The Responses of C4 Invasive Grass Eragrostis Curvula and C3 Native Australian Grass Austrodanthonia Racemosa Under Elevated CO2 and Water Limitation

The responses of C4 invasive grass Eragrostis curvula and C3 native Australian grass Austrodanthonia racemosa under elevated CO2 and water limitation.

Sara Elizabeth Lorraine Hely

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

School of Biological Science University of New South Wales

2008

Abstract

The concentration of atmospheric carbon dioxide (CO2) in the atmosphere has increased by 35% since pre-industrial levels. Projections for the next 100 years indicate an increase to levels between 490 and 1260 parts per million by volume

(ppm) of CO2, equating to a 75 % to 350 % increase in concentration since the year

1750. Associated with this increase in [CO2] will be a 1.4 to 5.8º C increase in lower atmospheric temperature. While past research has attempted to address the effects of such climatic changes on individual plant responses, predictions of plant responses at the ecosystem level are still highly uncertain. Difficulties lie in the enormous variation of plant responses to climate change variables among and within species, and between and within environmental conditions.

Past research assumed that plants using either the C3 or C4 metabolic pathways would respond differently but predictably to climate-change variables based on their metabolic pathway. Recent evidence has suggested however, that the added interactions of external environmental variables and species-specific sensitivities to climate change make it difficult to predict plant and ecosystem responses to climate change.

To investigate the mechanisms behind responses of Australian grasses to climate change, 2 pot experiments was conducted using growth cabinets to compare the effect of elevated CO2 and water-limitation on the invasive C4 grassland plant,

Eragrostis curvula (E. curvula), native Australian C3 grassland plant,

Austrodanthonia racemosa (A. racemosa), and wheat species, Triticum aestivum (T. aestivum). The experiment was run at ambient levels of CO2 maintained at 390 ppm compared to elevated levels of 740 ppm. Imposed restrictions to water supply consisted of gradually drying the soil down to 30 % available soil water (ASW) followed by re-wetting to 50 % ASW. Well-watered conditions for the experiment

ii consisted of gradually drying the soil down to 50 % ASW, followed by rewetting to

95 % ASW. Plants were grown in mixtures and monocultures, consisting of 9 plants equally spaced in a grid design.

The three significant findings of the thesis were that:

1) the metabolic pathway (C3 versus C4) was not always an accurate predictor of

biomass accumulation under elevated CO2 in the plants studied. Previous

research suggested that CO2-stimulation of photosynthesis in C3 plants would

lead to greater increases in biomass under elevated CO2 compared to C4 plants,

though both C3 and C4 plants could benefit from any reduction in stomatal

conductance under dry conditions at elevated CO2. The results from the

experiments in this thesis showed a strongly significant biomass response to

elevated CO2 in both dry and wet conditions for C4 grass E. curvula. The C3

grass A. racemosa in dry conditions, did not. It was speculated that without the

CO2-induced water conservation effect, the C3 grass experienced

photosynthetic down-regulation and this precluded a positive biomass

response under elevated CO2.

2) the magnitude and direction of biomass response to elevated CO2 was

dependant on factors such as resource-availability and the phenotypic

variability of the plants species.

3) critical analysis of results from this thesis, combined with past research on

plant responses under elevated CO2 showed that there was a tendency for

researchers to repeatedly test plants from the Poaceae family, or close

relatives of the Poaceae family. As a result, when past data were corrected for

this lack of independence, there was no relationship between the evolution of

the C3 and C4 metabolic pathway and biomass response to elevated CO2.

Instead, other factors (such as growth rate, plant height, leaf number, etc) were

iii presented as being more important in determining biomass response. These observations were supported by results found in this thesis.

Acknowledgements

I would like to express great thanks to my supervisors Dr Roger Gifford and

Professor Ross McMurtrie for providing me with the opportunity to do this PhD. Dr

Stephen Bonser and Dr Terry Bolger also provided a great deal of encouragement, insightful comments and assistance in production of this thesis. Additional thanks must go again to Dr Stephen Bonser for his co-authorship of Chapter 6.

I would also like to thank the agencies responsible for the financial support throughout the project, The University of New South Wales, CSIRO Plant Industry,

The Cooperative Research Centre for Greenhouse Accounting, The Foundation for

Young Australians, and The Bureau of Rural Science. Without their assistance this project would not have been possible.

Thanks must also go to Dr Colin Jenkins and David Lewis for contributing greatly with analysis of leaf biochemistry, and CSIRO Discovery Centre for providing resources to me when asked at very short notice. Denys Garden kindly donated the grass seeds for the experiments. Emma Hely, Andrew Morrison, Dave Pritchard and

Angela Newey all volunteered their time with the set up and maintenance of the experiment. Thankyou, your assistance was invaluable.

I would also like to thank all the people who have contributed greatly to ensuring my personal wellbeing throughout the last 5 years. My family, Paul, Kathie,

Emma, Amanda, and Matthew Hely, have provided support and understanding throughout. Thanks also to devoted friends, Prue Leach, Fiona Hedgecoe, Michael

Treanor, Dave Pritchard, Danealle Lilley, and Paul Bywood. Colleagues, John

Kirkegaard, Angela Newey, Sarah Bruce, Megan Ryan, Astrid Volder, and Ed Cross, who gave me excellent advice and inspiration. A special thanks must also go to Simon

Thompson for being tolerant, helpful, and encouraging throughout the last 5 years.

Table of contents

Page

Abstract……………………………………………………………………… i

Acknowledgements ………………………………………………………… iv

Table of contents……………………………………………………………. v

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

1.1 Climate change predictions over the next 100 years………………… 1

1.2 Climate change research and plants………………….……………… 1

1.3 Climate change and Australian grasslands………………………….. 3

1.4 Novel ideas………………………………………………………….. 4

Chapter 2: Literature review……………………………………………… 6

2.1 General plant responses to elevated CO2…………………………… 6

2.2 Effect of water-availability on plant growth under elevated

CO2…………………………………………………………………. . 9

2.3 Effect of nutrient-availability on plant growth under elevated

CO2…………………………………………………………....……. . 10

2.4 Effect of elevated CO2 on competitive interactions ………………… 11

2.5 Pot and controlled environment experiments…………………..….... 15

2.6 Meta-analysis………………………………………………………… 16

2.7 Climate change in Australia…………………………………………. 17

2.8 Thesis outline………………………………………………………… 19

Chapter 3: The effect of elevated CO2 and water-limitation on the competitive interactions of C4 invasive species, E. curvula, and C3 native species, A. racemosa…………………………………………………………………….. 21

3.1 Overview of chapter…………………………………………………. 21

3.2 Introduction………………………………………………………….. 22

3.3 Methods and materials………………………………………………. 25

3.4 Results……………………………………………………………….. 34

3.4.1 The relative growth response to elevated CO2 by

E. curvula grown in competition with other E. curvula

plants, compared to when grown in competition with

A. racemosa plants ………………………………………….. 34

3.4.2 The below-ground effects of elevated CO2 and water-limitation on E.

curvula in competition with other E. curvula plants…………. 40

3.4.3 The below-ground effects of elevated CO2 and water-limitation

on competition between E. curvula with A. racemosa

plants…………………………………………………………. 42

3.5 Discussion…………………………………………………………… 47

3.5.1 The response of E. curvula to elevated CO2 and water-

limitation……………………………………………………… 47

3.5.2 The effects of competition on the responses of E. curvula to

elevated CO2 and water-limitation…………………………… 48

3.6 Conclusion………………………………………………………….… 52

vii

Chapter 4- Growth form and biomass allocation of C4 invasive species

E. curvula, C3 native species A. racemosa, and wheat species T. aestivum, in response to elevated CO2 and water-limitation…….………...……….. 54

4.1 Overview of chapter………………………………………………… 54

4.2 Introduction…………………………………………………………. 55

4.3 Methods and materials………………………………………………. 58

4.4 Results………………………………………………………………. 64

4.4.1 T. aestivum…………………………………………………. . 64

4.4.2 E. curvula……………………………………………………. 64

4.4.3 A. racemosa…………………………………………………. 65

4.5 Discussion………………………………………………………….. . 72

4.5.1 T. aestivum……………………………………………………. 72

4.5.2 E. curvula…………………………………………………….. . 73

4.5.3 A. racemosa…………………………………………………… 77

4.6 Implications for the competitive success of T. aestivum, E. curvula,

and A. racemosa……………………………………………………… 79

4.7 Conclusion…………………………………………………………… 80

Chapter 5- Plant physiological responses of E. curvula, A. racemosa, and T. aestivum under elevated CO2 and water-limited conditions….… 82

5.1 Overview of chapter………………………………………………... . 82

5.2 Introduction………………………………………………………….. 84

5.2.1 Leaf gas exchange measurements and plant responses……….. 85

5.3 Methods and materials…………………………………….………… 88

5.4 Results………………………………………………….……………. 91

5.4.1 T. aestivum…………………………………………………... 91

viii

5.4.2 E. curvula……………………………………………………. 97

5.4.3 A. racemosa………………………………………………….. 101

5.5 Discussion…………………………………………………………….. 107

5.5.1 T. aestivum………………………………………………….... 107

5.5.2 E. curvula…………………………………………………..… 108

5.5.3 A. racemosa………………………………………………..…. 113

5.6 Conclusion……………………………………………………….…… 116

Chapter 6- Extension of results: A meta-analysis of current research on the effects of elevated CO2 on plant responses………………………… 119

6.1 Overview of chapter…………………………………………………. 119

6.2 Introduction………………………………………………………….. 120

6.3 Materials and methods ……………………………………………… 123

6.4 Results ………………………………………………………………. 125

6.5 Discussion…………………………………………………………… 132

6.6 Conclusion…………………………………………………………... 134

Chapter 7- General conclusions and future research………………....….. 135

7.1 Overview of chapter………………………………………………….. 135

7.2 The responses of E. curvula and A. racemosa at elevated CO2…….. 135

7.3 The impact of external environmental variables ……………………. 137

7.4 The impact of species phenotypic variability..……………………… 138

7.5 Implications for Australian grasslands………………………………. 141

7.6 Future research………………………………………………………... 142

Bibliography…………………………………………………………………. 144

Appendix 1- Species list, metabolic pathway and references for species used in meta-analysis……………………………………………….. 154

Appendix 2- Source material used for the reconstruction of evolutionary relationships of species within families used in the comparative analyses……………………………………………………….. 157

List of tables

Chapter 3

Table 3.1 Summary of repeated measures ANOVA results for shoot

biomass of target plant. ……………………………………. 36

Table 3.2 Summary of ANOVA results for final target plant root

biomass of surrounding plants……………………………... 36

Table 3.3 Summary ANOVA results for surrounding shoot biomass

on a per-plant basis………………………………………… 36

Table 3.4 Summary ANOVA results for surrounding root biomass

on a per-plant basis………………………………………… 36

Table 3.5 The comparison of cumulative above-ground biomass

and final below-ground biomass of target E. curvula

plants with surrounding plants on a per-plant basis………... 37

Table 3.6 Summary repeated measures ANOVA results for the

Corrected Relative Competition Index (CRCI) of shoot biomass

comparing competitive ability of target E. curvula plants

between pots with and without root exclusion tubes……..... 45

Table 3.7 Summary ANOVA results for the Corrected Relative Competition

Index (RCI) of root biomass comparing competitive ability of

target E. curvula plants in pots with and without root

exclusion tubes………………………………………………. 45

Chapter 4

Table 4.1 Summary ANOVA results for cumulative whole-pot

shoot biomass……………………………………………….. 66

Table 4.2 Summary ANOVA results for final whole-pot shoot

biomass……………………………………………………… 66

Table 4.3 Summary of whole-pot biomass components of T. aestivum,

E. curvula and A. racemosa………………………………… 67

Table 4.4 Summary repeated measures ANOVA results for average

number of leaves of plants…………………………………... 68

Table 4.5 Summary repeated measures ANOVA results for average

plant height………………………………………………….. 68

Table 4.6 Summary information comparing duration, cutting frequency

and design of experiments in Chapter 3 and Chapter 4………. 75

Chapter 5

Table 5.1 Summary repeated measures ANOVA results for percent

nitrogen of leaves…………………………………………… 92

Table 5.2 Summary repeated measures ANOVA results for

transpiration of leaves………………………………………. 92

Table 5.3 Summary repeated measures ANOVA results for specific

leaf area ………..…………………………………………… 92

Table 5.4 T-test results for T. aestivum in ambient and elevated

CO2, dry and wet conditions………………………………… 93

Table 5.5 T-test results for A. racemosa in ambient and elevated

CO2, dry and wet conditions………………………………… 102

Table 5.6 Summary ANOVA results for assimilation at saturated

light………………………………………………………….. 106

Chapter 6

Table 6.1 Phylogenetic contrasts of families and sub-families used

in the meta-analysis…………………………………………... 128

xii

List of figures

Chapter 3

Figure 3.1 Species combinations of E. curvula and A. racemosa in

mixtures and monocultures with, and without root-exclusion

tubes….………………………………………………………. 29

Figure 3.2 Comparison of the effect of surrounding E. curvula plants

(monoculture) versus surrounding A. racemosa plants (mixture)

on target E. curvula biomass………………………….…….. 38

Figure 3.3 Whole-plant biomass enhancement ratios (BER) of E.

curvula target plants…………………………………………. 39

Figure 3.4 Comparison of the effect of surrounding E. curvula plants

on target shoot and root biomass with root interactions

(monoculture), versus without root interactions (monoculture

with tube) …………………………………………………… 41

Figure 3.5 Comparison of shoot and root biomass of target E. curvula

with root interactions (mixture), versus without root

interactions (mixture with tube)………………………………. 44

Figure 3.6 Comparison of Corrected Relative Competition Index (CRCI)

of target E. curvula plants between treatments with and

without root-exclusion tubes ………………………………… 46

Chapter 4

Figure 4.1 Experimental layout of species…………………………….. 63

Figure 4.2 a) Leaf number and b) height of T. aestivum………………… 69

Figure 4.3 a) Leaf number and b) height of E. curvula…………………. 70

Figure 4.4 a) Leaf number and b) height of A. racemosa………………. 71

xiii

Chapter 5

Figure 5.1 T. aestivum assimilation versus chamber CO2 concentration,

in a) dry conditions and, b) wet conditions.…………………… 94

Figure 5.2 T. aestivum assimilation versus intercellular CO2

concentration, in a) dry conditions and, b) wet

conditions………………………………………………………. 95

Figure 5.3 Physiological parameters of T. aestivum, a) percent leaf

nitrogen, b) specific leaf area and, c) transpiration…………. 96

Figure 5.4 E. curvula assimilation versus growth cabinets CO2

concentration in a) dry conditions and b) wet conditions……. 98

Figure 5.5 E. curvula assimilation versus intercellular CO2

concentration in a) dry conditions and b) wet conditions ……. 99

Figure 5.6 Physiological parameters of E. curvula, a) percent leaf

nitrogen, b) specific leaf area and, c) transpiration…….…... .. 100

Figure 5.7 A. racemosa assimilation versus growth cabinets CO2

concentration in a) dry conditions and, b) wet conditions…….. 103

Figure 5.8 A. racemosa assimilation versus intercellular CO2

concentration in a) dry conditions and, b) wet conditions …… 104

Figure 5.9 Physiological parameters of A. racemosa, a) percent leaf

nitrogen, b) specific leaf area and, c) transpiration…...….….. 105

Chapter 6

Figure 6.1 Comparison of geometric means of biomass

enhancement ratios (BER) at elevated CO2 for C3

and C4 plants across all taxa presented in a) a box plot

showing spread of data, b) pair-wise comparisons of

log BER between the 9 family and sub-family pairs

xiv

where C4 photosynthesis has evolved independently………. 127

Figure 6.2 Box plot comparison of phylogenetic contrasts of mean BER

across Pooideaes (Pooid), Panicoideaes (Panic) and

Chlorideaes (Chlor) grass taxas in CO2 enrichment

experiments…… ……………………………………………. 129

Figure 6.3 Correlation analysis between BER and growth rate of non-

independent species responses ……………………………… 130

Figure 6.4 Independent contrast analyses of the relationship between

growth rate and BER across all species used in the

meta-analysis…………………………………………………. 131

List of images

Chapter 3

Image 3.1 The comparative biomass of A. racemosa (left) and E. curvula

(right) at the same age, CO2, and watering treatments.…….. 50

Chapter 1- Introduction

1.1 Climate change predictions over the next 100 years

Projections of carbon dioxide concentrations [CO2] in the atmosphere for the next 100 years indicate an increase from current day concentrations to between 490 and 1260 ppm. This will equate to a 75 % to 350 % increase in CO2 since the year

1750. Associated with this increase in CO2, temperature will increase between 1.4 and

5.8ºC with an ocean level rise of between 0.09 and 0.88 metres (Houghton et al.

2001). These changes in global climate will have a substantial effect on the natural environment and human systems, leading to irreversible damage (Hughes 2003).

Evidence of climate change impacts has already been apparent. Data obtained from ice cores as well as visible signs such as shrinkage of glaciers, thawing of permafrost, earlier flowering of trees etc. all provide compelling evidence that the global climate is changing (McCarthy et al. 2001). These changes have the potential to lead to flow- on effects at the ecosystem-scale, causing widespread shifts in species diversity and ultimately impacting our way of life (Hughes 2003).

There is however, limited understanding of the ecosystem-scale flow-on effects resulting from climate change, making mitigation and adaptation difficult. By increasing our understanding of the impacts of climate change on ecosystems, possible negative effects can be reduced.

1.2 Climate change research and plants

Plants are the basis of terrestrial ecosystems and are directly affected by the projected increases in elevated CO2 and temperature associated with climate change

(Atwell et al.1999). This is because the physiological processes of plants rely directly on CO2, warmth and water for growth. Changes in such factors as a result of climate

2 change will therefore alter biomass accumulation and in turn affect species abundances, causing impacts at a much larger scale. These impacts, while difficult to predict, could potentially have enormous effects on native and managed ecosystems and could threaten food production across the world (Hughes 2003).

Significant research over the past 30 years has developed a strong knowledge

base of single plant responses to single factors associated with elevated [CO2].

However, as scale and complexity increases, the certainty with which predictions can be made of plant responses decreases. Communities and ecosystems contain a high level of interaction and therefore variability, making it difficult to gain a clear understanding of climate change impacts at the ecosystem level.

Even at the single-species level, only a few consistent trends in species responses have emerged. Functional-type classifications have provided some consistency in predictions of plant responses to elevated CO2. Plants using the carbon-

3 (C3) metabolic pathway, on average, are found to produce higher growth enhancements under favourable conditions than plants using the carbon-4 pathway

(C4) (Poorter & Navas 2003). Species-specific differences and interactions with resources and environmental conditions have however, led to numerous exceptions to this classification (Bazzaz & McConnaughay 1992; Ghannoum et al. 2000; Körner

2006). In addition, single species experiments provide little insight into whole- community and ecosystem responses and may not be indicative of elevated CO2 impacts on wider biota (Bazzaz & McConnaughay 1992).

More recently, but to a lesser extent, plant experiments have endeavoured to include multiple species with a variety of interacting variables (Lilley et al. 2001;

Wand et al. 2001; Ghannoum et al. 2002). While this gives a better indication of plant responses in natural systems, the increase in complexity makes trends increasingly

3 difficult to discern. As a result very little headway has been made in determining the

effects of elevated CO2 on wider plant systems.

1.3 Climate change and Australian grasslands

Climate change has already been observed in Australia. In the last century alone, annual average temperature has increased by 0.7°C and annual average rainfall has decreased in the south-west, and south east of Australia (Australian Bureau of

Meteorology 2006).

Australian agriculture is dominated by systems operating on the boundary of resource limits. With such vulnerable agricultural systems, changes in environmental conditions can lead to much larger implications (Hughes 2003). For this reason,

understanding the impacts of climate change and elevated CO2 in Australia agriculture is important for maintaining natural and agricultural viability.

Grasslands occupy a large part of Australia’s productive land and are an important commodity to the Australian economy. Australian grasslands are also a source of rich and unique biodiversity, and have been identified by the

Intergovernmental Panel on Climate Change (IPCC) as a ‘vulnerable’ system under climate change (Houghton et al. 2001). The vast majority of grassland ecosystems in

Australia are currently water-limited, making projected changes in rainfall patterns of great significance on viability, in addition to the impacts of elevated CO2.

Little research has however, been carried out on the effects of elevated CO2 on grassland ecosystems in Australia. The research that has been carried out is highly diverse and has produced results not consistent with previous classifications of plant responses, i.e. those based on metabolic pathway (Bolger et al. 1997; Ghannoum et al.

2001; Hely & Roxburgh 2005). With the regular co-occurrence of C3 and C4 species

4 in Australian grasslands, and the lack of consistency in results, little can be deduced from this existing research. It is apparent that there is a clear need for further research on the effects of elevated CO2 on Australian C3 and C4 plants.

1.4 Novel ideas

In consideration of the significant knowledge gaps outlined above, this thesis sought to address a specific interaction, where the effects of elevated CO2 were observed on a native C3, and an invasive C4 grassland species under water-limitation.

Increases in CO2 and temperature, accompanied by changes in precipitation patterns under climate change will impact plant productivity in Australia. Carbon dioxide, temperature and water-availability are fundamental drivers of resource-use and thus plant growth. This makes climate change an important issue for resource-limited systems (such as native grasslands) in Australia. Differential responses of species using the C3 and C4 metabolic pathways could also change natural balances which, in turn could affect viability and even the ability of a system to resist weed invasion

(Owensby et al. 1999; Ward et al. 1999; Wullschleger et al. 2002). This thesis examined the interaction of an example of a C3 and C4 grass species in order to increase the basic level of understanding of Australian grassland responses to climate change.

In addition, the use of a mechanistic approach for investigating plant responses through patterns of biomass allocation and detailed physiological measurements gave insight into the processes under-pinning biomass responses. These measurements are often overlooked in many climate change plant experiments, but are highly important in understanding the processes leading to plant responses.

A pot experiment was used in this investigation as a cost effective method to

5 address a working hypothesis whereby explicit control over external variables was possible. The limitations of pot experimentation are acknowledged however, and results are viewed in the context of such limitations.

The combining of the results found in this experiment with results from other research in a meta-analysis of plant growth responses to elevated CO2 conditions, allowed the determination of relationships between evolved plant traits such as metabolic pathway, growth rate and responsiveness to elevated CO2. By using this information to better design future experiments, stronger and more reliable predictions of plant responses to climate change can be made.

Chapter 2- Literature review

2.1 General plant responses to elevated CO2

The accumulation of biomass in plants is controlled by many variables. At the instantaneous level these variables are light, temperature, water and [CO2]. Over longer time-scales, availability of nutrients, interference from other plants, genotypic differences, patterns of biomass allocation and resource-use efficiency all play important roles in plant growth (Atwell et al. 1999). Most, if not all of these processes may be impacted by climate change. Global light dimming (caused by an increase of cloudiness and/or atmospheric particles), increased temperature and [CO2], changes in precipitation, increased extreme climatic events and higher rates of decomposition and therefore nutrient turnover, are all examples of factors that could affect basic plant processes under climate change (Hughes 2003).

Given the reliance of plants on atmospheric CO2 as a source of carbon, the effect of elevated CO2 on photosynthetic processes has received a large amount of research interest. A doubling in atmospheric CO2 concentration is predicted to occur in the next 50 to 100 years (Houghton et al. 2001). Carbon dioxide not only acts as substrate for rubisco, it is a catalyst in the photosynthetic carbon reduction cycle occurring in the leaf. Research has clearly shown that higher concentrations of CO2 in the atmosphere lead to an instantaneous increase in photosynthesis (Farquhar &

Sharkey 1982; Körner 2006). Such an increase in photosynthesis is expected then to result in increased biomass accumulation if interactions with time and resources do not undermine this biomass response. Due to the implications of such a biomass enhancement under elevated CO2 in natural and managed land systems, this area has received much scientific interest.

