxtail (Nutrient addition: p < 0.0002; No treatment p < 0.00074). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 63

Figure 3.21 Trait comparison of belowground nitrogen content (F7 = 37.13, p < 2.66454  10–15). Significant differences are between non-native Johnson grass and native wild sorghum under both nutrient treatments

The Roots of Invasion: Belowground traits of invasive and native Australian grasses ix

(Nutrient addition: p < 0.000002; No treatment: p < 0.00001) and buffel grass and swamp foxtail (Nutrient addition: p < 0.0000; No treatment: p < 0.00001). Non-native Rhodes grass and native windmill grass also had a significant difference only under the low-nutrient treatment (p < 0.0330). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 64

Figure 3.22 Trait comparison of aboveground carbon content (F7 = 23.63, p < 2.44249  10–15). Non-native buffel grass and native swamp foxtail had a significant difference under both nutrient treatments (Nutrient addition: p < 0.0015; no treatment: p < 0.00007). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 65

Figure 3.23 Trait comparison of belowground carbon content (F7 = 25.35, p < 2.22045  10–15). Non-native buffel grass and native swamp foxtail had a significant difference under both nutrient treatments (Nutrient addition p < 0.000008; No treatment p < 0.00044). Non-native Johnson grass and native wild sorghum had a significant difference only under the low nutrient treatment (p < 0.0010). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 65 Figure 3.24 Trait comparison of percent total nitrogen content by leaf area 2 –15 (cm ) (F7 = 158.4, p < 1.9984  10 ). A significant difference was found between non-native Johnson grass and native wild sorghum under both treatments (Nutrient addition: p < 0.00001; No treatment: p < 0.00001). A slight significant difference was found between non- native Rhodes grass and native windmill grass only under the high- nutrient treatment (p < 0.0524). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair...... 66 Figure 4.1 Diagram of experimental design for the microdialysis experiment. Each circle represents one 250 ml microcosm filled with substrate and either a non-native grass (yellow), a native grass (green) or substrate with no plant, referred to as a blank (blue). Microcosms were moved randomly each week of the experiment to avoid light, temperature and other potential bias...... 80 Figure 4.2 Leaf area (cm2) of the native (blue line) and non-native (red line) grasses over time using measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R- squared: 0.56)...... 83 Figure 4.3 Leaf area (cm2) of the native woodlands lovegrass, WLG (blue line) and non-native African lovegrass, ALG (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.822  10–13, adjusted R-squared: 0.73)...... 83

The Roots of Invasion: Belowground traits of invasive and native Australian grasses x Figure 4.4 Leaf area (cm2) of the native swamp foxtail, CP (blue line) and non- native buffel grass, CC (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.71)...... 84 Figure 4.5 Leaf area (cm2) of the native wild sorghum, SL (blue line) and non- native Johnson grass, SH (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.86)...... 84 Figure 4.6 Available ammonium over time in the blanks, shown in yellow, and the plants, regardless of origin, shown in blue, analysed using a linear regression model (p < 4.809  10–8, adjusted R-squared: 0.089)...... 85 Figure 4.7 Available nitrate over time in the blanks, shown in yellow, and the plants, regardless of origin, shown in blue, analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.43)...... 86 Figure 4.8 Available ammonium over time in the blanks, shown in yellow, the non-native grasses, shown in red, and the native grasses, shown in blue, analysed using a linear regression model (p < 3.65  10–7, adjusted R-squared: 0.087)...... 87 Figure 4.9 Available nitrate over time in the blanks, shown in yellow, non- natives, shown in red, and natives, shown in blue, analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.44)...... 87 Figure 4.10 Available ammonium over time in each congener pair. The red lines represent the non-natives (a) African lovegrass, (b) buffel grass and (c) Johnson grass. The blue lines represent the natives (a) Woodlands lovegrass, (b) swamp foxtail and (c) wild sorghum. The yellow line depicts the same blank microcosms in each figure. The results of the linear regression model analysis are included within each figure...... 89 Figure 4.11 Available nitrate over time in each congener pair. The red lines represent the non-natives (a) African lovegrass, (b) buffel grass and (c) Johnson grass. The blue lines represent the natives (a) Woodlands lovegrass, (b) swamp foxtail and (c) wild sorghum. The yellow line depicts the same blank microcosms in each figure. The results of the linear regression model analysis are included within each figure...... 90 Figure 4.12 Aboveground biomass (g) by species of native (blue) and non- –14 native (red) grasses (F5 = 54.45, p < 1.97  10 ). Significant differences were found between all congener pairs except African lovegrass and woodlands lovegrass (Johnson vs. wild sorghum: p < 0.00001; buffel vs. swamp foxtail: p < 0.0022)...... 91 Figure 4.13 Belowground biomass (g) by species of native (blue) and non- –10 native (red) grasses (F5 = 28.76, p < 1.36  10 ). Significant differences were found only between one congener pair: Johnson grass and wild sorghum (p < 0.00001)...... 92

The Roots of Invasion: Belowground traits of invasive and native Australian grasses xi Figure 4.14 Total nitrogen content of full-plant biomass (F5 = 14.21, p < 2.97  10–7). Significant differences were found only between the congener pair Johnson grass and wild sorghum (p < 0.00006)...... 92

The Roots of Invasion: Belowground traits of invasive and native Australian grasses xii List of Tables

Table 1.1 General characteristics and known trends of the four congener pairs...... 6 Table 3.1 Table of methods or calculations for each trait including units where applicable...... 43 Table 3.2 Summary of functional trait measurements and significant differences...... 46

The Roots of Invasion: Belowground traits of invasive and native Australian grasses xiii Statement of Original Authorship

QUT Verified Signature

The Roots of Invasion: Belowground traits of invasive and native Australian grasses xiv Acknowledgements

I would like to thank and acknowledge my supervisors for all their guidance and encouragement: Primary supervisor Associate Professor Jennifer Firn, Co-supervisor Professor Susanne Schmidt and Assistant Professor Amanda Rasmussen. I would also like to thank the laboratory groups of each of my supervisors for their help, encouragement and enthusiasm particularly Coral Pearce, Simone Yasui, Richard Brackin, Nicole Robinson, Aidan Chin, Joshua Buru and Eleanor Velasquez. I would also like to thank Joshua Bon for his help with learning and interpreting R. I could not have collected any of my data without the wonderful help from the University of Queensland Glasshouse staff, as well as the Queensland University of Technology (QUT) technical services staff. The team at QUT’s Central Analytical Research Facility also helped considerably with sample preparation and processing. I would also like to thank Laurel MacKinnon for her excellent proof reading.

I acknowledge that I am a recipient of the Australian Government Research Training Program Funding

The Roots of Invasion: Belowground traits of invasive and native Australian grasses xv

Chapter 1: Introduction

The biodiversity and agricultural economy in Australia are threatened by non- native grasses that were introduced for pasture improvement (Firn, 2009; Godfree et al., 2017). Non-native plants have long been known to cause a variety of detrimental impacts to native environments including declines in biodiversity and shifts in nutrient cycling. Species that are considered to have significant environmental, social and economic impacts are considered invasive (Vila et al., 2011).

Ecologists use plant functional traits to analyse the life-history strategies that allow non-native species to colonise new environments. In the past, many studies have focused on aboveground functional traits; however, a focus on belowground traits is currently emerging as an important field within invasion ecology (Broadbent, Stevens, Peltzer, Ostle, & Orwin, 2018; Mommer & Weemstra, 2012). Previous studies have suggested that successful non-native species can use nutrients more efficiently than native species, particularly in low-nutrient soils, as shown by their greater biomass (Funk, 2013). Much of Australia’s grasslands are characterised as having low-nutrient soils (Buckley, Wasson, & Gubb, 1987). Plants absorb most of the nutrients required for growth from their roots; therefore, the mechanism for greater efficiency is likely to be linked to belowground traits and belowground interactions (Bardgett, Mommer, & De Vries, 2014). Additionally, for grasses, most tissue growth is typically invested belowground (Blair, Nippert, & Briggs, 2013). Despite this knowledge, most research in invasive ecology has occurred aboveground until recently (Bardgett et al., 2014; Wilson, 2014).

Belowground traits are particularly important in invasion ecology for understanding the ability of non-native plant species to establish in new areas (Broadbent et al., 2018). It is difficult to devise efficient invasive control strategies without sufficient empirical data to support a strategic response (Firn et al., 2015). Research has shown that a comprehensive understanding of the mechanisms supporting the establishment of non-native species is vital when considering control strategies (Seabloom, Harpole, Reichman, & Tilmam, 2003). Therefore, quantification of the belowground mechanisms that facilitate the establishment of non-native grasses

Chapter 1: Introduction 1

in place of native biodiversity in Australia’s important and unique landscapes is essential for developing practical strategies that favour the native over the non-native.

The research presented in this thesis took a multidisciplinary approach by combining ecological and physiological hypotheses with experimental techniques to untangle the possible mechanisms that explain the success of non-native grasses over native congeners under traditionally low-nutrient soils. I examined the physiological and architectural differences between four sets of congener pairs of grasses, as well as the trends in functional traits and nitrogen uptake ability across the selected native and non-native grasses. The first objective was to examine the functional traits of all eight grasses to understand how these traits might impact nutrient use efficiency, which translates to above- and belowground biomass. The second objective was to examine the nitrogen depletion of soil surrounding each of the eight grasses during early growth stages to determine whether a difference in nutrient uptake ability between the native and non-native grasses can be inferred.

1.1 BACKGROUND

Globally, more than 40% of terrestrial landscapes can be classified as grasslands, but of that, only 4.6% are protected habitats. Some of the most extensive habitat loss is within temperate grassland and savannah ecosystems, making them some of the most threatened biomes in the world (Hoekstra, Boucher, Ricketts, & Roberts, 2004). About 80% of Australia can be classified as grassland or rangeland, but there is still much to learn about the processes and functions within these ecosystems (Guerin et al., 2017). For the purpose of this study, literature on grasslands and rangelands are used interchangeably because both are affected by the concepts discussed in this research.

Within this study, invasive plants are defined as plants that establish outside of their native ranges and have a detrimental impact on the native environment and other species (Mack et al., 2000). Weeds, which can be native or non-native, are often considered invasive plants and are estimated to cost over $2.2 billion per year in yield loss for Australian landholders (Sinden et al., 2004). Australia currently has 32 ‘Weeds of National Significance’ based on the impact of the plant, which are all non-native, and 28 weeds on the ‘National Environmental Alert’ list because of their potential for greater impact (Sielaff, Polley, Fuentes-Ramirez, Hofmockel, & Wilsey, 2019b).

Chapter 1: Introduction 2 Given this evidence, it is vital that the mechanisms of invasion are further understood to find ways to reduce the cost and ecological threats imposed by non-native grasses.

Australia’s grasslands sustain high biodiversity, and invasive species are known to affect biodiversity negatively (Isbell et al., 2018; Marshall, Lewis, & Ostendorf, 2012; van Klinken & Friedel, 2017). In 2003, one-third of the 15 ‘biodiversity hotspots’ announced by the Australian Government were classified as grasslands (Grice, 2006). Biodiversity is strongly linked to ecosystem functioning and ecosystem services that either directly or indirectly benefit humans (Isbell et al., 2017). Ecosystem services refer to the many benefits that humans enjoy, both directly and indirectly, from certain environments (Cardinale et al., 2012). Non-native grasses can be a threat to native biodiversity because they harden the landscape, which causes landholders to manage more intensely and results in detrimental consequences both ecologically and economically (Godfree et al., 2017).

Many non-native grasses from Africa were intentionally introduced to Australia for pasture improvement (Cook & Dias, 2006). These grasses have largely not held up to the expectations and are either not as palatable or nutritionally rich for cattle grazing as originally thought (Cook & Dias, 2006; Firn, 2007; Godfree et al., 2017). The introduction of these grasses, combined with extrinsic stressors such as tree clearing, grazing, irrigation, soil disturbance, fire and fertilisation, has caused native species richness to decline (Firn et al., 2015). This has created a situation in which non-native species become abundant and increase in dominance, resulting in the conversion of the ecosystem into a non-native -dominated grassland. In most cases, these non-native grasses negatively impact agricultural systems by decreasing biodiversity and shifting ecosystem processes and functioning (Cook & Dias, 2006). Previous studies have shown that intuitive or theoretically based approaches towards non-native grass management are not always successful and more knowledge is required to create effective conservation strategies (Firn, 2009).

A large proportion of studies in invasion ecology have focused on a single non- native species, which gives a limited view for non-native species dominance in general (Han, Buckley, & Firn, 2012; Reichmann, Schwinning, Polley, & Fay, 2016; Rout, Chrzanowski, Smith, & Gough, 2012). By contrast, this study looked at four established non-native grasses, as well as their native congeners experimentally, which allowed for a broader understanding of the traits that contribute to their ability to

Chapter 1: Introduction 3

colonise new environments beyond a single species outlook. The non-native grasses that I have studied were chosen because of their ecological and economic impact on Australia and their congeners were determined by the closest related congener, as well as comparable distribution.

The native grasses chosen are not known to be invasive elsewhere in the world, whereas several of the non-natives are considered invasive in other countries. The non- native grasses studied within this thesis were Eragrostis curvula (Schrad.) Nees (African lovegrass), Cenchrus ciliaris L. (buffel grass), Sorghum halepense (L.) Pers. (Johnson grass) and Chloris gayana Kunth. (Rhodes grass) along with their native counterparts Eragrostis sororia Domin (woodlands lovegrass), Cenchrus purpurascens Thunb. (swamp foxtail), Sorghum leiocladum (Hack.) C.E.Hubb. (wild sorghum) and Chloris truncata R.Br. (windmill grass) as seen in Table 1.1. As depicted in Figure 1.1, the native and non-native grasses have similar distributions, as documented by herbarium records, although the non-natives tend to have a wider distribution in most cases.

The purpose of this research was to examine the belowground traits of native and non-native grasses to understand how these traits contribute to the colonisation process and influence biotic interactions of the invaded community. Another purpose was to determine whether, as for the aboveground traits, there is a generic set of belowground traits that can be associated with non-native establishment success (Funk & Vitousek, 2007; Lee et al., 2017).

Chapter 1: Introduction 4

Figure 1.1 Distribution maps of all eight grasses grouped into their congener pairs. Distributions across congener pairs are comparable, and all of the native grasses are commonly found across multiple ecosystems (Herbaria, 2018). Non-native grasses are on the left within each pair.

Chapter 1: Introduction 5

Table 1.1 General characteristics and known trends of the four congener pairs. Eragrostis Eragrostis Cenchrus Cenchrus Chloris Chloris Sorghum Sorghum curvula sororia ciliaris purpurascens gayana truncata halepense leiocladum Common African Woodlands Buffel grass Swamp Rhodes grass Windmill Johnson grass Wild name lovegrass lovegrass foxtail grass sorghum Growth Tufted Tufted Tussock Tussock Tufted Tufted Tussock Tussock habit perennial (2) perennial (2) perennial (1) perennial (3) perennial (3) perennial (3) perennial perennial Photosynthe C4 (2) C4 (2) C4 (1) C4 C4 (4) C4 (4) C4 (4) C4 (4) tic pathway Height Up to 120 Up to 70 cm 20-150 cm (1) 1.5 m (5) Up to 1 m (3) >2 m (6) 1 m cm (2) (2) Flowering Spring to Summer (2) Varies Late summer- Summer & Varies (3) Summer Summer time Autumn (2) early Autumn Autumn (5) (3) Palatability Low (2) Moderate (2) Moderate (1) Unknown High in Unknown Toxic to Unknown to livestock young growth herbivores (6) (5) Native Africa (2) Australia (2) Tropical & China, Japan, Sub-Saharan Australia (3) Africa & Eastern distribution subtropical SE Asia, Africa (5) Mediterranea Australia (3) arid Africa & Eastern n (6) west Asia (1) Australia (3) Known Palatability NA Drought and NA Unknown NA Soil leachates NA invasive in early heavy grazing (6) attributes growth (2) tolerant (1) Weed Weed in NA ‘non- NA ‘non- NA Weed in some NA classification some states restricted restricted states invasive’ in invasive’ in QLD QLD Synonym NA NA Pennisetum Pennisetum NA NA NA Sarga ciliare (3) alopecuroides leiocladum (3) (3) Non-native grasses are shaded in red, and natives are shaded blue. Much of the information on the native grasses was unavailable or not applicable as noted (1) (Marshall et al., 2012), (2) (Han et al., 2012), (3) ("Atlas of Living Australia," 2017), (4) (Gibson, 2008), (5) ("Rhodes Grass," 2017), (6) (Rout et al., 2012).

1.2 LITERATURE REVIEW

1.2.1 The importance of grasslands

Grasslands are often underappreciated in their importance ecologically but are utilised extensively economically (Grice, 2006). The family of grasses, Poaceae, is one of the largest and most widely distributed families on earth with around 10,000 known species (van Klinken & Friedel, 2017). An estimated 80% of Australia can be considered grassland or rangeland and is home to a myriad of native grassland ecosystems (Guerin et al., 2017). Grasslands can be excellent indicators of change because they can respond rapidly to environmental shifts, including changes in climate (Blair et al., 2013).

The higher biodiversity found in grasslands can also be viewed as a kind of ‘insurance’ against the shifts associated with climate change (Loreau et al., 2001). When true roots evolved in the mid-Devonian era, a large subsequent decrease in

Chapter 1: Introduction 6 global atmospheric CO2 occurred because of the storage of carbon belowground (Bardgett et al., 2014). Carbon storage is more often associated with forests, but grasslands, which hold up to 80% of biomass underground, are considered important ecosystems within the carbon cycle globally (Ni, 2002; Scurlock & Hall, 1998). The unique qualities of grasslands, including their ability for carbon storage and high biodiversity, which provides a buffer and performance enhancer and provide insurance against change (Yachi & Loreau, 1999). At the same time, the ability for quick turnover and rapid shifts on a seasonal basis mean that grasslands can be important predictors of change. These attributes suggest there is an imperative need to understand the mechanisms that govern these systems.

Native grasslands contain a high level of biodiversity of plants, animals and invertebrates (Blair et al., 2013). By contrast, the dominance of non-native grasses has been linked to declines in biodiversity (Grice, 2006; Pyšek et al., 2012). Biodiversity is an important element within a sustainable and resilient ecosystem, both ecologically and agriculturally (Tilman, Reich, & Knops, 2006). Numerous studies have shown that high biodiversity benefits both ecosystems from an environmental perspective and humans through ecosystem services and ecosystem functioning, as described in Figure 1.2 (Isbell et al., 2017). For many years, landholders have thought that increasing the native biodiversity of their rangelands would result in decreased productivity, but ecological research suggests that increased biodiversity holds many potential benefits for landholders as well as the environment (Firn, 2007; Godfree et al., 2017). For example, grasslands exhibiting higher biodiversity have been shown to have greater drought resistance (Tilman & Downing, 1994), which is an important feature for Australia’s arid grasslands. By and large, supporting the biodiversity of native ecosystems, including grasslands, has ecological and economic benefits (Godfree et al., 2017).

Chapter 1: Introduction 7 Figure 1.2 Diagram of the potential effects of anthropogenic drivers such as non-native species introduction into native environments (Isbell et al., 2017, p. 65). It is important to note that the economy and human well-being are affected and entwined with ecosystem functioning and ecosystem well-being. Biodiversity loss is not only detrimental at an environmental level but has the potential to affect ecosystem services and human well-being negatively. The yellow blocks depict social components, the blue blocks refers to ecological components and the green blocks refer to socio- ecological components. The review by Isbell et al. (2017) focused on the relationships indicated by solid lines, although the relationships indicated by dashed lines are also important.

Together, the ecosystems of the world determine the processes that regulate the Earth’s systems, including nutrient cycling, climate and biogeochemical cycles, which makes them important considerations both environmentally and economically (Loreau et al., 2001). In addition to biodiversity loss, non-native plants utilise resources differently than native plants, and non-native grasses can create much greater biomass, which can affect fire regimes, nutrient cycling and other important ecosystem processes (D'Antonio & Vitousek, 1992; Rossiter-Rachor et al., 2009). These shifts in ecosystem functioning can negatively affect ecosystem services (refer to Figure 1.2) (Godfree et al., 2017). For example, buffel grass is known to create a high fuel load with greater biomass, which can result in more intense and frequent fires and thus

Chapter 1: Introduction 8

potentially altering the landscape by eventually shifting the overall ecosystem functioning (Marshall et al., 2012).