It has been commonly observed in past research that a difference in the instantaneous photosynthetic stimulation occurs between plants of differing metabolic pathways (Ainsworth & Long 2005). C4 plants in general, receive a lower photosynthetic enhancement as a result of a CO2 concentrating mechanism in the bundle sheath of their leaf mesophyll. Plants using the C3 metabolic pathway do not have this ability, leading to greater sensitivity to increases in atmospheric [CO2]. The reduced sensitivity of the C4 metabolic pathway has been the primary explanation for many reported lower responses of photosynthesis under elevated CO2 (Pearcy &

Ehleringer 1984; Soinit & Patterson 1984; Ehleringer et al. 1997). The lowered sensitivity of C4 photosynthesis compared to C3 to elevated CO2 is thus expected to lead to lower enhancements of biomass. A meta- analysis of 150 observations in 80 experiments by Poorter & Navas (2003) showed total biomass enhancements of 12 and 45% at twice ambient [CO2] by C4 and C3 plants respectively (Poorter & Navas

2003).

There have however, been many exceptions to the classification of responses of plants based on metabolic pathway (i.e. Wand et al. 1999; Poorter & Navas 2003;

Ainsworth & Long 2005). Such exceptions have demonstrated that C4 plants have the ability to show a photosynthetic and biomass enhancement equal to C3 plants under elevated CO2. In fact, Wand et al. (1991) found comparable stimulation of C3 and C4 plants in biomass (44% and 33% respectively) and assimilation (33% and 25% respectively) as a result of elevated CO2. Results however, indicate that there is considerable complexity in responses from plant experiments using elevated CO2 conditions. Despite the thousands of papers dedicated to understanding such complexities, there is a conspicuous lack of consistency between leaf-level photosynthetic stimulation at raised CO2 concentration and the resulting increase in

8 biomass reported in the literature (Körner 2006).

In a research paper on general plant responses to elevated CO2, plant responses were averaged across 120 experiments (Ainsworth & Long 2005). Light-saturated photosynthetic rates of grasses, legumes and trees were 38%, 21% and 47%, respectively. The corresponding above-ground dry matter production of C3 grasses, legumes and trees were 28%, 10% and 24%. The discrepancy between photosynthetic enhancements and biomass enhancements was simply hypothesised to be a culmination of many environmental and physiological factors demonstrating yet again, the need for further understanding of plant responses to elevated CO2.

The variation in plant responses in past research emphasises the need for careful consideration of the effects of factors other than physiological, on plant responses. Indirect factors such as differing resources and environmental conditions have been found to produce a great array of results (Campbell et al.1995; Teughels et al.1995; De Graaff et al. 2006; Körner 2006; Ainsworth & Long 2005; Dukes et al.

2005; Hikosaka et al. 2005). Light, temperature, water, nutrients and interference from other plants can all make large contributions to the variation seen in biomass measures. Ultimately however, instantaneous photosynthetic rates only lead to increased biomass if there is co-ordination between resources and physiological processes ( Ghannoum et al. 2000; Körner 2006). This can be best explained by a source-sink analogy. An increase in photosynthesis will lead to an increase in the demand for basic resources such as water and nitrogen (source). If the plant’s demand for these resources is commensurate with rates of photosynthesis, then carbohydrates are distributed evenly throughout the leaves and stems of the plant and growth occurs

(sink). If ‘sinks’ are not able to keep up with the increased photosynthesis, negative feedback results, leading to a down-regulation of photosynthesis (Arp 1991). The

9 occurrence of photosynthetic down-regulation or ‘acclimation’ has been observed by a number of authors (Sage et al. 1989; Arp 1991; Woodrow 1994; Xu et al. 1994;

Moore et al. 1999; Adam et al. 2000; Ghannoum et al. 2000). Understanding the mechanisms of photosynthetic down-regulation however, has been a challenging task.

Currently, the known causes of photosynthetic down-regulation range from water and thus nutrient-limitation, low light levels, carbohydrate accumulation, restricted rooting volume and species-specific differences, but as yet there is no clear general consensus on the causes (Ghannoum et al. 2001; Ellsworth et al. 2004). Understanding photosynthetic down-regulation is a high research priority however, due to the consequences it may have for crops, pastures and therefore food production.

2.2 Effect of water-availability on plant growth under elevated CO2

In addition to the effects of elevated CO2 on instantaneous photosynthesis, elevated CO2 has a significant effect on stomatal conductance. At higher concentrations of atmospheric CO2, stomatal conductance is reduced (Ainsworth &

Long 2005). This limits the amount of water lost during transpiration and as a result can prolong the growing season of plants in water-limited environments (Morison

1993). The water-conservation effect at elevated CO2 is a characteristic of both metabolic pathways and there are a growing number of cases where, under water- limitation, the growth response of C4 plants equals or exceeds that of C3 plants.

Research by Owensby et al. (1999) showed strong evidence of this in a tall grass prairie in the United States. Biomass enhancement in dry years was greater for the C4 grasses compared to C3 grasses under elevated CO2.

While the water-conservation property at elevated CO2 is able to explain most of the larger than expected responses in C4 plants, it is not able to explain all. A

number of experiments have reported increases in biomass by CO2-enriched C4 plants under well-watered conditions (Soinit & Patterson 1984; Ziska & Bunce 1997;

Ghannoum & Conroy 1998; Wand et al. 1999). Although C4 photosynthesis is believed to be saturated at current levels of atmospheric CO2, other factors could potentially lead to growth responses irrespective of water levels. Examples of such factors are vascular bundle sheath leakiness, fixation of CO2 directly in the bundle sheath, C3-like photosynthesis occurring in younger leaves and increased leaf temperature (Tremmel & Patterson 1993; Ziska & Bunce 1999; Ghannoum et al.

2000). Once again, due to the species-specific nature of the responses however, there is limited knowledge of the role of these other factors under elevated CO2 at the community or ecosystem-level.

2.3 Effect of nutrient-availability on plant growth under elevated CO2

The acquisition of nutrients plays an important part in photosynthetic processes with soil-nutrient heterogeneity strongly influencing the development of plant individuals as well as plant communities (Maestre & Reynolds 2006). Research on the effects of nutrient-availability has shown highly variable results. In C3 plants grown under elevated CO2 there is evidence that biomass accumulation is higher in nutrient-rich conditions then in nutrient-poor conditions (Bowler et al. 1996). In the few studies conducted on the C4 metabolic pathway, contrasting results have been observed (Wong & Osmond 1991). For both C3 and C4 plants however, there is by no means consensus on these trends, and many exceptions have been published

(Ghannoum & Conroy 1998; Maestre & Reynolds 2006). The effect of nitrogen on plant growth under elevated CO2 still requires greater attention. In addition, uptake of nutrients is linked to the uptake of water, whereby water and nutrients are acquired by

the roots in a mutually dependant fashion (Atwell et al. 1999). Elevated CO2 is predicted to cause changes in water relations in plants, which could therefore be an important factor affecting nutrient acquisition under climate change (Ghannoum &

Conroy 1998; Ainsworth & Long 2005).

The complexity and range of responses increase with the inclusion of other interacting and indirect effects of elevated CO2. Studies of interactions of elevated

CO2, temperature, water, nutrients, and light supply have revealed a high level of variability in plant responses (Poorter & Nagel 2000). Despite a need to broaden the scale of climate-change experiments to include greater detail in interacting factors and species responses, unfortunately no scientist has successfully addressed all interactions of environment and species in situ collectively.

2.4 Effect of elevated CO2 on competitive interactions

Factors affecting plant growth and resource acquisition affect the ability to occupy space and thus interactions with other plants (Poorter & Nagel 2000). The direct and indirect effects of elevated atmospheric CO2 on plant growth may alter its ability to suppress or resist competition from surrounding plants and therefore impact on competitive interactions. The most fundamental effect elevated CO2 could have on competitive interactions is as a result of changes in demand for resources. The capture of resources from the environment by plants is reliant on the capacity of roots and shoots to efficiently occupy space and uptake those resources (Poorter & Nagel 2000).

Under elevated CO2, some plants may experience an increase in demand for nutrients, while others may experience an increase in resource-efficiency (Shaw et al. 2002;

Maestre & Reynolds 2006). In either case, changes in biomass allocation to structures that will balance the uptake of the most limiting resource will best advantage the plant

under changed CO2 conditions. For example, limited below-ground resources tend to cause a diversion of biomass to the roots in order to maintain the functional balance between above and below-ground processes. If effective, the resulting increase in biomass of an individual plant may lead to a competitive advantage relative to its neighbour. The same would be true for a plant in an environment rich in below- ground nutrients. In this case, emphasis on light capture would be favoured and successful diversion of biomass to shoots could lead again to a relative competitive advantage (Harper 1977; Ryser et al. 2000).

The re-allocation of biomass by plant individuals in response to the environment is termed phenotypic variation. Phenotypic variability in biomass allocation can be highly important for resource uptake and changes in biomass allocation to improve resource uptake can thus lead to changes in the intensity of competition in plant communities under elevated CO2 (Bazzaz & McConnaughay

1992). By improving resource capture, biomass accumulation is enhanced, consequently leading to greater occupation of space and associated increased competitive ability. Overtopping in above ground competition and space-occupation in below ground competition resulting from the ability to plastically shift biomass may therefore become a potential selective advantage at elevated atmospheric CO2.

Generally speaking, invasive plants are reported to possess traits pertaining to high levels of phenotypic variability (Hastwell & Panetta 2005). Phenotypic variability can be expressed in a number of ways, but the re-allocation of biomass to roots or shoots in response to environment has been observed regularly in invasive species (Stewart & Potvin 1996; Dukes & Mooney 1999; Ghannoum et al. 2000;

Poorter & Nagel 2000; Thompson et al. 2001). An increase in demand for resources expected to occur as a result of climate change would therefore favour plants such as

13 weeds which exhibit the ability to adjust to changing resource demands (Hastwell &

Panetta 2005). As a result there is now some speculation that climate change will favour invasive species in ecosystems particularly after disturbances. Because the frequency of disturbance is predicted to increase under climate change, the potential risk of increased weed invasion is a significant issue for agriculture.

There is much debate over many of the definitions and traits that govern plant interactions, especially when interactions are observed over long timeframes or when comparisons of individual plant performance with populations in larger plant communities are made (Goldberg 1996). Generally speaking however, when comparing plants in communities at equilibrium, the ability of a plant to deny resources to neighbours and/or the ability of a plant to tolerate resource limitation imposed by neighbours, equates to greater species dominance and thus a superior competitive ability. At the individual level, competitive ability can then be quantitively defined as the per unit effect of one experimental unit on another (either in number of individuals, biomass, or other measure of abundance) (Goldberg 1996).

The ability of a plant to either deny neighbouring plants resources, or tolerate resource limitation imposed by neighbours is not always equal. In other words, the ability of a plant to suppress another is not always of the same magnitude as its ability to tolerate suppression. Therefore, to examine the mechanisms of competitive ability of a species of interest, the design of the experiment must allow the separation of species suppression, from a species’ ability to tolerate suppression.

The Target-Neighbour design is considered one of the simplest forms of the groups known as ‘Additive’ competition designs. The design allows both the observation of the ability of ‘Target’ or ‘Focus’ plant to resist competition from surrounding plants, as well as the competitive effect of the target on surrounding

14 plants. The target plant biomass is measured when in competition with a set density of plants of the same (monoculture) and different (mixture) species (Gibson et al 1999).

The Target-Neighbour design has significant advantages over other competition designs (such as the commonly used ‘Replacement Series’ or

‘Substitutive’ competition design (Gibson et al. 1999)). Replacement design competition experiments have a number of weaknesses which significantly reduce their power. For example, replacement designs lack the ability to remove size biases and also rely on a fixed, often arbitrarily chosen density of plants. Any variation in the size of plants can thus cause a bias not easily separated from the effects of competitive interactions (Gibson et al. 1999). The Target-Neighbour design overcomes size inequalities between experimental units by allowing measurements to be taken initially, at the time of harvesting as well as throughout the course of the experiment, giving it advantages over replacement designs (Goldberg & Werner 1983; Gurevitch et al. 2002). The Target-Neighbour design also maintains the same density of the same species of plants across all experimental replicates allowing the assessment of traits governing observed competitive interactions (Goldberg & Landa 1991). The limitation of the Target-Neighbour lies in its inability to address wider questions on the competitive outcome of plant interactions, such as the response of multi-species mixtures on plant competitive ability. The design is however, economic and powerful for interpreting features of interspecific competition. Given the above attributes of the

Target-Neighbour design, it is most appropriate for answering experimental questions asked in this thesis.

2.5 Pot and controlled environment experiments

Climate-change research necessitates the use of controlled environments. Only

in rare instances have experiments been carried out under elevated CO2 levels in natural field situations (Wand et al. 1999; Stock et al. 2005). While Free-Air CO2

Enrichment (FACE) is a system similar to field conditions, it is also associated with high up-front and maintenance costs. FACE has limitations with maintaining uniform

CO2 levels and with including other climate-change variables such as projected air temperature levels. Controlled environment experiments however, have the ability to reduce variability experienced in the field and allow control over interactions between plants and environment, as well as over soil, air and water conditions (Gifford 2004).

The trade-off however is the significant departure of conditions from the natural field environment.

Controlled environment experiments also require the use of pots for containment of the plants. In the past, it has been suggested that pot experiments may reduce the level of response to elevated CO2 by restricting rooting volume, which in turn limits sink development and thus reduces the accumulation of biomass (Arp

1991). It has been widely reported in the literature however, that pot experiments are able to validly assess the underlying mechanism of plants responses. Pot experiments remove variation and complex interacting variables in order to focus on a specific responses or mechanisms. Validation of these responses must then be carried out in the field. Research by Navas et al. (1999) and Gifford (2004) reported that pot and growth chambers are essential in identifying the underlying processes for plant responses to climate change variables and that trends found in pot experiments can be replicated more often than not in the field. As a result pot experiments can provide a sound basis for understanding plant processes and are highly useful for investigating processes in plants, granted the necessary caveats for their use are in place.

2.6 Meta-analysis

A meta-analysis was used in this study as a method for comparing responses to elevated CO2, including those in this study with those reported in the literature. Meta- analyses of data from existing work are powerful tools for identifying trends, and understanding their causes. The difficulties lie however, in standardising data collected from experiments that may vary widely in experimental conditions, plant types and growth forms. The choice of a measurement for comparing plant response to elevated CO2 can be crucial in overcoming these issues of standardising data, despite being restricted by the data available. Growth rate was chosen for comparing plant responses to elevated CO2 in this study as it is the most widely reported measurement. With initial plant biomass rarely presented in the literature, the preferred choice of using relative growth rate is not possible. Large-comparative datasets examining plant growth rates increasingly use rates of plant growth rather than standardised or relative growth rates (e.g. Niklas & Enquist 2001; Niklas &

Enquist 2002). This is due to the fact that relative growth rate used in comparative plant studies fail to consider non-photosynthesising tissue in assessing plant growth.

Growth rate indexes such as relative growth rate make comparisons of actual plant biomass partitioning across taxa impossible (Zens & Webb 2002).

It is however, acknowledged that simple growth rate comparisons do not overcome all the problems associated with the standardising of data for a meta- analysis, but for the comparison of plant response in this study, it is well supported by other research and is a valid approach.

2.7 Climate change in Australia

The water-limited nature of the Australian landscape may contribute to greater

17 impacts from climate change on natural and agricultural systems compared to other countries (Hughes 2003). Despite this, a limited amount of research has been carried out on Australian systems and existing research fails to answer key questions relating to the sustainability of the Australian agricultural estate under climate change. Lilley et al. (2001), Hely & Roxburgh (2005), and Navas et al. (1999) have conducted the only research on multiple species combinations representative of plant systems in

Australia to date. Their studies have indicated that the impact of climate change on plant growth will cause changes in net biomass accumulation, relative abundances of species, and changes in the boundaries of certain ecosystems. The low nutrient content that is a common feature of Australian soils is also expected to intensify competition, which could favour more plastic weed species over natives. Increased weed invasion is a costly problem for the agricultural industry.

The settlement of Australia by Europeans over 200 years ago has resulted in the introduction of a great variety of invasive species into Australia. Noxious weeds in

Australia appear to have characteristically high growth rates and demonstrate a high level of phenotypic variability, both of which could prove advantageous under climate change (Hughes 2003). In addition, possible differing sensitivities of C3 and C4 plants may also impact the success of C3 and C4 weeds under climate change (Ziska &

Bunce 1997). Research on particular C3 and C4 native and invasive species could therefore provide insight into two important issues. Firstly, in examining whether inherent characteristics of high growth rates and phenotypic variability in weed species E. curvula enhances responses to elevated CO2, based on past research indicating that C4 plants yield smaller biomass enhancements under elevated CO2 conditions compared to C3 plants. Secondly, to determine whether the differing sensitivities of examples of plants using a C3 and C4 metabolic pathway to elevated

CO2 can override invasive and non-invasive traits.

A C4 weed recently declared as ‘regionally prohibited’ in certain areas of

Australia is Eragrostis curvula (African lovegrass) (Eurobodalla Shire Council website 2005). E. curvula has been used for grazing in north-east Australia, but has become invasive in the cooler, drier parts of south-eastern Australia. Its widespread distribution in south-eastern Australian native grasslands has caused concern due to its ability to out-compete native grasses. Research suggesting a favouring of weeds under elevated CO2 indicates that such species could be a significant threat to biodiversity

(Hely & Roxburgh 2005).

The impact of elevated CO2 on growth of E. curvula has been studied only once previously (Wand et al. 2001). E. curvula grown in open top chambers under

CO2 conditions of 360ppm and 660ppm showed a 71% increase in photosynthetic rate and up to a 31% increase in non-leaf biomass in measurements taken between 6-12 weeks of plant growth. Wand et al (2001) proposed that the responses were a result of weedy characteristics such as high relative growth rate and heightened nitrogen-use efficiency at elevated [CO2] compared to ambient [CO2]. Such traits may explain the dominance of E. curvula in water-limited grasslands in Australia.

In grasslands located in south-eastern Australia some native species are experiencing competitive pressure from E. curvula species (Williamson & Faithfull

1998). An example of such species is Austrodanthonia or Wallaby grass. There have been several studies on the effects of elevated CO2 on the genus Austrodanthonia, but there is no known research carried out on the species Austrodanthonia racemosa.

In research by Lutze & Gifford (1998), responses of isolated Austrodanthonia richardsonii plants showed contrasting results to those found in research by Hely &

Roxburgh (2005) on Austrodanthonia racemosa. Lutze &Gifford (1998) compared

responses of plants grown in a glasshouse experiment running 37 days at CO2 levels of 365ppm and 735ppm respectively. Hely & Roxburgh (2005) used growth chambers to grow Austrodanthonia eriantha in mixtures and monocultures at 350ppm and

700ppm of CO2 concentration for 50 days. A. richardsonii had between a 28% and

103% increase in biomass (at low nitrogen and high nitrogen respectively) and an increase in nitrogen-use efficiency under elevated CO2. A. eriantha had no increase in plant volume (a non-destructive measure of plant biomass) or relative growth rate as a result of elevated CO2. The effects of temperature were incorporated into the experimental design by Hely & Roxburgh (2005) however, whereby a 3°C increase was imposed during the day and night. The interactive effect of temperature with CO2 was used as a possible explanation for the lack of positive response by plants to elevated atmospheric CO2 alone in Hely & Roxburgh (2005). Lutze & Gifford (1998) did not incorporate warming into the experimental design.

Research on the Panicum and Poaceae generas have also shown similar levels of complex responses (Wilson 1975; Ghannoum & Conroy 1998; Wand et al. 1999;

Rudmann et al. 2002), and are further examples of the difficulty in predicting plant responses when multiple factors are incorporated in experimental design. Use of the

Austrodanthonia genus in experimentation in this thesis may however provide further insight into the genus’ responses to elevated CO2.

2.8 Thesis outline

The impact of elevated levels of CO2 on photosynthesis, water-use efficiency, light, and nutrient acquisition has received significant research attention to date. Less attention has been devoted to the interaction of these factors with other important variables such as metabolic pathway, genotype, species-specific plant traits, and

20 multi-species combinations. There are even fewer studies examining the above factors on Australian grass species. This has resulted in a vast lack of solid information on the effects of elevated CO2 on Australian plant ecosystems needed for building resistance to negative, or taking advantage of positive climate-change impacts.

This thesis uses a mechanistic approach to understand the responses of C4 invasive species, E. curvula, and C3 native grass species, A. racemosa, to elevated

CO2 under water-limited conditions. Such an approach uses patterns of biomass allocation and measurements of physiological processes to understand measured growth responses. The project also enables close examination of the impacts of elevated CO2 and water-limitation on above- and below-ground competition between the two species, giving insight into the impacts of elevated CO2 and water-limitation on competitive interactions between an example of a weed, and a native species.

Research questions addressed in this thesis can therefore be summarised as follows:

1) To examine the responses to elevated CO2 and water-limitation by C4 invasive

grass, E. curvula, and C3 native Australian grass, A. racemosa.

2) To establish whether these responses have an impact on the competitive

interactions of the two species.

3) To understand how patterns of biomass allocation and photosynthetic

processes affect the responses of A. racemosa and E. curvula to elevated CO2.

4) To examine the evolutionary histories of A. racemosa and E. curvula and other

grass species commonly used in elevated CO2 experiments in order to help

explain broader plant responses to elevated CO2.

Chapter 3- The effect of elevated CO2 and water-limitation on the competitive interactions of C4 invasive species, E. curvula, with C3 native species, A. racemosa

3.1 Overview of chapter

A pot experiment was conducted in growth cabinets to compare the effect of elevated CO2 and water-limitation on competitive interactions between the invasive

C4 grassland plant, E. curvula, and the native Australian C3 grassland plant, A. racemosa. The experiment was run at ambient levels of CO2 maintained at 390 parts per million by volume (ppm) compared to elevated levels of 740 ppm. Imposed restricted water supply conditions consisted of gradually drying down the soil to 30 % available soil water (ASW) followed by re-wetting to 50 % ASW (ASW is the amount of soil water that is available for use by the plant. In other words, it is the water held between field capacity and the so-called plant wilting point). Well-watered conditions for the experiment consisted of gradually drying the soil down to 50 % followed by rewetting to 95 % ASW. Plants were grown in mixtures and monocultures using a

‘Target- Neighbour’ competition design (Goldberg & Werner 1983) and porous root- exclusion tubes were used to separate competition belowground. The arc sin of the

Relative Neighbour Effect (CRCI) (Markham & Chanway 1996; Callaway et al.

2002; Oksanen et al. 2006) was then used to assess E. curvula’s ability to suppress and tolerate suppression in mixtures and monocultures.

E. curvula showed a significant increase in root and shoot biomass in response to elevated CO2 in both wet and dry treatments when surrounded by A. racemosa plants. The response to elevated CO2 was diminished when E. curvula target plants were surrounded by other E. curvula plants. This showed that in addition to A. racemosa exerting little competitive pressure on E. curvula compared to pressure 22

exerted by other E. curvula plants, elevated CO2 may have altered the relative importance of resources required for biomass accumulation. CRCI values further supported these observations whereby E. curvula had an increase in competitive ability at elevated CO2 and water limitation.

The positive response of E. curvula to elevated CO2 in wet and dry conditions is contrary to much past research where biomass enhancements in C4 plants were found to occur only in water-limited conditions. Findings here where in support of recent literature suggesting that photosynthesis by C4 plants can increase under elevated CO2, leading to a positive biomass response. Morphological characteristics related to root and shoot biomass allocation were speculated to have also contributed to the responses in the C4 species.

3.2 Introduction

The classification of plants based on metabolic pathway has previously been reported to be a robust predictor of growth under elevated CO2 conditions (Ainsworth

& Long 2005). Plants using the C4 metabolic pathway are believed to have less sensitivity to increases of CO2 concentration in the atmosphere compared to C3 metabolism, due to the CO2-concentrating mechanism in leaf mesophyll (Pearcy &

Ehleringer 1984). Recent research however, has found that exceptions to this result are so numerous that alternative explanations are needed to explain the variation observed in plant responses to elevated CO2 (Ghannoum et al. 2000).

While consistent trends in C3 and C4 plant responses to elevated CO2 are evident in the literature, the complex interactions of CO2 concentration with photosynthetic processes, and with external environmental variables, produce highly variable plant responses to elevated CO2 across species and functional groups. There 23 is much uncertainty as to how external environmental variables (such as light, water and nutrient supply) interact to cause variations in plant responses and thus the flow- on, larger-scale effects that result. Understanding the role of these environmental variables on elevated CO2 effect is still required in order to make broader generalisations of plant responses under climate change (Poorter & Navas 2003).

The predicted changes in patterns of precipitation resulting from climate change (Hughes 2003) will likely have a large impact on Australian ecosystems

(Houghton et al. 2001). Photosynthetic processes depend on both water availability and concentrations of atmospheric CO2 (Farquhar & Sharkey 1982). The impacts of changes in CO2 and water availability resulting from climate change could therefore be large for natural and agricultural systems, particularly those that operate on the boundary of resource limits (Hughes 2003). In grassland ecosystems, where plants using different metabolic pathways compete with one another for limiting resources, a change in resource supply rate can alter the outcome of competitive interactions between species with different requirements for that resource (Tilman 1988).

At elevated levels of atmospheric CO2 associated with climate change, some plants experience an increase in demand for nutrients, while others may experience an increase in resource-use efficiency (Shaw et al. 2002; Maestre & Reynolds 2006). In either case, changes to resource demands by plants can lead to changes in the intensity of competition (Bazzaz & McConnaughay 1992; Wolfe et al. 1998; Poorter & Navas

2003). Plants with traits that help to maintain or increase their competitive ability when resource availability is changed, would thus be expected to increase in abundance under climate-change conditions.

The strategies used by invasive species which may improve competitive ability vary significantly with environment. Traits such as high growth rates and the 24 ability to plastically adapt to an environment may improve a species competitive ability (Ziska & Bunce 1997; Hastwell & Panetta 2005). Competitive ability can be defined as the capacity of a plant to deny resources to neighbours and/or the capacity of a plant to tolerate resource limitations imposed by neighbours in communities at equilibrium. At the individual level, competitive ability can be quantitively defined as the per unit effect of one experimental unit on another (either in number of individuals, biomass, or other measure of abundance) (Goldberg 1996). Under elevated [CO2], where environmental conditions will be more variable and competition intensity between plants is changed, the ability to adjust to environment is likely to be advantageous. There is potential therefore for weed species to prosper under certain conditions associated with climate change.

A Target-Neighbour competition design was used to establish whether invasive species, E. curvula, possessed traits that may confer greater competitive ability under elevated CO2 relative to native species A. racemosa. The design allows both the observation of the ability of a ‘Target’ or ‘Focus’ plant to resist competition from surrounding plants, as well as the competitive effect of the target on surrounding plants. The target plant biomass is measured when in competition with a set density of plants of the same (monoculture) and different (mixture) species (Gibson et al 1999).

The Target-Neighbour competition design was used in this experiment to assess the ability of a target C4 E. curvula species to suppress and tolerate suppression from surrounding E. curvula plants or from native C3 plant A. racemosa.

Measures of above and below-ground biomass at elevated CO2 and water- limitation were then corrected for community productivity by using a corrected index of the Relative Neighbour Effect (Markham & Chanway 1996; Callaway et al. 2002;

Oksanen et al. 2006). The Corrected Relative Competitive Index or CRCI 25 standardises the performance of a target species in mixtures relative to its performance in monocultures to account for environmental differences not related to inter-specific and intra- specific competition. The findings of the experiment were then interpreted in the context of the possible implications of elevated atmospheric

CO2 on Australian grassland species’ competitive ability.

3.3 Methods and materials

Species selection

The choice of species was based on selecting a native and an invasive grass species found to co-occur in the grasslands in and around the Australian Capital

Territory (ACT). The species chosen are not archetypes, and little (if any) past research has been carried out on the species. However A. racemosa is an example of a common native grass found in south-eastern Australia. Invasive C4 grass, E. curvula, was chosen due to its co-occurrence with A. racemosa and proliferation in south- eastern Australian grasslands. The species choice was based on gaining insight into a competitive interaction of selected Australian grassland species that could possibly be affected by changes in growth responses in the field as a result of elevated atmospheric CO2.

A. racemosa (R.Br) H.P Linder, (a Wallaby grass) is a cool-season plant that flowers with white fluffy seed heads and is a common constituent of grasslands in and around the ACT. A. racemosa is productive in pastures but can be susceptible to competition from invasive species (Eddy et al. 1998).

E. curvula (Schrad) Nees, (African Lovegrass) was chosen for its invasive properties and also because it is dominant in grasslands in south-eastern Australia.

This perennial species, native to Africa, was first introduced to the Australian state of 26

Victoria in the 1900s. Now it is found in most regions of Australia and has gained regionally prohibited status in these areas (Eurobodalla Shire Council Website 2005).

It is a densely tufted 20-120 cm high plant with high fecundity. E. curvula has been known to grow in a broad range of soils and climates and is highly persistent and unpalatable to stock in mature stages of its life cycle (Williamson et al.1998).

Controlled environment conditions

The experiment was conducted in two Canberra Phytotron LB controlled environment cabinets artificially lit with twenty-eight 80-Watt fluorescent light tubes

(Morse & Evans 1962). Light levels were maintained at 350 to 400 μmol m-2 s-1 over a

16-hour photoperiod. Carbon dioxide in the elevated CO2 treatment was controlled at

740 ppm using a Binos® 100 Non-dispersive Infrared Gas Analyser (Leybold-Heraus,

Hanau Germany) and purpose-made electronic control system. All CO2 and temperature levels were recorded using Series 600 Datataker® Data logger (Datataker,

Melbourne, Australia) measuring CO2 and temperature levels at intervals of 10 minutes through the entire experiment. Temperature levels were always within 1 degree of the target temperatures of 22°C day and 18°C night and averaged 21.94°C and 18.40°C respectively. Plants and CO2 conditions were exchanged between the two cabinets every week to avoid possible problems of pseudo-replication.

Plant germination

Seeds were germinated in 35 cm × 30 cm × 5 cm deep trays filled with standard potting mix in ambient and elevated CO2 controlled cabinets. They were sown at 2 mm depth and watered morning and night. Enough seed of each species was sown to enable selection of homogeneous seedlings for transplantation into pots after

10 days. 27

Pot treatments

The pots were filled with homogenised treated soil collected from the

Ginninderra Experimental Station at CSIRO Plant Industry, Canberra, Australia. It was a fine sandy loam with a pH of 5.9 (Kirkegaard et al. 1999). The pots were 25 cm high, 15 cm wide at the top, and 13 cm at the bottom. A layer of sand was placed in the bottom to aid drainage, and after soil had settled to 2 cm below the top of the pot, soil volume was calculated to be about 4530 cm3. Two and a half grams of slow- release Osmocote fertiliser were added to the surface of each of the pots at the beginning of the experiment, with root-exclusion tubes receiving a ninth of the total fertiliser. This amount was calculated to be approximately 300 kg of nitrogen per hectare, which is the typical amount applied to maintain improved pastural systems in

Australia (Watson et al. 2000).

The effect of competition on E. curvula was assessed using two competition treatments, monocultures and mixtures. Monoculture pots contained 9 E. curvula plants sown on a 3 x 3 grid design. Mixture treatments contained a centred ‘target’ E. curvula plant surrounded by eight A. racemosa plants. The arrangement of plants within individual pots was in accordance with the ‘Target-Neighbour’ competition design (Harper 1977; Goldberg & Werner 1983). The Target-Neighbour Competition design examines the performance of the centred or target plant in the presence of a set density of either the same or different surrounding species on a per-amount basis.

Plants were randomly assigned to one of the sixteen treatment combinations (4 competition × 2 water × 2 CO2). Each treatment combination was replicated 3 times and pots were randomly positioned within growth cabinets. The 4 competition treatments can be observed in Figure 3.1.

Belowground competition was assessed using root-exclusion tubes in two of the treatments. After a layer of sand was placed at the bottom of the pots, root- 28 exclusion tubes were centred within the sand and then filled with soil. Tube placement on the bottom of the pots was such that there was no gap between it and the underlying gauze. This ensured that roots could not grow out from underneath the tube bottom into the surrounding soil. 29

1. 2. 3. 4.

C4 E. curvula Root-exclusion tube

C3 A. racemosa

Figure 3.1 Species combinations of E. curvula and A. racemosa in mixtures and monocultures with, and without root-exclusion tubes. Treatment 1- Monoculture of C4 invasive grass, E. curvula, 2- Mixture of E. curvula target plant surrounded by C3 native, A. racemosa plants, 3- Monoculture of E. curvula with a root-exclusion tube, 4- Mixture of a E. curvula target plant surrounded by A. racemosa with a root- exclusion tube.

Root-exclusion tubes consisted of a 5 cm outside diameter by 25 cm long piece of polyvinyl chloride (PVC) pipe. The tube was 3mm thick, containing 10 holes of 1 cm in diameter covered in 300 μm gauze. The 300 μm gauze prevented the penetration of roots, but allowed the free flow of water and nutrients between the target and surrounding plants following a watering event. Selection of the tube diameter for the root exclusion tube was based on ensuring a larger per-plant volume of soil for the surrounding plants compared to the volume of the target plant so that the surrounding plants were given sufficient resources to inflict a competitive influence on the target plant during the course of the experiment. The root-exclusion tube partitioned off 340 cm3of soil compared to the outside 8 plants sharing 4190 cm3 of soil giving outside plants 524 cm3 each.

The effect of water supply was assessed using 2 treatments, Dry and Wet.

These were based on the percentage available soil water of each pot and using wilting point and field capacity as the respective lower and upper limits. Wilting point was measured using a pressure plate apparatus at -15 bars on disturbed soil samples sieved to 2 mm. Field capacity of the soil was determined by saturating a pot with water and covering the top surface to prevent evaporation. It was then allowed to drain freely with the pot being weighed daily. Field capacity was determined as the soil water content at which the pot’s weight had stabilised. The wilting point of the soil was assigned 0 % ASW and field capacity was 100 % ASW. 'Dry' treatments were based on drying down to 30 % and watered up to 50 % ASW and 'Wet' treatments were dried down to 50 % and watered up to 95 % ASW. Pot watering was based on individually calculated weights using the dry soil volume at the beginning of the experiment. Every two to three days, all pots were weighed, and those below the lower water threshold for dry and wet treatments were watered up to their respective 31 upper thresholds. Watering was carried out in increments by filling pots up to the brim and allowing water to soak through before weighing again. This process was repeated until pot weight had reached the desired upper limit, water had soaked in and minimal dripping was occurring.

The drying down of soil based on percent soil moisture was chosen instead of a set addition rate over the course of the experiment so as to maintain consistent moisture levels. These water treatments remove the water-conservation effects of elevated CO2 because plants with high transpiration rate (e.g. ambient CO2 plants) are watered more frequently than plants with low transpiration rate (e.g. elevated CO2 plants). While such a watering treatment is not representative of precipitation inputs in natural ecosystems, it does allows clear differentiation between biomass enhancements caused by photosynthetic enhancement over those caused by enhanced water-use efficiency. There are however, significant challenges in maintaining comparable soil moisture conditions between pots and treatments as a result of varying growth rates, evaporation effects, and vertical distribution of moisture with in each pot. Some of these difficulties were counteracted by precise and regular watering of pots, as well as the use of sand in the bottom of pots to aid in drainage. It must be noted, however that some level of variation of moisture levels may have occurred between pots in the experiments.

The effect of CO2 was assessed using two CO2 treatments, Elevated (740 ppm) and Ambient (390 ppm). Levels were maintained within 5 % of the target CO2 concentration (740 ppm) in the elevated CO2 treatments, and the ambient CO2 stayed consistently within 5 % of 390 ppm by day. Higher than global average ambient CO2 levels can be attributed to the close proximity of the experimental site to the central 32 business district of the city of Canberra and to human traffic in the Phytotron from which air was extracted.

Measurements

Root, shoot and whole-plant biomass of individual plants were chosen to be measured in this experiment not only because it allows a direct measure of the amount and efficiency of carbon uptake in a plant, but also the examination of possible strategies used in gaining competitive advantages through biomass allocation

(Goldberg & Werner 1983).

The experiment ran for 6 months with destructive biomass harvests taken at the end of each month after the second month of growth. Plants were clipped to 2 cm above the soil surface and watered to their upper thresholds to avoid plant death. The target plant biomass clippings were separated from outside plant biomass for competition estimates calculated later, oven-dried and weighed. Roots were harvested only at the end of the experiment, with target plant roots separated as best as possible from outside plants and then also oven-dried and weighed.

Data analysis

Repeated measures analysis of variance (ANOVA) was used to assess significant (P < 0.05) differences in biomass measured monthly across treatments.

Repeated measures ANOVA is appropriate for these variables as shoot biomass from the same individuals was harvested 5 times over the duration of the experiment.

Interactions that were not significant were not included in the ANOVA model. The following effects were examined: Harvest date, Competition, Water, CO2,

Competition x Water, Competition x CO2, Water x CO2, Competition x Water x CO2.

All effects in the model were fixed and significant differences in final root biomass across treatments were assessed by ANOVA. The model for root biomass was the 33 same as above except harvest date was not included because repeated measures were not conducted on root biomass.

To correct for the effects of community productivity on the target plants examined in this thesis, the arc sin of the Relative Neighbour Effect (RNE) index was calculated for each mono/mix treatment pair (Markham & Chanway 1996; Callaway et al. 2002; Oksanen et al. 2006). The advantage of the arc sin RNE or Corrected

Relative Competition Index (CRCI) is it’s ability to overcome high variability in plant performance at the individual level when competition from surrounding plants is very high, or very low. CRCI also reduces the effects of fixed upper and lower limits when the effects of competition are linear (Oksanen et al. 2006). CRCI values were calculated for E. curvula species based on the biomass measures using the equation:

CRCI = Arc sin(Xmono – Xmix)/(max Xmono,Xmix)

Where Xmono is the performance (g) of plants in a monoculture and Xmix is the performance (g) of the plants in a mixture. This value is then divided by the monoculture or mixture with the highest biomass (max Xmono, Xmix) and arc sin converted.

Values close to 1.7 indicate strong competition from surrounding species and values close to or below -1.7 indicate weak competition from surrounding species.

Significant (P < 0.05) differences between means within each ANOVA model were assessed using Tukey’s HSD post hoc test (JMP, SAS Institute, Cary North

Carolina).

3.4 Results

3.4.1 The relative growth response to elevated CO2 by E. curvula grown in

competition with other E. curvula plants, compared to when grown in competition

with A. racemosa plants

Target E. curvula plant size was dependant on the identity of the species

surrounding it, irrespective of water and CO2 treatments (Table 3.1-3.5, Figure 3.2a).

The mean root and shoot biomasses across water and CO2 treatment of E. curvula

target plants were significantly higher when surrounded by A. racemosa plants

(mixture) compared to when surrounded by E. curvula plants (monoculture).

Biomass was 3.01g of root and 1.88g of shoot biomass compared to 0.17g of root and

0.19g of shoot biomass, respectively.

Under elevated CO2, a strong positive response by E. curvula target plants was apparent for above-ground (Figure 3.2b) and below-ground (Figure 3.2c) biomass only when in competition with A. racemosa, and was unresponsive when surrounded by other E. curvula plants. Surrounding E. curvula plants greatly suppressed the target

E. curvula plants in monocultures compared to mixtures.

The additional water in the wet treatments under ambient and elevated CO2 treatments increased the absolute above and below-ground biomass produced by the target E. curvula plants when surrounded by A. racemosa plants. However, there was no effect of watering regime when surrounded by other E. curvula plants (Figures

3.2d and 3.2e). This was also reflected in biomass enhancement ratios (defined as the cumulative shoot plus final root biomass in elevated CO2 divided by cumulative shoot biomass plus final root biomass at ambient CO2 in both wet and dry treatments) of target plant shoot material. Mixture treatments showed a 75 % (or 1.75 on Figure 3.3) 35 enhancement of biomass in comparison to no enhancement in monocultures (less than

1.0 on Figure 3.3). 36

Table 3.1 Summary of repeated measures ANOVA results for shoot biomass of target plant. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 4 4 34.155064 14.5139 <.0001 Competition 3 3 75.094403 42.5477 <.0001 Water 1 1 5.650605 9.6047 0.0022 CO2 1 1 6.915471 11.7547 0.0007 Competition*Water 3 3 3.737835 2.1178 0.0995 Water*CO2 1 1 0.363480 0.6178 0.4329 Competition*CO2 3 3 13.072211 7.4066 0.0001 Competition*Water*CO2 3 3 4.998590 2.8321 0.0397 Error 184 108.25015 Total 223

Table 3.2 Summary of ANOVA results for final target plant root biomass. Bold values represent P<0.05.

Source Nparm DF Sum of Squares F Ratio Prob > F Competition 4 4 197.16738 7.2838 0.0002 Water 1 1 1.58558 0.2343 0.6312 CO2 1 1 22.00640 3.2519 0.0795 Competition*Water 4 4 2.08545 0.0770 0.9888 Competition*CO2 4 4 37.81958 1.3971 0.2540 Water*CO2 1 1 0.60490 0.0894 0.7666 Competition*Water*CO2 4 4 1.86471 0.0689 0.9910 Error 37 250.39148 Total 56

Table 3.3 Summary ANOVA results for surrounding shoot biomass on a per-plant basis. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 4 4 1.5116040 13.9713 <.0001 Competition 3 3 1.5264604 18.8114 <.0001 Water 1 1 0.5123258 18.9410 <.0001 CO2 1 1 0.0049906 0.1845 0.6680 Competition*Water 3 3 0.0590120 0.7272 0.5369 Water*CO2 1 1 0.0803508 2.9706 0.0865 Competition*CO2 3 3 0.2626219 3.2364 0.0235 Competition*Water*CO2 3 3 0.0571661 0.7045 0.5505 Error 208 323.28751 Total 223

Table 3.4 Summary ANOVA results for surrounding root biomass on a per-plant basis. Bold values represent P<0.05

Source Nparm DF Sum of Squares F Ratio Prob > F Competition 4 4 2.0823555 6.1302 0.0008 Water 1 1 0.8407654 9.9005 0.0034 CO2 1 1 0.0106254 0.1251 0.7257 Competition*Water 4 4 0.5769129 1.6984 0.1731 Competition*CO2 4 4 0.0760341 0.2238 0.9232 Water*CO2 1 1 0.0004329 0.0051 0.9435 Competition*Water*CO2 4 4 0.1663973 0.4899 0.7431 Error 34 2.8873324 Total 53

Table 3.5 The comparison of cumulative above-ground biomass and final below-ground biomass of target E. curvula plants with surrounding plants on a per-plant basis. Values are the mean across all CO2 and water treatments. Different letters represent significant differences at the 0.05 level of probability and an asterisk denotes < 0.001 level of probability. Significant differences are assessed between values down each column and not across column pairs.

Species Combination Target C4 Target C4 Root Surrounding Surrounding Shoot Biomass (g) Shoot Biomass Root Biomass (g) Biomass (g) (g) Per- Plant Per -Plant E. curvula monoculture 0.19 (a) 0.17 (a) 0.36 ns 0.57 ns

E. curvula and A. 1.88 (c)* 3.01 (c)* 0.20 ns 0.37 ns racemosa mixture E. curvula monoculture 0.87 (b) 0.26 (a) 0.25 ns 0.46 ns with root-exclusion tube E. curvula and A. 0.96 (b) 1.18 (b) 0.14 ns 0.51 ns racemosa mixture with root-exclusion tube

5 4 Shoots a) Roots target 3 ** 2 1 0 -1 -2 **

Biomass (g) of E. curvula -3 -45 Ambient CO target 2 4 b) Elevated CO2 * 3 E. curvula E.

2 **

0 ns

Shoot biomass (g) of (g) biomass Shoot ** 5 ** Ambient CO target 4 2 c) Elevated CO2 3 ** E. curvula E. 2

0 ns Root Biomass (g) of of (g) Biomass Root ** 5 Monoculture Mixture d) 4 Ambient Dry Elevated Dry target (g) 3 * 2 E. curvula E. + 1

0 ns + Shoot biomass 5 Monculture Mixture e) target Ambient Wet 4 Elevated Wet ** 3 ** 2

0 ns * E. curvula curvula E. of biomass (g) Shoot

Monculture Mixture

Figure 3.2 Comparison of the effect of surrounding E. curvula plants (monoculture) versus surrounding A. racemosa plants (mixture) on target E. curvula biomass without the presence of root exclusion tubes. Shoot biomass in a) is the cumulative biomass of target E. curvula plants (g) and root biomass is the final biomass (g) at the end of the experiment across both CO2 and water treatments. Shoot biomass in b), c), d) and e) is the cumulative biomass and root biomass is the final biomass at the end of the experiment for each CO2 by water treatment. Error bars are ± 1 SE of the mean. P < 0.001 is represented by **, P < 0.05 is *, P < 0.07 is +, and not significant is ‘ns’. Significance levels across pairs are on the right of each bar comparison and below the line for between pair comparisons.

3.0

Dry 2.5 Wet

2.0 * * *

1.5

1.0

BER Shoots ns ns 0.5

0.0

-0.5 Mono. Mono. with tube Mixture Mixture with tube

Figure 3.3 Whole-plant biomass enhancement ratios (BER) of E. curvula target plants. BER is calculated by dividing cumulated shoot biomass (across all harvests) plus final root biomass in elevated CO2 by cumulative shoot biomass plus final root biomass in ambient CO2 for both wet and dry treatments. Line at 1.0 represents the point where no growth enhancement has occurred and asterisks denotes a significant increase or decrease in growth enhancement at P < 0.05 level, and not significant is ‘ns’. ‘Mono.’ indicates monoculture pots and ‘tube’ indicates a root-exclusion tube.

3.4.2 The below-ground effects of elevated CO2 and water-limitation on E. curvula in competition with other E. curvula plants

The effect of direct root-root interactions between E. curvula target plants and surrounding E. curvula plants was compared by examining the performance of the target plants in monoculture pots with, and without root-exclusion tubes (Figure 3.4a).

The use of the root-exclusion tubes did not change the total water and nutrients supplied to the group of nine plants, as the amount was equal for all plants regardless of whether they were inside or outside the tube.

The presence of the root-exclusion tube resulted in a significant increase of above-ground biomass of the target E. curvula plants compared with when the root- exclusion tube was not present (Figure 3.4a and Table 3.5). Final root biomass measurements produced more variable results. However, trends in root biomass were consistent with the above-ground measurements (Table 3.5).

The effects of elevated CO2 on the target E. curvula plants in the presence of root interactions resulted in no significant growth enhancement. When root interactions were prevented (through the presence of a root exclusion tube), there was an increase in E. curvula target plant shoot biomass (Figure 3.4b). E. curvula plants growing with root-exclusion tubes had on average, a 46 % increase in biomass under elevated CO2 compared with ambient CO2. This result dovetails with the previous section where the lower competition pressure from surrounding plants (which in this case was due to the presence of the root-exclusion tubes) saw a positive response to elevated CO2. The effect of the dry and wet treatments was also similar to the previous section’s results. Figure 3.4c and 3.4d show that a positive response to elevated CO2 was apparent in wet and dry treatments when root competition was excluded. Elevated CO2/wet treatments gained 30 % more biomass than the elevated/dry. This translates into a 75 % biomass enhancement for both water treatments in the presence of root-exclusion tubes (Figure 3.3).

3.4.3 The below-ground effects of elevated CO2 and water-limitation on competition between E. curvula with A. racemosa plants

The effects of the root-root interactions between target E. curvula plants when surrounded by A. racemosa plants can be compared by examining their performance in mixture pots with and without root-exclusion tubes (Figures 3.3 and 3.5a). The results here show that the presence of the root-exclusion tubes were not advantageous to the growth of target E. curvula plants (Table 3.5). Biomass of the target E. curvula plants in mixtures without the tube was 1.88 g and with a tube it was 0.96 g.

In the comparison of these treatments under elevated CO2 in Figure 3.5b an increase in above-ground biomass was observed only where the tube was not present.

Further analysis of the water/CO2 effect observed in Figure 3.5c and 3.5d showed that the addition of water increased shoot biomass in the ambient CO2 conditions with and without the presence of the tube. A trend however, of a reduction of biomass observed in the elevated CO2, wet treatments with root-exclusion tubes suggests that root growth may have been restricted in these treatments. Biomass enhancement ratios demonstrate the same trend where there was no biomass enhancement as a result of elevated CO2 where additional water was added (Figure 3.3).