Despite their importance, grasslands have been heavily exploited and are some of the most threatened ecosystems in the world (Blair et al., 2013; Hoekstra et al., 2004). In Australia, many native grassland ecosystems have been converted to grazing pastures, which are more likely to be negatively affected by non-native plants, drought and overgrazing caused by anthropogenic drivers. Non-native grasses have a tendency to shift nutrient cycling and community dynamics over time, which can negatively affect ecosystem services within grasslands (Godfree et al., 2017). A focused approach towards native rehabilitation in maintained and natural landscapes can improve the sustainability of nutrient cycling, as well as the sustainability of production rates (Firn, 2007). To conserve and maintain Australia’s unique and varied grassland ecosystems, a more comprehensive understanding of their ecosystem processes and functions, with and without the presence of non-native grass invasions, is needed.

1.2.2 The grasses examined in this study

The research presented in this thesis compared and contrasted the traits of four congeneric pairs of Australian grasses, including four native and four non-native species, as depicted in Figure 1.3. Following van Kleunan’s (2010) framework for the comparison of native and non-native species, all the native grasses are commonly found in Australia and are not considered invasive anywhere, whereas the non-natives have established successfully outside of Australia and some are considered invasive in other parts of the world (van Kleunen, Dawson, Schlaepfer, Jeschke, & Fischer, 2010). This framework provides a foundation for linking the differences in the native and non-native groupings to the qualities associated with invasive capabilities rather than simply differences between species. In addition, comparison of congener pairs minimised the misinterpretation of common adaptations made by non-native species as we would expect to see similar levels of adaptation for their respective congeners (Burns & Strauss, 2011; Garcia, Callaway, Diaconu, & Montesinos, 2013).

Chapter 1: Introduction 9

Figure 1.3 This research focuses on four congener pairs of native and non-native grasses ("Atlas of Living Australia," 2017). The non-native grasses include (from the top left) African lovegrass, Rhodes grass, Johnson grass, and buffel grass. The native grasses include woodlands lovegrass, windmill grass, wild sorghum and swamp foxtail.

Despite being closely related as congener pairs, non-native African lovegrass and woodlands lovegrass show striking differences in their known height and biomass from previous research (Table 1.1) (Han et al., 2012). African lovegrass was originally introduced as a pasture grass because it produces large amounts of biomass quickly and can tolerate drought and low-resource environments; however, it is unpalatable after the early growth stages and requires intense management as a pasture grass (Firn, 2009). This congener pair co-exists within ecosystems, although it is common to find environments dominated by African lovegrass (Han et al., 2012).

Non-native buffel grass and swamp foxtail are both common across much of Australia and are used commercially for pasture and landscaping, respectively. Buffel grass is an important pasture grass economically and a prolific weed environmentally (Marshall et al., 2012). Buffel grass has a particularly negative impact on indigenous communities, which struggle to prevent it from dominating their land and driving out their native food sources (Smyth, Friedel, & O'Malley, 2009). Despite this, its high productivity, tolerance for heavy grazing and drought, and moderate palatability make it a valuable pasture grass (Marshall et al., 2012). Swamp foxtail has been criticised as

Chapter 1: Introduction 10 possibly being an early introduction by European colonisers in Australia. However, recent genetic analysis has revealed that, despite its existence in Asia, it has been in Australia through the Pleistocene epoch, well before European colonisation (Toon, Sacre, Fensham, Cook, & Zhan, 2018). As the name implies, swamp foxtail thrives on deep, poorly drained soils (Jarman, Johnson, Southwell, & Stuart-Dick, 1987). Swamp foxtail is found around spring wetlands and provides essential habitat for a myriad of species (Toon et al., 2018).

Johnson grass, originally native to Africa and the Mediterranean, is widely studied because it is considered invasive in various countries around the world, unlike its congener counterpart, wild sorghum. Johnson grass is known to propagate clonally while suppressing the growth of neighbouring plants by releasing allelochemicals into the soil (Rout et al., 2012). Wild sorghum is found primarily along Australia’s Great Dividing Range and can grow on a variety of soil types (Robinson & Dowling, 1976; Ryley, 2001). Although recent sources have classified it as being more productive than initially thought by early European colonisers, wild sorghum is still highly susceptible to downy mildew (Ryley, 2001). Despite this, populations of wild sorghum can be found commonly along the Australian east coast hinterlands.

Native windmill grass and non-native Rhodes grass are both found commonly throughout Australia, and both prefer moist soil. Rhodes grass is often used in mine- site rehabilitation (Sheldon & Menzies, 2005) and remains a commonly used pasture grass, despite its negative impacts ecologically (Godfree et al., 2017). Windmill grass is the only native grass included in this study that is considered a weed in any capacity, although, this is primarily within agricultural settings in its native range (Chauhan, Manalil, Florentine, & Jha, 2018). It becomes weedy in these agricultural landscapes because of the favourable conditions created including higher water resource availability (Ngo, Boutsalis, Preston, & Gill, 2017). Although windmill grass is considered a weed in some agricultural settings, other landholders may choose to utilise it as a summer pasture crop in rotation to reduce nitrogen loss (Syme, Acuña, Abrecht, & Wade, 2007). In this study, windmill grass was considered a native that is not known as invasive elsewhere because its weedy tendencies are circumstantial and reserved to agricultural systems.

The non-native grasses included in this study have varying levels of suitability for pasture use, as well as a range of detrimental impacts on native Australian ecology.

Chapter 1: Introduction 11 All of the non-native grasses in this study were originally introduced from Africa for pasture improvement but have since established outside of their introduced range (Firn, 2009; Godfree et al., 2017; Grice, 2006). They are all considered as non-restricted invasive or a weed in at least one Australian state or territory (Table 1.1). Buffel grass and Rhodes grass are still considered agriculturally beneficial and can be purchased as pasture grass. Buffel grass and African lovegrass have been shown to increase only a few factors of agro-economic value while decreasing all factors of conservation value (Godfree et al., 2017). Both buffel and African lovegrass are listed as ‘high-impact’ species environmentally because they have spread unintentionally and have come to dominate land that is valued environmentally (van Klinken & Friedel, 2017).

As noted in Table 1.1 many of the non-native grasses that were originally introduced for pasture improvement are actually not substantially palatable for livestock. African lovegrass is particularly unpalatable after its early growth stages, an attribute that is thought to contribute to its invasive tendency (Firn, 2009). The native and non-native grasses included in this study were carefully selected for a range of reasons including their common distribution, invasive qualities, method of introduction and congeneric pairing, which will further understanding of the impacts of and differences between the native and non-native grasses of Australia.

1.2.3 Invasion ecology

With the rise of globalisation, the field of invasion ecology continues to grow and direct attention to the increasing spread of non-native species around the world (van Kleunen et al., 2015). Before the early 20th century, the introduction of non-native species to Western colonised environments was common practice and viewed as resourceful (Cook & Dias, 2006; Firn, 2009; Simberloff et al., 2013). Many introduced plants can grow outside of their native range without detrimental impact to the native ecosystem. However, when a plant species affects an ecosystem negatively, often by dominating a site or landscape, altering fire regimes or nutrient cycling and decreasing native species abundance, it is considered an invasive species (Mack et al., 2000). Globally, invasive plants are deemed to negatively impact the abundance and diversity of native species in ecosystems (Vila et al., 2011). Australia’s unique environments have become home to a host of invasive plant and animal species in the last century, and ecologists are tasked with revealing the ‘why’ and ‘how’ behind the invasions (Cook & Dias, 2006).

Chapter 1: Introduction 12

Ecologists have proposed a number of theories to explain how invasive non- native species can outperform native species (Catford, Jansson, & Nilsson, 2009; Mitchell et al., 2006). Van Klinken and Friedel (2017) suggest that the factors allowing Australia’s ‘high-impact’ plant species to invade outside of their introduced ranges can be attributed to propagule pressure, ecological novelty and an ability to adapt or alter disturbance patterns. Ecological novelty is defined as any trait or life-history strategy that allows a species to outcompete a native species. Other similar theories include both pre- and post-adaptation of invader traits that contribute to their ecological novelty as seen in Figure 1.4 (MacDougall et al., 2018). The concept of post- adaptation includes the release from co-evolved pathogens and herbivores resulting from being introduced into a new habitat (Callaway & Aschenhoug, 2000). Invaders that occupy a different niche to that of native species might be able to establish successfully, but it is thought that they still need some form of ecological novelty or advantage to become dominant; this requires successful invaders to exhibit a fitness and niche difference compared with the native occupants (MacDougall, Gilbert, & Levine, 2009).

Many of these theories focus on the invading species. Conversely, fluctuations in resource availability of environments is a key factor in allowing them to establish and thrive (Davis, Grime, & Thompson, 2000). Non-native plant invasions depend on both the qualities of the invading species and the qualities of the habitat being invaded (Davis et al., 2000; Funk & Vitousek, 2007). Phenology can also play an important role in invasion success, and early emergence can be the key factor in giving native species competitive advantage (Firn, MacDougall, Schmidt, & Buckley, 2010; Seabloom et al., 2003). Ecologists have untangled many of the routes that species can take to become invasive (Mitchell et al., 2006), yet the fine details of the differences in functional traits across a gradient of environments remain equivocal.

Chapter 1: Introduction 13

Figure 1.4 Diagram of the ways in which a non-native species may invade new habitats as modified from (MacDougall, et al., 2018, p. 2). Invasions can occur through a single pathway, but a combination of ‘opportunity’ pathways is more likely to take place.

Propagule pressure is recognised as a main contributor to plant invasion success, particularly in Australia (Catford et al., 2009; Driscoll et al., 2014). Propagule pressure refers to the number of individuals introduced into an environment where they are not native, the number of times that species is introduced and the number of individuals introduced at a given time (Lockwood, Cassey, & Blackburn, 2005). The non-native grasses included in this study were all introduced intentionally and have therefore received a sufficient number of introductions into the environment for successful establishment (Cook & Dias, 2006). Following their introduction, they have also become established outside the ranges of their initial introduction (Godfree et al., 2017). Although propagule pressure can help to explain initial invasions, phenology of seed dispersal, capacity for seed production and germination success can also contribute to the ongoing propagule pressure that drives and sustains the steps in the invasion process (Cleland, Chuine, Menzel, Mooney, & Schwartz, 2007; Firn et al., 2010).

One of the other major contributing factors supporting plant invasions in Australia is the ecological novelty of the invading species (van Klinken & Friedel, 2017). This theory of ecological novelty in plant invasions acknowledges that invaders tend to have some mechanism or functional trait advantage that the existing native

Chapter 1: Introduction 14

species do not have. Many ecological studies have found a link between certain plant functional traits that indicate investment trade-offs and a species’ ability to successfully invade environments outside of its native range (Funk & Vitousek, 2007; Legay et al., 2016; Leishman, Haslehurst, Ares, & Baruch, 2007; MacDougall et al., 2009).

Numerous researchers have implicated disturbance as a key factor in initiating and maintaining plant invasions (Buckley, Bolker, & Rees, 2007; Davis et al., 2000; Firn, Rout, Possingham, & Buckley, 2008; Lake & Leishman, 2004). Disturbance can be defined as any physical event that disrupts the natural abiotic or biotic interactions within an ecosystem. Disturbances that favour invasive species are often, but not always, anthropogenically driven. Some common forms of disturbance that can impact plant invasion are fire, floods, changes to grazing regime including herbivory increase or decrease, introduction of invasive non-native species, shifts in nutrient cycling and changes in climate (Buckley et al., 2007; Firn et al., 2008; Lake & Leishman, 2004; Mack et al., 2000). Studies have shown that invasive grasses within Australia have the potential to alter the nitrogen cycling of a system due to differences in nitrogen uptake compared with decomposition of the grass (Rossiter-Rachor et al., 2017).

The theory of invasibility proposed by Davis et al. (2000) posits that changes in resource availability, including that of water, light and nutrients, drives plant invasions; this theory explains how invasions are more common following a disturbance. In Australia, plant invasions have contributed to shifts in natural disturbance such as fire regime (van Klinken & Friedel, 2017). For example, the presence of some invasive grasses results in an increased frequency and intensity of fires (D'Antonio & Vitousek, 1992; Rossiter-Rachor et al., 2009). Additionally, Australia’s land management changed drastically with the introduction of Western management practices during European colonisation in place of indigenous management strategies. Indigenous land management utilised fire to maintain beneficial landscapes, and this shift in disturbance regime likely influenced shifts in plant species communities, which allowed non-native species to capitalise on the changed landscape (Buckley et al., 2007; Gammage, 2008). Examining the belowground traits offers a unique perspective on disturbance because these are the only structures on plants that may be unaffected by disturbance (Klimešová,

Chapter 1: Introduction 15

Martínková, Ottaviani, & Field, 2018). Disturbance is an important and complicated factor to consider when devising control strategies for invasive grasses.

Different management strategies have been used to help mitigate the detrimental impacts of invasive non-native plants in Australia and around the world (Buckley et al., 2007; Firn et al., 2008; Guido, Pillar, & Souza, 2017). Some research suggests that simply removing the disturbance will return a particular ecosystem to its natural state, whereas others argue that this simply creates a novel ecosystem (Firn et al., 2008). Removing the invasive plant can be a disturbance that creates a ‘weed- shaped hole’, in which the same plant can re-establish or another potentially invasive plant can establish (Buckley et al., 2007). Although the initial impact of plant invasions can be extremely detrimental, some studies suggest that this impact is likely to decline with time if the initial disturbance does not persist (Flory, Bauer, Phillips, Clay, & D'Antonio, 2017). There is no definitive evidence that this scenario has taken place in Australian grasslands and rangelands, which indicates that the understanding of the key factors involved in this process is incomplete. In most cases, eradication of invasive species is not realistic, and researchers suggest the strategy of promoting a novel ecosystem with novel forms of conservation and restoration management (Hobbs, Higgs, & Harris, 2009).

It is often assumed that invasive species are more abundant and more successful in their invaded environments than in their native environments (Parker et al., 2013; van Kleunen, Dawson, et al., 2010); however, several studies have refuted this supposition. One global study suggests that species that are abundant in their native ranges are more likely to be abundant and potentially invasive in new environments (Firn et al., 2011). Other studies found that, although some species perform better in their new ranges, this is not true for all invasive species (Parker et al., 2013). For example, plants that are more widely distributed throughout South Africa are more likely to become invasive or weedy plants in Australia (Scott & Panetta, 1993). Abundant species from other parts of the world, particularly with similar environments such as sub-Saharan Africa and Australia, are likely to become abundant in Australia (Firn et al., 2011; Scott & Panetta, 1993).

The main aim of the research presented in this thesis was to measure possible trait advantages that non-native grasses in Australia have over their native counterparts. Plant invasion undoubtedly involves multiple factors and interactions,

Chapter 1: Introduction 16

and the initial establishment of a species is difficult to measure because it represents a historical event. It has been shown that climate change will likely favour invasive grass species as temperatures rise and water availability decreases (Duell, Wilson, & Hickman, 2016). Most ecologists agree that the success of plant invasions is context dependant and multifaceted (Catford et al., 2019; Pyšek et al., 2012), which is why it is critical to understand further the interactions between Australia’s unique native grasses and the non-native grasses that are successfully establishing in these particular low-resource environments. It is important that conservation biology includes an understanding of trait-based invasion ecology to untangle the forces driving biodiversity loss (van Kleunen & Richardson, 2016).

1.2.4 Low-resource environments

High-resource environments are theoretically more likely to be colonised by non-native plants because of the lack of nutrient limitation. A large proportion of Australia is characterised by its nutrient-poor soil and arid climate (Buckley et al., 1987; Orians & Milewski, 2007). Theoretically, plants that have evolved in harsh, low- nutrient environments including Australian grasslands (Guerin et al., 2017) should be able to outcompete potential non-native plants that have not evolved under the same conditions; however, this is not the case (Funk, 2013). In low-nutrient environments, non-native plants tend to have greater nutrient use efficiency (Funk & Vitousek, 2007). For example, African lovegrass has been shown to thrive and create large amounts of biomass on infertile, sandy soils with relatively low annual rainfall (Firn, 2009). Understanding the mechanisms behind non-native species’ capacity to thrive under resource limitation is an essential step towards understanding invasive ecology in Australia’s grasslands.

Many studies on plant invasions in low-nutrient environments have focused on aboveground traits (Han et al., 2012; Reichmann et al., 2016; Rout et al., 2012), although the mechanisms that allow and initiate nutrient use efficiency start belowground with nutrient uptake (Bardgett, 2017; Funk & Vitousek, 2007). Some studies have shown that soil biota, nutrient availability and water availability increase following the establishment of non-native plants in new ecosystems, which suggests that non-native plants are equipped to increase these resources for themselves (Pyšek et al., 2012). When combined with disturbance, resource availability greatly affects the success of plant invasions into a new environment, as seen in Figure 1.5 (Davis et

Chapter 1: Introduction 17

al., 2000; Funk, 2013). Understanding the belowground functions of native and non- native grasses will be particularly important when devising conservation management plans in low-nutrient environments.

Figure 1.5 Diagram adapted from Funk (2013) showing the interaction of disturbance and resource availability, and the effect on non-native species invasion (Funk, 2013, p. 3). It is noted here that disturbance often correlates with a temporary increase in resource availability.

Nitrogen is essential for successful plant growth because of its importance in photosynthesis, cell wall structure and genetic material (Dong et al., 2017; Vitousek & Howarth, 1991). It is one of the main limiting nutrients in regards to net primary production and, when shifts in nitrogen cycling occur, it nitrogen can affect community assemblage (Vitousek & Howarth, 1991). Plants absorb nitrogen primarily in two forms: ammonium and nitrate. Nitrate is typically more easily absorbed by plants but is also more easily lost from the soil as it is very mobile (Gigon & Rorison, 1972). Ammonium can be converted into nitrate in the soil through nitrification, and together these are the two most available and important forms of nitrogen for plants. Some plants, including some grasses, exude nitrate inhibitors into the soil and thus prefer ammonium, which can give them an advantage in nitrate-limited soils (Rossiter- Rachor et al., 2009). Nitrogen use efficiency can be thought of in two ways: first, the processes and factors that affect uptake and assimilation and, second, the processes that affect nitrogen mobilisation and use within the plant (Kant, Bi, & Rothstein,

Chapter 1: Introduction 18

2011). Agricultural research has focused on nitrogen and its importance in crop production in great detail (Brackin et al., 2015; Robinson et al., 2011). By incorporating physiological and agricultural research techniques, research can help uncover the interactions between plants and nitrogen at greater depth and accuracy in ecological systems.

Researchers believe that the ability of a species to adapt to different environmental conditions determines its success (Leishman, Thomson, & Cooke, 2010). Different plants have different nitrogen absorption capacities and, given the greater biomass of most non-native grasses (Godfree et al., 2017), it is likely that they are better at obtaining nutrients from the soil. Nutrient cycles are changing worldwide because of anthropogenic activities such as non-native plant invasions, agricultural development and climate change. Consequentially, shifts in nitrogen availability have the potential to transform whole ecosystems (Hawkes, Wren, Herman, & Firestone, 2005). Studies have shown that native and non-native species may display different levels of trait plasticity in low-nutrient environments, which could increase their ability to capitalise on resources under various conditions (Funk, 2008). Trait plasticity refers to the range of expression occurring in one trait given different environmental conditions (Richards, Bossdorf, Muth, Gurevitch, & Pigliucci, 2006). In addition to trait plasticity, invasive non-native plants are generally found to utilise faster growth strategies, which allows for successful establishment in new ecosystems when resources are not limited (Leishman et al., 2007). However, in Australia’s resource- limited environments, an abundance of non-native invasive grasses have established successfully.

It seems paradoxical that invasive plants are able to outcompete natives in low- nutrient environments where the natives have evolved to adapt (Funk & Vitousek, 2007), but this is happening in Australia’s grasslands (Han et al., 2012). The lack of evolved pathogens to non-native plants in new environments provides one explanation for their ability to invade (Callaway & Aschenhoug, 2000). Much of these plant– pathogen interactions occur belowground and involve the entire native and non-native microbiome (Rout & Callaway, 2009). Additionally, many studies have implicated disturbance as one of the key factors of invasion (Firn et al., 2008). Some species, such as buffel grass, can handle heavy grazing better than native grasses in Australia, which reflects conditions of their native ranges (Marshall et al., 2012). The mechanisms

Chapter 1: Introduction 19

providing the nutrients for the non-native grasses greater nutrient use efficiency are belowground, especially in grasslands that typically have more biomass belowground (Bardgett et al., 2014; Blair et al., 2013). Many of the mechanisms that contribute to the establishment of non-native plants are unknown, although there has been extensive research on the aboveground functional traits that correlate with this, as well as the environmental factors that may lend themselves to being colonised.