Final root biomass in most cases, reflected above-ground responses, with minor explicable exceptions (Table 3.5). When the target plant was not being suppressed by surrounding plants, root biomass in the presence of root-exclusion tubes is limited to the volume of the tube. This can cause a confounding effect on above and below-ground biomass explaining the more than 50 % less root biomass observed in target plants of mixture pots with root-exclusion tube, compared to target plants in mixtures pots without root-exclusion tubes (Table 3.5). 43

These results are further supported by analysis of shoot biomass using CRCI values (Table 3.6 and Figure 3.6a and b). The ability of E. curvula to compete was increased when water was limiting and when CO2 was elevated. This is consistent with previous conclusions, indicating that E. curvula strongly suppressed A. racemosa competitors. Shoot biomass CRCI values in the presence of the root exclusion tubes were also consistent with biomass responses. Significant interactions in the ANOVA analysis between competition, water and CO2 showed the presence of the root exclusion tubes increased the competitive ability of E. curvula in all cases except for wet treatments of elevated CO2. At elevated CO2 and wet conditions, there was a significant reduction in the competitive ability of E. curvula (Figure 3.6b). This is in line with earlier observations of a possible root binding effect. The limited data collected from final root biomass measurements yielded no significant ANOVA results for root CRCI (Table 3.7).

Table 3.6 Summary of repeated measures ANOVA results for the CRCI of shoot biomass comparing competitive ability of target E. curvula plants between pots with and without root exclusion tubes. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 4 4 1.0280254 0.6383 0.6365 Competition 1 1 0.3640962 0.9043 0.3442 Water 1 1 1.9874782 4.9363 0.0289 CO2 1 1 1.5019413 3.7304 0.0566 Competition*Water 1 1 0.4950669 1.2296 0.2705 Water*CO2 1 1 1.2948707 3.2161 0.0764 Competition*CO2 1 1 2.1633399 5.3731 0.0228 Competition*Water*CO2 1 1 1.8113496 4.4989 0.0367 Error 88 35.430830 C. Total 99

Table 3.7 Summary ANOVA results for the CRCI of final root biomass, comparing competitive ability of target E. curvula plants in pots with and without root exclusion tubes.

Source Nparm DF Sum of Squares F Ratio Prob > F Competition 1 1 0.13015107 0.6810 0.4306 Water 1 1 0.21211767 .1099 0.3196 CO2 1 1 0.32687932 1.7103 0.2234 Competition*Water 1 1 0.14475507 0.7574 0.4068 Water*CO2 1 1 0.43835275 2.2936 0.1642 Competition*CO2 1 1 0.07361910 0.3852 0.5502 Competition*Water*CO2 1 1 0.06806039 0.3561 0.5654 Error 9 1.7200989 C. Total 16

3.5 Discussion

3.5.1 The response of E. curvula to elevated CO2 and water- limitation

A positive response in root and shoot biomass to elevated CO2 was observed in both wet and dry treatments for the C4 species E. curvula. In the past, growth responses of C4 plants to elevated CO2 have often been small as a result of the CO2- concentrating mechanism in their leaves (Hatch 1987). However, due to the water- conserving effect of stomatal closure under elevated CO2, C4 plants have been found on a number of occasions to increase biomass under water-limited conditions (Poorter

& Navas 2003).

The watering treatment used in this experiment (see section 3.3) removes any effects of conserved soil water by basing watering treatments on percentage content of the soil water rather than applying a set amount of water over the duration of the experiment. The biomass enhancement that was observed therefore, is likely to have resulted from photosynthetic stimulation caused by elevated levels of [CO2] in the cabinets. This is similar to a result found by Wand et al. (2001) whereby E. curvula plants grown in a glasshouse experiment had a positive growth enhancement in biomass, accompanied by photosynthetic up-regulation under elevated CO2.

Growth enhancements of C4 plants by as much as 28 % under elevated CO2 in well-watered conditions have been documented (Ghannoum et al. 1997; Ghannoum et al. 2001). These enhancements are theorised to result from factors such as increased intercellular CO2 concentration, improvements in water balance and increased leaf temperature (Ghannoum et al, 2000). Further, traits related to patterns of biomass allocation have been shown to significantly contribute to biomass enhancements of C4 plants under elevated CO2 (Ghannoum et al. 1997; Ziska & Bunce 1997; Wand et al.

1999; Ziska et al.1999; Poorter & Navas 2003). 48

In this experiment, the confounding effect of lesser soil volume for plants within root-exclusion tubes, may have biased biomass allocation patterns in some treatments. In monoculture treatments, however, elevated CO2 caused a significant increase in root biomass (Figure 3.2c). Increased allocation of biomass to the roots has been shown to increase nutrient uptake, and under elevated CO2 could lead to a biomass enhancement (Poorter & Nagel 2000). It can be therefore speculated that the observed increases in biomass under elevated CO2 in E. curvula was either a result of photosynthetic stimulation or species-specific morphological traits.

3.5.2 The effects of competition on the responses of E. curvula to elevated CO2 and water-limitation

The previous section demonstrated that E. curvula experienced a significant enhancement of biomass at elevated CO2 under both dry and wet conditions. There were however, instances where E. curvula did not experience a growth enhancement to elevated CO2. E. curvula plants did not show a biomass enhancement under elevated CO2 when surrounded and in competition with other E. curvula plants (Fig

3.3). The results suggest instead, that levels of competitive pressure played a strong role in the absence of E. curvula’s response. In the monoculture treatments, the competitive pressure on target E. curvula plants was far greater with neighbouring E. curvula plants than with neighbouring A. racemosa plants. Target E. curvula plants experienced a 75 % enhancement in shoot biomass when surrounded by A. racemosa plants, but experienced a 40 % decrease in shoot biomass when surrounded by E. curvula plants.

A larger stature is considered a significant competitive advantage in plant interactions whereby the greater volume of biomass has greater surface area for 49 absorbing resources (Bengtson et al. 1994). The greater biomass of E. curvula compared to A. racemosa would therefore indicate that E. curvula could exert greater competitive presence, as shown in Image 3.1 where the denser foliage of E. curvula is visible when compared to A. racemosa. The lack of competitive pressure exerted by

A. racemosa allowed E. curvula plants to take advantage of available resources resulting in a significant growth enhancement under elevated CO2. When E. curvula was surrounded by other E. curvula plants, it is speculated that competition for resources was much greater, limiting access and leading to a diminished growth enhancement. Such differences in competition intensity have also been found to alter the magnitude and direction of biomass enhancements to elevated CO2 across species

(Teughels et al. 1995; Connolly et al. 2001; Poorter & Navas 2003) and support the observed responses of E. curvula under strong competition here.

A lack of significant enhancement of biomass under elevated CO2 conditions in E. curvula target plants was also observed in mixture treatments with root- exclusion tubes (Figure 3.5a, 3.5b, and 3.5c. Here, once again the over-riding factor affecting E. curvula responses was competition for resources. The root-exclusion tube restricted overall plant growth thus making it less competitive with surrounding plants

(Figure 3.6). Although the purpose of the root-exclusion tube was to ultimately act as barrier between root interference on the target plant, in Figure 3.5a and 3.6 it was observed to disadvantage the target plant. 50

Image 3.1 The comparative biomass of A. racemosa (left photo) and E. curvula (right photo) at 12 weeks of age, elevated [CO2], and dry watering treatments. 51

A comparison of target E. curvula plant biomass in a mixture and monocultures of E. curvula with root-exclusion tubes, revealed that the presence of root-exclusion tubes in mixture treatment limited the total biomass of target E. curvula. The lesser competitive pressure asserted by A. racemosa and the small relative volume of the tube allowed the target plant to grow with very little interference by surrounding plants until the tube became filled, limiting further growth. Resources are likely to have been greatly reduced at this point, and growth was down-regulated in line with resource-availability. CRCI values for mixture treatments with root-exclusion tubes further support this (Figure 3.6). The competitive ability of E. curvula was noticeably less in the presence of root-exclusion tubes negating the positive effects of elevated CO2.

In contrast, root-exclusion tubes provided a good barrier to competition when

E. curvula target plants were surrounded by other E. curvula. Above-ground competition limited growth to some extent, which prevented the occurrence of root growth limitation in this treatment. As a result, the tube partitioned off enough resources to support a positive growth response under elevated CO2 (Figure 3.4a and b). Such a growth response resulted also in a significant increase in competitive ability (demonstrated by a decline in CRCI values, Figure 3.6) by E. curvula under wet conditions of elevated CO2,

This experiment provides evidence that resource-limitation (either imposed by competitive interference of surrounding plants, or restriction of rooting volume) can alter the magnitude and direction of biomass responses to elevated CO2. The amount of resources available to a plant can therefore have a strong impact on the level of biomass enhancement it can experience under elevated CO2.

3.6 Conclusion

The results from this experiment support mounting evidence for positive responses of biomass by C4 plants to elevated CO2 in wet and dry conditions. The results also suggest that the invasiveness of E. curvula may increase under elevated

CO2 leading to significant implications for the management of this species in the future.

Several other authors have reported greater biomass enhancements under elevated CO2 in weeds compared to native or crop species (Ziska & Bunce 1997;

Ghannoum et al. 2001; Weltzin et al. 2003). This is attributed to their greater physiological and morphological variability in response to environmental factors. The results found here suggest that elevated CO2 enhances biomass accumulation in E. curvula possibly through a stimulation of physiological processes and accompanied by changes in other morphological characteristics such as the allocation of biomass.

Continued research is required to validate these identified traits, which could potentially lead to greater invasiveness in Australian agriculture systems under climate change.

Competition and availability of resources was also seen to play a significant role in determining the magnitude of biomass response of E. curvula plants. In agricultural systems, where additional water and nutrients are provided to support crops and pastures, weeds with similar responses to E. curvula may become more abundant. In natural systems, where resources are more limiting, and competition between species is intense, weed species may not retain the same level of competitive advantage. Given the water conservation effects of elevated CO2 were not expressed in this experiment, there is still clearly the capacity for significant biomass responses to occur when water is conserved. Field experiments that address environmental and 53 species-specific factors will be required to confirm indications that the outcome of plant growth and competition may change under future elevated CO2 environments. 54

Chapter 4- Growth form and biomass allocation of C4 invasive species, E. curvula, C3 native, A. racemosa, and wheat species, T. aestivum in response to elevated CO2 and water-limitation

4.1 Overview of chapter

In Chapter 3, the effects of elevated CO2 and water-limitation were examined on a target E. curvula plant in mixture and monoculture pots. It was observed that E. curvula responded positively to elevated CO2 in wet and dry conditions provided space, water, and nutrients were not limiting.

In this chapter C4 invasive grass, E. curvula, was again examined along with native C3 species, A. racemosa, and wheat species, T. aestivum using a pot experiment where whole-pot biomass was measured. This experiment was conducted in controlled environments to examine patterns of biomass allocation in response to elevated CO2 and water-limited conditions. The experiment was run at ambient levels of CO2 averaging 390 ppm and elevated levels of 740 ppm. Water-limited conditions consisted of drying the soil down to 30 % and wetting up to 50 % ASW. Well- watered conditions involved drying the soil down to 50 % and wetted up to 95 %

ASW. Plants were grown in monocultures consisting of 9 plants in each pot. Biomass allocation was measured by comparing harvested above-ground leaf material, stem material and roots. This was complemented with non-destructive measures of leaf number and plant height.

T. aestivum was used for purposes of a control species in this experiment because it has been well studied in the past and has produced strong biomass increases to elevated CO2 in dry and wet conditions in growth chambers. The biomass enhancement of T. aestivum in wet and dry conditions was similar in magnitude to

55 previous research on the species. Based on this, the cabinet effects were not constraining the potential of E. curvula and A. racemosa to respond to elevated CO2.

A non-significant growth enhancement for T. aestivum under dry conditions however, was theorised to be a result of the high variation in measurements and low sample size resulting in large standard errors. This result may thus not have been a true indicator of biomass response to elevated CO2.

The C4 species, E. curvula, responded positively in above and below-ground biomass under elevated CO2 in both dry and wet treatments. However the C3 species,

A. racemosa, showed positive growth responses to elevated CO2 only under the wet conditions. Increases in root biomass, plant height, leaves and stems were consistent with the increases in biomass for E. curvula and T. aestivum, and in line with theories of optimality. Optimality theories suggest a diversion of biomass to areas of the plant needed to increase uptake of limiting resources. This trend did not occur in A. racemosa and the lack of CO2 response in the dry conditions was hypothesised to be caused by low soil moisture which in turn can limit nutrient uptake (Atwell et al.

1999).

Results in this project raise interesting ecological issues relating to the lack of

CO2 response by a C3 native and the presence of a positive CO2 response by a C4 invader. Biomass allocation appears to play a significant role in the above-ground responses.

4.2 Introduction

Chapter 3 illustrated that the growth of a target E. curvula plant under elevated

CO2 varied greatly depending on resource availability. Where water and nutrients were relatively abundant, E. curvula produced a significant biomass response to

elevated CO2. However, if these resources were limited, increased CO2 did not increase biomass accumulation.

Results in Chapter 3 are consistent with hypotheses that biomass will only be enhanced if photosynthetic stimulation is matched by increased resource-uptake

(Ghannoum et al. 2000). Environmental and physiological factors can affect the uptake of water and nutrients necessary to support enhanced photosynthesis, and factors such as optimal root/shoot biomass allocation leading to a biomass enhancement under elevated CO2 have received significant attention (Lutze & Gifford

1998; Wand et al. 1999; Poorter & Nagel 2000; McDonald et al. 2002; Chapin 2003)

Optimal root/shoot allocation of biomass has been suggested as varying greatly depending on environmental conditions (Atwell et al.1999). Generally speaking however, patterns of biomass allocation should maximise resource-uptake and hence the competitive ability compared to neighbours.

The effects of elevated atmospheric CO2 concentration on above and below- ground biomass can be reflected in a variety of different plant attributes. Increases in leaf thickness (through carbohydrate accumulation), number of stems, and proliferation of fine roots and fine root turnover have all been observed (Poorter &

Navas 2003; Ainsworth & Long 2005). Changes in these plant attributes can have large impacts on the competitive success of the plant when the demands for nutrients are increased at elevated CO2.

The competitive ability of a plant is determined by its potential to capture nutrients relative to its neighbours (Grime 1977; Harper 1977). For example, a plant that diverts a greater amount of biomass to the roots compared to shoots would be expected to increase the capture of below-ground resources, enhancing its competitive ability in a nutrient-poor environment. Similarly, a plant that diverts biomass to shoots

57 in an environment rich in below-ground nutrients, may enhance its ability to capture light, and CO2 resulting also in a competitive advantage (Harper 1977; Ryser et al.

2000). Patterns of biomass allocation can therefore indicate how environmental conditions are affecting the demand for resources and hence growth.

Particular environmental conditions may favour certain biomass allocation patterns leading to some plants gaining a competitive advantage. Demands for resources, and thus, optimal allocation strategies should change with increasing atmospheric CO2. The ability of a plant to plastically respond to these changes will play an important role in determining its competitive success (Teughels et al. 1996).

Patterns of biomass allocation can be a useful indicator of a plant’s ability to cope with variable environmental conditions and thus their competitive success.

This experiment addressed whether elevated CO2 and water-limitation can alter patterns of biomass allocation in E. curvula and A. racemosa plants. Studying the allocation of biomass in E. curvula may reveal plant growth strategies contributing to the results observed in Chapter 3 and in addition, may give insight into the effects of elevated CO2 and water-limitation on phenotypic variability and the competitive capacities of both species. The comparison of native grass, A. racemosa, and co- occurring invasive grass E. curvula, also provides information on the possible effects of elevated CO2 on species abundances in the field.

& Patterson 1984; Kimball et al. 1995; Wolf 2002; Derner et al. 2004). This species thus provides a benchmark for determining cabinet effects on the other two species used in this experiment.

4.3 Methods and materials

T. aestivum (cv. Janz) is an annual C3 semi-dwarf, spring bread wheat. In previous experiments, it has shown strong positive biomass responses to elevated CO2 in controlled environment experiments (Gifford 1979; Soinit & Patterson 1984;

Kimball et al. 1995; Wolf 2002; Derner et al. 2004). In this experiment T. aestivum was used as a control species to assess the cabinet effects on plant responses to CO2 of

E. curvula and A. racemosa.

E. curvula is an invasive perennial C4 species and A. racemosa is a C3 species native to Australia. These two species were chosen for their ecological significance in grasslands in the Australian Capital Territory (ACT) and surrounding regions. E. curvula (African Lovegrass) is native to Africa and was first introduced to the

Australian state of Victoria in the 1900s. It is now found in most regions of Australia and has reached regionally prohibited status in many of those areas (Eurobodalla

Shire Council Website 2005). E. curvula is a densely tufted 20-120 cm high plant known to have high fecundity. It can grow in a broad range of soils and climates and is highly persistent and unpalatable to stock in mature stages of its life cycle

(Williamson et al. 1998). E. curvula co-occurs in grasslands with A. racemosa and directly competes with this species.

A. racemosa (a Wallaby Grass) is a perennial most common in south-eastern

Australia. It is a cool-season plant with white fluffy inflorescence and is a common constituent of grasslands in and around the ACT. It is considered quite productive for grazing and pasture systems, but also susceptible to weed invasion in these systems

(Eddy et al. 1998).

The choice of species was based on gaining insight into a competitive interaction that could possibly be impacted by changes in growth responses in the

field as a result of elevated atmospheric CO2.

Controlled environment conditions

The experiment was carried out for 3 months in two Canberra Phytotron LB controlled environment cabinets, artificially lit with twenty-eight 80-Watt fluorescent light tubes (Morse & Evans 1962). The same cabinet conditions were maintained in this experiment as per the experiment in Chapter 3 (page 26). Photon-flux densities were maintained at between 350 and 400 μmol m-2 s-1 over a 16-hour photoperiod.

The effect of CO2 was assessed using two CO2 treatments, Elevated (740 ppm) and

Ambient (390 ppm). Carbon dioxide was controlled using a Binos® 100 Non- dispersive Infrared Gas Analyser (Leybold-Heraus, Hanau Germany) and a purpose- built electronic control system. CO2 levels were maintained within 5 % of the target

CO2 concentration in the elevated CO2 treatments, while the ambient cabinet CO2 stayed consistently within 5 % of 390 ppm in the day. Relatively high ambient CO2 levels can be attributed to the close proximity of the experimental site to the central business district of the city of Canberra and to human traffic in the Phytotron, from which air was extracted.

® All CO2 and temperature levels were recorded using Series 600 Datataker

Data logger (Datataker, Melbourne Australia) measuring CO2 and temperature levels at intervals of 10 minutes through the experiment. Temperature levels were maintained within 1 °C of the target temperatures of 22°C day and 18°C night, and with averages of 21.94°C and 18.40°C, respectively.

Pot treatments

A. racemosa, E. curvula and T. aestivum seeds were germinated in 35 cm × 30 cm × 5 cm trays filled with standard potting mix. They were sown at a depth of 2 mm

60 in sufficient numbers for selection of homogeneous seedlings when transplanted into pots. The seedlings were germinated in equal proportions in both ambient and elevated-CO2 cabinets. Seedlings were watered morning and night for two weeks, and were then transplanted into larger pots.

Larger pots were filled with homogenised treated soil collected from the

Ginninderra Experimental Station in Canberra, Australia. The soil was a fine sandy loam with a pH of 5.9 (Kirkegaard et al. 1999). The pots were 25 cm high and 15 cm wide at the top and 13 cm wide at the bottom. A layer of sand was placed in the bottom to aid drainage. Liquid nutrient was added on a 3-weekly basis in substitution of their normal daily water application for that day using ‘Hoaglands’ solution

(Hewitt 1966). This amount was calculated to be approximately 300 kg of nitrogen per hectare per year, which is the typical amount applied to maintain improved pastural systems in south-east Australia (Watson et al. 2000).

Three monocultures were used with 9 plants of each species per pot. The effect of water was assessed using two watering treatments, Dry and Wet. These were based on the percentage ASW of each pot using wilting point and field capacity as the respective lower and upper limits as explained in Section 3.3 (pg 25). The drying down of soil based on percent soil moisture was chosen over a set addition rate over the course of the experiment in order to maintain consistent moisture levels and remove the water-conservation effects of elevated CO2. While such a watering treatment is not representative of precipitation inputs in nature, it does allows clear differentiation between biomass enhancements caused by photosynthetic stimulation over those caused by enhanced water-use efficiency.

There are also significant challenges in maintaining comparable soil moisture conditions between pots and treatments as a result of varying growth rates, evaporation effects and vertical distribution of moisture within each pot. Some of

61 these difficulties were counteracted by precise and regular watering of pots, as well as the use of sand in the bottom of pots to aid in drainage. It must be noted, however that some level of variation of moisture levels may have occurred between pots in the experiments.

The pot treatments were replicated 5 times in ambient and elevated CO2, dry and wet conditions. T. aestivum was the exception. Due to space limitation, it was decided that T. aestivum would only have 2 replicates in each CO2 and watering treatment.

A total of 48 pots were used in the experiment (20 pots for E. curvula and A. racemosa, and 8 for T. aestivum). Problems of pseudo-replication were reduced by replicating the whole experiment twice temporally with treatments rotated between the two cabinets. Pots were randomly placed in the cabinets with pots and CO2 conditions switched between cabinets weekly to avoid cabinet effects. Monoculture pot layout can be seen in Figure 4.1.

Measurements and data analysis

After 6 weeks of growth, shoots of E. curvula and A. racemosa were harvested by clipping to 3 cm and dried in a 70º C oven for 7 days. T. aestivum was not harvested at this time and was allowed to reach maturity before harvesting with other plants in the second harvest at 12 weeks. At 12 weeks, all three species were harvested for both shoot and root material, oven-dried then weighed.

Data were analysed using JMP Statistical package version 5, multi-factor

ANOVA analysis (JMP, SAS Institute, Cary North Carolina). Repeated measures

ANOVA was used for biomass traits, and non-destructive measurements to assess the significance (P < 0.05) of CO2, water, and the interaction between CO2 and water.

The following factors were assessed: Species, CO2, Water, CO2 × Water, CO2 ×

Species, Water × Species, CO2 × Water × Species. Comparisons of above-ground measures of biomass are made on the sum of the two harvests. Biomass enhancement ratios were calculated by dividing the mean species whole-pot biomass for roots and shoots in elevated CO2, wet and dry conditions, by the mean species biomass for roots and shoots in ambient CO2, wet and dry conditions. Root percent values were expressed as the quotient of root biomass by total biomass multiplied by 100.

Significant (P < 0.05) differences between means within each ANOVA model were assessed using Tukey’s HSD post hoc test (JMP, SAS Institute, Cary North Carolina).

E. curvula (C ) A. racemosa (C ) T. aestivum (C ) 4 3 3

Figure 4.1 Experimental layout of species. Design consisted of monocultures of three species, E. curvula, A. racemosa, and T. aestivum. Nine, pre-emerged homogeneous seedlings were selected for sowing into a 3 × 3 grid configuration in each pot. All seedlings were the same age and size upon sowing into pots.

4.4 Results

4.4.1 T. aestivum

Biomass of T. aestivum significantly increased under elevated CO2 in roots, shoots and whole-plant measurements in the wet conditions only (Table 4.1, 4.2 and

4.3). Wet conditions had a mean overall enhancement of biomass by 52 % (P < 0.001) and there was an accompanying increase in absolute root and shoot biomass measurements with increasing CO2. There was a similar trend in dry conditions.

However, the biomass enhancement was not significant.

Measurements of leaf number showed high levels of variation in T. aestivum data and large variability in both water treatments obscured any trends (Table 4.4,

4.5). Plant height and biomass measurements were in agreement with biomass results at week 8 (Figure 4.2b), where elevated CO2/wet treatments produced significantly taller plants compared to those in the ambient/control treatments. Differences were lost, however, by the end of the 3-month experiment when T. aestivum plants reached maturity and plant heights were similar across treatments.

4.4.2 E. curvula

A significant increase in whole-pot biomass as a result of elevated CO2 was observed under elevated CO2 in dry and wet treatments for E. curvula plants (Table

4.3). The biomass enhancement at elevated CO2 was between 25 and 35 % in wet and dry treatments respectively, and the CO2 enhancement across water treatments was 28

% (Table 4.3).

Absolute biomass increased in both roots and shoots under elevated CO2.

However, there was significantly less root biomass in wet treatments compared to dry

(P < 0.05). Root fraction was not significantly different between wet and dry

treatments at elevated CO2 (Table 4.3).

Measurements of height and number of leaves (Figure 4.3a and b) show responses that are in agreement with the biomass increases. In addition, leaf number recovered to pre-harvest levels within 2 weeks of harvesting in elevated CO2 conditions whereas dry and wet ambient CO2 conditions took an extra 2 weeks to reach the pre-harvest leaf number.

4.4.3 A. racemosa

Under elevated CO2, A. racemosa plants showed a significant increase in whole-pot shoot and whole-plant biomass in wet treatments but not in dry treatments

(Table 4.3). While elevated CO2 increased biomass by 14 % averaged across both watering treatments, dry conditions of elevated CO2 showed only minor (and not significant) enhancements shown particularly in the roots.

Significant increases in biomass occurred predominantly in the shoots of wet, elevated CO2 plants (27 %) and even though root biomass was higher in wet conditions compared to dry, there was no enhancement of root biomass as a result of elevated CO2 (Table 4.3).

A. racemosa produced a greater number of leaves in wet treatments at both ambient and elevated CO2 conditions (Figure 4.4a and b). Overall, A. racemosa’s recovery of leaf number after harvest was quicker than E. curvula plants.

Plant height was significantly greater at elevated CO2 conditions (Figure 4.4b) making height the only measurement that showed a positive effect of elevated CO2 in both water conditions.

Table 4.1 Summary ANOVA results for cumulative whole pot shoot biomass. Bold values represent P<0.05.

Source Nparm DF Sum of Squares F Ratio Prob > F CO2 1 1 238.17077 12.5394 0.0007 Water 1 1 232.41085 12.2362 0.0008 Species 2 2 9.19594 0.2421 0.7855 CO2*Water 1 1 15.79664 0.8317 0.3644 Water*Species 2 2 68.57831 1.8053 0.1707 CO2*Species 2 2 28.28483 0.7446 0.4780 CO2*Water*Species 2 2 1.61110 0.0424 0.9585 Error 84 1595.4779 Total 95

Table 4.2 Summary ANOVA results for final whole pot root biomass. Bold values represent P<0.05.

Source Nparm DF Sum of Squares F Ratio Prob > F CO2 1 1 26.84747 3.0900 0.0824 Water 1 1 3.05332 0.3514 0.5549 Species 2 2 342.98351 19.7376 <.0001 CO2*Water 1 1 0.00377 0.0004 0.9834 Water*Species 2 2 59.17313 3.4052 0.0378 CO2*Species 2 2 14.82169 0.8529 0.4298 CO2*Water*Species 2 2 0.71234 0.0410 0.9599 Error 84 729.8395 Total 95

Table 4.3 Summary of whole-pot biomass components of T. aestivum, E. curvula and A. racemosa. Root material is the whole-pot biomass at the final harvest, shoot material is the sum of the two harvests. Biomass enhancement ratio (BER) is the biomass in elevated CO2 wet and dry conditions divided by the biomass in respective ambient CO2 wet and dry conditions. Significance for BER is derived from the ANOVA for above-ground, below-ground and, whole-plant biomass for each species. The ‘mean’ column represents the mean effect of CO2 on biomass averaged across water treatment. Units in the body of the table are grams of dry matter (g) per pot. P < 0.001 is represented with **, P < 0.05 is *, P < 0.10 is +, and not significant either by ‘ns’ or unmarked (significant results mean that BER is significantly different from unity).

Dry Wet Mean Roots Shoots Root + % Root Roots Shoots Root +% Root Root +

CO2 shoot shoot shoot T. aestivum Ambient Mean 2.09 6.63 8.72 23.97 2.66 10.49 13.15 20.22 10.94 S.E 0.39 1.15 1.32 0.39 1.15 1.32 1.32 Elevated Mean 3.15 9.25 12.4 25.4 4.35 15.69 20.04 21.71 16.23 S.E 0.39 1.15 1.32 0.39 1.15 1.32 1.32 BER Mean 1.51 ns 1.39 ns 1.42 ns 1.63 * 1.49 * 1.52 ** 1.48 *

E. curvula AmbientMean 7.99 12.09 20.09 39.77 6.54 13.4 19.94 32.8 20.02 S.E 0.6 1.02 1.55 0.6 1.02 1.55 1.55 ElevatedMean 9.99 15.24 25.24 39.588.35 17.9 26.25 31.81 25.74 S.E 0.6 1.02 1.55 0.6 1.02 1.55 1.55 BER Mean 1.25* 1.26* 1.26* 1.28+ 1.34** 1.32* 1.28*

A. racemosa Ambient Mean 4.36 10.4 14.76 29.54 6.36 13.92 20.28 31.36 17.52 S.E 0.34 0.81 1.02 0.34 0.81 1.02 1.02 Elevated Mean 4.73 11.29 16.01 29.54 6.39 17.63 24.02 26.6 20.02 S.E 0.34 0.81 1.02 0.34 0.81 1.02 1.02 BER Mean 1.08 ns 1.09 ns 1.08 ns 1.01 ns 1.27 ** 1.19 * 1.14 *

Table 4.4 Summary repeated measures ANOVA results for average number of leaves of plants. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 7 7 58958.400 20.2213 <.0001 CO2 1 1 714.063 10.1923 0.0015 Water 1 1 1010.389 14.4219 0.0002 Species 2 2 18424.302 131.4906 <.0001 CO2* Water 1 1 45.373 0.6476 0.4212 Water *Species 2 2 984.482 7.0261 0.0010 CO2*Species 2 2 446.164 3.1842 0.0420 CO2* Water *Species 2 2 33.622 0.2400 0.7867 Error 590 31116.90 C. Total 685

Table 4.5 Summary repeated measures ANOVA results for average plant height. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 7 7 70694.828 158.9996 <.0001 CO2 1 1 1731.679 7.2630 <.0001 Water 1 1 399.905 6.2960 0.0123 Species 2 2 3137.242 6959 <.0001 CO2* Water 1 1 32.316 0.5088 0.4759 Water *Species 2 2 25.210 0.1985 0.8200 CO2*Species 2 2 322.583 2.5393 0.0797 CO2* Water*Species 2 2 64.093 0.5045 0.6040 Error 614 23909.63 Total 709

14 a) Ambient CO2 Dry 12 ns Ambient CO2 Wet Elevated CO Dry 10 2 Elevated CO2 Wet 8 T. aestivum T.

Mean number of leaves leaves of number Mean -2

60-4 b) ns

50 (cm) 40

T. aestivum T. aestivum 30 Height Height 20

10 Wk 3 Wk 4 Wk 5 Wk 6 Wk 8 Wk 9 Wk 10 Wk 12

Time

Figure 4.2 Leaf number a) and height b) of T. aestivum. Ambient CO2 (circles) is 390 ppm, and Elevated (triangles) is 740 ppm. Dry treatments (filled) were dried down to 30 % and watered up to 50 % ASW and Wet treatments (open) were dried down to 50 % and watered up to 95 % of ASW. Values represent the mean of the measurements and error bars are ± 1 SE of the mean. ‘ns’ indicates a lack of significant difference between CO2 and water treatments within measurement week.

60 a) AmbientAmbient CO CO2 2Dry Dry b 50 AmbientAmbient CO CO2 2Wet Wet Plants harvested to 3cm ElevatedElevated CO CO2 2Dry Dry b ElevatedElevated CO CO Wet Wet 2 2 c E. curvula E. 40 b b 30 a b b a b ab b a 20 ab a a a a a 10 a Mean number of leaves leaves of number Mean

600 b b) Plants harvested b 50 to 3cm b a

(cm) 40 b b 30 b a

E. curvula a a 20

Height b

10 a

0 Wk 3 Wk 4 Wk 5 Wk 6 Wk 8 Wk 9 Wk 10 Wk 12 Time

Figure 4.3 Leaf number a) and height b) of E. curvula. Ambient CO2 (circles) is 390 ppm, and Elevated (triangles) is 740 ppm. Dry treatments (filled) were dried down to 30 % and watered up to 50 % ASW and Wet treatments (open) were dried down to 50 % and watered up to 95 % of ASW. Mean values with different letters are significantly different (P<0.05) between treatments within the measurement week.

70 a) Ambient CO2 Dry 60 c Ambient CO2 Wet Elevated CO Dry 2 a 50 Elevated CO Wet b 2 Plants harvested to 3cm A. racemosa A. b 40 b a

30 b b a b a

20 ab Missingab values a a a 10 a Mean number of leaves Figure 4.4 A. raemosa mean pot height over time (cm) b) 600 0 b b) Plants harvested 50 to 3cm b a b

(cm) 40 ab

a 30 A. racemosa A. 20 Height

0 0 Wk 3Wk 4Wk 5Wk 6Wk 8Wk 9Wk 10Wk 12 Time

Figure 4.4 Leaf number a) and height b) of A. racemosa. Ambient CO2 (circles) is 390 ppm, and Elevated CO2 (triangles) is 740 ppm. Dry treatments (filled) were dried down to 30 % and watered up to 50 % ASW and Wet treatments (open) were dried down to 50 % and watered up to 95 % of ASW. Mean values with different letters are significantly different (P<0.05) between treatments within the measurement week.

4.5 Discussion

In Chapter 3, a positive increase in biomass by E. curvula under elevated CO2 was evident under both wet and dry conditions, a finding supported by recent research

(Ghannoum et al. 1997; Ziska & Bunce 1997; Wand et al. 1999; Ziska et al.1999;

Poorter & Navas 2003). In the previous chapter, positive responses in biomass were hypothesised to be caused by several factors: enhancement of physiological processes or morphological characteristics such as biomass allocation. Further information was needed however, to determine which of the above were the drivers of the observed responses.

In this chapter, patterns of biomass allocation of E. curvula in comparison to

A. racemosa were examined. T. aestivum was also observed in this chapter as a means to compare the effects of the growth cabinets. The history of significant enhancements of growth by T. aestivum under elevated CO2 in controlled environments (Gifford

1979; Soinit & Patterson 1984; Kimball et al. 1995; Wolf 2002; Derner et al. 2004) can give an indication of whether cabinet conditions could affect the magnitude of growth response of E. curvula and A. racemosa. Consequently, T. aestivum responses are examined first, followed by E. curvula, and then A. racemosa.

4.5.1 T. aestivum

T. aestivum showed a positive growth enhancement (by up to 50 %) at elevated CO2 in both wet and dry treatments. However, the enhancement was significant only in wet treatments (Table 4.3). Dry treatments produced quite variable results and the low sample size (n = 2) restricted the interpretation of differences across treatments. Despite this, the magnitude of elevated CO2 enhancement observed in the results is comparable with past experimental results for T. aestivum grown

under elevated CO2 in cabinet conditions. It can therefore be deduced that cabinet effects in this experiment were not significant to the point where cabinet effects could influence the validity of results for E. curvula and A. racemosa.

4.5.2 E. curvula

Significant enhancements in biomass by the C4 plant, E. curvula, at elevated

CO2 under dry and wet conditions have been hypothesised to be caused by combinations of physiological and morphological characteristics (Wand 1999;

Ghannoum et al. 2001). This chapter looks at the role of morphological characteristics such as biomass allocation and development in the responses of E. curvula under elevated CO2 and water-limitation.

The results showed a significant enhancement of whole-pot biomass in response to elevated [CO2] in both wet and dry treatments for E. curvula plants (Table

4.3). Biomass enhancement was approximately 20 % in dry and 30 % in wet conditions. In Chapter 3, E. curvula target, and whole pot biomass in monocultures

(i.e. under intense competition for space and resources) did not show an enhancement under elevated CO2 in either wet or dry conditions. Differences in experimental duration, harvest frequency, and the presence or absence of root exclusion tubes

(summarised in Table 4.6), is likely to have contributed greatly to some of the irregularity in responses. However, E. curvula target plants under less pressure to access resources (i.e. in the presence of root-exclusion tube or surrounded by a species less competitive) showed comparable responses to the previous chapter, thus indicating a resource availability effect.

Optimality theories predict that plants should plastically shift biomass to maximise uptake of the most limiting resources in any given environment. For

74 example, high shoot biomass should be favoured in habitats where light is limiting, and high root biomass should be favoured in habitats were water or nutrients are limited (Bloom et al. 1985; McConnaughay & Coleman 1999; Ryser et al. 2000;

Bassirirad et al. 2001). However, in order to sustain conditions optimal for growth, a balance is needed between above- and below-ground biomass allocation (Ryser et al.

2000). The phenotypic variability of biomass allocation can also vary greatly from species to species (Wand 1999).

Patterns of biomass allocation by E. curvula in this experiment show trends consistent with optimality theories. A greater proportion of biomass was diverted to the roots when water was limiting and a lesser amount was diverted to the roots when water was not limiting. This suggests that the water and/or nutrient demand were not being met in dry conditions compared to wet conditions and biomass was allocated to overcome this. Interestingly, elevated CO2 did not alter the percent of biomass allocated to roots from that seen between watering treatments. Such a response would indicate that demand for nutrients was not differentially affected by elevated CO2.

Absolute values of biomass showed increases in roots by 26% and shoots by 34% under elevated CO2. Therefore elevated CO2 may have increased the efficiency of resource-use without altering the relative importance of above or below-ground resources for growth.

Table 4.6 Summary information comparing duration, cutting frequency and design of experiments in Chapter 3 and Chapter 4.

Exp Species Experiment Design Shoots Shoot cutting frequency Roots Root cutting length (g/day) (g/day) frequency 1 E. curvula 168 days Monoculture 0.018 At 56 days then every 0.030 Final 28 days × 4 2 E. curvula 84 days Monoculture 0.163 At 42 days then at 42 0.107 Final days again 2 A. racemosa 84 days Monoculture 0.129 At 42 days then at 42 0.054 Final days again

Changes in biomass allocation to optimise resource-uptake under elevated CO2 have been reported by a number of authors (Teughels et al. 1995; Ghannoum et al. 2000;

Ryser & Eek 2000). Increased resource-use efficiency has also been found under elevated CO2 conditions (Lutze & Gifford 2000; Ghannoum et al. 2000). Biomass allocation in E. curvula was speculated to be able to overcome the effects of resource limitation in drier conditions and as a result, under elevated CO2, supported a biomass enhancement.

A. racemosa illustrated a different biomass allocation strategy compared to E. curvula (Table 4. 3). There were no consistent trends in the proportion of biomass diverted to roots in response to elevated CO2 or water. A noticeable decrease in the proportion of biomass diverted to roots was observed at elevated CO2 in wet treatments (reflected also in absolute measures of root biomass), which is contrary to the optimality theory given that nutrients were previously speculated to be in higher demand at elevated CO2. Such a result might also reflect limited phenotypic variability in morphology and biomass allocation, thus contributing to the lack of biomass response to elevated CO2 when resources were limited in dry treatments.

This will be discussed in more detail in the next Section.

Other growth strategies were also demonstrated in E. curvula under elevated

CO2, which could have possibly contributed to the enhanced biomass. Increases in leaf number and plant height were observed (Figures 4.4a, b), whereby more leaves and elongated stems not only allowed an increase in capture of above-ground resources, but increased the capacity of the plant to store carbohydrates. The increase in number of leaves and plant height was particularly prominent after the plants were harvested to 3 cm. In both dry and wet elevated CO2 treatments, the height of plants and the rate of appearance of leaves were greater in plants at elevated CO2 compared

77 to ambient. This was also reflected in a basic calculation of growth rate (Cumulative biomass (g) divided by the number of days). Such a developmental response would be highly important for plant recovery after clipping, when light-harvesting biomass is urgently needed.

Leaf number is strongly correlated to the number of stems a plant produces and there have been many reports of significant biomass responses by species with enhanced morphological variability under CO2 in both increased leaf and tiller numbers (Wand et al. 1999; Poorter & Navas 2003). These traits are a further indication of heightened growth capabilities of E. curvula, contributing to a biomass enhancement at elevated CO2 under dry and wet conditions.

4.5.3 A. racemosa

In general elevated CO2 increases assimilation and reduces stomatal conductance of C3 plants (Atwell et al. 1999), but variation between species in the accumulation of biomass, interactions with resources and environmental conditions make predictions of broader C3 plant responses difficult (Hanley et al. 2004). In this experiment, A. racemosa under conditions of non-limiting water, showed a significant enhancement of whole-pot biomass as a result of elevated CO2, reflected mainly in shoot material. Under limiting water, there was no significant enhancement of biomass in A. racemosa.

The lack of growth enhancement by A. racemosa under elevated CO2 in dry treatments of this experiment is contrary to previous research on C3 plants. The water- conservation effects at elevated CO2 that accompany increases in assimilation by C3 plants are predicted to lead to biomass enhancements as great as 20 to 30 % under dry conditions (Morison 1993; Ward et al. 1999; Poorter & Navas 2003). Water treatments applied in this experiment were not designed to accommodate any water-

conservation under elevated CO2. As a result the measured response can only have occurred through photosynthetic or morphological processes.

In Section 4.5.2 of this chapter the comparative allocation of biomass by A. racemosa compared to E. curvula showed a clear diversion of biomass to roots by E. curvula plants under dry conditions, but was not evident in A. racemosa. A. racemosa plants, however, were taller (Figure 4.4a) and had faster recovery of leaf number after harvest under elevated CO2 in dry conditions (Figure 4.4b). Such a response would normally correspond with a biomass response. In this experiment it did not and may therefore indicate inherent favouring of above-ground development processes and be a limiting factor to biomass accumulation under limited water and elevated CO2 conditions. Without appropriate phenotypic variability to overcome resource limitation, increased demand for below-ground resource uptake under elevated CO2 would mean limitations on growth. In addition, the experimental water treatments did not allow the water-conservation effect of elevated CO2 to be expressed, potentially making demand for below-ground resources under dry conditions even greater.

A lack of phenotypic variability in biomass allocation when resources are limiting has been found to restrict growth by compromising leaf biomass and thus the potential for further overall carbon gain (Bloom et al. 1985; Arp 1991; Wolfe et al.

1998; Moore et al. 1999; Poorter & Nagel 2000; Bassirirad et al. 2001). Such research has shown that plants that are genetically limited by their capacity to change the allocation of biomass in response to environment may have a reduced ability to meet the resource demands imposed by changed environmental conditions (such as climate change). Physiological down regulation can compound a lack of phenotypic variability at elevated CO2 (Poorter & Nagel 2000; Poorter & Navas 2003). It is therefore plausible that the lack of diversion of biomass to roots under dry conditions

of elevated CO2 could have contributed to a lack of biomass enhancement by A. racemosa.

4.6 Implications for the competitive success of T. aestivum, E. curvula, and A. racemosa

The allocation of photo-assimilates to various parts of a plant structure can have implications for the competitive ability of that plant (Ryser et al. 2000). For example, under conditions where light is limited, a plant that diverts carbon to roots rather than shoots may be more efficient at gathering water and nutrients, but it is also more likely to be overtopped by neighbouring plants. Ideally a plant seeks a balance between above and below-ground biomass, diverting biomass one way or another in response to environment (Bengtson et al. 1994).

The responses of plants in this experiment have indicated that a species’ ability to modify biomass allocation in response to environmental changes may affect its response to elevated CO2. E. curvula and A. racemosa co-exist in grasslands around the ACT, making the different responses to elevated CO2 and water supply, leading to a competitive advantage, particularly relevant. T. aestivum plants are generally grown in monocultures in the field and while competition of T. aestivum with invasive species is a costly problem for growers, it would not be expected to compete with the two grasses researched here. Attention will thus be focused on the competitive interactions of E. curvula and A. racemosa only.

Both E. curvula and A. racemosa show an absolute increase in above-ground biomass in response to elevated CO2. While this occurred in both dry and wet conditions for E. curvula, it only appeared in wet conditions for A. racemosa. E. curvula had accompanying increases in root biomass, leaf number and plant height

80 resulting in an overall increase in size and space occupation. This is a strategy likely to result in a competitive advantage, whereby larger plants are expected to gain more resources both above and below-ground compared to smaller plants (Bengtson et al.

1994). Research on other weed species has shown similar growth strategies under elevated CO2 conditions (Ziska & Bunce 1997; Ghannoum et al. 2001). Weed species have been shown to have traits that not only lead to an increase in resource uptake, but also to greater occupation of space and thus have a competitive advantage. Such results emphasise the importance of considering both physiological and morphological characteristics in the prediction of plant responses to elevated CO2.

The lack of phenotypic variability in growth strategies (namely biomass allocation) in A. racemosa plants appeared to contribute to a lack of biomass enhancement at elevated CO2 under dry treatments, placing it at a significant competitive disadvantage compared to E. curvula. If future climates are drier due to climate change, species with the ability to quickly adapt to these altered conditions will be favoured over species which demonstrate a lack of phenotypic variability like

A. racemosa. If there is a tendency for a lack of phenotypic variability in other

Australian natives and the predictions from climate change models indicating changes in seasonal precipitation in the next 50 to100 years are correct (Hughes 2003), then future work should address the responses of other native Australian species under elevated CO2.

4.7 Conclusion

A major difficulty in plant experimentation is the great variety of plant responses that can occur to differing environmental conditions such as elevated CO2 and water-limitation. Biomass is an obvious measure of a plant’s responses in

elevated CO2 studies by providing a succinct integration of a set of very complex processes. It has been used in this chapter to give insight into the effect that elevated

CO2 and water-limitation can have on the responses of two co-occurring species, possibly leading to a competitive advantage.

Owing to the great many factors affecting biomass accumulation in plants, broad generalisations are difficult. In this instance, however, A. racemosa showed different patterns of biomass accumulation to E. curvula at elevated CO2 under dry and wet conditions. This difference may have resulted in a lack of enhancement of biomass under dry conditions for A. racemosa with the potential to alter its competitive ability. The positive responses of E. curvula to elevated CO2 under varying water levels would imply a significant competitive advantage over a native grass species such as A. racemosa. Such a trend could potentially occur in other co- existing species combinations, and therefore warrants further investigation.

Despite these findings, there is still much to be learnt about physiological and morphological factors affecting growth responses of plants to elevated CO2. The next chapter of this thesis will look closely at the physiological responses of E. curvula and

A. racemosa to elevated CO2 under water-limitation.

Chapter 5 -Plant physiological responses of E. curvula, A. racemosa and, T. aestivum under elevated CO2 and water-limited conditions

5.1 Overview of Chapter

Chapter 4 examined the patterns of biomass allocation of E. curvula, A. racemosa, and T. aestivum at elevated CO2 under wet and dry treatments. Additionally, the effects of competition on the responses of E. curvula under the same conditions were examined in Chapter 3. This chapter explores possible physiological explanations for responses observed in these previous chapters. That is, the lack of significant response of T. aestivum in dry treatments, the lack of response of the C3 plant, A. racemosa, to elevated CO2 also in dry treatments, and a positive response by the C4 plant, E. curvula, to elevated CO2 in both dry and wet treatments.

In previous chapters, it was found that the fractional biomass enhancement of

T. aestivum at elevated CO2 was similar in both wet and dry conditions, although the biomass enhancement in dry conditions was not significant at the 0.05 level of probability. Measures of photosynthetic assimilation of T. aestivum at 390 and 740 ppm indicated there was no evidence of photosynthetic down-regulation at elevated

CO2 at the leaf level, with assimilation at growing CO2 concentrations of 740 ppm being higher than that at 390 ppm. Also, plots of assimilation versus intercellular CO2 concentration showed no evidence of down-regulation. Results thus indicate that the lack of significant response by T. aestivum in dry conditions was not related to a physiological response and could possibly be a result of insufficient replication and high variability in data collected. The low replication and high variability were also reflected in measures of leaf nitrogen, specific leaf area (SLA), and transpiration rates in this chapter, whereby differences were non-significant between CO2 treatments.