1.2.5 Functional traits and niche

In its most basic definition, traits are a measure of an organism’s ability to perform (Violle et al., 2007). Plant functional traits can be defined as any feature that can be measured, such as height or biomass, which can be described as a type of surrogate measure for a plant’s life-history strategy and explains a response to or effect on environmental factors. Functional traits are often used in ecology to answer questions about scales, which range from individual populations and localised communities to global trends (Pérez-Harguindeguy et al., 2013). The research presented in this thesis is focused on plant functional traits above- and belowground to compare growth strategies in relation to a plant’s ability to colonise new environments. Weiher (1999) suggests that researchers and even ancient philosophers have long sought to classify plants by how they function; however, the idea of globally generalised plant traits in the modern sense has been a part of the field of ecology for several decades, and understanding of the implications of functional traits is still expanding (Weiher et al., 1999).

With the growing popularity of trait-based ecology came the need for generalisable patterns across ecosystem gradients worldwide. The ‘Leaf Economic Spectrum’ (LES) was one of the first global studies to find correlateions between plant functional traits across biomes that explained life-history strategy trade-offs (Wright et al., 2004). The LES provides a foundational spectrum of traits running from fast growing to slow growing or nutrient acquisition specialist to nutrient conservation specialist. The LES and other studies identified traits such as specific leaf area, height, biomass and leaf area as standard measures for describing plant growth strategies (McGill, Enquist, Weiher, & Westoby, 2006; Pérez-Harguindeguy et al., 2013; Wright et al., 2004). However, the implications of these traits are still under review. A recent study suggests that a common trait measurement, specific leaf area, may not indicate the investment trade-offs that it has traditionally been given credit for when applied

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within a single biome such as grasslands and used to explain responses to short-term perturbations (Firn et al., 2019). The use of traits, and the strategies for which they can be considered measures for, are still being fine tuned by the ecological community, and the addition of belowground traits as standard measurements in ecological enquires is rapidly taking form (Funk et al., 2016; Mommer & Weemstra, 2012; Reich & Cornelissen, 2014).

Research within invasion ecology has confirmed that not all plants within a functional group, such as grasses, exhibit the same functional traits (Funk et al., 2016). Functional traits have been used extensively across various ecosystems to provide evidence for differences in fitness between native and non-native invasive species (van Kleunen, Weber, & Fischer, 2010). It is believed that non-native invasive plants are most likely to fall towards the acquisition or fast-growing end of the spectrum, rather than the conservative slow-growing end, particularly in environments where resources are not limited (Han et al., 2012). Native plant species within Australia are usually thought to have a more conservative growth strategy compared with non-native invasive plants that have traits on the faster growth strategy end of the LES (Leishman et al., 2007). Functional traits allow comparisons of native and non-native species based on their differences in function rather than their differences in taxonomic classification, and the study of functional traits can be particularly useful when comparing species that are more closely related.

Much of the literature discusses functional traits within the theoretical framework of competitive advantage. Direct competition depends on a multitude of abiotic and biotic factors, which led me to focus more on the mechanistic advantage of non-native and potentially invasive species in this thesis research. My study focused on resource acquisition at an individual species level without direct competition, although I note that certain species may have advantages when in direct contact with a competitor (Li, Liu, McCormack, Ma, & Guo, 2017). For example, research has shown that Johnson grass has allelopathic effects on plants growing in the same vicinity, giving it a unique advantage in supressing competition (Rout et al., 2012). It is theorised that invasive species are able to succeed in new environments because of some form of ecological novelty, which may be measurable in the form of functional traits (van Kleunen, Dawson, et al., 2010). Mechanistic advantages drive non-native

Chapter 1: Introduction 21

dominance and more thorough understanding of these mechanisms will be useful for creating more efficient control strategies (Firn et al., 2010).

Focusing on non-native species and their native congeners reduces the possibility that the non-native species are dominant because of the niche complementarity hypothesis. Niche complementarity theory states that ecosystems with greater species diversity can more efficiently utilise the resources that are limiting (Firn, 2007; Loreau, 2000). Non-native species are thought to be able to establish from two methods: niche difference from native occupants or fitness advantage (MacDougall et al., 2009). The fitness advantage aspect refers to competitive or novel traits that outcompete the native species. Congeners are more likely to be functionally similar, thus reducing the likelihood that non-native dominance can be explained through the occupation of difference niches (Burns & Strauss, 2011). Although biodiversity is undoubtedly an important factor, several studies have noted that, as research moves forward, it will be better to focus on functional classifications than species richness or biodiversity because functional classifications have a greater impact on ecosystem functioning (Bardgett, 2017; Díaz & Cabido, 2001; Loreau et al., 2001).

Functional traits are influenced by environmental factors but can also feedback to influence the environment through nutrient and carbon cycling and soil aggregation, as seen in Figure 1.6 (Bardgett et al., 2014; Mommer & Weemstra, 2012; Wright et al., 2004). The list of measurable functional traits is seemingly endless, and one challenge in trait-based ecology is to identify the most important set of traits to measure (Funk et al., 2016). Many traits correlate with each other, and ecologists strive to find the fewest independent traits needed to represent the important life-history strategies of a given plant (Laughlin & Wilson, 2014). As trait-based ecology shifts its focus to include belowground traits, the correlations between above and belowground traits, and how they affect their environments together and independently, become increasingly important.

Chapter 1: Introduction 22

Figure 1.6 Belowground plant functional traits can impact various processes such as carbon cycling, nutrient cycling and structural stability (Bardgett, et al., 2014, p. 697). The belowground plant functional traits from this figure are broken down into morphological, architectural and physiological traits, which are evolved ecological strategies associated with the response of a plant to its environment, as well as the effect of a plant on its environment (Bardgett et al., 2014).

1.2.6 Root economic spectrum

Much like the leaf economic spectrum (Wright et al., 2004), a root economic spectrum that is based on functional traits aims to provide a framework of life-history strategies from which ecologists can make informed predictions, assumptions and inferences. However, finding generalisable trends in belowground traits is challenging (Mommer & Weemstra, 2012). Studies have shown that leaves and roots have uncorrelated lifespans, which suggests that both above- and belowground traits need to be considered. Laliberte (2017) suggested that more research is needed to quantify root trait dimensionality to identify a possible root economic spectrum. Root traits tend to exhibit even higher plasticity than leaves and do not always correlate directly with the common aboveground traits (Bardgett et al., 2014; Mommer & Weemstra, 2012). Roots respond to their environments and, therefore, root traits cannot be studied without their corresponding abiotic influences. Invasive species can show greater phenotypic trait plasticity in general, regardless of their position above- or belowground (Ruprecht, Fenesi, & Nijs, 2013). As evidence emerges to suggest that morphological leaf traits may also be poor indicators of a plant’s response to environmental factors (Firn et al., 2019), it has been suggested that trait-based research

Chapter 1: Introduction 23

should move more towards indexing processes than morphology (Figure 1.7) (Mommer & Weemstra, 2012).

Figure 1.7 Mommer and Weemstra (2012) proposed a set of trait measurements for a ‘whole plant resource economy perspective’. Rather than focusing above or below, this framework incorporates key traits from both above- and belowground into a process-focused framework (Mommer & Weemstra, 2012, p. 725).

Many researchers have sought to create a framework for a root economic spectrum and, despite some similarities across ecosystems and species, a comprehensive generalisable framework has yet to be widely agreed upon (Mommer & Weemstra, 2012; Roumet et al., 2016; Tjoelker, Craine, Wedin, Reich, & Tilman, 2005; Weemstra et al., 2016; Withington, Reich, Oleksyn, & Eissenstat, 2006). Root traits are likely to be essential for understanding how resources such as water and nutrients are acquired and used by plant species. Recent research has shown that shifts in the environment can result in different responses above- and belowground, which can be manifested in changed plant traits (Cleland et al., 2019). Additionally, root traits may be especially important in low-resource environments because they play a key role in nutrient acquisition and conservation (Funk et al., 2016). Despite the challenges

Chapter 1: Introduction 24

of creating a comprehensive resource economic spectrum that is suitable across myriad ecosystems, ecologists agree that root systems greatly affect ecosystem processes and therefore should be included in trait-based ecological research (Bardgett, 2017; Klimešová et al., 2018; Weemstra et al., 2016). Overall, the research presented in this thesis does not aim to help create a root economic spectrum but rather acknowledges the need for a greater understanding of belowground traits, belowground root and nutrient interactions, and a belowground-focused approach to understanding Australia’s native grasses.

1.2.7 Belowground traits

As of 2014, fewer than 20% of studies on plant ecology consider roots and root structures, despite the fact that much of a plant’s biomass along with many important physiological processes can be found belowground (Wilson, 2014). The inclusion of belowground traits is a clear gap within trait-based ecology research at the global and community level (Funk et al., 2016). As such, belowground traits have become increasingly recognised in recent years, and many researchers are developing different ways of using these measurements and advising which traits are most useful within the field of ecology (Bardgett et al., 2014; Klimešová et al., 2018; Laliberte, 2017; McCormack et al., 2015; Weemstra et al., 2016). In their most basic function, roots exist to acquire nutrients and water for the plant, as well as to support the plant architecturally (Bardgett et al., 2014). However, there are challenges when attempting to compare the belowground structures and function of different species such as grasses and trees, for example. Grasses typically store most of their biomass underground (Blair et al., 2013), which is where many critical nutrients for the plant are acquired (Bardgett et al., 2014). Much of invasive ecology and plant sciences in general have focused on the portion of plants that can be seen—the aboveground portion of the plant—yet belowground mechanisms are vital for the aboveground outputs.

The mechanisms for aboveground success start belowground, and there is a clear gap in knowledge and understanding, particularly with regards to invasion ecology. Belowground traits are useful for making ecological predictions across a multitude of environments and can be used to help predict species success and location shifts in the face of climate change (Li et al., 2017). Belowground competition for resources is linked to the impact of invasive plant species (Broadbent et al., 2018) and is more

Chapter 1: Introduction 25

important than previously thought in driving ecosystem processes, as seen in Figure 1.8 (Bardgett et al., 2014). A study conducted using non-native plants of European origin growing in the USA focused on the nutrient foraging differences among clonal plants and suggests that non-native plants may have greater capacity for nutrient accumulation than natives, as well as more phenotypic plasticity in response to nutrient differences (Keser et al., 2014). Belowground interactions can drive ecosystem shifts and make it imperative to understand the impact that non-native species may have both globally and locally (Bardgett et al., 2014). To understand how non-native grasses sculpt landscapes, the differences in belowground traits and their effects on belowground processes need to be understood.

Figure 1.8 The feedback cycles associated with root traits and community assemblage, as well as global change are shown (Bardgett, et al., 2014, p. 696).

Nutrient use efficiency is an important aspect of success and begins belowground with nutrient uptake. Nitrogen is important for net primary production because of its

Chapter 1: Introduction 26

vital role in photosynthesis and is needed in larger quantities than most other nutrients (Vitousek & Howarth, 1991). Recent studies have shown that different root types have different abilities to absorb water and nutrients from the soil (Ahmed et al., 2018). Most root systems in monocots, including grasses, typically comprise adventitious roots including seminal and crown roots, which help plants tolerate myriad stressful conditions (Ahmed et al., 2018; Bardgett et al., 2014; Steffens & Rasmussen, 2016). Different types of roots respond differently to stressors, including nutrient deficiency. Understanding these belowground responses to stresses, such as drought, nutrient limitations, and other conditions (Steffens & Rasmussen, 2016), is important for understanding the mechanisms that contribute to non-native plant establishment.

Belowground traits present numerous challenges when searching for generalisable trends across ecosystem gradients. Although a global trend may be difficult to define (Mommer & Weemstra, 2012), I approached this with a narrower focus in Australia’s unique grassland ecosystems by comparing traits and functions across a set of native and non-native grass species given the same conditions. Non- native grasses have the ability to dominate Australia’s grasslands and create more biomass despite the low-resource conditions (Godfree et al., 2017). The knowledge and understanding of belowground traits and processes will help to paint the picture of ‘how’ they are able to do this.

1.2.8 Measuring belowground traits

One of the challenges with studying belowground traits is accessibility; however, novel technology is emerging to help alleviate these challenges. Most methods for measuring belowground traits rely on destructive harvesting and can only be done once during a plant’s lifetime. Software such as WinRhizo (Regent Instruments, Quebec, Ontario, Canada) has helped make belowground trait quantification more accessible by automatically analysing scanned root images for a suite of traits such as root surface area, root diameter and root length (Li et al., 2017; Pérez-Harguindeguy et al., 2013; Withington et al., 2006). A novel technique using microdialysis allows the sampling of nutrient flux in the soil in real time to record the changes in nutrient availability over time (Buckley, Brackin, & Schmidt, 2016; Inselsbacher, Öhlund, Jämtgård, Huss-Danell, & Näsholm, 2011). The depletion of nitrogen can be used to help infer a difference in uptake ability of the plant (Brackin, Atkinson, Sturrock, & Rasmussen, 2017; Jin et al., 2014). Microdialysis is a novel

Chapter 1: Introduction 27

technique in invasive ecology, but it has been used to determine belowground interactions in agricultural and plant physiological research (Brackin et al., 2015; Buckley et al., 2016; Inselsbacher & Nasholm, 2012).

Often, in ecology, the trends of plant communities are studied without identifying the actual mechanism behind those trends. Studies within agriculture have had a much greater mechanistic focus belowground than studies within ecology. For example, research on cereal crops using x-ray microscale computed tomography (CT) imaging along with genetic, genomic and cellular analysis has shown that different types of branching in root systems provide different types of growth strategies and are controlled by different mechanisms (Atkinson et al., 2014). The use of microdialysis in situ has helped to elucidate the gap between supply and uptake ability in sugar cane crops (Brackin et al., 2015; Robinson et al., 2011). My project aimed to apply this knowledge and novel technique experimentally to examine nutrient interactions from the perspective of ecological theory. The combination of sampling physiological processes and quantifying belowground traits along with aboveground traits will lend a more comprehensive understanding of how functional traits correlate with function in Australia’s low resource grasslands.

Grasses present a unique challenge when incorporating global trait-based criteria because they are able to grow clonally as well as sexually (Klimešová & Klimeš, 2007; Klimešová et al., 2018). This clonal growth adds an extra dimension to trait-based research and increases their ability to spread across new areas, thereby moving the belowground trait conversation away from simple acquisition-oriented strategies (Klimešová et al., 2018). Root traits can be organised into acquisition and non- acquisition categories, with a subsection of structural and non-structural within the non-acquisition category (Klimešová et al., 2018). Belowground structures are not solely for nutrient acquisition and play important roles in nutrient storage, re-sprouting ability and structural integrity. When comparing clonal to non-clonal species, it is simple enough to record this trait as present or absent. However, if all species within a study are clonal, more complex delineations need to occur (Zheng, Bai, & Zhang, 2019). Tiller production compared with above- and belowground biomass can be indicative of the species investment in clonal regeneration (Klimešová & Klimeš, 2007). Tillers are not an appropriate trait measure for all plants but can provide an important insight into investment trade-offs for grasses and other clonal species.

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Much emphasis is placed on the measurement of fine roots given that they are thought to be the most important in terms of resource absorption and loss within roots (Freschet, Roumet, & Treseder, 2017). Traditionally, fine roots are defined as being less than 2 mm in diameter, although this is based on simple morphology rather than function (McCormack et al., 2015). It has been suggested that fine roots should be categorised according to their absorptive ability for more distal fine roots and transportive ability for more proximal fine roots. These classifications are important in perennial ecosystems, where most roots, such as grass roots, fall into the fine root category of size (Freschet et al., 2017). For the purpose of this study, I assumed that the primary purpose of roots in early growth stages is resource acquisition, and all roots were classified as fine roots, despite the potential presence of some roots over 2 mm in diameter.

Measuring belowground traits in unique and informative ways is increasingly more accessible and can lead to novel insights into the role that roots play in invasion ecology and overall plant success. The research presented in this thesis used several traditional and novel techniques and measurements to provide a comprehensive underground snapshot of Australia’s grasslands.

1.3 PURPOSES

My research aim was to identify a suite of above- and belowground functional traits of four native and non-native congener pairs of Australian grasses during early growth stages to determine whether the traits exhibited by each group follow the current understanding of invasive trait-based ecology. I also aimed to compare the trend in functional traits to a more accurate measure of function by using a novel microdialysis technique to examine nitrogen depletion in the soil over time during early growth stages.

The overarching objective was to gain a more comprehensive understanding of the ecological processes and functions that underlie the widespread dominance of non- native grasses throughout Australia compared with native grasses whose populations are decreasing. Within this objective was the aim to compare function with functional traits in grasses that traditionally succeed in low-resource environments.

Chapter 1: Introduction 29

1.4 SIGNIFICANCE AND SCOPE

Ecologists have long focused on trait-based studies, as opposed to taxonomic- based studies, to find trends and thus predictions across ecosystems globally. The recent emergence of a belowground focus within this ideology and my research aim to contribute to the growing body of knowledge with a focus on Australia’s unique grasslands (Godfree et al., 2017; Mommer & Weemstra, 2012). Many non-native African grasses in Australia have greater biomass than Australian native grasses, but the belowground equivalent traits and the mechanisms that lead to this difference are largely unknown (Godfree et al., 2017). The significance of this study lays in a deeper understanding of the contribution that belowground traits and functions make to the invasive qualities of non-native African grasses in Australia.

In most cases described in this thesis, the word non-native is used to define the plants included in this study that are not native to Australia but, because of the nature of the subject and previous research terminology, it is difficult not to include the term ‘invasive’ when discussing the literature. In this document, invasive refers to non- native species that have established outside of their native and intended range to the detriment of the existing ecosystem. The term invasive can encompass both native and non-native plants; however, in this study I focus on invasive non-native grasses versus non-invasive native grasses (Vila et al., 2011).

The terms grassland and rangeland are used interchangeably, although rangelands refers specifically to grasslands that have been converted or utilised for commercial purposes, particularly for use with livestock. The concepts, traits and interactions discussed throughout this thesis are applicable to natural, managed and commercially utilised grasslands.

1.5 THESIS OUTLINE

The next three chapters (Chapters 2, 3 and 4) outline the three experiments and three datasets associated with my Master of Philosophy research. These experiments relate to each other, used the same species and in many ways support the same conclusions but are different and are therefore discussed independently. The final chapter (Chapter 5) discusses the conclusions and implications of the three experiments in conjunction with each other, as well as the larger field of research from

Chapter 1: Introduction 30

the literature. This final chapter also discusses future directions for using microdialysis and belowground functional trait-based research in ecology.

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Chapter 2: Germination probability of native and non-native Australian grasses

2.1 AIMS AND OBJECTIVES

The aim of this experiment was to quantify the time to germination for the seeds of all eight grass species (Table 1.1) under the same conditions in order to determine if germination success as a functional trait contributes to the ability of that species to establish populations in non-native ranges. The non-native grasses included in this experiment were African lovegrass, Rhodes grass, buffel grass and Johnson grass, and their native congeners were woodlands lovegrass, windmill grass, swamp foxtail and wild sorghum. Faster and higher germination success rates indicate a higher propagule pressure on a system and a head start in resource acquisition and growth (van Klinken & Friedel, 2017). Higher and faster germination rates were hypothesised in the non- native grasses than in the native grasses (Wainwright & Cleland, 2013).

Propagule pressure is thought to be an important component in the ability of a novel species to establish outside of its native range (Callaway & Aschenhoug, 2000; Catford et al., 2016; Lockwood et al., 2005). Propagule pressure refers to the number of viable individuals from a species that are introduced or released into an area. The number of viable and successfully germinated seeds is one aspect of propagule pressure (Lockwood et al., 2005). Germination rates for several target grasses have been studied before, but these studies have focused on the non-native grasses for the purpose of pasture use under different conditions and the studies were therefore not comparable to each other or to the germination success of native grasses (Chauhan et al., 2018; Firn, 2009; Kaur & Soodan, 2017; Marshall et al., 2012). The germination trial described in this chapter was conducted to gain a better understanding of the potential role of germination rates in aiding the establishment of non-native grasses in Australia’s grasslands compared with their native congeners.