It was found in the previous chapters that the C4 invasive species, E. curvula,

responded significantly (more than 25 % growth response) to elevated CO2 in both dry and wet conditions in above and below-ground biomass. Results from leaf gas exchange measurements in this chapter show evidence of physiological responses to elevated CO2 in the significantly lower transpiration rates (especially under dry conditions) and increased nitrogen-use efficiency. This was despite measurements of assimilation versus chamber and intercellular CO2 concentration showing complete down-regulation to elevated CO2. It was therefore suggested that assimilation may have been enhanced under elevated CO2 early in plant development (as has been shown by authors with similar results) and the late timing of leaf gas exchange measurements did not allow detection of this effect.

In Chapter 4, elevated CO2 enhanced biomass in A. racemosa plants in conditions of high water availability, but there was no enhancement as a result of elevated CO2 in conditions of low water availability. This result was supported by measurements of photosynthesis at growth cabinet and intercellular leaf CO2 concentration in this chapter. There was full down-regulation and a reduction in rubisco activity and rate of carboxylation under dry conditions of elevated CO2 in dry treatments, but not in wet conditions. Significantly lower leaf nitrogen concentrations in the dry treatments indicate that the down-regulation of assimilation under elevated

CO2 may have been a result of nutrient limitation. While transpiration and conductance were reduced under elevated CO2, the watering treatments may have negated the water-conservation effect, possibly leading to reduced soil water and limited uptake of nutrients.

The findings from this project demonstrate the complexity and variability of plant responses. It also illustrates the value of leaf gas exchange measurements in understanding species-specific responses to elevated CO2 and their interaction with other environmental factors such as water-limitation.

5.2 Introduction

In Chapter 4, T. aestivum (wheat) was used to standardise plant responses to growth-cabinet conditions because of its history of demonstrating large responses to elevated CO2 in both wet and dry conditions using growth cabinets. A positive biomass response to elevated CO2 observed in Chapter 4 was of similar magnitude to other studies on T. aestivum, indicating that cabinet conditions were unlikely to limit biomass enhancements at elevated CO2 in E. curvula and A. racemosa. T. aestivum clearly showed biomass enhancements in both dry and wet conditions (up to 60 % in some cases), but high levels of variability and low sample size may have prevented enhancements from being statistically significant in dry conditions. This chapter attempts to use measures of photosynthesis to further examine this result.

Chapters 3 and 4 of this thesis examined the responses of the invasive C4 species, E. curvula, to elevated CO2 under dry and wet conditions. In Chapter 3, E. curvula target plants responded significantly to elevated CO2 but the magnitude and direction of the response depended on resource-availability. Restriction of available resources as a result of intense competition or the presence of the root-exclusion tubes resulted in no enhancement of biomass under elevated CO2. When the E. curvula target plant experienced less resource limitation from surrounding A. racemosa plants, and root-exclusion tubes were absent, a large CO2-induced enhancement in biomass occurred.

In Chapter 4, responses observed in Chapter 3 were confirmed by positive enhancements of whole-pot biomass at elevated CO2 in E. curvula monocultures.

Examination of biomass allocation in Chapter 4 showed that phenotypic variability in the diversion of biomass to above and below-ground parts of the plant may have facilitated uptake of limiting resources and supported growth enhancements at elevated

CO2 under both dry and wet treatments.

Chapter 4 also examined responses of the native C3 grass, A. racemosa, to elevated CO2 and water limitation. The lack of positive CO2-response by A. racemosa in dry conditions was hypothesised to be related to phenotypic variability and the lack of allocation of biomass to roots to compensate for increased nutrient demand. This lack of positive CO2-response was also believed to be accentuated by watering treatments, which prevented any expression of benefit from the water-conservation effects of elevated CO2.

5.2.1 Leaf gas exchange measurements and plant responses

Measurements of photosynthesis can assist in gaining a mechanistic understanding of biomass responses of plants. Photosynthesis represents the process of the fixation of CO2 from the atmosphere into the foliage, and measurements of photosynthesis can be used to provide insight into the cause of many plant responses.

Photosynthetic rates underlie biomass responses in plants so the classification of plants based on their metabolic pathway (C3 or C4) can be a robust predictor of biomass responses to elevated CO2 (Ainsworth & Long 2005). Plants using the C3 metabolic pathway fix CO2 directly in the chloroplast and at current atmospheric CO2 concentrations are sub-optimal for the most efficient carbon gain. At future elevated

CO2 concentrations, photosynthetic rates of C3 plants will increase, which could therefore lead to greater biomass. In contrast, because C4 plants have a CO2- concentrating mechanism in leaf mesophyll that increases CO2 concentrations to 15 times that of ambient atmospheric CO2, rates of photosynthesis can be largely unaffected by elevated CO2 (Farquhar & Sharkey 1982; Pearcy & Ehleringer 1984).

Elevated CO2 also reduces stomatal aperture resulting in water-conservation potentially prolonging growth under water-limiting conditions (Morison 1993). Since all plants obtain CO2 through stomates, C4 plants have been found to also benefit from

86 this water-conservation effect (Farquhar & Sharkey 1982). In addition, recent research has demonstrated other physiological and morphological factors can influence the degree of biomass response of plants to elevated CO2 causing a vast array of plant responses. For example, the leakiness of bundle sheaths that normally act to concentrate CO2 is correlated with a greater than expected response of C4 plants

(Ghannoum et al. 2000). Such responses have fuelled speculation that a great variety of species-specific characteristics can interact with elevated CO2, and that biomass accumulation is not always directly proportional to the level of photosynthetic stimulation as was previously assumed (Ghannoum et al. 2000; Poorter & Navas 2003;

Ainsworth & Long 2005).

Regardless of the level of photosynthetic enhancement a species may receive under elevated CO2, it is common to observe decreased enhancements over time as a result of photosynthetic down-regulation or other physiological feedbacks (Poorter &

Navas 2003). A plant experiences photosynthetic down-regulation, or ‘acclimation’ when, following a period of stimulated assimilation, the plant adjusts back towards the original rate of assimilation through a process of negative feedback (Atwell et al.

1999). Often the activity of rubisco and rate of carboxylation are also lowered during down-regulation. The causes of down-regulation are still unclear however, resource limitation or a reduction in sink strength, or reduced leaf N have been theorised as common contributors (Wolfe et al. 1998; Atwell 1999; Ellsworth et al. 2004). In addition, down-regulation does not preclude a biomass enhancement, and plants may experience various levels of down-regulation caused by different environmental and species-specific factors (Woodrow 1994). As a result, it is extremely difficult to generalise the causes of plant responses to elevated CO2. It is clear however, that theories based on photosynthetic processes alone are losing favour.

In this experiment, analyses of leaf biochemistry and leaf gas exchange are

87 used to further explore the responses of T. aestivum, E. curvula, and A. racemosa to elevated CO2 under different water levels. This experiment tests the hypotheses presented in previous chapters for explaining the positive response of C4 invasive species E. curvula to elevated CO2 in dry and wet treatments and the lack of response of C3 native species A. racemosa in dry treatments.

5.3 Methods and materials

Species selection, controlled environment conditions and pot treatments were all replicated from Chapter 4. Detailed methodology can be seen in the Methods and materials section (4.3) on page 58.

Measurements

Leaf gas exchanges measurements were taken using the Li-Cor Model LI-6400 portable photosynthesis system (Li-cor, Lincoln Nebraska). This system measures leaf photosynthesis, transpiration and stomatal conductance, whilst controlling CO2, humidity, leaf temperature and light in a specially designed leaf chamber. Irradiance was maintained by the Li-6400 leaf chamber at 1600 μmol m-2 s-1 (based on preliminary measurements indicating photosynthesis was light-saturated for all species at 1200 to1600 μmol m-2 s-1), leaf temperature at 22°C (the day temperature at which the plants were grown) and humidity was not controlled. The Li-6400 was used for determining A/Ci curves (i.e. net CO2 assimilation (A) as a function of intercellular leaf CO2 concentration (Ci)) and A/Ca curves (net CO2 assimilation (A) as a function of leaf chamber CO2 concentration (Ca)). Assimilation was measured at 9 different leaf chamber CO2 concentrations to form curves ranging from 0 to 1200 ppm of [CO2] for

C4 species, and 50 to 1200 ppm for C3 species. Upon commencing measurements for curves, leaves were allowed to equilibrate at a leaf chamber CO2 concentration of 50 ppm until assimilation and stomatal conductance were constant (approximately

20mins). After leaves had equilibrated sufficiently to allow for accurate measurement at each concentration, assimilation and transpiration were measured automatically by the Li-6400 at each of the 9 leaf chamber CO2 concentrations. The curves were then stored in the Li-6400 for later transfer into Microsoft Excel.

Assimilation curves were used to estimate electron transport capacity (Jmax) and

RuBisCO (rubisco) activity (Vcmax) following Farquhar et al. (1980). Measures of leaf gas exchange were taken once a week, beginning 5 weeks after the plants were sown into pots. Over the course of the entire experiment all replicates for each species were measured at least 3 times. T. aestivum pots had measurements taken weekly because there were only two pots in each treatment combination. If leaves were small and narrow, up to 3 leaves were placed in the leaf chamber to ensure reliable measures of

CO2 assimilation. If leaves were larger, only 1 or 2 leaves were required. Wherever possible the youngest, fully expanded leaves were used for measurements of photosynthesis. The leaf segments (where leaf gas exchange was measured) were marked immediately after the leaf chamber was removed, measured for their length and width and harvested. The segment was then labelled and frozen in liquid nitrogen in preparation for freeze-drying. Freeze-drying of samples was done over a 48- hour period before being placed in a desiccator for weighing. After weighing, leaf segments were stored at -12ºC for further analysis.

An analysis of non-structural carbohydrate concentration was carried out on freeze dried leaf material following Li-6400 measurement and harvesting. This was intended to complement measures of leaf gas exchange to support acclimation responses in plant leaves. The high variability in the results of the analysis of non- structural carbohydrates in leaf material revealed data were not sufficiently meaningful for use in the writing of the thesis and was omitted.

Leaf area and specific leaf area (SLA) were calculated on leaf segments using the dimensions of each segment. Leaf area was calculated by using the following formula for a trapezium:

where a and b are the lengths of the parallel sides and h is the perpendicular distance between the parallel sides. SLA was then calculated by dividing the leaf-area measurements by the dry weight of the segment in milligrams (mg).

After 7 weeks of growth, shoot biomass of E. curvula and A. racemosa were harvested to 3 cm and dried in a 70º C oven for 7 days. T. aestivum was not harvested at this time and was allowed to reach maturity before harvesting with other plants in the second harvest. At 12 weeks, all three species were harvested for both shoot and root material, oven-dried then weighed.

Statistical analysis

Data were analysed using JMP Statistical package version 5, multi-factor

ANOVA analysis (JMP, SAS Institute, Cary North Carolina). Repeated measures

ANOVA was used for leaf gas exchange measurements, percent leaf nitrogen and SLA to assess the significance (P < 0.05) of CO2, water, and the interaction between CO2 and water (Tables 5.1,5.2 and 5.3). The following factors were assessed: Species, CO2,

Water, CO2 × Water, CO2 × Species, Water × Species, CO2 × Water × Species.

Significant (P < 0.05) differences between means within each ANOVA model were assessed using Tukey’s HSD post hoc test (JMP, SAS Institute, Cary North Carolina).

Because the ambient growing concentration of 390 ppm was not included in the

9 leaf chamber CO2 concentrations, assimilation for 390 ppm was calculated by fitting a polynomial regression to assimilation of the other 9 leaf chamber and intercellular

CO2 concentrations. It was then possible to extrapolate the rate of assimilation for

ambient growing CO2 by solving the equation for the line. The rate of assimilation at ambient CO2 on the ambient curve and the rate of assimilation at elevated growing

CO2 on the elevated CO2 curve were then compared statistically for evidence of photosynthetic down-regulation.

5.4 Results

5.4.1 T. aestivum

Plots of assimilation rate versus leaf chamber CO2 concentration (Ca) showed that T. aestivum plants had not experienced significant photosynthetic down-regulation at the leaf level at elevated CO2 under dry and wet conditions (Figures 5.1a and b). In addition, there was no evidence of significant down-regulation when stomatal effects were accounted for in the A/Ci analysis (Figures 5.2a and b). In both Figures 5.1 and

5.2, there was an increase in assimilation observed between ambient and elevated growing CO2 concentrations (represented by the grey boxes in Figure 5.1). A slightly less steep line between ambient and elevated growing concentrations in Figure 5.1b

(grey boxes) indicates a lower photosynthetic stimulation but this was only minor.

Statistical analysis of Jmax and Vcmax showed a similar lack of down-regulation between ambient and elevated CO2 treatments in both wet and dry conditions (Table 5.4). These results illustrate that photosynthesis in T. aestivum plants in both dry and wet conditions was still responsive to increases in CO2 concentration throughout the duration of experiment.

Percent leaf nitrogen (Figure 5.3a), specific leaf area (5.3b), and transpiration measurements (Figure 5.3c) showed no clear differences between water and CO2 treatments. The low replication (n = 2) and high variation in T. aestivum data caused significant differences between CO2 and water treatments to be difficult to detect.

Table 5.1 Summary of repeated measures ANOVA results for percent nitrogen of leaves. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob> F Harvest 1 1 0.0664011 .8366 0.3670 CO2 1 1 0.8923824 11.2439 0.0020 Water 1 1 0.0121763 0.1534 0.6978 Species 2 2 5.6252923 35.4391 <.0001 CO2*Water 1 1 0.0000286 0.0004 0.9850 Water*Species 2 2 0.0133522 0.0841 0.9195 CO2*Species 2 2 0.6288558 3.9618 0.0287 CO2*Water*Species 2 2 0.0226873 0.1429 0.8673 Error 33 2.6190642 C. Total 45

Table 5.2 Summary of repeated measures ANOVA results for transpiration of leaves. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob>F Harvest 4 4 41.782952 0.6721 0.6129 CO2 1 1 1.435351 0.0924 0.7619 Water 1 1 31.609730 2.0338 0.1570 Species 2 2 39.198301 1.2610 0.2880 CO2*Water 1 1 38.293753 2.4639 0.1197 Water*Species 2 2 8.848892 0.1757 0.8392 CO2*Water*Species 2 2 62.412769 2.0079 0.1398 Error 33 2.6190642 C. Total 45

Table 5.3 Summary of repeated measures ANOVA results for specific leaf area. Bold values represent P<0.05. Interactions between ‘Harvest’ and all other factor were not significant and therefore not presented in the table.

Source Nparm DF Sum of Squares F Ratio Prob> F Harvest 4 4 98669.80 8.5738 <.0001 CO2 1 1 354.85 0.1233 0.7262 Water 1 1 16029.33 5.5714 0.0202 Species 2 2 100920.95 17.538 <.0001 CO2*Water 1 1 1773.48 0.6164 0.4342 Water*Species 2 2 163.47 0.0284 0.9720 CO2*Species 2 2 278.14 0.0483 0.9528 CO2*Water*Species 2 2 3917.29 0.6808 0.5086 Error 100 287706.55 C. Total 115

Table 5.4 T-test results for T. aestivum in ambient and elevated CO2, dry and wet conditions. The P value (P) is given in the top line of each Ambient and Elevated CO2 pair. The ‘Analysis’ column indicates the leaf gas exchange measurement statistically compared (Jmax being the electron transport capacity and Vcmax is the rubisco activity), ‘n’ is the number of measurements compared in each treatment, ‘Mean’ is the mean value in each treatment, s.d. is the standard deviation, t is the T-value for the sample set, df is the degrees of freedom in the analysis and P is the probability in which the result occurred by chance alone.

CO2 Water Analysis n Mean s.d. t df P

Ambient Dry Jmax 8 114 28.3 -1.29 16 0.227

Elevated Dry Jmax 10 133 33.6

Ambient Wet Jmax 9 126 26.9 1.95 16 0.555

Elevated Wet Jmax 9 143 26.2

Ambient Dry Vcmax 6 66.6 18 1.29 14 0.218

Elevated Dry Vcmax 10 80.1 21.6

Ambient Wet Vcmax 9 92.3 24.3 1.4 16 0.180

Elevated Wet Vcmax 9 77.4 20.6

) a) -1

/s 30 -2 25 * * 20

15 Assimilation (μmol/m 10 DRY DRY

Ambient CO2 5 Elevated CO2

0 T. aestivum 35-5

) b) -1

/s 30 -2

25 *

15 Assimilation (μmol/m Assimilation 10 WET 5

0 T. aestivum T. aestivum -5 0 200 400 600 800 1000 1200 1400 1600 1800

Chamber CO2 Concentration (ppm)

Figure 5.1 T. aestivum assimilation versus growth cabinet CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and, b) wet conditions. The dotted lines represent the growth cabinet CO2 levels at ambient and elevated CO2 on the curve, and the two grey squares have been statistically compared for evidence of photosynthetic down-regulation between growing CO2 concentrations. The dashed line indicates 0 on the X-axis. Points on the curve are the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean. * represents P < 0.05 (Note: some error bars are obscured by symbols).

35 )

-1 a) s

-2 30

mol m 25 μ * 20

15 Assimilation ( Assimilation 10 DRY

Ambient CO2 5 Elevated CO2

0 T. aestivum 35-5-5

) b) -1 s

-2 30

mol m 25 μ

15 Assimilation ( Assimilation 10 WET

0 T. aestivumT. -5 0 200 400 600 800 1000 1200 1400 1600

Intercellular CO2 Concentration (ppm)

Figure 5.2 T. aestivum assimilation versus intercellular CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and, b) wet conditions. The dotted lines represent the growth cabinet CO2 levels at ambient and elevated CO2 on the curve. The dashed line indicates 0 on the X-axis. Points on the curve are the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean and * is P < 0.05 (Note: some error bars are obscured by symbols).

3.0

AmbientAmbient CO CO2 2 2.5 a) ElevatedElevtaed CO CO2 2

2.0 ns ns

1.5

Nitrogen (%) 1.0

0.5

3500.00

300 ns b) ns 250 ) -1 g

2 200

150 SLA (cm 100

00 4.0 ) Dry Wet 1 ns ns s- c)

-2 3.5

O m 3.0 2

2.5

2.0

1.5

1.0

0.5 TranspirationH (mmol 0.0 Dry Wet

Figure 5.3 Physiological parameters of T. aestivum, a) percent leaf nitrogen, b) specific leaf area and, c) transpiration. Ambient CO2 (black bars) is 390 ppm and elevated (grey bars) is 740 ppm. Dry treatments (LHS) were based on drying down to 30 % and watered up to 50 % ASW and Wet treatments (RHS) were dried down to 50 % and watered up to 95 % ASW. Values are the mean across all harvests within each CO2 and water treatment and error bars denote ± 1 SE. ‘ns’ indicates no significant difference between means.

5.4.2 E. curvula

When photosynthesis data were presented as A/Ca curves, E. curvula in dry conditions (Figure 5.4a) showed that while there was a trend of down-regulated photosynthesis across a number of CO2 concentrations, it was not significant. There was however, no increase in assimilation between ambient and elevated CO2 growing conditions (grey boxes), indicating that partial down-regulation at the leaf scale had occurred. In wet conditions (Figure 5.4b), a similar trend occurred across a number of concentrations on the A/Ca curve, but were not significant at 0.05 level of probability.

When the stomatal effects were removed and assimilation was examined at the relative cabinet intercellular CO2 concentrations (Figures 5.5a and b) there again appeared to be no evidence of lowered assimilation across the whole curve, but the comparison of assimilation at growing Ci values, indicated that partial down-regulation had occurred.

Percent leaf nitrogen measurements were significantly lower under elevated

CO2 in both wet and dry conditions for E. curvula with a mean decrease of 6 % across both water treatments (Figure 5.6a). SLA measurements were not significantly different between CO2 treatments or water levels (Figure 5.6b). However, elevated

CO2 caused a significant reduction of transpiration in wet conditions (P < 0.001) and a significant difference at the .10 level of probability in the dry (Figure 5.6, Tables 5.1,

5.2 and 5.3).

) a) -1

/s 25 -2 ns

mol/m 20

10 Assimilation ( μ Assimilation

Ambient CO DRY 5 2 Elevated CO2

0 E. curvulaE.

35-5 )

-1 b) Reference CO2 (ppm) /s 30 -2

25 * ns 20

Assimilation Assimilation (μmol/m 10 WET 5

0 E. curvulaE.

-5 0 200 400 600 800 1000 1200 1400

Chamber CO2 concentration (ppm)

Figure 5.4 E. curvula assimilation versus chamber CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and b) wet conditions. The dotted lines represent the cabinet CO2 levels at ambient and elevated CO2 on the curve and the two grey squares have been statistically compared for evidence of photosynthetic down-regulation between growing CO2 concentrations. Dashed line indicates 0 on the X-axis. Points on the curve represent the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean and * is P < 0.05. (Note: some error bars are obscured by symbols).

35 ) -1

/s 30 -2 a) 25 mol/m μ 20

Ambient CO2 Assimilation ( Assimilation 10 Elevated CO2 DRY

0 E. curvula 35-5-5 ) -1

/s 30 -2 b)

25 mol/m μ

Assimilation ( 10 WET 5

0 E. curvula -5 0 200 400 600 800 1000 1200

Intercellular CO2 Concentration (ppm)

Figure 5.5 E. curvula assimilation versus intercellular CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and, b) wet conditions. The dotted lines represent the growth cabinet CO2 levels at ambient and elevated CO2 on the curve. The dashed line indicates 0 on the X-axis. Points on the curve are the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean. Non-significant results are unmarked (Note: some error bars are obscured by symbols).

100

4.0

Ambient CO22 3.5 Elevated CO2 2 * * a) 3.0

2.5

2.0

1.5 Nitrogen (%) 1.0

0.5

3500.00 Dry nsWet 300 ns 250 ) -1

g b) 2 200

150 SLA (cm 100

04 )

-1 ** s

-2 ns 3 O m 2

2 c)

1 Transpiration (mmol H (mmol Transpiration 0 Dry Wet

Figure 5.6 Physiological parameters of E. curvula, a) percent leaf nitrogen, b) specific leaf area and, c) transpiration. Ambient CO2 (black bars) is 390 ppm and elevated (grey bars) is 740 ppm. Dry treatments (LHS) were based on drying down to 30 % and watered up to 50 % ASW and Wet treatments (RHS) were dried down to 50 % and watered up to 95 % ASW. Values are the mean across all harvests within each CO2 and water treatment and error bars denote ± 1 SE. ** is P < 0.001, * is P < 0.05 and ‘ns’ indicates no significant difference between means

101

5.4.3 A. racemosa

The plot of assimilation rate versus leaf chamber CO2 concentration for A. racemosa in dry conditions showed significant down-regulation of assimilation at a number of CO2 concentrations (Figure 5.7a) and complete down-regulation in the comparison of assimilation of growing CO2 between ambient and elevated CO2 (grey boxes). In wet conditions (Figure 5.7b) there was no evidence of down-regulation with photosynthesis still responsive to elevated CO2 across the span of the experiment.

When stomatal effects were excluded in the analysis of assimilation versus intercellular

CO2 concentration (Figures 5.8a and b), the same trend occurred in dry and wet treatments at elevated CO2. In dry treatments (Figure 5.8a) the lowered assimilation between elevated and ambient CO2 curves was significant in the majority of Ci values.

Examination of electron transport capacity (Jmax) and rubisco activity (Vcmax) using the

Farquhar et al. (1980) model indicated that in dry conditions Jmax and Vcmax were significantly lower (Jmax P < 0.001, and Vcmax, P < .005) in elevated CO2 and dry conditions (Table 5.2).

The lack of enhancement of assimilation in dry conditions is in line with lower percent leaf nitrogen in this treatment (a reduction on average from 3 % in ambient

CO2, to 2.6 % in elevated CO2 under dry conditions (Figure 5.9a). In the wet, where down-regulation of photosynthesis was not apparent, nitrogen levels were not significantly different between ambient and elevated CO2 (Figure 5.9a). There were no significant trends in SLA (Figure 5.9b), but a clear decrease in transpiration rate under dry conditions at elevated CO2 was observed (Figure 5.9c). Repeated measures

ANOVA results are displayed in Tables 5.1, 5.2 and 5.3.

102

Table 5.5 T-test results for A. racemosa in ambient and elevated CO2, dry and wet conditions. The P value (P) is given in the top line of each Ambient and Elevated CO2 pair. The ‘Analysis’ column indicates the leaf gas exchange measurement statistically compared (Jmax being the electron transport capacity and Vcmax is the rubisco activity), ‘n’ is the number of measurements compared in each treatment, ‘Mean’ is the mean value in each treatment, s.d. is the standard deviation, t is the T-value for the sample set, df is the degrees of freedom in the analysis and P is the probability in which the result occurred by chance alone.

CO2 Water Analysis n Mean s.d. t df P

Ambient Dry Jmax 10 146 17.9 3.26 18 0.004

Elevated Dry Jmax 10 113 27.3

Ambient Wet Jmax 10 122 23.8 0.938 18 0.360

Elevated Wet Jmax 10 134 31.3

Ambient Dry Vcmax 10 90.3 12.6 4.06 18 0.001

Elevated Dry Vcmax 10 64.3 16.6

Ambient Wet Vcmax 10 79.2 15.3 0.03 18 0.973

Elevated Wet Vcmax 10 79 20.1

103

35 )

-1 a) s

-2 30 * 25 * * 20 ns * 15 * (μmolAssimilation m 10

DRY Ambient CO2

5 Elevated CO2

0 A. racemosa 35-5 )

-1 b)

s X Data

-2 30 25 * 20

15 Assimilation (μmol Assimilation m 10 WET 5

0 A. racemosa racemosa A. -5 0 200 400 600 800 1000 1200 1400 1600 1800 Chamber CO concentration (ppm) 2

Figure 5.7 A. racemosa assimilation versus growth cabinet CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and, b) wet conditions. The dotted lines represent the growth cabinet CO2 levels at ambient and elevated CO2 on the curve, and the two grey squares have been statistically compared for evidence of photosynthetic down-regulation between growing CO2 concentrations. The dashed line indicates 0 on the X-axis. Points on the curve are the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean. ** P < 0.001, * P < 0.05 and ‘ns’ indicates no significant difference between means (Note: some error bars are obscured by symbols).

104

35 ) -1 s

-2 30 a)

mol m mol 25 μ

15 Ambient CO2

Assimilation ( Assimilation * Elevated CO 10 2 DRY

0 A. racemosa A. 35-5-5 ) -1 s

-2 30 b)

mol m 25 μ

15 Assimilation ( Assimilation 10 WET

0 A. racemosa A. -5 0 200 400 600 800 1000 1200 1400

Intercellular CO2 Concentration (ppm)

Figure 5.8 A. racemosa assimilation versus intercellular CO2 concentration. Filled circles are ambient (390 ppm) CO2 and triangles are elevated (740 ppm) CO2 in a) dry conditions and, b) wet conditions. The dotted lines represent the growth cabinet CO2 levels at ambient and elevated CO2 on the curve. The dashed line indicates 0 on the X-axis. Points on the curve are the mean values across all harvests for each species within the CO2 and water treatments. Error bars are ± 1 SE of the mean. * P < 0.05 and non significant results difference in means are unmarked (Note: some error bars are obscured by symbols).

105

3.5 * ns a) 3.0

2.5

2.0

1.5 Nitrogen (%) 1.0

0.5

3000.00

Ambient CO2 Dry Wet b) ns ns Elevated CO 250 2

) 200 -1 g 2 150

SLA (cm 100

) c) -1 ** s

-2 5 ns O m 2 4

1 Transpiration (mmol H 0 Dry Wet

Figure 5.9 Physiological parameters of A. racemosa, a) percent leaf nitrogen, b) specific leaf area and, c) transpiration. Ambient CO2 (black bars) is 390 ppm and elevated (grey bars) is 740 ppm. Dry treatments (LHS) were based on drying down to 30 % and watered up to 50 % ASW and Wet treatments (RHS) were dried down to 50 % and watered up to 95 % ASW. Values are the mean cross all harvests within each CO2 and water treatment and error bars denote ± 1 SE. P < 0.001 is represented by **, P < 0.05 is * and non-significant results have either ‘ns’ or are unmarked.

106

Table 5.6 Summary ANOVA results for assimilation at saturated light. Bold values represent P<0.05.

Source Nparm DF Sum of Squares F Ratio Prob > F Harvest 1 1 78.02241 2.2941 0.1358 Water 1 1 156.23730 4.5939 0.0367 Species 2 2 40.47515 0.5950 0.5552 CO2 1 1 73.38472 5.0120 0.0281 CO2*Water 1 1 7.84650 0.2307 0.6330 Water*Species 2 2 53.19548 0.7821 0.4627 CO2*Species 2 2 141.87921 2.0858 0.1343 Harvest*CO2 4 4 58.29873 0.4285 0.7874 Harvest*Water 4 4 43.63836 0.3208 0.8628 Harvest*Species 8 8 527.54528 1.9389 0.0731 Harvest*CO2*Water 4 4 44.71891 0.3287 0.8575 Harvest*CO2*Species 8 8 193.14754 0.7099 0.6816 CO2*Water*Species 2 2 149.58677 2.1992 0.1209 Harvest*Water*Species 8 8 122.42473 0.4500 0.8851 Harvest*CO2*Water*Species 8 8 377.32285 1.3868 0.2238 Error 53 1802.5352 Total 112

107

5.5 Discussion

5.5.1 T. aestivum

Previous research on T. aestivum has shown large growth responses to elevated

CO2 under dry and wet conditions (Gifford 1979; Kimball 1983; Soinit & Patterson

1984). These authors convincingly showed that elevated CO2 enhances assimilation leading to an increase in biomass. In dry conditions, reduced stomatal aperture permits the conservation of soil water leading to an increase in growing time and an even greater relative enhancement of biomass under elevated CO2.

A biomass enhancement was observed in T. aestivum in both dry and wet conditions at elevated CO2 in this experiment, but in dry treatments this enhancement was not statistically significant (Table 4.2, Chapter 4). The CO2-induced enhancement across both water treatments was however, up to 50 %, which is similar to previous research on T. aestivum under elevated CO2 (Gifford 1979; Kimball 1983). The enhancement of biomass observed in this experiment by T. aestivum would thus indicate that cabinet conditions were not limiting the CO2 responses of E. curvula and

A. racemosa.

The lack of significant difference in biomass between ambient and elevated

CO2 in dry treatments was hypothesised in Chapter 4 to be an artefact of low replication. Limited space in the controlled environment cabinets meant that a compromise was made on the number of control pots used in order to permit greater replication of E. curvula and A. racemosa pots. The results from this chapter support this hypothesis. Figures 5.1a) and b) show no evidence of photosynthetic down regulation of T. aestivum plants to elevated CO2 conditions and only a minor trend of photosynthetic down-regulation in a few leaf chamber concentrations on the A/Ca curves. This was also reflected in plots of assimilation over intercellular CO2 (Figures

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5.2a and b) indicating that when assimilation is corrected for the possible effects of lowered stomatal conductance, assimilation was still higher under elevated CO2. Leaf- level measurements of nitrogen concentration, SLA and transpiration (Figures 5.3a, b, and, c) show the high level of variability, leading to a lack of significant differences between CO2 and water treatments. There was a lack of significant trend in leaf-level measurements of nitrogen concentration, SLA and transpiration. A/Ca and A/Ci curves however, showed a significant photosynthetic capacity to produce greater biomass under elevated CO2 in both dry and wet conditions. As a result, it can be concluded that the cabinet conditions did not appear to alter the magnitude of responses of T. aestivum from that anticipated from previous studies. The low replication of CO2 and water treatments, coupled with high variability in T. aestivum data, led to a non- significant difference in responses of this species to elevated CO2 in dry conditions.

5.5.2 E. curvula

Previous chapters in this thesis have shown a significant enhancement of biomass in both wet and dry conditions at elevated CO2 for C4 invasive grass species

E. curvula.

In Chapter 4, allocation of biomass appeared to favour parts of the plant that aided in the uptake of below-ground resources under elevated CO2 conditions. It was theorised that this plastic response in biomass allocation allowed necessary resource-uptake to sustain an enhancement of biomass under elevated CO2 in wet and dry conditions. In this chapter, measures of photosynthesis were carried out to gain evidence of increased photosynthetic rate or reductions in stomatal conductance to support the observed biomass enhancement. E. curvula showed no increase in assimilation between ambient and elevated CO2 growing conditions in wet and dry conditions, indicating that partial down-regulation at the whole-leaf level had occurred.

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Leaf nitrogen and transpiration were reduced at elevated CO2, though this was only significant in the wet treatment (Figures 5.6a and c). This suggests that while measurements of assimilation did not indicate a photosynthetic enhancement at elevated CO2 concentrations (in a comparison of means of measurements taken on plants throughout the entire experiment), reduced leaf nitrogen and lowered transpiration show that a change in physiological processes did occur at elevated CO2.

While past research supports the absence of photosynthetic stimulation, in light of the

CO2 ‘pump’ that exists in the leaf mesophyll of C4 plants, there was evidence in this experiment of a 28 % increase in biomass under elevated CO2.

Cases of a lack of photosynthetic enhancement in the presence of a biomass enhancement have been reported in several studies (Carlson & Bazzaz 1980; Rogers et al. 1983; Ghannoum et al. 2000). A number of hypotheses have been put forward by authors to explain such C4 plant responses. For example leakiness in the bundle sheath of leaf cells (Ziska & Bunce 1999) and C3-like photosynthetic occurring in young C4 plant leaves (Ghannoum et al. 1998) have both been theorised to increase sensitivity of photosynthesis to atmospheric CO2 concentrations. Another hypothesis presented by

Ghannoum et al. (2000) was that elevated CO2 leads to increases in leaf temperature enhancing photosynthetic rates and leading to a positive biomass response.

In a reassessment of C4 plant responses to elevated CO2 (Ghannoum et al.

2000), the absence of an increase in assimilation when a biomass enhancement had occurred is theorised to result from the effects of developmental stage at the time that instantaneous leaf gas exchange measurements are taken. Ghannoum et al (2001) and

Baxter et al (1995) demonstrated that assimilation, enhanced by elevated CO2, is down-regulated with ontogeny, a finding shown by several other authors (Wand et al.

1999; Lecain et al. 2003). Leaf gas exchange measurements that are not carried out in the early development of the plant have the potential to ‘miss’ early photosynthetic

110 stimulation. Work by Wand & Midgely (2004) found that measurements taken on

Themeda triandra 3 weeks after defoliation showed significant photosynthetic down- regulation at that early stage. However, a biomass response flowed on into later plant development. The capturing of such early developmental responses to elevated CO2 can therefore be difficult.

Further to this, research in a glasshouse experiment in South Africa showed that

E. curvula species has the capacity to receive a stimulation of photosynthesis under

elevated CO2 (Wand et al. 2001). Photosynthetic up-regulation was observed in plants

measured at the equivalent of 2 weeks younger than plants used in this experiment (i.e.

plant age in this experiment excluded the 2 week germination phase that occurred

before seedling were placed in pots, despite germination occurring in their assigned

CO2 treatments).

The possibility of a reduction in photosynthetic stimulation of E. curvula over

time can be examined in the results of the Repeated Measures ANOVA presented in

Table 5.6. It is shown that there was a significant interaction between harvest and

species and closer observation of means indicate earlier measurements show higher

rates of assimilation (but not significant) at elevated CO2 compared to later

measurements. The interaction of harvest with species was significant, but there were

no significant trends over time. The data was consequently pooled for greater

statistical power. Due to the lack of data being collected prior to week 5 and the

possibility that the greatest photosynthetic stimulation may have occurred during this

time frame, the ANOVA analysis does not provide any further quantitative support

that photosynthesis enhancement occurred prior to week 5.

Reductions in photosynthetic response under elevated CO2 through time have,

been observed widely in C3 plants, but are reported less frequently in C4 plants.

Despite this however, Baxter et al. (1995) proposed that C4 plants probably suffer

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similar constraints to photosynthetic capacity as C3 plants. That is, the process of

photosynthetic down regulation occurs through an accumulation of carbohydrates as a

result of increased assimilation under elevated CO2 concentrations. This accumulation

of carbohydrates causes feed-back inhibition, reducing sink strength and diluting

concentrations of leaf nitrogen needed for photosynthetic machinery. While nitrogen

concentrations were observed to be significantly lower under elevated CO2 in E.

curvula plants in this experiment (Figure 5.6a), a number of other factors would

indicate this is a reflection of increased efficiency in the use of nitrogen rather than a

cause for photosynthetic down-regulation.

Specific leaf area has been used in the past as a component of carbohydrate

accumulation in the leaf (Poorter & Navas 2003). A decrease in SLA (or thicker leaves

caused by carbohydrate accumulation) may cause a dilution of leaf nitrogen, and in

some cases cause an increase in leaf density. Past research however has shown a

negative relationship between leaf thickness and leaf nitrogen levels (Wolfe et al.

1998).

There was no significant difference in SLA of E. curvula plants under elevated

CO2 (Figure 5.6b) indicating that a dilution of leaf nitrogen concentration was

unlikely. There were also no obvious signs of nitrogen limitation reflected in plant

health or in accumulation and allocation of biomass under elevated CO2. The

proportion of biomass diverted to the roots changed with water levels, but not with

CO2 (Chapter 4, Table 4.2) suggesting that nitrogen was not limiting under elevated

CO2.

The accumulation of carbohydrates in C4 plants under elevated CO2 is also believed to be small and unlikely to result in negative-feedback on photosynthesis

(Wand 1999; Ghannoum et al. 2000). Rather, changes in the concentration of enzymes responsible for initial CO2 fixation in the leaves could potentially cause an acclimatory

112 response. The lack of research conducted in this area, indicates considerable further work is required before this can be established as a cause for the down-regulation observed in C4 species. It is however, plausible that changes in leaf enzymes may have caused a lack of photosynthetic response in E. curvula in this experiment.

Separate to photosynthetic responses, the observed reduction in transpiration in

E. curvula leaf gas exchange measurements would normally cause soil water to be conserved, increasing the length of time for growth before water is limiting (Poorter &

Navas 2003). In this experiment the method for applying water to pots (see Chapter 4,

Methods and Materials) does not support a water-conservation effect. This is because watering treatments were based on drying soil down to a set percentage of moisture, counteracting any water conserved in the soil profile resulting from the water- conservation effects of elevated CO2. The greater biomass response observed in this thesis by E. curvula under elevated CO2 can therefore be confidently attributed to an enhanced photosynthetic response rather than due to a water conservation effect.

Recent work by Ghannoum et al. (2003) indicated that stomatal effects do not solely drive C4 plant responses. Physiological measurements taken in this experiment show that an enhancement of assimilation may have occurred in E. curvula plants early in plant development and this, coupled with increased nitrogen-use efficiency probably enabled a greater accumulation of biomass under wet and dry treatments at elevated

CO2. While leaf gas exchange measurements did not capture this response adequately as a result of the short time-frame in which the possible stimulation occurred, the increase in assimilation by E. curvula supports research indicating that C4 plants may experience photosynthetic stimulation at elevated CO2 leading to a biomass response.

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5.5.3 A. racemosa

A positive biomass response to elevated CO2 was only observed under wet conditions for A. racemosa (Chapter 4). This equated to a 19 % whole plant and 27 % shoot biomass enhancement in wet, compared to a non-significant 8 % enhancement across roots and shoots in the dry conditions (Chapter 4, Table 4.3).

The additional effects if water-conservation occurs along with increased assimilation under elevated CO2, can induce greater enhancement of biomass in dry conditions compared to wet for C3 and C4 plants (Morison 1993; Morgan et al 2001;

Poorter & Navas 2003). In the majority of these experiments plants are grown in conditions where the water conserved under elevated CO2 is retained in the soil. In this experiment, re-watering was based on percent soil moisture, whereby watering of pots occurred only when soil water had dropped to the lower ASW limit. While measures of transpiration indicated that there was potential for water to be conserved in the soil under dry conditions at elevated CO2 (Figure 5.9c), this conserved soil water was lost as a result of the watering treatments. Because of the exclusion of the soil water- conservation effects, biomass increases could therefore only be a product of photosynthetic enhancements under elevated CO2.

Measurements of assimilation (Figure 5.7a) demonstrated a significant down- regulation of photosynthesis by A. racemosa plants at elevated CO2 in dry conditions, which was not apparent in wet conditions. There was also significantly lower electron transport capacity (Jmax) and rubisco activity (Vcmax) observed under dry conditions for

A. racemosa (Table 5.5). While the negation of conserved soil water can account for the lowered relative biomass response compared to wet conditions in A. racemosa, the causes for the lowered photosynthetic response requires further examination.

Down-regulation of photosynthesis or acclimation under elevated CO2 in C3 plants is a widely observed phenomenon (Sage et al. 1989; Arp 199l; Woodrow 1994;

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Xu et al. 1994; Baxter et al. 1995; Moore et al. 1999; Poorter & Navas 2003; Ellsworth et al. 2004). The fundamental premise of down-regulation of C3 plants is through source-sink imbalances. That is, the rate of intake of nutrients cannot match demand for nutrients required in the production of sinks to store by-products of photosynthesis.

This leads to down-regulation of photosynthesis under elevated CO2 over time. The accumulation of carbohydrates can also act as negative-feedback on photosynthetic processes with the potential to cause other side effects such as the rupturing of cell walls, and dilution of leaf nitrogen necessary to sustain higher levels of photosynthesis

(Woodrow 1994).

Measurement of SLA in this experiment did not support an accumulation of carbohydrates in A. racemosa leaves grown in elevated CO2/dry conditions as there was evidence of a decrease in SLA values. However, it must be noted that in the past, measures of SLA have not always adequately represented carbohydrate concentrations in some plant species (Seneweera et al. 2001). Lower percent leaf nitrogen was clearly observed under dry treatments of elevated CO2, indicating that the dilution effect may still be a possible cause of photosynthetic down-regulation of A. racemosa plants if

SLA had not adequately captured carbohydrate levels in the leaves.

In comparison, there was no similar evidence of reduced levels of percent leaf nitrogen in wet treatments at elevated CO2 for A. racemosa plants (Figure 5.9a).

Measurements of leaf gas exchange in wet treatments demonstrated also that an up- regulation of assimilation under elevated CO2 had occurred (Figures 5.7a and b and

5.8a and b), which is in line with the 19 % enhancement in whole-plant biomass observed in A. racemosa plants under wet conditions at elevated CO2 (Chapter 4, Table

4.3). Such a lack of biomass and photosynthetic response in the dry conditions may therefore suggest nitrogen was limiting.

Nitrogen limitation has been found widely in the past to limit photosynthetic

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capacity under elevated CO2 (Ghannoum & Conroy 1998; Lutze & Gifford 2000;

Poorter & Navas 2003; Ainsworth & Long 2005). Nitrogen is a necessary resource for the development of sinks to deposit photo-assimilates during the growth of plants

(Ghannoum & Conroy 1998). The down-regulation of photosynthesis, reduced Jmax and

Vcmax, and lowered levels of percent leaf nitrogen all therefore support a nitrogen- limitation effect on growth of A. racemosa in this study.

There are a number of possible causes for the lowered percent leaf nitrogen levels in A. racemosa plants under dry conditions. In Chapter 4 it was hypothesised that the observed lack of phenotypic variability in allocation of biomass by A. racemosa could have prevented the needed investment of carbon to the roots of the plant to allow adequate uptake of water and thus nitrogen. This would in turn have supported enhanced photosynthesis under elevated CO2. The impact of limited phenotypic variation on biomass enhancement cannot be determined here. However, it is clear that in dry conditions, an increase in demand for nitrogen through enhanced assimilation (and given the water conservation effect was not expressed) could logically lead to difficulties in the plant maintaining nutrient uptake to support enhanced photosynthesis. It may also be that A. racemosa plants experienced an accumulation of carbohydrates in the plant structure (diluting nitrogen concentrations) but simple measurements of SLA were not able to identify this.

The reduction in transpiration in dry conditions (absent in wet conditions) may also have alleviated some of the nutrient-limitation if watering treatments had allowed the water-conservation effects of elevated CO2 to be retained. In the absence of water conservation however, the reduced transpiration could not overcome deficits in soil water and hence lead to possible nutrient deficits. Under abundant water in wet conditions, a CO2-induced biomass enhancement occurred, indicating that the limiting factors in dry conditions were not present. Such a response explains the absence of

116 lowered transpiration in wet conditions (Figure 5.9c). Previous research supports a lack of lowered transpiration under elevated CO2 in wet conditions, whereby there is a reduced need for water-conservation when water is abundant (Morison 1993).

Further research is required to confirm nutrient-limitation as the likely cause of the down-regulation of photosynthesis in A. racemosa under dry conditions. However, it is likely that the combination of the experimental watering treatments and difficulties in nutrient-uptake by the plant, led to a down-regulation of photosynthetic processes and reduced biomass enhancement under elevated CO2 in dry conditions.

5.6 Conclusion

Measurements of physiological processes in this chapter have provided important insight into the mechanisms behind biomass responses observed in three species (T. aestivum, E. curvula and, A. racemosa) at elevated CO2 and water- limitation.

The increased rate of assimilation by T. aestivum supports much past work, whereby enhancements of photosynthesis occurred in both dry and wet conditions at elevated CO2. The lack of replication however, made it difficult to determine the role of other leaf processes (percent leaf nitrogen, SLA and, transpiration) in enhancing assimilation and biomass under elevated CO2 for this species. Ultimately, measurements indicated that T. aestivum’s response to elevated CO2 was similar in magnitude to other research conducted on the species. Therefore cabinet conditions

were not limiting to possible growth enhancements at elevated CO2 in the other species grown in this study.

Measurements of leaf gas-exchange were taken on E. curvula in this chapter to find evidence of an enhancement of photosynthesis in line with the significant increases in biomass at elevated CO2 in dry and wet conditions in Chapter 4.

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Assimilation rates showed a trend of down-regulated photosynthesis to elevated CO2 in both water treatments. It appeared however, that a reduction of transpiration and increased efficiency in the use of nitrogen supported a physiological enhancement under elevated CO2, but was not reflected in assimilation measurements. It was thus concluded that an enhancement of assimilation probably occurred in early plant development, but was down-regulated as time went by. While further research is needed to support such a hypothesis, the results highlight the level of species-specific differences that can occur in plant responses. In this case the response suggests that photosynthetic enhancement can occur very early in plant development of some species.

Species-specific effects were also apparent in leaf gas exchange measurements in A. racemosa species. Measures of photosynthesis complemented biomass responses consistently in dry and wet conditions at elevated CO2. A lack of biomass enhancement was supported by significant down-regulation of photosynthesis combined with lower percent leaf nitrogen in dry conditions. This was despite a reduction in transpiration, which under field conditions would act to conserve water and lead to biomass enhancement. However, it was negated in this experiment as a result of watering treatments. The species was not able to overcome higher demands for nutrients through biomass allocation when under enhanced levels of photosynthesis in dry conditions.

When water was not limited however, sufficient nutrient uptake is likely to have occurred and measurements of photosynthesis showed a significant biomass enhancement.

In retrospect, measurement of water usage by plants during experimentation could have provided compelling evidence of the CO2 effects on plants water use.

However given the labour intensive nature of water usage measurements and limited resources available, it was not possible to collect these measurements for support of

118 conclusions.

The results here demonstrate the vast complexity of plant responses to elevated

[CO2], especially when in combination with other environmental variables. While leaf- level measurements are extremely important in understanding plant response to factors such as CO2 and water-limitation, it must be stressed that they cannot provide conclusive evidence to all. These measurements are most valuable when analysed and interpreted in combination with other observations.

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Chapter 6- Extension of results: A meta-analysis of current research on the effects of

elevated CO2 on plant responses

Sara E.L Hely1, 2 and Stephen P. Bonser1†

1. University of New South Wales, Sydney

2. CSIRO Plant Industry, Canberra

6.1 Overview of chapter

Functional-type classifications have been used to predict plant responses to

elevated CO2 frequently in the past. Occurrences of plant responses that do not conform to

such functional-type classifications are, however, numerous.

We conducted a meta-analysis on data collected from published studies and results

from this thesis to determine the extent to which photosynthetic pathway could account

for biomass responses to elevated CO2. Other plant traits that could pertain to a biomass

enhancement under the same conditions were also tested. Plant data used in the meta-

analysis included a broad cross-section of results from experiments in a range of elevated

CO2 systems and conditions. Plant species used also included numerous independent

evolutionary transitions between C3 and C4 photosynthesis. The lack of independence

resulting from repeated use of closely evolved species in past research was controlled for

by using species in which metabolic pathway had evolved independently in their

phylogenetic history.