2.2 RESEARCH DESIGN

The germination trials were conducted at Queensland University of Technology (Gardens Point campus) using growth chambers set to a 12-hour day and night cycle

Chapter 2: Germination probability of native and non-native Australian grasses 32

with a maximum temperature of 28°C and a minimum temperature of 18°C. All seeds were collected locally from wild populations in Northern New South Wales and South East Queensland. All seeds were collected within several weeks of each other from December 2017 to January 2018 and were stored in a laboratory refrigerator in envelopes within plastic bags to avoid light, water, and temperature exposure. Seeds for each species were collected from a minimum of two different populations and two different plants from each population, though when possible 20 individual plants were sampled from at least three populations all within the northern NSW and south east Queensland areas. African lovegrass and woodlands lovegrass were collected from three populations in Millerman, QLD. Buffel grass was collected from three populations in Ingleside, QLD. Swamp foxtail was collected from three populations surrounding Brisbane, QLD. Rhodes grass seeds were collected from two locations in Samford QLD, and one in Nudgee QLD. Windmill grass was collected from three populations within Brisbane and Woodford, QLD. Johnson grass was collected from a minimum of three populations in Brisbane, QLD. Wild sorghum was the most difficult to locate and was therefore collected from a smaller number of individuals at two locations in Carney’s Creek, QLD. All seeds were used in subsequent experiments in Chapter 3 and Chapter 4.

Twenty seeds of each of the eight grasses (Table 1.1) were placed into petri dishes with a single layer of filter paper and were kept moist using deionised water. Petri dishes were checked daily for germination (Han et al., 2012). Each of the eight grasses had six replicates of petri dishes (120 seeds each). Each day, the number of seeds that had produced a radicle were counted for a total of 21 days. On each day, the petri dishes were moved randomly to different locations within the growth chamber to avoid light and temperature bias.

This experiment was conducted twice because of the unavailability of swamp foxtail seeds at the time of the first trial. The same growth chamber was used under the same conditions and settings. Swamp foxtail in South East Queensland produced seeds later than the other grasses. The second trial consisted of another full set of non- native African lovegrass, non-native Rhodes grass and native swamp foxtail. Rhodes grass was highly susceptible to mould in the first trial; therefore, a second trial was performed to ensure that this mould did not affect germination rates. The Rhodes grass seeds in the second trial did not differ from those of the first trial. Therefore, only the

Chapter 2: Germination probability of native and non-native Australian grasses 33

results from the first trial were used in the final results. The non-native African lovegrass also did not differ significantly from the first trial, which indicated that the conditions were similar enough to confidently include the results from swamp foxtail in the second trial along with the results from the first trial.

2.3 ANALYSIS

All data analyses were conducted in R statistical computing package (R Development Core Team, 2013).

The germination trial data was analysed using a Kaplan–Meier survival analysis and the ‘survival’ package, which is a commonly used non-parametric method for estimating the probability of survival but can be adapted for estimating the probability of germination rates (referred to as germination rates from here) (Han et al., 2012). The Kaplan-Meier survival analysis allows me to model the probability of germination rates over time for each species while taking into account that some will not germinate within that given time period. This model was designed for medical trials where patients may not exhibit a death or survival status only and can include patients who have left the study for some reason or another. While this model projects the probability of germination rates inclusive of seeds that may be dormant or unviable. This analysis will also show the percent survival/germination referred to as germination success. My dataset comprised of day-to-germination results for each individual seed and followed the requirements for a Kaplan–Meier time-to-event analysis because my study used a continuous time scale with consistent monitoring. Results were plotted using the ‘survfit()’ function from the ‘survminer’ package, which plots the data using point-wise 95% confidence limits (McNair, Sunkara, & Frobish, 2012).

2.4 RESULTS

Overall, when comparing grasses grouped together by origin, the native grasses germinated at a higher rate, although this difference was not significant as seen in (Figure 2.1). Most congener pairs did not display a large difference in germination rate or overall germination success (Figure 2.2). Most of the non-natives germinated at a similar rate and speed as the natives. On day 4, the non-native grasses displayed a higher rate of germination; however, the native grasses quickly met and surpassed this rate.

Chapter 2: Germination probability of native and non-native Australian grasses 34

Figure 2.1 Probability of germination rate of native and non-native germination. When all grasses were grouped together by origin, the germination rates did not differ significantly. Native grasses are depicted in blue and non-native grasses are depicted in red.

Except for wild sorghum, all of the native grasses had a higher germination rate than their non-native counterparts (Figure 2.2). Analysis of the data by individual species showed that Johnson grass had the highest germination success followed by woodlands lovegrass, swamp foxtail, African lovegrass, windmill grass, buffel grass, wild sorghum and, lastly, Rhodes grass (Figure 2.2). The only non-native grasses to perform better than its native congener was Johnson grass. All seeds germinated at more than 75%, except for Rhodes grass, which had the slowest and lowest germination rate, and its seeds also appeared to be particularly susceptible to mould.

Chapter 2: Germination probability of native and non-native Australian grasses 35

Figure 2.2 Probability of germination rate by individual species. Johnson grass, woodlands lovegrass, swamp foxtail, windmill grass and African lovegrass all had a very similar percentage of germinated seeds at the end of the 3-week experiment, although there were some differences in time to germination between them. Buffel grass and wild sorghum had a lower, but still reasonably high germination rate. Rhodes grass had a germination rate around 50% and, unexpectedly, had the lowest germination rate.

2.5 DISCUSSION AND CONCLUSION

The difference in germination rates between native and non-native grasses were mostly negligible, which suggests that germination rate is likely not a key trait associated with the invasiveness of non-native grasses in Australia. Contrary to my hypothesis, the non-native grasses did not show higher or faster germination rates. In most congener pairs, the native grasses germinated at higher success rates, except for non-native Johnson grass and its congener wild sorghum. Previous studies have found that non-native African lovegrass and its congener woodlands lovegrass germinate at similar rates (Han et al., 2012). My results were different in that woodlands lovegrass was overall more successful. Higher germination rates can be essential for establishing a population in a new environment (Lockwood et al., 2005). It must be noted that most of the seeds came from a single or small number of different locations and could represent specific genetic variations that may differ across the ecosystem gradients of

Chapter 2: Germination probability of native and non-native Australian grasses 36

Australia (Lavergne & Molofksy, 2007). It was determined that this limitation of seed source was negligible given that all seeds were collected from a variable amount of populations from the same general region creating an ample comparison across all eight species. Additionally, genetic variation is considered less likely to determine invasiveness in plants that grow clonally, such as the grasses of this study (Poulin, Weller, & Sakai, 2005).

This germination trial provides an idea about the comparable germination capabilities under the same conditions across all eight grasses. However, each species grows in different environments, and the conditions used in this study were likely not optimal for all species. A full germination trial would need to include different day/night timing, temperature variations, soil depth and watering conditions that simulate different environments where the grasses typically occur (Cleland et al., 2007; Han et al., 2012; Ngo et al., 2017; Wainwright & Cleland, 2013). For example, non-native Rhodes grass has spread widely throughout Australia (Firn, 2007), yet it was surprising to find here that it had the lowest and slowest rate of germination. Additionally, non-native species have been shown to display plasticity in traits, including germination success rates; given the conditions, the non-native grasses of this study may maintain their germination success rate whereas the natives’ rate may decrease (Wainwright & Cleland, 2013). For the purpose of this experiment, the limitations of experimental conditions instead of field conditions outweighed the risk of unfavourable conditions as it allowed for a clear comparison across all species. All species showed a relatively low dormancy rate within this trial with all germination success over 50% and therefore the effect of dormancy rates within these species is considered negligible (Han et al., 2012). While a more complex germination trial may be helpful in understanding the physiology and agricultural aspects of these species, this trial provides a foundational understanding of the impact that germination rates may have on the success of these grasses under the same conditions in relation to their ability to establish populations within Australia.

The concept of propagule pressure in invasion ecology is more robust than simple germination rates. Phenology, particularly time to flowering and time to seed, are important factors in population success for both native and non-native species (Cleland et al., 2007). Colonisation success can hinge on flowering time and recruitment limitation rather than resource limitation (Seabloom et al., 2003). It has

Chapter 2: Germination probability of native and non-native Australian grasses 37

been shown that simply giving the native species a head-start can effectively supress the non-native invader (Firn et al., 2010). The phenology of different species can be affected by changes in environmental conditions, particularly changes in climate (Cleland et al., 2007). This may be important information when considering management strategies and should be noted for each environment and species. Windmill grass is known to produce seed earlier in wetter environments, which can affect seed production timing in different climates and with changes in climate (Ngo et al., 2017).

As the climate continues to shift, changes in phenology are expected to occur among plants, and this has the potential to feedback into further shifts in community assemblage (Cleland et al., 2007). Grassland ecologist, Jennifer Firn, has observed that most of the non-native grasses included in this study flower earlier in the season and often multiple times per year when compared with their native counterparts (Pers. Comm. Jennifer Firn, Queensland University of Technology, 2019). Early and more frequent flowering times may overshadow germination rates in importance when unravelling the comparative differences between native and non-native species. More information about seed production capacity of individual species, as well as germination timing and frequency, would together create a comprehensive understanding of propagule pressure within a given system.

Chapter 2: Germination probability of native and non-native Australian grasses 38

Chapter 3: Functional traits of native and non-native Australian grasses

3.1 AIMS AND OBJECTIVES

The aim of this experiment was to examine the above- and belowground traits of native and non-native Australian grasses to understand how these traits might impact nutrient use efficiency and how differences in traits translate to above- and belowground biomass. I examined experimentally a suite of functional traits of four congener pairs of native and non-native grasses under high- and low-nutrient conditions during early growth stages to compare these traits both by congener pair and by origin. The non-native grasses included in this experiment were African lovegrass, Rhodes grass, buffel grass and Johnson grass, and their native congeners were woodlands lovegrass, windmill grass, swamp foxtail and wild sorghum.

Australia’s non-native grasses typically display greater aboveground biomass than their native counterparts (Grice, 2006). At the same time, the information available on the aboveground structures tells only half of the story, leaving the belowground structures and interactions to speculation (Klimešová et al., 2018; Roumet et al., 2016). Over time, most grasses allocate a larger proportion of their biomass belowground, making belowground traits particularly important in these systems (Blair et al., 2013; Mommer & Weemstra, 2012). The early growth stages of these structures are vital by allowing them to establish dominance in Australia’s native ecosystems (Badalamenti, Militello, La Mantia, & Gugliuzza, 2016). Additionally, C4 grasses tend to grow relatively quickly and can reach mature stages of growth in a short time (Firn et al., 2010). Due to their importance in invasion ecology, particularly in grass species, this experiment focused primarily on the functional traits within early growth stages.

The non-native grasses were expected to display more fast-growing or acquisition specialist traits compared with the expected conservative traits of the native grasses (Firn et al., 2010; Funk et al., 2016; Wright et al., 2004). Higher values for traits including aboveground biomass, belowground biomass, height, specific leaf area, root length, root surface area, root nitrogen content and specific root length were

Chapter 3: Functional traits of native and non-native Australian grasses 39

expected in the fast-growing non-native grasses. Lower values were expected in belowground carbon content and root diameter from the same acquisition specialist non-native grasses (Bardgett, 2017; Bardgett et al., 2014; Pérez-Harguindeguy et al., 2013). Traits associated with clonal growth have not been included in many trait-based studies and, when included, are often only indicated as presence or absence (Larson & Funk, 2016). All of the grasses in this study can reproduce clonally, and their tillers are associated with the regenerative investment of biomass (Klimešová & Klimeš, 2007). Because tillers can aid in the clonal spread of a plant, I assumed that the non- native grasses would create more tillers overall; however, because their expected greater height (Godfree et al., 2017), I expected fewer tillers per unit of above- and belowground biomass.

3.2 RESEARCH DESIGN

This experiment was conducted at the University of Queensland (St Lucia campus) in a temperature-controlled glasshouse. The glasshouse was subject to natural light and temperature was set on a 12 hour day/night cycle of 28°C and 18°C. Post- harvest samples were processed at both University of Queensland Saint Lucia and Queensland University of Technology Garden’s Point campuses. All seeds were collected locally from wild populations in Northern New South Wales and South East Queensland. All seeds were collected within several weeks of each other from December 2017 to January 2018 and were stored in a laboratory refrigerator in envelopes within plastic bags to avoid light, water, and temperature exposure. The seeds from Chapter 2 were also used within this experiment.

I planted six replicates of each of the eight grasses and applied two different nutrient treatments (high and low), totalling 96 pots, as seen in Figure 3.1. I grew the plants in standard 20 cm ANOVApot (ANOVApot Pty. Ltd., Brisbane, QLD, Australia). The soil substrate used for this experiment was made and sterilised at the University of Queensland following a standard University of California (UC mix) recipe: 1/3 cubic metre of sand, 1/6 cubic metre of peat, 4.068 kg of stock fertiliser mix comprising 2 kg of calcium nitrate, 1 kg of potassium sulphate, 9.33 kg of supersulfate, 10 kg of dolomite, 6 kg of hydrated lime, 3 kg of gypsum and 1.2 kg of Micromax (Scotts-Sierra Horticultural Products Co., Marysville, OH). The nutrient treatment consisted of administering 10 grams of Aquasol (Hortico Pty Ltd.,

Chapter 3: Functional traits of native and non-native Australian grasses 40

Melbourne, Australia) per 9 litres of water every other week evenly across all high- nutrient replicates (150 ml per plant). Seeds were germinated in seedling trays with the same soil mix and then transplanted after about 1 week in early September 2018. Plants were watered equally by hand daily. Watering amounts depended on the amount of sun hitting the glasshouse each day. To keep the plants moist but not overly saturated, watering applied between 100 ml and 300 ml daily, with consistent amounts administered across all plants each day. To avoid any light bias, pots were moved randomly within the glasshouse each week.

Figure 3.1 Diagram showing the initial experimental design for the trait quantification experiment. There were six replicates of each of the eight grasses for each of the two nutrient treatments (high and low). Pots were moved randomly each week to avoid light and temperature bias. Pots were spaced evenly apart to avoid light bias from neighbouring grasses.

Plants were harvested after 6 weeks in mid-October 2018. During harvesting, a leaf area meter was used to scan the leaf area of the two largest leaves from each plant. These leaves were placed in separate bags, dried at 45°C for at least 24 hours and then weighed for specific leaf area (leaf dry weight/leaf area) (Pérez-Harguindeguy et al., 2013). I also measured height of the grass when the tallest leaf was held completely vertical and counted the inflorescences present if applicable. Inflorescences were cut

Chapter 3: Functional traits of native and non-native Australian grasses 41

and dried separately from the rest of the aboveground biomass as to not affect the leaf nutrient analysis. Roots were washed thoroughly of all soil. Roots were splayed out between two pieces of cardboard as to not lose their form during drying.

After harvest, all biomass was dried at 45°C for at least 24 hours. The total aboveground and belowground dry biomass was recorded. The aboveground biomass included the biomass measured from the leaves used to calculate specific leaf area and the inflorescences. WinRhizo 2012b (Regent Instruments, Quebec, Canada) and an Epsom Expression 10,000 XL scanner (Epson America Inc., Long Beach, CA) were used to scan and analyse the root images for root surface area, root average diameter and root length. The above- and belowground tissues were ground using a TissueLyser II (QIAGEN, Valencia, CA) and analysed for total nitrogen and carbon content using a LECO TruMac CNS analyser (LECO Corporation, USA). The nitrogen and carbon content were measured as percentages and this is considered an indicator of content. All functional traits were measured according to methods listed in Table 3.1.

Chapter 3: Functional traits of native and non-native Australian grasses 42

Table 3.1 Table of methods or calculations for each trait including units where applicable. Trait Method of measurement Aboveground Dry weight of all aboveground structures of the plant (g) biomass Height Height of the tallest leaf held to its vertical height (cm) Specific leaf Leaf area (cm2) divided by the dry weight (g) of that leaf. I measured SLA for two leaves area (SLA) from each plant (cm2/g) Tiller count The number of tillers present on each plant Inflorescence The number of inflorescence present, regardless of maturity, on each plant number Belowground Dry weight of all belowground structures of the plant after all soil was washed from the biomass roots (grams) Root surface Analysed using WinRhizo 2012b from dry root scans (cm2) area Root length Analysed using WinRhizo 2012b from dry root scans (cm) Root Analysed using WinRhizo 2012b from dry root scans (mm) diameter Specific root Root length divided by belowground biomass (cm/g) length Root shoot Belowground biomass divided by aboveground biomass (cm/g) ratio Root mass Belowground biomass divided by the biomass of the whole plant (aboveground biomass fraction and belowground biomass) (g) Aboveground Percent total nitrogen of the aboveground structures of the plant using a LECO TruMac nitrogen CNS Aboveground Percent total carbon of the aboveground structures of the plant using a LECO TruMac CNS carbon Belowground Percent total nitrogen of the belowground structures of the plant using a LECO TruMac nitrogen CNS Belowground Percent total carbon of the belowground structures of the plant using a LECO TruMac CNS carbon Tiller by Tiller count divided by the aboveground biomass (tiller/g) aboveground biomass Tiller by Tiller count divided by the belowground biomass (tiller/g) belowground biomass Leaf nitrogen The percent total nitrogen of the aboveground structures of the plant multiplied by the by leaf area average leaf area (cm2) taken at harvest.

3.3 ANALYSIS

All data analyses were conducted using R statistical computing package (R Development Core Team, 2013).

The trait measurements were analysed first using analysis of variance (ANOVA) between all combined native and non-native grasses together to identify any overall differences between the two groups. Another ANOVA was then performed across all individual species followed by a Tukey honest significant difference (HSD) post hoc

Chapter 3: Functional traits of native and non-native Australian grasses 43

test to identify significant differences between each congener pair for each trait. Species and nutrient treatments are fixed variables and the growth variables measured as traits were the variables of interest in each model. Random variables were not included within this analysis.

In order to minimise the probability of Type 1 family-wise error, due to the number of traits being measured from the same group of species, a sequential Holm Šídák analysis was performed to correct the p-values using the equation below (Abdi, 2010):

PSidák =1−(1−P)C−i+1

All p-values reported are adjusted using this method. A table of original and adjusted p-values can be found in Appendix A (page 124). ‘C’ is the number of values in the sequence,‘i’ is the sequence position, and ‘P’ is the original p-value from the ANOVA.

To examine several unexpected trends, correlations between several pairs of traits were analysed using linear regression models. The pairs analysed were belowground biomass and specific root length, aboveground biomass and aboveground nitrogen content, and belowground biomass and belowground nitrogen content.

A principle components analysis (PCA) was performed on all traits that were not compounded measurements across native and non-native groupings independently of their individual species allocation. The traits included in this analysis were specific leaf area, belowground biomass, aboveground biomass, specific root length, root diameter, height, tiller, aboveground nitrogen content, aboveground carbon content, belowground nitrogen content and belowground carbon content. Because of technical difficulties, four nitrogen or carbon content measurements were lost, and these replicates were not included in the PCA, which is not equipped to handle missing data.

3.4 RESULTS

Most traits revealed the non-native grasses to be resource acquisition specialists with a few exceptions. Many traits displayed significant differences between each congener pair, and the non-natives had greater biomass both above- and belowground in most pairs, as seen in Table 3.2. When a significant difference was found, the non-

Chapter 3: Functional traits of native and non-native Australian grasses 44

native grasses tended to have higher values, as acquisition specialists, than the native grasses, as conservation specialists, except for specific root length, belowground nitrogen percentage and tiller count by both above- and belowground biomass.

Most differences were found regardless of nutrient treatment; however, several traits were affected by treatments. Root-to-shoot ratio, belowground nitrogen, belowground carbon content, and tiller by aboveground biomass all showed a significant difference in at least one pair only under the low-nutrient condition (Table 3.2). Additionally, three congener pairs differed significantly in specific root length only under the high-nutrient treatment. Differences were also seen in Johnson grass and wild sorghum under the high-nutrient treatment for the traits nitrogen by leaf area and tiller by belowground biomass.

Specific leaf area and tiller count were not effective predictors of invasiveness and did not differ when analysed according to species origin or nutrient treatments. Traits that showed a significant difference only between one set of pairs, and are therefore not likely to be good indicators of invasiveness, were inflorescence number, root length, root-to-shoot ratio, root mass fraction and aboveground total carbon percentage (Table 3.2).

Chapter 3: Functional traits of native and non-native Australian grasses 45

Table 3.2 Summary of functional trait measurements and significant differences.

Red indicates a significant difference under the high nutrient treatment only. Yellow indicates a significant difference under the low nutrient treatment only. Purple indicates a significant difference under both nutrient treatments. Significant differences were found between congener pairs using ANOVA and Tukey HSD post hoc analysis. The upward arrow (↑) indicates a higher value between the congener pair when a significant difference was found. Congener pairs are separated by solid grey lines.

Chapter 3: Functional traits of native and non-native Australian grasses 46

Belowground biomass correlated strongly and negatively with specific root length (Figure 3.2). Although the linear regression analysis showed no significant difference between native and non-native origin, the data points of native and non- native grasses clustered towards opposite ends.

Aboveground nitrogen content and belowground nitrogen of the native grasses correlated slightly positively with above- and belowground biomass, respectively, however, this trend was not found in the non-native grasses (Figure 3.3 and Figure 3.4). The non-native grasses correlated negatively between the traits both above- and belowground.

The PCA revealed a difference in the clustering of the native and non-native principle components in both PC1 and PC2 with only a small cross over (Figure 3.5). This suggests that the combination of traits displayed a distinct difference in habit across the native and non-native groups while maintaining some overlap in trait characteristics. PC1, which explained 54.39% of the variance, is driven by aboveground biomass, belowground biomass and belowground carbon content. PC2 explained 37.12% of the variance and is driven by belowground biomass. Refer to Appendix B (page Error! Bookmark not defined.) for full PCA outputs.

Figure 3.2 Linear regression model of correlation between specific root length and belowground biomass. Overall there was a significant difference between the two traits –16 (F3 = 140.9, adjusted R-squared: 0.817, p < 2.2  10 ). The correlation between the

Chapter 3: Functional traits of native and non-native Australian grasses 47

traits is driven by the difference in traits rather than the covariate origin (specific root length p < 2  10–16; native p < 0.617).

Figure 3.3 Linear regression model of correlation between aboveground biomass and aboveground nitrogen content. Overall there was a significant difference between the –16 traits (F3 = 50.16, adjusted R-squared: 0.6184, p < 2.2  10 ). The correlation between the traits is driven by the difference in traits as well as origin (aboveground nitrogen p < 7.07  10–8; native p < 2.12  10–9).

Chapter 3: Functional traits of native and non-native Australian grasses 48

Figure 3.4 Linear regression model of correlation between belowground biomass and belowground nitrogen content. Overall there was a significant difference between the –16 traits (F3 = 48.76, adjusted R-squared: 0.6064, p < 2.2  10 ). The correlation between the traits is driven by the difference in traits as well as origin (belowground nitrogen p < 5.93  10–10; native p < 3.15  10–6).

Chapter 3: Functional traits of native and non-native Australian grasses 49

Figure 3.5 The principle components analysis of functional traits suggested a moderate level of correlation between functional traits across the native and non-native groupings, which is driven by both below and aboveground biomass. PC1 is driven by belowground biomass (0.5879), aboveground biomass (0.4954) and belowground carbon (0.4069) and explains 54.39% of the variance overall (standard deviation: 2.088). PC2 is driven by belowground biomass (0.3213) and explains 37.12% of the variance (standard deviation: 1.697). Refer to Appendix B (page 124) for PCA outputs.

3.4.1 Aboveground traits

The non-native grasses generally followed a nutrient acquisition specialist trend in the aboveground traits. Aboveground biomass (Figure 3.6), height (Figure 3.8) and tiller by aboveground biomass (Figure 3.10) all displayed a significant difference between at least three congener pairs, which made them the strongest predictors of invasiveness in aboveground traits. Interestingly, specific leaf area (Figure 3.7) did not result in significant differences between any congener pairs. Additionally, there was no trend across native and non-native groupings.

Chapter 3: Functional traits of native and non-native Australian grasses 50

– Figure 3.6 Trait comparison of aboveground biomass (F7 = 44.34, p < 4.21885  10 15). The only congener pairs not to have a significant difference in aboveground biomass were non-native African lovegrass and native woodlands lovegrass. All significant differences were present under both nutrient treatments as found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: Rhodes vs. windmill p < 0.00025, Johnson vs. wild sorghum p < 0.0000002, buffel vs. swamp foxtail p < 0.000004; No treatment: Rhodes vs. Windmill p < 0.00098, Johnson vs. wild sorghum p < 0.000012, buffel vs. swamp foxtail p < 0.000004).

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Figure 3.7 Trait comparison of specific leaf area. The species exhibited a significant difference overall, although the Tukey HSD post hoc analysis showed that none of –6 the congener pairs differed significantly (F7 = 6.812, p < 5.69999  10 ). Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Figure 3.8 Trait comparison of height showed a significant difference overall (F7 = 35.04, p < 3.9968  10–15). All pairs of congeners showed a significant difference under both treatments except for non-native buffel grass and native swamp foxtail. Significant difference between congener pairs was found using a TukeyHSD.

Chapter 3: Functional traits of native and non-native Australian grasses 52

Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: African vs. woodlands p < 0.0006, Rhodes vs. windmill p < 0.0100, Johnson vs. wild sorghum p < 0.0005, No treatment: African vs. woodlands p < 0.0000017, Rhodes vs. windmill p < 0.00008, Johnson vs. wild sorghum p < 0.0020). Non-native buffel grass and native swamp foxtail only showed a significant difference between each other in height under the low-nutrient treatment (p < 0.0016).

The tiller counts did not differ significantly between congener pairs, but analysis of the tiller counts by above and belowground biomass showed several differences (Figure 3.9, Figure 3.10, and Figure 3.13). All of the significant differences in tiller count by aboveground biomass occurred only under the low nutrient environment, and the native grasses all had higher values than their non-native counterparts. These data suggest that, under low-nutrient conditions, native grasses create more tillers with less biomass, which means they grew wider instead of taller, following a more resource conservation specialist route.

–10 Figure 3.9 Trait comparison of tiller count (F7 = 11.99, p < 5.16  10 ). None of the congener pairs differed significantly in tiller count under either nutrient treatment. Significant difference between congener pairs was found using a TukeyHSD.

Chapter 3: Functional traits of native and non-native Australian grasses 53

Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Figure 3.10 Trait comparison of tiller count by aboveground biomass. Tiller count per plant was divided by aboveground biomass (F7 = 4.155, p < 0.00055). All congener pairs except for non-native Rhodes grass and native windmill grass displayed a significant difference in tiller count by aboveground biomass under the low-nutrient treatment only. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (No treatment: African vs. woodlands p < 0.0062, Johnson vs. wild sorghum p < 0.0125, buffel vs. swamp foxtail p < 0.038).

A number of non-native African lovegrass, native woodlands lovegrass, and native windmill grass samples produced inflorescences but not seed within the 6-week experiment (Figure 3.11). Unrecorded observations showed that both the native grasses in this case produced inflorescences first. Windmill grass was the only species to display significantly higher values for inflorescence number than its congener, but this trend was not seen across all native grasses and its non-native congener Rhodes grass did not produce any inflorescences.

Chapter 3: Functional traits of native and non-native Australian grasses 54

– Figure 3.11 Trait comparison of inflorescence count (F7 = 22.72, p < 3.77476  10 15). Non-native Rhodes grass and native windmill grass were the only pair to have a significant difference in inflorescence count (Nutrient addition: p < 0.0001, no treatment: p <0.000001). However, this reflected the observation that very few of the grasses matured to produce flowers within the time span of this experiment. Only species that produced flowers were included in this figure. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

3.4.2 Belowground traits A greater number of significant differences were observed belowground than aboveground. The native grasses had significantly higher values in specific root length across all four congener pairs under at least one nutrient treatment (Figure 3.14). Two of the congener pairs showed significant differences in tiller by belowground biomass, but again the natives in both cases had the higher values (Figure 3.13). Traits such as belowground biomass followed a resource acquisition specialist trend in non-native grasses (Figure 3.12). All congener pairs except non-native African lovegrass and native woodlands lovegrass had a significant difference in root surface area (Figure 3.15). Both root length and root diameter did not reveal significant differences between most congener pairs except for non-native Johnson grass and native wild sorghum, as

Chapter 3: Functional traits of native and non-native Australian grasses 55

well as non-native buffel grass and native swamp foxtail in root diameter only (Figure 3.16and Figure 3.17).

Figure 3.12 Trait comparison of belowground biomass (F7 = 84.03, p < 3.55271  10–15). All congener pairs differed significantly in belowground biomass except for non-native African lovegrass and native woodlands lovegrass under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: Rhodes vs. windmill p < 0.00022, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.00001, No treatment: Rhodes vs. windmill p < 0.0100, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.0023).

Chapter 3: Functional traits of native and non-native Australian grasses 56

Figure 3.13 Trait comparison of tiller count by belowground biomass. For this trait measure, tiller count per plant was divided by aboveground biomass (F7 = 18.81, p < 3.73035  10–14). Non-native African lovegrass and native woodlands lovegrass had a significant difference under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: p < 0.0011; No treatment: p < 0.0014). Johnson grass and wild sorghum had a significant difference under the high nutrient treatment only (p < 0.0012).

Chapter 3: Functional traits of native and non-native Australian grasses 57

– Figure 3.14 Trait comparison of specific root length (F7 = 38.72, p < 3.33067  10 15). Non-native African lovegrass and native woodlands lovegrass had a significant difference under both nutrient treatments (Nutrient addition: p < 0.00001; No treatment: p < 0.0008). The other three congener pairs had a significant difference in specific root length under the high-nutrient treatment only (Rhodes vs. windmill p < 0.0018, Johnson vs. wild sorghum p < 0.0023, buffel vs. swamp foxtail p < 0.0016). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Chapter 3: Functional traits of native and non-native Australian grasses 58

Figure 3.15 Trait comparison of root surface area was overall significantly different –15 (F7 = 41.67, p < 3.10862  10 ). All congener pairs had a significant difference in root surface area under both treatments except for non-native African lovegrass and native woodlands lovegrass. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient treatment: Rhodes vs. windmill p < 0.0055, Johnson vs. wild sorghum p < 0.00001, buffel vs. swamp foxtail p < 0.00004, No treatment: Rhodes vs. windmill p < 0.0124, Johnson vs. wild sorghum p < 0.000001, buffel vs. swamp foxtail p < 0.0127).

Chapter 3: Functional traits of native and non-native Australian grasses 59

Figure 3.16 Trait comparison of root length had a significant difference overall (F7 = 8.352, p < 6.61999  10–6). The only pairs with a significant difference in root length under both treatments were non-native Johnson grass and native wild sorghum (Nutrient addition: p < 0.00008, No treatment: p < 0.0308). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Figure 3.17 Trait comparison of root diameter was overall significantly different (F7 = 26.76, p < 2.88658  10–15). Non-native Johnson grass and native wild sorghum

Chapter 3: Functional traits of native and non-native Australian grasses 60

had a significant difference under both nutrient treatments. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair. (Nutrient addition: p < 0.0000002, No treatment: p < 0.0010), as did buffel grass and swamp foxtail (Nutrient addition: p <0.00001, No treatment: p <0.0015).

3.4.3 Full plant traits

Root mass fraction and root-to-shoot ratio represent the relative allocation of biomass either above- or belowground. The only significant differences between any congener pairs were for Johnson grass and wild sorghum (Figure 3.18 and Figure 3.19).

–14 Figure 3.18 Trait comparison of root mass fraction (F7 = 18.72, p < 3.65263  10 ). Non-native Johnson grass and native wild sorghum had a significant difference under both nutrient treatments (Nutrient addition: p < 0.0117; No treatment: p < 0.00002), and none of the other congener pairs displayed a difference under either nutrient treatment. Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Chapter 3: Functional traits of native and non-native Australian grasses 61

–11 Figure 3.19 Trait comparison of root-to-shoot ratio (F7 = 14.37, p < 1.68598  10 ). Non-native Johnson grass and native wild sorghum were the only congener pairs to have a significant difference and it was only under the low-nutrient treatment (p < 0.0000003). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

3.4.4 Nutrient content

A greater number of differences was found in belowground nutrient content than aboveground nutrient content, and several differences belowground occurred only under the low-nutrient treatment. The congener pairs of Johnson grass and buffel grass both displayed a significant difference in all nutrient content measurements except for Johnson grass with aboveground carbon content (Figure 3.20, Figure 3.21, Figure 3.22, Figure 3.23, and Figure 3.24). The only other congener pairs to show a significant difference in nutrient content were Rhodes grass and windmill grass in belowground nitrogen content under the low-nutrient treatment (Figure 3.21).

The native congeners wild sorghum and swamp foxtail had significantly higher values of above- and belowground nitrogen content, which suggests that native grasses may absorb similar amounts of nitrogen as the non-natives, but do not produce the larger biomass that the non-natives do (Figure 3.21). This was further suggested by

Chapter 3: Functional traits of native and non-native Australian grasses 62

the larger percentage of above- and belowground carbon in their respective non-native congener pair (Figure 3.22).

Figure 3.20 Trait comparison of aboveground nitrogen content (F7 = 13.43, p < 8.10002  10–11). Significant differences are between non-native Johnson grass and native wild sorghum (Nutrient addition: p < 0.0365; No treatment p < 0.00028) and buffel grass and swamp foxtail (Nutrient addition: p < 0.0002; No treatment p < 0.00074). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Chapter 3: Functional traits of native and non-native Australian grasses 63

Figure 3.21 Trait comparison of belowground nitrogen content (F7 = 37.13, p < 2.66454  10–15). Significant differences are between non-native Johnson grass and native wild sorghum under both nutrient treatments (Nutrient addition: p < 0.000002; No treatment: p < 0.00001) and buffel grass and swamp foxtail (Nutrient addition: p < 0.0000; No treatment: p < 0.00001). Non-native Rhodes grass and native windmill grass also had a significant difference only under the low-nutrient treatment (p < 0.0330). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Chapter 3: Functional traits of native and non-native Australian grasses 64

Figure 3.22 Trait comparison of aboveground carbon content (F7 = 23.63, p < 2.44249  10–15). Non-native buffel grass and native swamp foxtail had a significant difference under both nutrient treatments (Nutrient addition: p < 0.0015; no treatment: p < 0.00007). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Figure 3.23 Trait comparison of belowground carbon content (F7 = 25.35, p < 2.22045  10–15). Non-native buffel grass and native swamp foxtail had a significant difference under both nutrient treatments (Nutrient addition p < 0.000008; No

Chapter 3: Functional traits of native and non-native Australian grasses 65

treatment p < 0.00044). Non-native Johnson grass and native wild sorghum had a significant difference only under the low nutrient treatment (p < 0.0010). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

2 Figure 3.24 Trait comparison of percent total nitrogen content by leaf area (cm ) (F7 = 158.4, p < 1.9984  10–15). A significant difference was found between non-native Johnson grass and native wild sorghum under both treatments (Nutrient addition: p < 0.00001; No treatment: p < 0.00001). A slight significant difference was found between non-native Rhodes grass and native windmill grass only under the high- nutrient treatment (p < 0.0524). Significant difference between congener pairs was found using a TukeyHSD. Asterisks (*) indicate the non-native congener and dashed vertical lines separate each congener pair.

Chapter 3: Functional traits of native and non-native Australian grasses 66

3.5 DISCUSSION AND CONCLUSION

Overall, the data from the investigation of this suite of traits imply that this selection of common Australian grasses displays differences in both above- and belowground functional traits at the seedling stage of development in terms of native and non-native origins. However, not all differences followed my initial expectations. The traits of the non-native grasses tended to follow a fast-growing trend, whereas the traits of the native grasses tended towards a slow-growing trend, although not all traits followed this generalisation. Several traits did not follow these expected conventions, which suggests that the non-native grasses utilise a combination of fast-growing and slow-growing traits that allow them to outcompete native grasses in Australia’s unique low-resource environments. This information may be vital for devising effective control strategies because studies have shown that understanding life-history trade- offs can help to target strategically an invasive species weaknesses while favouring the native species’ strengths (Firn et al., 2015; Ramula, Knight, Burns, & Buckley, 2008).

What is invasive in one ecosystem may not contribute to invasiveness in another ecosystem. Although many previous studies point towards a single spectrum of fast to slow production trade-offs to explain processes in plants across biomes and climates (Wright et al., 2004), the data from this study suggest a more complex interaction of traits that may suit individuals particular to a certain ecosystem (Funk, 2013). A global resource economic spectrum of traits that combines both above- and belowground traits may be useful and accurate for most ecosystems; however, different environments may favour non-native species with traits from different ends of the spectrum (Funk & Vitousek, 2007; Reich & Cornelissen, 2014). Non-native species may display a unique set of traits containing a combination of fast- and slow-growth- associated traits, as observed within this dataset. It is also possible that certain fast- growth traits override other more conservative traits in some ecosystems. In addition, belowground traits are considered to have higher plasticity than aboveground traits, which may also contribute to a blurred line between fast and slow growth depending on the contributing abiotic factors (Bardgett et al., 2014).

Despite the fact that specific leaf area has long been considered to be an important trait for predicting the response of individual species to environmental factors (Pérez-Harguindeguy et al., 2013), it did not have a strong correlation to plant

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origin or nutrient treatment in this study. Recent research has shown that specific leaf area is a poor indicator of the species response to environmental changes, particularly nutrient changes within a single biome (Firn et al., 2019), and my results are consistent with this study. In addition to the lack of significant differences in specific leaf area under any nutrient treatment, the values did not show a trend across native and non- native groupings as expected (Pérez-Harguindeguy et al., 2013). From previous research, non-native grasses were expected to have a higher specific leaf area as a resource acquisition specialist but, in this experiment, some of the natives had higher specific leaf area values. The results of this experiment suggest that, in addition to being a poor indicator of invasiveness, specific leaf area may not be an effective indicator of the response to nutrient shifts (Firn et al., 2019).

Because I had expected a greater biomass for the non-native grasses, I had also expected to find fewer tiller count per above- and belowground biomass than in the natives but higher overall tiller counts. Contrary to my expectations, I found no significant difference in tiller count overall, although this may have changed with a longer growing time. As expected, where there was a significant difference in tiller count by above- or belowground biomass, the higher values were consistently for native grasses, which suggests that instead of investing resources to grow upwards, they grow outwards in shorter tufts. Tiller count and tiller count by biomass are not widely used functional trait measures, although recent research into the clonal abilities of plants suggests that these measures may contribute to a plant’s ability to colonise new areas (Aspinwall et al., 2013; Klimešová et al., 2018). In grasslands, the belowground portion of the plant is directly related to the plant’s ability to regrow after disturbance because it is protected from these effects and represents an important life- history strategy (Klimešová et al., 2018).

Tiller morphology has been shown to correlate with other commonly used traits, such as leaf mass per area and leaf nitrogen per unit mass in switchgrass (Panicum virgatum) (Aspinwall et al., 2013). In clonal plants, creating more tillers can allow for the creation of more roots at the surface level and can contribute to the plant’s ability to reproduce vegetatively (Klimešová & Klimeš, 2007). This can in turn create competition for light with direct neighbours, as well as competition for resources as roots and shoots spread horizontally.

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Previous research suggests that non-natives should have higher values for specific root length, belowground nitrogen content and aboveground nitrogen content (Bardgett et al., 2014; Leishman et al., 2007; Pérez-Harguindeguy et al., 2013); however, the opposite was found in my study. Interestingly, the native grasses consistently had higher specific root length compared with the non-natives, although this was only found under the high-nutrient treatment for most congener pairs. High specific root length and belowground nitrogen content are both associated with faster growing, acquisition-oriented plants (Bardgett et al., 2014). Higher aboveground nitrogen content can be considered synonymous in this case with higher leaf nitrogen content, which is also typically associated with fast-growing species. A positive correlation was expected between each of these traits and biomass; however, the correlation was negative in non-native grasses, especially between belowground biomass and specific root length. Interestingly, the natives showed a very slight positive correlation between biomass and nitrogen content above and belowground respectively. Previous studies have suggested that invasion is more complicated than a simple acquisition to conservation trait spectrum allows. Instead, the qualities of the potentially invaded environment, as well as its current populations need to be considered, as they may favour some traits over others (Funk & Cornwell, 2013).

Specific root length, not unlike specific leaf area, has questionable assumptions attached to its associated life-history strategies. Although it is typically associated with fast-growing, acquisition specialists, other studies suggest specific root length is independent of growth rate (Fransen, Blijjenberg, & de Kroon, 1999; Kramer-Walter et al., 2016). My results show that belowground biomass and specific root length are negatively correlated in both native and non-native grasses. Studies have shown similar responses in grasses from both nutrient-rich and nutrient-poor soils, where the expected changes in biomass did not correlate with a change in root morphology (Fransen et al., 1999). Along with being associated with fast growth rate, higher specific root length is also associated with non-mycorrhizal plants, as opposed to plants with mycorrhizal associations (Kong et al., 2014). Therefore, information about the mycorrhizal associations, which were not quantified in this study, may shed light on this unexpected result.

It is also possible that different environments have different implications for the function of a structure (Leishman et al., 2007). Many global trait studies incorporate

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plants from a variety of functional groups, and it is possible that each functional group may utilise structures differently across a gradient of habitats. A global standard of measurement might not be possible for belowground traits because they are highly responsive to environmental factors (Mommer & Weemstra, 2012). Although the native grasses had higher specific root length, they had overall lower belowground biomass, which suggests that the belowground carbon was used conservatively in the most efficient way for obtaining necessary resources.

Nutrient availability is not likely to be the main driving force for non-native establishment for this set of grasses (Funk & Vitousek, 2007). There was little difference between the responses of each congener pair to the nutrient treatments. Previous research suggests that nitrogen availability can be the driving force behind establishment success of some non-native grasses (Suding, LeJeune, & Seastedt, 2004). Other studies show that higher nutrient availability leads to increased biomass without affecting root morphology of grass species (Fransen et al., 1999). The theory of invisibility structures plant invasions around the temporary or permanent increase of resources in a system (Davis et al., 2000). Conversely, in my experiment, the nutrient treatment had very little effect on the biomass and most belowground morphological traits, but it did seem to have an effect on some features such as specific root length. Although some studies have suggested that increased nutrient availability leads to increased invasions, others have found that invasions can happen regardless of shifts in nutrient availability (Broadbent et al., 2018; Davis et al., 2000; Funk & Vitousek, 2007). It is possible that the difference between nutrient treatment and background nutrient availability in the soil was not large enough to show as much of a difference in nutrient treatment as expected in my study. However, other studies that focused on invaders of low-resource environments found that resource additions did not predict invasiveness as strongly as ecological novelty, which is congruent with my results (Broadbent et al., 2018; Funk & Vitousek, 2007).

Although most traits did not respond to nutrient treatment, several traits responded, which suggests that particular traits may exhibit a mild response to shifts in nutrient availability. There were seven instances of significant differences between congener pairs under the low-nutrient treatment and five significant differences between pairs under the high-nutrient environment, and most of the significant differences occurred under both nutrient treatments. As expected, the traits most

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effected by treatment were traits that directly measure nutrient content above and belowground, but other traits such as specific root length also responded. These traits are associated with allocation of resources, which suggests that the non-native plant’s ability to allocate resources differently according to nutrient availability might give them a mechanistic advantage over native grasses. One study suggests that root tissue density, rather than specific root length or root diameter, are indicative of the ability to establish in low-resource environments because the other traits are more variable within individual species (Kramer-Walter et al., 2016). On the other hand, the results of other studies imply that both specific root length and root tissue density display plasticity in resource-limited environments, which suggests that they may both be associated with the ability to capitalise on resource in these environments (de Vries, Brown, & Stevens, 2016). It is unclear whether these belowground traits exhibit a generalisable pattern in response to nutrient shifts in a global sense; however, trends across these native and non-native grasses in Australia suggest that most traits respond independently of nutrient availability and that others respond moderately to changes in nutrient availability.

Given that nitrogen is a key component for photosynthesis, it is thought that higher nitrogen availability can be associated with higher photosynthetic capacity, which increases a plant’s productivity (Vitousek & Howarth, 1991). However, studies have shown that leaf nitrogen per leaf area is only weakly related to photosynthetic capacity. Photosynthetic capacity relies more on the given climate, specifically light availability and environmental conditions, than nitrogen availability (Smith et al., 2019). It has also been suggested that plants respond to limited nitrogen by making fewer leaves rather than making leaves with less nitrogen content (Dong et al., 2017). It may be informative to measure photosynthetic capacity via the maximum Rubisco carboxylation rate along with a trait study and nutrient analysis to see how these factors link with each other in Australia’s native and non-native C4 grasses, particularly in response to nitrogen limitation (Smith et al., 2019).

Despite the clear trend in traits across native and non-native grasses, there is still evidence of additional unique traits that may be equally important for predicting invasion success. African lovegrass is a prolific weed that is detrimental to many landowners and has the ability to quickly dominate native ecosystems (Firn, 2009; Godfree et al., 2017). It is known to grow much larger than its native counterpart,

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woodlands lovegrass. However, I found that the pair were quite similar, with only four significant differences between them across all traits. Unlike woodlands lovegrass, African lovegrass, is unpalatable to livestock and is less moderated by herbivory (Firn, 2009). The most limiting factors for the success of African lovegrass are the presence of grazing and increased nutrient availability (Han et al., 2012). Given that the pair’s early growth habits appear to be fairly similar, it is likely that this natural lack of herbivory because of its unpalatability may be a key factor in the success of African lovegrass. Additionally, African lovegrass and woodlands lovegrass had fairly similar specific root length, which aids in water sequestration in water-limited environments where African lovegrass is known to thrive (Firn, 2009). Non-native species that become invasive are likely to have unique beneficial traits, such as the allelopathic effects that Johnson grass has on its neighbouring plants (Huang, Liu, Wei, Wang, & Zhang, 2015). Native and non-native plants can utilise comparable traits within disturbed and undisturbed areas (Leishman et al., 2010), which suggests that their dominance may depend on ecological novelties as much as disturbance or predictable traits.

Non-native Johnson grass and buffel grass both had roots that exceeded the traditional definition of fine roots (<2 mm) at 6 weeks and, therefore, it can be assumed that their large belowground biomass was not entirely acquisitively focused (McCormack et al., 2015). The larger root system can be assumed to contribute to the structural integrity of the plant as well as the transport of nutrients. These grasses both evolved in places with potentially higher grazing frequency and more compacted soil, as well as higher resource availability, factors that may all contribute to the evolution of a more substantial belowground physiology (Aspinwall et al., 2013; Bowen & Chudleigh, 2018). It must be noted that roots do not exist solely for the purpose of nutrient acquisition (McCormack et al., 2015). In this study, I assumed that the main role of roots during the early stages of growth is to acquire nutrients; however, roots are used structurally, for nutrient storage, re-sprouting ability and other functions (Klimešová et al., 2018). For the purpose of this study, all roots were considered to be fine roots, although a more detailed analysis of different root structures of plants grown for longer may provide a more comprehensive understanding of the differences between each species’ belowground structures. Undoubtedly, the large belowground

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structure of Johnson grass and buffel grass, even at such early stages of growth, contributes to their ability to dominate ecosystems.

Research has shown that the seedling and early stages of plant growth can be the most critical stages, particularly when discussing community-level shifts (Harrison & LaForgia, 2019). My study suggests that the functional traits displayed by grass seedlings differ across native and non-native grasses and are likely to contribute to their overall colonisation success in Australia. This is especially true given that drought and elevated temperatures are predicted to increase in the future, putting even more pressure on already vulnerable seedlings (Duell et al., 2016; Ni, Liu, Chu, Xu, & Fang, 2018). Non-native Johnson grass can have detrimental competitive effects on neighbouring native species within the first 50 days of growth (Schwinning, Meckel, Reichmann, Polley, & Fay, 2017). Furthermore, the belowground traits are particularly important during this stage because they are directly involved in resource capture to ensure the longevity of the plant (Harrison & LaForgia, 2019; Ni et al., 2018). These early growth stages are arguably the most important in understanding plant species invasion from a trait-based perspective (Badalamenti et al., 2016), and my results contribute to this growing body of knowledge.

The effects of different root traits on nutrient cycling and soil aggregate are varied and inconsistent across diverse ecosystems (Bardgett et al., 2014). Non-native grasses can affect the soil communities and qualities that they grow in, and the soil will, in turn ,affect the plants that subsequently establish there, creating a feedback loop between plant communities, soil aggregate, nutrient cycling and ecosystem shifts (Bardgett, 2017; Duell et al., 2016; Ehrenfeld, 2004). When harvesting my experiment after 6 weeks, I found that this difference was clear and most of the non-native grasses had very dry, loose soil, whereas the natives had denser, moist soil. This observation suggests that the non-natives capitalised on the available water and sculpted the soil environment differently (Gibbons, et al., 2017; Orians & Milewski, 2007; Richardson, 2009). The complex plant–soil interactions were only partially captured here because microbial associations were not quantified, and native soil was not used. The use of native soil could have an array of effects on the growth of each species, though it was decided that these effects outweighed the potential of using a consistent growing medium in order to compare the eight target species under the same conditions. Belowground traits both respond to and have the potential to influence soil aggregate

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and soil functioning, which in turn can influence the existent plant communities within a given system (Ehrenfeld, 2004). For this reason, root traits can be considered ecosystems drivers (Bardgett et al., 2014). It is essential to consider soil type and nutrient availability in restoration management (Fry et al., 2017). However, the trait differences between and among the grasses when grown under the same conditions imply that a strong feedback system between non-native plants and soil is possible. This provides the foundation for a more accurate measure of nutrient differences in the soil of native and non-native grasses.

Belowground functional traits cannot be assumed by looking aboveground, and there are still many questions regarding the native grasses of Australia in comparison with ecologically threatening non-native grasses. Ecologists look for patterns and trends that help to explain processes across various ecosystems (Mommer & Weemstra, 2012; Wright et al., 2004). In this case, some of the accepted trends in functional traits hold true for the eight target grasses, whereas others suggest a more complex dynamic.

Conclusions

In this study, I have shown that there is a difference in the above- and belowground functional traits of native and non-native Australian grasses and, more importantly, the trends seen belowground are not necessarily what were expected given the trends seen aboveground. These differences suggest that the native and non- native grasses can absorb and utilise resources differently when given the same conditions and that they rely on a unique combination of fast-growing and slow- growing traits to thrive in Australia’s low-resource grasslands. The difference in traits also suggests that the groups will respond differently to shifting environmental factors, such as changes in nutrient availability and climate (Cleland et al., 2007; Davis et al., 2000; MacDougall et al., 2018; van Kleunen, Weber, et al., 2010). As there are few differences between high- and low-nutrient treatments, it is likely that the non-native species are just as good at invading low-resource environments as they are at invading high-resource environments (Funk & Vitousek, 2007).

The current perceived trends in belowground traits may not hold true across all plant groups and ecosystems, as evidenced by this study. In light of these findings, I conclude that the belowground traits associated with invasiveness are more likely to be context dependent than globally standardised (Catford et al., 2019; Funk &

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Cornwell, 2013; Pyšek et al., 2012). Functional traits provide a surrogate measure for functional processes of each species, and it is essential that the physiological functions behind these morphological measurements are quantified more accurately to understand not only what the plants are doing with their limited resources but, more importantly, the differences in how they obtain their limited resources.

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Chapter 4: Microdialysis reveals the function behind functional traits

4.1 AIMS AND OBJECTIVES

The main objective of this study was to measure nitrogen depletion surrounding the roots of both native and non-native Australian grasses during early growth stages to compare nitrogen uptake ability. By examining the ability of each grass species to alter nitrate fluxes in the substrate surrounding their roots, given a background supply of available nitrate, it may be inferred that faster depletion levels coincide with greater uptake ability. The non-native grasses included in this experiment were African lovegrass, Rhodes grass, buffel grass and Johnson grass, and their native congeners were woodlands lovegrass, windmill grass, swamp foxtail and wild sorghum.

Nitrogen is important for plant growth and function because it is a key component of proteins in chlorophyll used in photosynthesis as well as the genetic material of the plant (Vitousek & Howarth, 1991). Both nitrate and ammonium are available for absorption by plants. Most plants prefer nitrate over ammonium (Gigon & Rorison, 1972), although some plants, including certain grasses, preferentially acquire ammonium as an energetically favourable source (Rossiter-Rachor et al., 2017). Nitrate is typically more easily absorbed by roots because it is more mobile in soil than ammonium, which also means that nitrate is more easily leached from the soil (Macdonald, Powlson, Poulton, & Jenkinson, 1989). Some plants prevent nitrification by either exuding nitrate inhibitors from their roots or effectively acquiring ammonium before nitrification can occur (Lata et al., 2004). However, in many ecosystems, nitrogen availability fluctuates in response to environmental conditions. For example, in tropical ecosystems, nitrification increases at the start of the wet season, which allows nitrate to remain as a highly available form of nitrogen for plants (Schmidt, Stewart, Turnbull, Erskine, & Ashwath, 1998). A plant’s ability to absorb nitrate more efficiently generates a competitive advantage over plants with lower nitrate uptake ability, and the ability to absorb ammonium efficiently provides an advantage in nitrate-limited environments.

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Not all soil nitrogen is available to plants. Processes such as mineralisation, ammonification and nitrification convert nitrogen to the available forms of nitrate and ammonium, whereas immobilisation renders nitrogen unavailable (Vitousek & Matson, 1988). Additionally, plants can boost mineralisation rates in their surrounding soil, which increases the available nutrients including nitrate and ammonium (Schmidt et al., 1998). These processes constantly shift the available soil nitrogen pool. Over time, ammonium can be absorbed by soil particles, making it unavailable, as well as converted to nitrate through the process of nitrification (Vitousek & Matson, 1988). The complex dynamics of nitrogen availability are difficult to sample, yet these greatly affect the success of a plant. Shifts in resource availability have been identified as a key factor in invasion ecology (Davis et al., 2000).

Traditional methods of sampling nitrogen availability in the soil are less accurate than microdialysis (Inselsbacher et al., 2011). By using this novel technique, I was able to sample diffusive nitrogen fluxes around native and non-native grass seedlings over time. Microdialysis has been used in a medical context for decades and is traditionally used to either administer chemicals into specific tissue or to sample specific tissue for certain elements (Chaurasia et al., 2007). This technique has been modified more recently to sample nutrients and metals found in soil (Inselsbacher et al., 2011). The microdialysis probes are small (0.5 mm  30 mm) and are considered to be root simulators, although in contrast to the action of roots, nutrients diffuse passively into the microdialysis probes. By inputting water into the microdialysis probe within the soil, it is possible to sample the nitrate and ammonium concentrations available in the soil. The strength of microdialysis is that it allows the researcher to sample fluxes of mobile nitrogen forms that are available to the roots and how these fluxes change over time (Brackin et al., 2015; Buckley et al., 2016). Microdialysis is a novel technique that is advancing the understanding of soil processes and will provide new insights into the field of invasion ecology, including the focus here on nitrogen processes associated with Australian native and non-native grasses.

Microdialysis has been used previously in plant physiology and agricultural research to determine nitrogen fluxes in soil to quantify nitrogen absorption, litter decomposition and differences between nitrogen sources (Brackin et al., 2015; Buckley, Brackin, Näsholm, Schmidt, & Jämtgård, 2017; Inselsbacher et al., 2011). Several experiments using microdialysis have examined differences in nitrogen flux

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rates in different soils, under different conditions and after different applications of nitrogen, but few have included the variable of time in their analysis (Buckley et al., 2016; Inselsbacher, Oyewole, & Näsholm, 2014). Even then, most studies consider differences applied to the soil rather than differences in plant composition experimentally. This use of microdialysis is novel and may allow one to infer the effect of different plants on the soil under controlled conditions and at the early stages of plant development, one of the most significant stages in invasion cycles (Badalamenti et al., 2016).

The trait comparison study described in Chapter 3 set the foundations for this experiment as I found that non-native grasses tend to display traits above- and belowground of faster growing plant species, particularly in biomass accumulation. These measures imply function but do not actually confirm whether the non-native grasses have a greater capacity to absorb nutrients from the soil as uptake and internal use efficiencies contribute to faster biomass accumulation. Resource use and resource availability have been strongly linked to plant invasions across various ecosystems (Davis et al., 2000). I expected to see the same overall resource acquisition specialist trends from the non-native grasses that were seen in the previous trait comparison experiment, as well as other supporting studies (Godfree et al., 2017; Lee et al., 2017). All microcosms containing plants were expected to display a faster decline in available nitrogen compared with microcosms without plants. I expected that, because of their greater biomass and other acquisition specialist traits, non-native grasses would also display a faster decline in available nitrogen from the substrate during the early growth stages compared with native grasses (Leishman et al., 2007; Reichmann et al., 2016). I also expected the availability of nitrate to fluctuate more over the course of the experiment than ammonium because nitrate is more mobile in the soil (Macdonald et al., 1989). The non-native grasses were expected to capitalise on the nitrate availability at a faster rate than the native grasses (Gigon & Rorison, 1972).

4.2 RESEARCH DESIGN

This experiment began in late September 2018 and was conducted at the University of Queensland (St Lucia campus) in temperature-controlled glasshouses and a research laboratory. All seeds were collected locally from wild populations in Northern New South Wales and South East Queensland. All seeds were collected within several weeks of each other and were stored in a laboratory refrigerator in

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envelopes within plastic bags to avoid light, water, and temperature exposure. The seeds from Chapter 2 were also used within this experiment. The glasshouse was subject to natural light and temperature was set on a 12 hour day/night cycle of 28°C and 18°C. Seeds were germinated in petri dishes, and then six replicates of each of the eight grasses were planted into 250 ml forestry pots, along with six blank pots containing only substrate, as seen in Figure 4.1. The pots without plants were considered controls and are referred to as ‘blanks’. To keep the growing medium consistent, a substrate was used for this experiment and was made at the University of Queensland following a standard University of California (UC mix) recipe (1/3 cubic metre of sand, 1/6 cubic metre of peat, 4.068 kg of stock fertiliser mix comprising 2 kg of calcium nitrate, 1 kg of potassium sulphate, 9.33 kg of supersulfate, 10 kg of dolomite, 6 kg of hydrated lime, 3 kg of gypsum and 1.2 kg of Micromax (Scotts- Sierra Horticultural Products Co., Marysville, OH)). The substrate was used as a simulated soil and was steam sterilised before use and provides a background availability of nitrate. A total nitrogen analysis of the substrate using a LECO TruMac CNS analyser (LECO Corporation, USA) revealed an average nitrogen concentration of 4.12%. Seeds were germinated in petri dishes, transplanted into the microcosms and then left to grow for 3 days before the experiment began to ensure the transplant was successful and to give each plant time to establish in the substrate environment. Preliminary experiments revealed that several grasses did not all survive after the first week of growth; therefore, additional replicates were included to ensure at least six replicates were available for each grass. Only data from the plants that survived the duration of the experiment were included in the final dataset. Each 250 ml pot was considered to be a microcosm for this experiment, and the substrate was considered to be soil.

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Figure 4.1 Diagram of experimental design for the microdialysis experiment. Each circle represents one 250 ml microcosm filled with substrate and either a non-native grass (yellow), a native grass (green) or substrate with no plant, referred to as a blank (blue). Microcosms were moved randomly each week of the experiment to avoid light, temperature and other potential bias.

Preliminary results suggested that the first 3 days of the experiment showed a large change in the available nitrogen. Therefore, each microcosm was sampled using the microdialysis at 5 l per hour for 1 hour each day using a 0.5 mm by 30 mm probe for the first 3 days (Brackin et al., 2017; Buckley et al., 2017; Inselsbacher et al., 2011). The same procedure was repeated on a weekly basis on days 6, 13, 20 and 34. Each microcosm was watered regularly by hand. The glasshouses were subject to natural sun availability, and some days the plants were watered more than others because of higher evaporation rates. Watering amounts ranged from 10 ml to 30 ml for each microcosm on each day. Occasionally, watering was skipped if the substrate remained saturated. I used the preliminary trials to estimate how much water was needed to avoid excess leaching of the substrate, but occasional minor leaching still occurred. Probes were placed about halfway between the centre of the pot, where the plant was placed,

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and the corner of the pot. A microdialysis needle was used to create a gap in the substrate for the probe before sampling to ensure the probe was not damaged during this process. This needle was replaced and left in the substrate after each sampling to ensure that I sampled the same place in the substrate every time. Before each day of sampling, all microdialysis lines were checked to ensure they were not damaged or compromised.

On days 1, 6, 13, 20 and 34, a photograph was taken of the largest leaf from each plant in front of a white paper with a photocopied ruler on it. These images were then used to measure leaf area using ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, MD, USA). The leaf growth on days 2 and 3 was considered negligible enough to forego the leaf area measurements on these days.

After harvest, the roots were washed of all substrate particles and the plant from each microcosm was dried for at least 24 hours at 45°C. The dried above- and belowground biomass was then weighed separately. The above- and belowground tissues were ground using a TissueLyser II (QIAGEN, Valencia, CA) and analysed for total nitrogen and carbon content using a LECO TruMac CNS analyser (LECO Corporation, USA). The nitrogen and carbon content were measured as percentages and this is considered an indicator of content.

Unfortunately, the conditions of this experiment were not optimal for all eight species of grass. Only one of the windmill grass replicates survived to the end of the experiment. With only one replicate, this congener pair could not be included in the final analysis. However, these data are included in the native and non-native groupings. I attempted to do a smaller version of the experiment with only this congener pair, but the windmill grass replicates failed to survive under the same conditions a second time.

4.3 ANALYSIS

All data analyses were conducted in R statistical computing package (R Development Core Team, 2013).

The dialysate or outflow collected from each sampling event comprised 300 l of liquid and was used to analyse ammonium and nitrate concentrations. The ammonium assay was derived from the Schmidt laboratory group at the University of Queensland School of Agriculture and Food Sciences (SAFS) and revised by J. Vogt,

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May 2013. The nitrate assay was adapted from Miranda et. al. and revised by J. Vogt, Schmidt Lab, SAFS, May 2013 (Miranda, Espey, & Wink, 2001).

The nitrate and ammonium concentration data were log transformed with a linear regression model to interpret the trend in substrate concentration over time. All nitrate concentration values were increased by 0.01 to avoid any zero values becoming infinite during log transformation. Data were analysed first as all plants against all blanks. Next the data were split into blanks, native and non-native groups. Finally, the data were organised by congener pair with blanks included alongside each pair.

Leaf area (cm2) was analysed for the largest leaf from each plant on each sampling day except for days 2 and 3 using ImageJ. Leaf area was also analysed using linear regression models both by origin and by congener pair.

The biomass and nitrogen content measurements were analysed separately using analysis of variance (ANOVA) across all individual species and then with a Tukey honest significant difference (HSD) post hoc test to identify the significant differences between each congener pair for each measurement.

4.4 RESULTS

4.4.1 Leaf area analysis

The leaf area measurements followed the same trends that were found in the trait comparison experiment (Chapter 3) in that the non-native grasses consistently had significantly larger leaves, as seen in Figure 4.2. The increase in leaf area over time provides a surrogate measure of growth rate and indicated that the non-native grasses grew faster than the natives. African lovegrass and woodlands lovegrass differed the least from each other (Figure 4.3). However, the other two congener pairs were substantially different in leaf size even at the early growth stages (Figure 4.4 and Figure 4.5).

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Figure 4.2 Leaf area (cm2) of the native (blue line) and non-native (red line) grasses over time using measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.56).

Figure 4.3 Leaf area (cm2) of the native woodlands lovegrass, WLG (blue line) and non-native African lovegrass, ALG (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.822  10–13, adjusted R-squared: 0.73).

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Figure 4.4 Leaf area (cm2) of the native swamp foxtail, CP (blue line) and non-native buffel grass, CC (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.71).

Figure 4.5 Leaf area (cm2) of the native wild sorghum, SL (blue line) and non-native Johnson grass, SH (red line) grasses over time measured in the microdialysis experiment and analysed using a linear regression model (p < 2.2  10–16, adjusted R- squared: 0.86).

4.4.2 Nitrate and ammonium analysis

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Nitrate and ammonium concentration decreased at a faster rate in the microcosms with plants than in the blanks ( Figure 4.6 and Figure 4.7), and this difference was smaller in ammonium than in nitrate. First, all plants regardless of origin were grouped together and compared against the blank microcosms. The ammonium and nitrate concentrations in the blank microcosms also decreased along with those of the plants, although more slowly. Given that the experiment provided nitrate and not ammonium as the primary source for nitrogen, the ammonium that was present was likely to have been generated from the chemical reduction of nitrate by microbes and plants within the substrate (Gigon & Rorison, 1972). I assumed that the faster rates of nitrogen depletion in this case resulted from the presence of the plant because all other factors were kept consistent. Therefore, the more negative the correlation between days and nitrogen concentration, the greater the uptake ability of the plant. The single surviving replicate of windmill grass and all replicates of Rhodes grass were also included in this analysis because they represented a plant in the substrate as opposed to the substrate only.

Figure 4.6 Available ammonium over time in the blanks, shown in yellow, and the plants, regardless of origin, shown in blue, analysed using a linear regression model (p < 4.809  10–8, adjusted R-squared: 0.089).

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Figure 4.7 Available nitrate over time in the blanks, shown in yellow, and the plants, regardless of origin, shown in blue, analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.43).

There was a significant difference between native and non-native grasses in regards to their interaction with nitrogen in the substrate (Figure 4.8 and Figure 4.9). Similar trends were found when plants were separated into their native and non-native groupings, and the differences were more pronounced for the nitrate concentrations than for the ammonium concentrations, although both differences were significant. The single surviving replicate of windmill grass and all replicates of Rhodes grass were also included in this analysis because they represent a native and non-native Australian grass.

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Figure 4.8 Available ammonium over time in the blanks, shown in yellow, the non- native grasses, shown in red, and the native grasses, shown in blue, analysed using a linear regression model (p < 3.65  10–7, adjusted R-squared: 0.087).

Figure 4.9 Available nitrate over time in the blanks, shown in yellow, non-natives, shown in red, and natives, shown in blue, analysed using a linear regression model (p < 2.2  10–16, adjusted R-squared: 0.44).

The same overall trends were found when uptake levels were compared within each congener pair. African lovegrass and woodlands lovegrass had very similar uptake levels compared with the other congener pairs (Figure 4.10a and Figure 4.11a).

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African lovegrass and woodlands lovegrass had the smallest difference between them, and they were the only pair without a significant difference in ammonium depletion (Figure 4.10a). This coincides with their growth rate, as seen in the leaf area measurements (Figure 4.3).

The trends across the native and non-native groupings did not appear to be driven by one congener pair, rather each congener pair followed the same overall trend. In all cases, the nitrate depleted at a faster rate than the ammonium, and the significant differences between the plants and the blanks, as well as between the native and non- native grasses were larger in the nitrate than in the ammonium sampling. Johnson grass started at a higher level for both ammonium and nitrate but both of these levels ended up lower than those in the native wild sorghum (Figure 4.10c and Figure 4.11c). Most congener pairs displayed the non-native trend line of starting lower and ending lower than the natives.

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Figure 4.10 Available ammonium over time in each congener pair. The red lines represent the non-natives (a) African lovegrass, (b) buffel grass and (c) Johnson grass. The blue lines represent the natives (a) Woodlands lovegrass, (b) swamp foxtail and (c) wild sorghum. The yellow line depicts the same blank microcosms in each figure. The results of the linear regression model analysis are included within each figure.

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Figure 4.11 Available nitrate over time in each congener pair. The red lines represent the non-natives (a) African lovegrass, (b) buffel grass and (c) Johnson grass. The blue

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lines represent the natives (a) Woodlands lovegrass, (b) swamp foxtail and (c) wild sorghum. The yellow line depicts the same blank microcosms in each figure. The results of the linear regression model analysis are included within each figure.

In addition to nitrate and ammonium sampling over time analysed using microdialysis, several measurements were taken after harvest. Above- and belowground biomass followed the same trends across native and non-native congener pairs as shown in the trait comparison experiment (Chapter 3). That is, the non-native grasses consistently had higher values, which suggested a more nutrient acquisition specialisation (Figure 4.12 and Figure 4.13). The nitrogen content also followed the same trend as shown in the previous experiment in that the native grasses consistently had higher nitrogen content, although most congener pairs did not differ significantly from each other (Figure 4.14).

Figure 4.12 Aboveground biomass (g) by species of native (blue) and non-native (red) –14 grasses (F5 = 54.45, p < 1.97  10 ). Significant differences were found between all congener pairs except African lovegrass and woodlands lovegrass (Johnson vs. wild sorghum: p < 0.00001; buffel vs. swamp foxtail: p < 0.0022).

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Figure 4.13 Belowground biomass (g) by species of native (blue) and non-native (red) –10 grasses (F5 = 28.76, p < 1.36  10 ). Significant differences were found only between one congener pair: Johnson grass and wild sorghum (p < 0.00001).

–7 Figure 4.14 Total nitrogen content of full-plant biomass (F5 = 14.21, p < 2.97  10 ). Significant differences were found only between the congener pair Johnson grass and wild sorghum (p < 0.00006).

4.5 DISCUSSION AND CONCLUSION

The results from this experiment suggest that plants, even when congeners, can have very different effects on the soil within the first few weeks of growth. The substrate from the non-native grasses showed a significantly faster nitrogen depletion rate than that of the native grasses, which implies that non-natives absorb nitrogen at

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a faster rate. Nitrate depletion was faster than ammonium depletion, which suggests that the non-native plants are better at absorbing nitrate and that they may prefer it when it is not limited. Although some grasses prefer ammonium (Rossiter-Rachor et al., 2009), their ability to absorb nitrate at faster rates when it is available gives them a competitive advantage over other plants. Given that the primary nitrogen source provided was nitrate, conclusions cannot be made regarding nitrogen source preference; however, the data suggest that non-natives have a superior nitrate uptake capacity compared with native grasses.

All of the plants studied in this experiment fill a similar functional niche within their respective environments, yet the difference in occupants of that niche can cause major shifts in the nutrient cycling of that system, especially if the difference is between a native and non-native species (Gibbons et al., 2017; Schmidt et al., 1998). Ecosystems around the planet are already experiencing vast shifts in community assemblage (Cardinale et al., 2012), and nitrogen, a limiting resource within many systems, will play an important role in these shifts (Lee et al., 2017). Nitrogen cycling is complicated, and several components should be discussed to understand the implications and limitations of this study.

In interpreting these results, I assumed that, as the available nitrogen decreases, some portion of it is absorbed by the plant, although other processes are inevitably happening, as well. Immobilisation, nitrification and denitrification may have all affected the nitrogen available in the substrate to some extent (Bardgett et al., 2014; Kant et al., 2011). Although these were avoided as much as possible through careful watering regimes, consistent substrate content and similar climactic conditions, some nitrogen, particularly nitrate, was likely to have been lost via leaching from the substrate (Vitousek & Howarth, 1991). The methods were consistent across all microcosms, and I assumed that the amount of nitrogen that was lost or unavailable was similar between microcosms. Nitrate and ammonium decreased from the microcosms containing plants faster than from those without, which suggested that the plants were responsible for the faster decline in available nitrogen. Other processes may have been happening, but it was assumed that they happened consistently to all microcosms, regardless of occupant.

Additionally, larger plants will inevitably absorb more nitrogen and a faster growth rate can explain some level of nitrogen uptake ability through a larger available

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root surface area (Atkinson et al., 2014). The leaf area measurements show that, as expected, the non-natives, grew faster than the natives, and larger plants require more resources, particularly nitrogen. Likewise, a higher concentration of nitrate transport proteins in the root membrane allow for a greater uptake capacity (Nasholm, Kielland, & Ganeteg, 2009). The combination of greater root surface area that comes with higher growth rates and higher concentrations of nitrate transport proteins in the roots should both contribute to the greater nitrate uptake capacity of non-native grasses. However, given the difference in growth rate the difference in absorptive capacity measured in this study cannot be attributed to a more efficient root system alone (Atkinson et al., 2014; Brackin et al., 2017; Nasholm et al., 2009).

Another aspect of both ammonium and nitrate sampling is the ‘pulsing’ of nitrogen concentration in the substrate over time. The available nitrogen sampled undulates in a similar pattern for each species. Days 2, 13 and 34 showed lower nitrogen availability on average compared with the other sampling days. This pulsing of nutrients is not unexpected because nitrogen cycling involves multiple possible forms of conversion and nitrate is a particularly mobile form (Abbadie & Lata, 2006). Water content of the substrate will affect the uptake of nutrients by the probe membrane, and slightly different water content in the substrate on each sampling day may explain some of this undulation or pulsing (Inselsbacher et al., 2011). I aimed to keep the watering amounts as consistent as possible as to not affect the microdialysis results, but there is a chance that differences in watering regimes may have had an effect on the available nitrogen. This pulsing was present across native, non-native, and blank microcosms, and I believe is therefore negligible when analysing the overall trend of nitrogen concentration over time.

Within this pulsing of nitrogen, there was a particularly notable increase in nitrogen concentration, in both nitrate and ammonium, around day 20. This was apparent across all species and blanks, and may have resulted from several factors. By this time, microbial communities within the substrate had likely started to affect nutrient availability, which may explain the spike in nitrogen availability in the previously sterilised substrate (Leff et al., 2015). It could also indicate an accumulation zone within the rhizosphere of the roots as they grew closer to the probes (Brackin et al., 2017). The small pot size was a limitation in the sense that roots of the larger grasses became somewhat root bound during the experiment; however, it also meant

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that roots were more likely to grow within close proximity to the probe. Research using microCT imaging to place microdialysis probes directly adjacent to roots, as well as away from roots, has reported the accumulation of nitrate in the rhizosphere and depletion of ammonium (Brackin et al., 2017). On the other hand, the blank samples also displayed a pulse of nutrients on day 20, although considerably smaller, which suggests that the pulse was related more to the presence of microbial communities than direct rhizosphere sampling because the blanks would have been able to form microbial associations but not rhizospheres.

Understanding the difference in nitrogen usage between native and non-native grasses will be critical for understanding larger system shifts in grasslands where non- natives are already dominant and systems where they can become dominant. Each species has a different nitrogen uptake capacity, and each environmental system may have different forms of available nitrogen (Ehrenfeld, 2003). Research has shown that Australia’s tropical grasslands tend to have more available ammonium than nitrate during the dry season and, in these environments, a greater ability to absorb ammonium may be more advantageous (Schmidt et al., 1998). It is theorised that environments with frequent shifts in resource availability are more easily invaded by non-native plants (Davis et al., 2000) and that an ability to capitalise on different forms of nitrogen could be an essential mechanistic advantage (Rossiter-Rachor et al., 2009). Shifts in community assemblage, grazing patterns, climate and any disturbance can affect the nitrogen supply within a system, and that supply may not match the uptake ability of all plants, which can further drive shifts within that system (Bardgett et al., 2014; Brackin et al., 2015; Ehrenfeld, 2003). Previous research suggests that invasive plants that are successful in low-resource environments tend to have greater resource use efficiency (Funk & Vitousek, 2007). These results suggest that Australia’s non- native grasses are resource acquisition specialists and likely thrive on even short-term increases in resource availability because their uptake capacity surpasses that of the native grass occupants.

Comparison of these results with what is known about other grasses of African origin shows some distinct similarities and a few noticeable differences. One of the most prolific invasive grasses in Australia is gamba grass (Andropogon gayanus Kunth.), which is native to sub-Saharan Africa. Much like the grasses included in this study, gamba grass can create large amounts of biomass both above- and belowground,

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and is associated with changes in nitrogen cycling (Rossiter-Rachor et al., 2017). The presence of gamba grass, compared with native Australian grasses, results in significantly lower amounts of nitrate in the soil but significantly higher amounts of ammonium, which suggests that it can inhibit nitrification in the soil and prefers ammonium (Rossiter-Rachor et al., 2009). Studies have shown that other African grasses can also produce more biomass under high ammonium availability (Ruess, McNaughton, & Coughenour, 1983; Schmidt et al., 1998). Another study found that a number of African grasses can transform their soil environment to suit their preferred nutrient cycling, which can potentially affect the community-level composition (Lata et al., 2004). Alternatively, it has been found that the preference for nitrate or ammonium of several African grasses is more dependent on climate than species (Wang & Macko, 2011). Despite providing consistent climatic conditions in my study, I found significant differences in nitrogen uptake across native and non-native grasses. Even though all eight grasses evolved in similarly low-resource environments (Buckley et al., 1987), it is likely that the African grasses used in this study have evolved to interact and affect nitrogen differently than native Australian grasses. Despite being collected from wild populations, the non-native grasses are likely to have been affected by selective breeding regimes for their use as pasture grasses that can enhance their ability to thrive in low-resource environments (Cook & Dias, 2006; Davis et al., 2011; Godfree et al., 2017). The difference in nitrogen utilisation even in the first few weeks of growth can have a feedback effect that further benefits non- native grasses (Godfree et al., 2017; Lata et al., 2004).

Phosphorus content was not analysed in this experiment, although it has been shown to be a limiting element, particularly in Australia’s arid soils, and could be important for understanding the advantages non-natives may have over native grasses (Buckley et al., 1987). Nitrogen and phosphorus can be co-limiting, particularly in grasslands (Craine & Jackson, 2009; Harpole et al., 2011). Different plant species can adapt different functions according to this limitation or co-limitation within a system (Harpole et al., 2011). Phosphorus limitation can alter the nitrogen cycle and should be considered when discussing resource limitations. In this study, phosphorus content was assumed to be consistent across all microcosms and, therefore, its effects on the occupants should be similar.

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This experiment provides a strong foundation for the concept that non-native grasses have a greater nitrogen uptake capacity in Australia, and this research could be taken further in several ways. Using either openable growing boxes or microCT imaging in the future can allow for more accurate probe placement. With the probe placed directly adjacent to the root, it would be possible to measure within the accumulation zone of the rhizosphere more accurately (Brackin et al., 2017). Some of the spread of the data might be explained by the lack of accuracy in probe placement, and using a consistent placement in relation to roots may result in more consistent data. As mentioned previously, microbial communities could have an effect on nutrient availability, and it is possible that the microbial associations differ between native and non-native grasses (Laliberte, 2017). Quantifying the microbial community could also be an interesting next step in this line of enquiry (Gibbons et al., 2017; Sheik et al., 2011). This experiment provides a foundation for the idea that native and non-native grasses interact with soil nutrients differently and that this is an important aspect of invasion ecology to understand.

Disturbance is another key factor affecting the nitrogen cycling within Australia (Orians & Milewski, 2007). Many of Australia’s grasslands are prone to disturbance including fire, development, non-native species establishment and different forms of land management. Different disturbances can have different effects on nitrogen cycling compared with undisturbed areas (Schmidt et al., 1998). Disturbances that increase nutrient availability are likely to encourage non-native plant invasions (Davis et al., 2000). Previous studies have shown that plant invasions are often followed by an increase in soil nutrient availability (Pyšek et al., 2012; Vitousek, 1990), which can itself be considered a disturbance and can be associated with successive colonisation following disturbance (Buckley et al., 2007). Non-native grasses are associated with greater biomass and fuel loads, which increase the risk of large, uncontrolled fires that affect litter decomposition and nitrogen availability in the soil (van Klinken & Friedel, 2017). Understanding the implications of disturbance factors along with non-native species establishment for the nitrogen cycling of grasslands will help to illuminate the importance of effective and well-informed control strategies in these threatened ecosystems.

Available soil nitrogen is affected by many factors such as disturbance, co- limitation, conversion and microbial communities. However, there is strong evidence

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that non-native grasses have a greater capacity for nitrogen uptake than native Australian grasses. The three non-native grasses studied here, African lovegrass, buffel grass and Johnson grass, are particularly adapted for rapid absorption of nitrate when it is available. This may have numerous implications for the ecosystems that they invade and dominate because this difference in resource utilisation can affect fire intensity, grazing differences, decomposition rates and a number of other processes.

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Chapter 5: Discussion and Conclusions

5.1 DISCUSSION

My research compared belowground functional traits and signs of resource uptake from the soil as functions that likely contribute to non-native grass species establishment in Australia’s low-resource environments, as well as how these trends follow and deviate from current beliefs regarding invasive plants. The functional traits differ between Australia’s native and non-native grasses during early growth stages, and these differences correlate with how effective they are in taking up nitrogen from the soil. While much of invasion ecology in Australia has been focused aboveground, understanding the process by which new species invade low-resource environments, such as Australia’s arid grasslands, requires a multifaceted approach including study of both above- and belowground traits, as well as abiotic factors, germination success, potential past or present disturbance, existing populations and shifts in climate (Catford et al., 2019; Funk & Cornwell, 2013; Pyšek et al., 2012).

The body of knowledge encapsulated by the field of invasion ecology is rife with contradictions and exceptions to theoretical generalisations. Long-term studies reveal the complex nature of plant invasions by evidencing the many factors involved, including the status of the invaded environment, traits of the native community and route of invasion. Even when these factors are defined, the outcome cannot always be predicted accurately (Catford et al., 2019). In this study, contrary to expectation, the native and non-native grasses displayed a mixture of traits indicative of both nutrient acquisition and conservation growth specialists, although the non-natives tended towards the nutrient acquisition specialist end of the resource economic spectrum (Funk et al., 2016; Mommer & Weemstra, 2012). A small set of traits did not follow the expected trend, which suggested that these traits may not always indicate the assumed adaptations in every environment (Funk, 2013). Additionally, the non-native grasses did not have higher germination success as expected. It is likely that context- dependent combinations of traits allow for nutrient use efficiency, greater nutrient uptake ability, and overall success in Australia’s low-resource environments. Studies have shown that an understanding of life-history strategies can be vital when creating effective management strategies for invasive species. Understanding the intricate

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similarities and differences between native and invasive species can inform management decisions and improve conservation efforts on both economically and ecologically valued land (Firn et al., 2015; Godfree et al., 2017; Ramula et al., 2008; Seabloom et al., 2003).

Despite this apparent mixture of traits, biomass measurements and nitrogen uptake ability followed the nutrient acquisition specialist trends expected from non- native invasive species (Funk, 2013; Leishman et al., 2007; Reichmann et al., 2016). The trends found in both the microdialysis experiment (Chapter 4) and the functional trait study (Chapter 3) are congruent, and this agreement suggests a relationship between functional traits measured in the second experiment and an increased ability for nitrogen uptake, mainly in the form of nitrate, as seen in the third experiment. For example, non-native African lovegrass and its congener woodlands lovegrass displayed the fewest differences in functional traits and nutrient depletion from the substrate, whereas non-native Johnson grass and its congener wild sorghum had the most differences in both experiments. The trait measurements made during the microdialysis experiment also reflect the traits measured in the functional trait comparison experiment. It is not possible to untangle all 19 traits to determine exactly which traits refer to this exact phenomenon of nitrogen uptake ability, and it is likely that a combination of traits is responsible for this function. Regardless, a generalisable trend across functional traits and function emerges, despite some traits not following the expected conventions.

The literature suggests that specific root length, belowground nitrogen content and aboveground nitrogen content are associated with resource acquisition specialists, but they have the opposite association in this set of Australian grasses (Bardgett et al., 2014; Funk & Cornwell, 2013). Regardless, the non-native grasses displayed greater nitrogen uptake capacities in my studies. Therefore, it can be inferred that either the traits associated with resource acquisition might not be consistent across all environments or these particular traits are not consistently associated with resource acquisition specialists (Catford et al., 2019; Funk, 2013). The non-native grasses may be successful because they utilise both nutrient acquisition and conservation traits within low-resource environments. These traits may develop differently in later growth stages. However, it is noteworthy that in the early growth stages, they demonstrated

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the development of a unique combination of traits, even when grown under similar conditions in the glasshouse.

Many of the non-native invasive grasses of Australia are of African origin. Much of Africa, like Australia, is dominated by low-resource grassland or savannah where nitrogen is a limited resource (Buckley et al., 1987; Lata et al., 2004). Therefore, it is not surprising that these grasses are well adapted to Australia, despite not having evolved there. However, they are more than well adapted and are increasingly dominating the Australian landscape (Grice, 2006). Interestingly, Australia is among the many countries that are home to non-native invasive grasses from Africa, but Africa is not home to nearly as many invasive grasses (Visser et al., 2016). It is thought that this one-way phenomenon reflects the high demands of the environment in Africa on plant species to adapt to fire, disturbance and high grazing pressure; all traits that correlate with invasive capabilities (van Kleunen, Weber, et al., 2010; Visser et al., 2016). Additionally, the introduction of these grasses occurred intentionally in only one direction, which explains the imbalance in establishment success (Visser et al., 2016). This one-way introduction can also help explain why germination rate does not seem to be a critical factor in invasive success. The success of non-native invasive grasses from Africa is at least partly related to propagule pressure from intentional introduction (Grice, 2006); however, mechanistic advantages that can be measured using functional traits have allowed them to persist and eventually dominate (Leishman et al., 2007). In addition, release from co-evolved pathogens may also contribute to their ability to thrive (MacDougall et al., 2018).

Although ecologists agree that the four non-native grasses included in this thesis research have detrimental impacts on native ecosystems, many landholders still consider Rhodes grass and buffel grass to be valuable pasture grasses despite their ability to dominate and transform ecosystems (Godfree et al., 2017; Orians & Milewski, 2007). This is not an uncommon phenomenon among invasive species, as they are often introduced intentionally for their productive abilities (Simberloff et al., 2013). Regardless, there is a growing body of research that suggests that native grasses may be more beneficial over the long term and that the non-native alternatives are not as helpful given their ability to transform the Australian landscape and their eventual requirement for intensive management (Firn, 2007, 2009; Godfree et al., 2017). My

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results support this by quantifying the difference in nitrogen utilisation and biomass accumulation between the two groups of grasses.

Taken together, the results of these three experiments will help to elucidate which functional traits are contributing to the invasive nature of Australia’s non-native grasses as well as help to understand the associations between growth traits and function within low-resource environments. Firstly, my research shows that germination success alone is not likely a cause for invasive success. This research shows how microdialysis can be used to measure nitrogen fluxes over time across native and non-native grass-occupied soils. A difference between nitrogen use and uptake across native and non-native grasses was found, and this difference is corroborated by the commonly measured plant functional traits compared between the native and non-native grasses. Furthermore, the difference in nitrogen uptake ability implies that non-native grasses create a shift in the nitrogen cycling regime when they invade native dominated grasslands. My research contributes to a growing need to understand how non-native species function, as well as how those functions can be measured and compared within Australia’s unique grassland habitats.

5.2 LIMITATIONS

Several limitations were discussed in individual chapters, but some apply to all or most of the experiments. One of the most obvious limitations is the conformity of the experimental design. To standardise as many factors as possible, the growing conditions may not have been ideal for each species. Experimentally based research will inevitably include some level of limitation. In this case, not all grasses were grown under optimal conditions, but all grasses were grown under reasonable conditions for their growth, with the exception of windmill grass in the microdialysis experiment. This may be a limitation from an applied perspective, but it is important to quantify the species differences under similar conditions before quantifying them under field conditions because abiotic factors can play a substantial role in the expression of functional traits (Goldberg, Rajaniemi, Gurevitch, & Stewart-Oaten, 1999; Keddy, 1990). Furthermore, studies have found similar trends in field experiments that had previously been found in glasshouse experiments (Catford et al., 2019).

The soil medium and watering regimes of the trait comparison (Chapter 3) and microdialysis experiments (Chapter 4) were not optimal for all of the species. Both

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experiments used a standard UC mix, which may not simulate a realistic environment for each grass, but all species grew to the expected standards regardless. The soil mix had a high water-holding capacity, which likely favoured the grasses that prefer wetter habitats, such as Johnson grass, Rhodes grass, swamp foxtail and windmill grass (Chauhan et al., 2018; Leguizamón & Acciaresi, 2014; Toon et al., 2018). The watering regime was not ideal across all species, but again this did not place excessive stress on any particular species. The method for watering was consistent across each pot, but because of the different water-use capacity of each species, this meant that some plants dried out more while others remained wetter. A method of watering to each pot’s carrying capacity might have helped to avoid this issue, although none of the plants was extremely under- or over-watered. To manage 96 pots total in the trait experiment and 54 microcosms in the microdialysis experiment, in a short growth period, the pots could not feasibly exceed a certain size. In addition, to ensure that the local soil environment was sampled in the microdialysis experiment, pots were kept relatively small (250 ml). Roots are very responsive to their environment, and the presence of a boundary to growth can affect growth habits (Bardgett et al., 2014). This high level of responsiveness makes roots difficult to standardise on a global scale and to study experimentally (Mommer & Weemstra, 2012). The root-bound effect that smaller pot sizes create is a larger concern in longer experiments and was considered negligible when examining early growth stages in these studies.

Because the seeds were collected locally, there may have been genetic differences given the particular varieties sourced for this experiment compared with the populations found throughout Australia, and these differences may affect the invasiveness of that variety (Lavergne & Molofksy, 2007). Many of the non-native species in this study have undergone breeding programs to enhance their suitability as pasture grass, which may contribute to their invasiveness and may also produce distinct varieties (Godfree et al., 2017). Rhodes grass and buffel grass are both sold commercially for pasture, and different varieties of each are available depending on the desired environment and usage. Studies have shown that trait expression is dependent on a mixture of genetic and environmental factors (Bardgett, 2017; Olsen, Caudle, Johnson, Baer, & Maricle, 2013; Pérez-Harguindeguy et al., 2013). However, it has been suggested that, in plants that can grow clonally and asexually, genetic variation is less likely to be a factor that determines invasiveness (Poulin et al., 2005).

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There is no reason to believe that any of the species in this study produce primarily asexually, and I assume that genetic variation plays a role in the non-native’s ability to become invasive. It is likely that most traits will display a similar pattern across varieties within the grasses found outside of their native range, although this was not specifically quantified in this study.

Overall, as with any glasshouse experiment, there are inherent limitations that must be considered when extrapolating the results into real-world applications. However, these limitations were carefully considered and could not be eliminated while keeping all species under the same conditions. Furthermore, trait-based studies have shown similar results between field and glasshouse conditions, which supports the value of controlled glasshouses (Catford et al., 2019). These results provide information about the grasses’ capabilities without external factors, which provides foundational knowledge for moving forward.

5.3 FUTURE RESEARCH

A larger dataset involving more Australian grass species would make these conclusions more robust and would be helpful for devising generalisable patterns in belowground traits across Australia’s grasses. A dataset that incorporates environmental factors, such as soil type, resource availability and climate of the grasses in situ, would help to bridge the gap between experimental and applied research. It is important that belowground functional traits are analysed alongside soil processes because they are highly correlated (Bardgett, 2017). Quantification of trait plasticity of the eight grasses examined here would help to elucidate the role that plasticity plays in Australia’s native and non-native grasses (Funk, 2008). Unlike aboveground, where most leaves are representative of all leaves, different types of roots have different functions and should be considered accordingly (Bardgett et al., 2014; Mommer & Weemstra, 2012). The inclusion of belowground traits and nutrient cycling into invasion ecology is a vast emerging field with growing importance and interest.

One aspect of belowground trait-based ecology that was not possible to determine within the scope of this thesis was the microbial associations in the soil. It has been suggested that plants are not in direct competition with each other for nutrients as much as their associated soil microbes are in direct competition for

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nutrients (Wilson, 2014). Bacteria and mycorrhiza can aid the plant’s absorption of limiting nutrients and can be an important factor when considering nutrient acquisition in resource-limited environments (Laliberte, 2017). Changes in plant community composition, including the introduction of non-native species, can change the quantity and type of microorganisms in the soil, which can affect nitrogen mineralisation and nitrification rates (Chaparro, Sheflin, Manter, & Vivanco, 2012; Hawkes et al., 2005; Toole et al., 2017). Studies have shown that root traits and arbuscular mycorrhizal communities are two of the best indicators of various ecosystem properties and together are ecosystem drivers (Bardgett et al., 2014; Legay et al., 2016). For example, the presence of non-native grasses in a Californian grassland increased the amount of bacteria in the soil, which greatly increased nitrification rates (Hawkes et al., 2005). Alternatively, another study suggests that soil microbial communities are poor indicators of the success of a target species in management plans (Fry et al., 2017). The expected shifts in temperature and precipitation because of climate change will significantly affect the microbial communities of grasslands (Duell et al., 2016; Sheik et al., 2011). In addition, native and non-native species can colonise different microbial communities in their soil, and these differences are likely to contribute to their success in new environments (Reinhart & Callaway, 2006; Sielaff, Polley, Fuentes-Ramirez, Hofmockel, & Wilsey, 2019a; Toole et al., 2017). Research incorporating root traits, nutrient uptake ability and microbial associations over time would greatly increase understanding of the systems that govern native and non-native grasslands.

In addition to microbial quantification, experiments using the native soils that each grass is found in could shed more light on these processes and how the traits respond to abiotic factors. However, there is a risk that such experiments could produce highly variable results given the large distributions of these grasses across the country and, therefore, these experiments would also have limitations. Soil type and nutrient availability are important factors in restoration management (Fry et al., 2017). It will be important to understand how roots affect soil as much as how soil affects roots (Funk et al., 2016). Studies have shown that non-native invasive plants can transform grassland soils quickly and that each species has the potential to change the soil environment in a different way (Gibbons et al., 2017; Orians & Milewski, 2007; Richardson, 2009). That is, the same species can have different effects on different environments, which suggests that it is not enough to simply study the invading species

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and not the potentially invaded environment (Ehrenfeld, 2003). Previous experiments have used native and non-native grasses in their respective soils and then swapped soils, and have found that both types of grasses are able to cultivate a soil aggregate and microbial community more like their native soil than their newly established soil (Lata et al., 2004). Studying the soil from various wild populations of each species, then using it for similar growth experiments, will help to elucidate the role that different soils play in invasion success.

This study focused on competitive advantage from a mechanistic perspective, and direct competition is another factor to consider. A number of factors are associated with direct above- and belowground competition that could explain further the invasive capabilities of the non-native species in this study (Broadbent et al., 2018). The theory that invasive plants can survive because they introduce novel mechanisms into native communities can apply to other forms of competition (Callaway & Aschenhoug, 2000). Studies suggest that non-native and invasive native plants are able to dominate grasslands because of allelopathic effects (Ghebrehiwot, Aremu, & Van Staden, 2013; Schwinning et al., 2017). For example, Johnson grass tissue can exude allelochemicals into the soil that prevent native grasses from growing (Rout et al., 2012). Larger biomass may play an important role in direct competition because it can reduce the light available to neighbouring plants (Pérez-Harguindeguy et al., 2013). Certain mechanisms are only advantageous in direct competition and, therefore, were not considered in this study but may be an unquantified factor for several grass species.

Photosynthetic capacity, which is affected by environmental conditions and varies between different species, is linked to nitrogen availability and was not specifically quantified in this study (Poorter et al., 2012). Nitrogen plays an important role in photosynthesis, cell wall structure and genetic material of plants (Dong et al., 2017; Vitousek & Howarth, 1991). It was previously thought that nitrogen availability is a strong indicator for photosynthetic capacity within a given species, but more recent research suggests that climate alone can account for a higher percentage of photosynthetic capacity (Smith et al., 2019). This suggests that photosynthetic capacity may be independent of nitrogen availability and should be considered in addition to nitrogen availability in ecology research on trait-based invasion.

As previously mentioned in Chapter 2, how early in a plant’s life-history cycle it can produce viable seeds within a given system is an important factor in its overall

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population success, particularly when establishing new populations (Cleland et al., 2007; van Klinken & Friedel, 2017; Wainwright & Cleland, 2013). Propagule pressure refers to both the timing that a species creates seeds relative to its neighbouring species and the number of seeds that it can release into a system (Lockwood et al., 2005). Greater rates of propagule pressure increase the likelihood of a non-native plant becoming invasive (Catford et al., 2016). Some non-native species have been shown to produce more seeds per biomass and per flower, which can give them a mechanistic advantage over natives that produce fewer seeds (Corbin & D’Antonio, 2010; Pyšek & Richardson, 2007). In addition to a species capacity for producing seeds, germination rates can also play an important role because species may germinate at a different rate given the same conditions. Despite, its low germination success rate, Rhodes grass may produce enough seeds to out-compete native alternatives.

Timing is another key element of propagule pressure. Through observations, it is known that non-natives, such as African lovegrass, can produce seeds multiple times throughout the year and may potentially release higher volumes of seeds into their environment (Pers. Comms. Jennifer Firn, Queensland University of Technology, 2019). The earlier seed producer within a system may have an advantage simply because of this head start on resource capture. Seed production and germination time can be key factors in plant invasion and effective land management (Seabloom et al., 2003). Lastly, climate can also be a factor in propagule pressure because it can affect the timing when a species produces seeds (Cleland et al., 2007). Along with functional traits and environmental conditions, the seed-producing properties of each species should be considered as part of effective conservation management.

As previously mentioned in Chapter 4, phosphorus and nitrogen can be co- limiting nutrients, and future research should include both phosphorus and nitrogen when considering low-resource environments (Harpole et al., 2011). Phosphorus is limited throughout Australia’s grasslands, and species that are well adapted to limited phosphorus availability may have an advantage in these habitats (Buckley et al., 1987). To ensure a more a more applied approach, future research should incorporate field experiments, including phenology and microbial communities, as well as a more robust nutrient analysis and photosynthetic capacity. This experimental approach creates a foundation of expectations for these Australian grasses under the same

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conditions; however, to extend this knowledge into a more applied realm, field studies across the various habitats where they are naturally found should be included.

Microdialysis is a fairly new technique in invasive ecology. It has been used successfully in field research concerning crop sciences and can definitely be adapted for field research in ecology in future studies (Brackin et al., 2015). Microdialysis allows for a more accurate, less destructive and temporal analysis of nitrogen fluxes in soil compared with traditional methods (Inselsbacher et al., 2011). This technique can be applied in different environments, across different species and even under different management treatments to quantify the effects of such differences on the nutrient cycling of a system (Brackin et al., 2015; Buckley et al., 2017; Inselsbacher et al., 2014).

5.4 CONCLUSIONS

Grasslands worldwide are reacting to shifts in climate, community assemblage and various anthropogenic drivers, including the introduction of non-native, potentially invasive species (Hoekstra et al., 2004; Isbell et al., 2017; Tilman et al., 2006). Non-native grasses are among the major threats to Australia’s grasslands. These non-native grasses were originally introduced for pasture improvement and now harden the landscape and drastically change nutrient cycling, fire regimes and ecosystem functioning (Gibbons et al., 2017; Godfree et al., 2017; Orians & Milewski, 2007). Trait-based ecological research provides insights into the functional differences that allow non-native grasses to outcompete native grasses in Australia’s low-resource environments (Buckley et al., 1987; Funk, 2013). The acquisition of nutrients is one of the main functions of roots in grasses, and nitrogen is one of the most limiting nutrients in many grasslands is (Atkinson et al., 2014; Bardgett et al., 2014). Belowground soil interactions and functional traits have recently emerged as an important and understudied aspect of trait-based invasion ecology (Bardgett et al., 2014; Mommer & Weemstra, 2012).

The research presented in this thesis combined traditional trait-based ecology methods with novel plant physiology techniques to compare functional traits and nutrient uptake abilities across four congener pairs of native and non-native Australian grasses during critical early growth stages (Bardgett et al., 2014; Inselsbacher et al., 2011; Wright et al., 2004). The non-native grasses followed the expected convention

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as nutrient acquisition specialists for most aboveground traits; however, including belowground traits revealed a unique combination of conservation and acquisition specialist traits (Funk et al., 2016; Wright et al., 2004). This combination of traits resulted in superior nitrogen uptake abilities by the non-native grasses when sampled using microdialysis. Even though most functions followed a nutrient acquisition specialist trend, these studies revealed that the selected non-native grasses are not as superior as expected across all functional traits and life-history strategies. The non- native grasses of Australia are transforming the native landscapes (Godfree et al., 2017). These research findings will help to elucidate the mechanisms contributing to this change within low-resource grasslands and provides foundational knowledge for devising effective control strategies.

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Appendices

Appendix A

Table of original p-values from and ANOVA and adjusted p-values using a sequential Holm Sidak analysis for each functional trait in Chapter 3. Original p- Holm Sidak adjusted p- Trait value sequence value Aboveground biomass 2E-16 1 4.21885E-15 Height 2E-16 2 3.9968E-15 Inflorescence 2E-16 3 3.77476E-15 Belowground biomass 2E-16 4 3.55271E-15 Specific root length 2E-16 5 3.33067E-15 Root surface area 2E-16 6 3.10862E-15 Root diameter 2E-16 7 2.88658E-15 Belowground nitrogen 2E-16 8 2.66454E-15 Aboveground carbon 2E-16 9 2.44249E-15 Belowground carbon 2E-16 10 2.22045E-15 Nitrogen by leaf area 2E-16 11 1.9984E-15 Tiller by belowground biomass 4.7E-15 12 3.73035E-14 Root mass fraction 5.27E-15 13 3.65263E-14 Root shoot ratio 2.81E-12 14 1.68598E-11 Aboveground nitrogen 1.62E-11 15 8.10002E-11 Tiller count 1.29E-10 16 5.16E-10 Specific leaf area 0.0000019 17 5.69999E-06 Root length 0.00000331 18 6.61999E-06 Tiller by aboveground biomass 0.00055 19 0.00055

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Appendix B

PCA Outputs for trait analysis in Chapter 3.

Appendices 124