The results indicated that experiments on plant responses under elevated CO2 to

date show a tendency to make repeated comparisons of species from the same

predominantly C3 versus C4 taxa. Consequently any general predictions on how plants

should respond to climate change are likely to be based on a very limited number of

evolutionary comparisons. The results also indicated that responsiveness of plants to

† Stephen Bonser contributed 50 % to the writing of this chapter. This was through assistance in constructing the phylogenies, the meta-analysis and interpretation of results

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elevated CO2 were related, to some degree, to metabolic pathway, but that C3 responses to elevated CO2 were sometimes weaker than the response of C4 species. This indicated that metabolic pathway may be less important in predicting biomass responses to climate change than was previously expected. In addition, the use in current studies of plant species in a narrow cross-section of taxa means that results may not be representative of wider species responses. Broader evolutionary comparisons must therefore be used in future experimentation of plants under elevated CO2 to gain better predictions of whole- plant system responses to climate change.

6.2 Introduction

Understanding how plants will respond to climate change is critical in making predictions and projections of the impacts of climate change. However, broad/multi- species comparisons of how plants will respond to climate change are infrequent and contradictory. In order to take advantage of the positive effects, and prepare or adapt to negative effects of elevated CO2, critical analysis of research to date is required to improve predictive capacity and methods of experimentation.

Currently, critical analyses of research conducted on plants at elevated CO2 are few (Bazzaz 1990; Drake et al. 1997; Rötter & Van De Geijn 1999; Wand et al. 1999;

Ghannoum et al. 2000; Poorter & Nagel 2000; Hughes 2003; Gifford 2004; Ainsworth &

Long 2005), and there is little agreement between these reviews as to solid determinants of plant growth under elevated CO2 conditions. No past studies simultaneously examine functional group data with phylogenetic relationships. This is however, a powerful tool since we can examine if plant responses to elevated CO2 are correlated to functional group evolution.

Current classifications of plant responses based on functional-type characteristics have provided reasonable predictions of plant responses to certain environmental 121 conditions (Wilson 1999). The primary functional-type classification used frequently in predicting responses to elevated CO2 is metabolic pathway. The C3 and C4 mode of photosynthesis is consistently represented by the same plant families and genera. Sage

(2004) identified the evolution of the C4 metabolic pathway from the C3 pathway over 45 times in 19 families of angiosperms. The evolution of C4 photosynthesis from C3 photosynthesis was driven by CO2 levels in the atmosphere (Beerling 2005), leading to the expectation that marked differences in responses between the C3 and C4 plants would occur under changed CO2 conditions.

Early initial research supported the use of metabolic pathways for predicting plant responses to CO2 enrichment. The C3 mode of photosynthesis was found to consistently exhibit greater biomass response to elevated CO2 than C4 (e.g. Carlson & Bazzaz 1980;

Zangerl & Bazzaz 1984; Morison 1993). The higher CO2 saturation point in C3 photosynthesis permits greater accumulation of biomass under favourable conditions at elevated CO2 (Carlson & Bazzaz, 1980). More recently however, the occurrence of large variability and exceptions to classifications based on metabolic pathway have increased

(Wand et al. 1999; Ghannoum et al. 2000; Poorter & Navas 2003; Ainsworth & Long

2005). This has prompted uncertainty in the use of metabolic pathway as a predictor of plant responses to elevated CO2.

Qualitative examination of past high CO2 research reveals a large majority of experiments focus predominantly on plants from the Poaceae family (grasses) or close relatives of the Poaceae family (Author’s Pers obs). The Poaceae family is physiologically diverse, economically important in agriculture and in maintaining natural diversity, and occupies 20 % of the vegetation cover on the earth (Winslow 2003). This makes it unsurprising that it is well represented in elevated CO2 research. The Poaceae family consists of 7 sub-families in which a number have representatives of C3, C4 and

C3/C4 intermediates. It is however, limited by only one major evolutionary divergence of 122

the C4 metabolic pathway. The sub-families Panicoideaes and Chloridoideae (most represented subfamily of predominately C4 species in the elevated CO2 literature) represent a single evolutionary divergence with a common ancestor in the subfamily

Pooideaes (the most represented subfamily of C3 species in the elevated CO2 literature)

(Hilu et al. 1999). The increasing number of exceptions of predicted responses to elevated CO2 based on metabolic pathway, and the variability of responses observed in research to date could therefore be caused by this clear lack of independence in the data sets. Contrasts made within the Poaceae family generally represent a single evolutionary event of C4 photosyntheses. Since C4 photosynthesis has evolved at least 45 times independently in the angiosperms, generalisations about the importance of metabolic pathway in understanding plant responses to elevated CO2 must include a wider range of taxa. Other plant traits (such as growth rate, leaf area and root fraction etc.) may evolve independently of photosynthetic pathway, and may help explain plant responses to elevated CO2 within the major grass subfamilies and across flowering plant taxa.

The role of other traits in plant responses to elevated CO2 has been the subject of research by a number of authors. A meta-analysis of research from 1980 to 1997 conducted by Wand et al (1999) compared the responses of approximately 40 wild

Poaceae species of C3 and 24 wild Poaceae species of C4 species under elevated CO2. A rigorous analysis of these species revealed that the responses of C3 and C4 grass species only adhered to what was expected based on metabolic pathway to some extent.

Differences between C3 and C4 biomass responses were marginal. Larger than expected responses of C4 species suggested that traits, not necessarily related to metabolic pathway, can control how plants respond to elevated CO2. This is a theory supported recently by others, who suggested that factors such as growth rate, nutrient availability, and species- specific phenotypic variability traits could be stronger drivers of plant growth under elevated CO2 (Ghannoum et al. 2000; Poorter & Navas 2003; Hely & Roxburgh 2005). 123

We examined plant responses to elevated CO2 through a meta-analysis of published experimental data. We tested for significant relationships between plant responses to elevated CO2 and plant traits. Phylogenetically independent contrasts were used to control for the confounding effects of the data from previous research being focused predominantly on the Poaceae family. Such closely related species would show greater similarity in trait expression than more distantly related species. However, phylogenetic contrasts allow control over the non-independence of species data and the confounding effects of the shared descent of the plants. Any trait differences between two closely related taxa will be independent of any two other closely related taxa (Felsenstein

1985). Thus, we can examine the evolution of traits such as metabolic pathway to determine if they are associated with the evolution of plant responses to CO2 enrichment.

6.3 Materials and methods

Data were collected from 94 species used in past experiments that assessed the effects of elevated CO2 on the growth of plants (Appendix 1). The data were obtained from scientific papers published from 1980 onwards. Species data were assembled to represent the greatest number of evolutionary transitions between C3 and C4 photosynthesis. Data were collected from plants growing in elevated atmospheric CO2 concentrations (i.e. 600 to 800 ppm) versus control ambient CO2 concentrations (350 to

400 pmm). Carbon dioxide treatments were established with growth cabinets, glasshouses, tunnels, open top chambers and free air CO2 enrichment (FACE) systems.

All data were collected from plants in favourable resource treatments (i.e. high nutrients and water) and at very low density or solitary plant experiments. Measurements (where available) were collected from elevated and control CO2 treatments from each study: Dry weight above-ground biomass (g), biomass enhancement ratio (BER) and, growth rate

(g/day). Biomass enhancement ratios (BER) were calculated by dividing the biomass in 124

elevated CO2, by the respective biomass in ambient CO2 conditions. Growth rate (g/day) was recorded as the biomass of plants divided by the number of days of growth until the plants were harvested. The calculation of relative growth rate, while desirable, was not possible because it requires an initial starting biomass. The data available in current literature very rarely includes such information and as a result growth rate per day was used instead of relative growth rate.

Other plant physiological and growth form measures of plant responses to elevated

CO2 (e.g. root fraction, percent leaf nitrogen, leaf area and specific leaf area measurements) are often reported in CO2 enrichment studies but they were not consistently reported across studies, and we could not test for evolutionary responses in these traits here.

Phylogenetic relationships across the plant species included in our analyses were obtained from published phylogenetic data (Appendix 2).

Data analysis

We tested for a significant (P < 0.05) relationship between biomass enhancement ratio (BER) to increased CO2 and plant metabolic pathway across species using a T-test.

Analysis of variance (ANOVA) was used to test for significant differences in geometric

BER means to increased CO2 across the Poaceae taxa. Pearson Product Moment

Correlations were used to test for significant relationships between growth rate and BER.

Outliers in the correlation analysis were removed (where appropriate) using Mahalanobis outlier distance plots (JMP, SAS Institute, Cary North Carolina).

Phylogenetically independent contrasts were conducted to control for the non- independence of interspecific comparisons. These were conducted using the Comparative

Analysis for Independent Contrasts (CAIC) program (Purvis & Rambaut 1995). For contrasts where the independent variable is categorical (i.e. C3 versus C4 metabolic pathways) the independent contrast method returns values for BER in taxa for each 125 independent evolutionary event of metabolic pathway evolution. We tested for a significant (P < 0.05) association between the evolution of metabolic pathway (e.g. from

C3 to C4 photosynthesis) and the evolution of a shift in BER to increased CO2 using a

Wilcoxon Signed Rank test (JMP, SAS institute, Cary North Carolina). For independent contrasts between continuous variables, BER was the dependent variable. The independent variable (growth rate) was made positive by convention. Changes in BER varied positively or negatively. This depended on whether the evolution of greater differences in growth rate between diverging taxa was consistently associated with an increase or decrease in BER. Significance of the independent contrast analysis was assessed by regression analysis. Regression lines were forced through the origin due to the symmetry arising from converting the dependent contrast values to positive numbers

(Garland et al. 1992).

6.4 Results

The broad analysis of mean biomass enhancement ratios across all species of C3 and C4 plants collected (see Appendix 1) indicated that C3 plants had marginally higher biomass enhancement to increased CO2 compared to C4 plants (Figure 6.1a T-test, T = 2, d.f = 81, P = 0.05). However, when species data were corrected for their lack of independence only 9 evolutionary independent events of metabolic pathway were found

(Table 6.1). Phylogenetically independent contrasts showed no significant difference between BER and the evolution of C3 and C4 photosynthesis (Figure 6.1a and b, Wilcoxon

Signed Rank Test, Hypothesised Value = 0, Actual Value = 0.0078, Signed Rank Test

Statistic = -11.5, d.f = 8, P = 0.2). Of the 94 species surveyed for this analysis more than

50 % were grass species from one of three subfamilies of Poaceae: Panicoideaes

(predominantly C4 grasses), Pooideaes (C3 species), and Chloridoideaes (predominantly

C4 grasses). The Pooideae evolved from a larger clade of C4 species (most recently the 126

Bambusoideae) but also the C4 Chloridoideae. So, investigations including C3 Pooidaea’s are essentially sampled from the same C3 evolutionary event and represent a single sample. If all plant experiment data were collected from elevated CO2 experiments to date, the proportion of plants from the Panicoideaes, Pooideaes, and Chloridoideaes would be expected to be as high as 90 %.

There were no significant differences in BER as a result of elevated CO2 for the

Pooideae, Panicoideae, and Chloridoideae families (Figure 6.2, d.f error = 53, F-ratio =

0.26, P = 0.77). However, there is considerable variability within sub-families. Thus, it is entirely possible to find significant differences in response in any pair of species - but these differences are likely to be due to the chance selection of the species.

Figure 6.3 illustrates that a correlation exists between growth rate and the biomass enhancement of plants used in the meta-analysis (Pearson correlation, R = -0.27, n = 59, P

= 0.036). In the independent contrast analysis between growth rate with BER (Figure 6.4), it was found that growth rate was evolutionarily associated with an increase in biomass at elevated CO2 (R = -0.27, n = 59, P = 0.04).

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2.0 a) 1.8

1.6

Contrasts: Means (Log BER) 1.4 C3 C4 1- Within the genus Panicum 0.085395 0.056825 1.2 2- Within the genus Euphorbia 0.1557 0.15608 3- C3 evolution in the genus Molina (within Arundinoideae) 0.22321 0.12045 1.0 4- Between Atriplex and Chenopodium 0.061095 -0.09388

Biomass Enhancement Ratio Enhancement Biomass 0.8 5- C3 evolution of Oryza within Bambusoideae 0.135125 0.121535 0.250.6 6- C3 evolution of Austrodanthonia 3 within Arundinoideae 0.117715 0.142825 0.20 b) 7- Between Fabaceae and 8 8 Chenopodaceae 0.086605 0.079675 2 2 8- C4 evolution in Flaveria 0.15 5 6 within the Asteraceae 0.17947 0.18295 3,5 6 9- Evolution of predominantly 0.10 C Pooideae 0.070395 0.073125 1,7 7 3 9 9 0.05 4 1

Log BER 0.00

-0.05

-0.10 4

-0.15 C3 C4

Figure 6.1 Comparison of geometric means of biomass enhancement ratios (BER) at elevated CO2 for C3 and C4 plants across all taxa presented in a) a box plot showing spread of data (T-test, T = 2, d.f = 81, P = 0.05). The box plot represents the 25th (bottom) and 75th percentile (top) and the line in the middle represents the median), b) Pair-wise comparisons of Log BER between the 9 family and sub-family pairs where C4 photosynthesis has evolved independently (Wilcoxon Signed Rank Test, Hypothesised Value = 0, Actual Value = 0.0078, Signed Rank Test Statistic = -11.5, d.f = 8, P = 0.2). Individual variances from the experiments were not included as the analysis was intended as a review of the change in average biomass enhancement in elevated CO2 associated with the evolution of C3 or C4 photosynthesis.

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Table 6.1 Phylogenetic contrasts of families and sub-families used in the meta-analysis. The table shows families and sub-families which have independent contrasts between C3 and C4 evolution.

Families of plants with independent divergences of C4 photosynthesis Euphorbeaceae Chenopodium within Atriplex Amaranthaceae Ambrosia, Flaveria, Cirsium, Centauria within Asteraceae

Grass sub-families with independent divergences of C4 photosynthesis Bambusoideae Panicaceae, Chlorideae, Arundinaceae within Pooidadeae Panicaceae Panicaceae, Chlorideae within Arundinaceae Austrodanthonia within Aristida

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2.0

1.5

1.0

0.5 Biomass Enhancement Ratio

0.0

Panic Chlor Pooid

Figure 6.2 Phylogenetic contrasts of mean BER across the three primary sub-families, Pooideaes (Pooid, n = 17), Panicoideaes (Panic, n = 27) and Chlorideaes (Chlor, n = 12) found in past research. The box plots represent the 25th (bottom) and 75th percentile (top) and the line in the middle represents the median (d.f. error = 53, F-ratio = 0.26, P = 0.77). 130

0.3

0.2 E.C Exp 2

0.1

A.R Exp 2 BER Log 0.0

E.C Exp 1 -0.1

-0.2 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

Growth Rate (g / day)

Figure 6.3 Correlation analysis between log BER and growth rate of non-independent species responses shows that log BER tends to be greater in species with relatively low growth rate (Pearson correlation, R = - 0.27, n = 59, P = 0.036). Growth rate units are in g/day. Grey circles labelled E.C (E. curvula) and A.R (A. racemosa) represent data points taken from Experiment 1 (Exp 1) and Experiment 2 (Exp 2) in the thesis.

131

0.10

0.05

0.00

-0.05

contrasts BER Log

-0.10

-0.15 0.00 0.01 0.02 0.03 0.04 0.05 0.06

Growth rate contrasts

Figure 6.4 Independent contrast analyses of the relationship between growth rate and log BER across all species used in the meta-analysis. Log BER tends to be greater in species with relatively low growth rate (n = 35, P = 0.0058). Growth rate units are in g/day. 132

6.5 Discussion

Past research has found many exceptions to predicted plant responses of C3 and C4 metabolic pathway plants to elevated CO2 (Ziska & Bunce 1997; Wand et al. 1999;

Ghannoum et al. 2000; Poorter & Navas 2003; Hely & Roxburgh 2005). The results of the meta-analysis conducted in this chapter supports previous observations that repetitive sampling of the Poaceae (grass) family is largely the cause of this variation. Figure 6.1 shows that only a marginal difference in biomass enhancement ratio occurred between results for C3 and C4 plants (Figure 6.1a and b), with the difference being driven largely by only a few evolutionary independent events (Figure 6.1). The lack of independent evolution of C4 photosynthesis in the grass family has effectively reduced the sample size.

This reduces the power of the analysis and predictions that may be drawn from the vast amount of research focussed on C3 and C4 species responses to elevated CO2. When the analysis was corrected using phylogenetic contrasts for the lack of independence in the data, there were no significant differences in BERs between C3 and C4 plant species

(Figure 6.1).

The comparison of biomass enhancement ratios of the most commonly represented families in the literature, the Pooideaes, Panicoideaes and the Chloridoideaes (Figure

6.2), supports the lack of independence of data observed in the previous analysis. The

Panicoideaes and the Chloridoideaes share a common ancestor with the Pooideaes, making this a sample size of one for this analysis. There is relatively high variability of species responses within these subfamilies, but no significant differences in BER across sub-families (Figure 6.2). Thus, any differences in species responses are likely to be due to differences in species-specific traits but little to do with the evolution of metabolic pathway. Differences in BER clearly exist between some C3 and C4 taxa (Fig 6.1).

However, to develop a clear understanding of how metabolic pathway is related to plant responses to elevated CO2, a broader range of plant taxa needs to be examined. 133

There are a number of studies suggesting that plant responses to elevated CO2 are not based purely on photosynthetic process (i.e. Wand et al. 1999; Ghannoum et al. 2000;

Poorter & Nagel 2000). These workers have found evidence to suggest that species- specific traits such as growth rate, leaf area, and root fraction can all play an important role in determining the magnitude of response of plants to elevated CO2. The results from the meta-analysis conducted in this study indicated a correlation between growth rate and responsiveness to elevated CO2. Figures 6.3 and 6.4 show that the degree of biomass enhancement decreases as the growth rate of plants sampled increases. Such a response is supported by other authors (Drake et al. 1997; Poorter & Nagel 2000; Ryser & Eek 2000), who theorised that higher growth rates cause a limitation of the capacity of a plant to respond to an increase in resources such as elevated CO2. Higher growth rate-plants operate at the upper threshold of their resource use, limiting possible increases in metabolism. Results in this thesis were contrary to this conclusion whereby E. curvula showed a greater biomass response to elevated CO2 compared to the slower growing A. racemosa plants. Exceptions such as this are consistent however in that often the response of a plant species to elevated CO2 is dependant on more than one trait , or a combination of several.

Growth rate is however, just one example of a plant trait that has been found to correlate with a biomass enhancement under elevated CO2. There is potential for other traits (not tested here) to play a large role in biomass responses, potentially more so than metabolic pathway. While it would be desirable to look at other traits such as plant height, leaf number and RGR, it was not feasible in this case because these traits are less frequently available in the literature. Such other traits could be considered in a more comprehensive analysis of the literature in future work, but would however, require significant work in the standardising of all data for light, nutrients, plant density, level of

CO2. As a result, it is beyond the scope of this thesis but it is recommended that further 134 research look at existing data sets for similar trends in other plant traits and thus allow stronger future predictions of plant responses.

6.6 Conclusion

This meta-analysis of phylogenetic information has highlighted the importance of future studies to not only include species form a larger cross section of plant taxa, but also include measurement of a variety of different plant traits. The high level of variability that exists in current research, whilst sometimes problematic, can in fact give important information about species-level responses to elevated CO2. It should however, be a high priority to extend current research to include measurement of plant traits as well as diverse phylogenetic information. This will allow better understanding of the causes of species-specific responses and variability occurring in future experimentation, and lead to better planning and adaptation for a future world of climate change.

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Chapter 7- General conclusions and future research

7.1 Overview of chapter

This thesis has addressed competitive interactions, patterns of biomass allocation, and the physiological responses of a C3 native species, Austrodanthonia racemosa, and C4 invasive species, Eragrostis curvula under elevated CO2 and water- limitation. Wheat species, Triticum aestivum, was examined briefly as a control species for checking for cabinet effects. The use of only two Australian grassland species in these experiments restricts potential generalisations that could be made about responses of Australian plant systems. However, the mechanistic approach taken in this thesis offers insight and identification of important plant processes that can be broadly applied to larger plant systems.

In this chapter, the major findings and their implications for grasslands in

Australia will be discussed. The final section will then make suggestions for future research in the area.

7.2 The responses of E. curvula and A. racemosa at elevated CO2

The C3 and C4 metabolic pathway has been generally found to produce consistent biomass responses to elevated CO2. This is based on the fundamental premise that C3 and C4 plants differ in how CO2 is fixed from the atmosphere by the plant. The CO2-concentrating mechanism present in C4 photosynthesis but absent in

C3 photosynthesis results in C3 plants showing a greater sensitivity to increased CO2 in the atmosphere (Poorter & Navas 2003). Under favourable conditions, a greater increase in biomass by C3 plants compared to C4 plants at elevated CO2 would thus be expected. Such a response would clearly provide widespread benefits for agricultural

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industries reliant on C3 crops (Ainsworth & Long 2005) and thus justification for the distinct focus of past research on the Poaceae family of plants.

While there is no doubt that differing metabolic pathways can play a role in plant responses to elevated CO2, many other factors can affect the rate and fate of the carbon fixed by the plant from the atmosphere (Ghannoum et al. 2000). Metabolic pathway is only important in the acquisition of carbon from the atmosphere. A growth enhancement relies on how that carbon is then utilised by way of investment into sinks. The photosynthetic and biomass responses observed in this thesis led to the conclusion that the majority of the plant responses that differed from what was expected based on metabolic pathway, could be explained either by external environmental variables or species-specific traits resulting in modification of plant structure to environment.

In Chapter 3, the responses of C4 invasive species E. curvula was examined in a competition experiment where a positive biomass response to elevated CO2 was observed when resources were not limiting (i.e. when competition from surrounding plants were low and root exclusion tubes were absent).

In Chapter 5, it was observed that the rates of photosynthesis of C3 plants T. aestivum and A. racemosa (in wet conditions) was in agreement with past theories of accumulation of biomass under elevated CO2 (Chapter 5.5.1 and 5.5.3). Under dry conditions however, C3 grass A. racemosa showed a lack of photosynthetic and biomass enhancement to elevated CO2 altogether (Chapter 5.5.2 and Chapter 5.5.3).

E. curvula, in Chapter 5 while demonstrating a significant growth enhancement, had no observed photosynthetic stimulation.

Results observed in these chapters indicated that interactions with other factors aside from photosynthetic processes have a significant influence on observed biomass

137 responses. The following sections will summarise these effects in greater detail.

7.3 The impact of external environmental variables

Past research has identified the availability of resources as highly important in determining the magnitude of biomass responses to elevated CO2. The availability of water, nutrients, and light have all been shown in previous literature to have large effects on physiology and the accumulation of biomass under elevated CO2 (Cotrufo et al. 1998; Ghannoum & Conroy 1998; Lutze & Gifford 1998; Atwell et al.1999;

Poorter & Nagel 2000; Ryser & Eek 2000; Wall et al. 2000; Daepp et al. 2001;

Mcdonald et al. 2002; Chapin 2003; Poorter & Navas 2003; Nowak et al. 2004; Wand

& Midgley 2004; Ainsworth & Long 2005). A plant’s ability to overcome resource- limitation and the variability in environmental conditions is also often species- specific.

Resource-limitation impacted on CO2 responses on numerous occasions in the results obtained in this thesis. In Chapter 3, intense competition in dry conditions caused above and below-ground resources to be limited to a target E. curvula plant surrounded by other E. curvula plants. Sink strength and hence growth is greatly limited without necessary water and nutrients to sustain photosynthesis. Thus this intense competition precluded a biomass response to elevated CO2. While E. curvula was grown in dry conditions in subsequent experiments under the same competitive pressure, the shorter timeframe of the experiment (168 days in Chapter 3 compared to

84 days in Chapter 4) is likely to have depleted a lesser amount of below ground resources leading to an overall biomass enhancement (Chapter 4.5.2). Growth rate of plants (based on cumulative biomass over number of days) supported this whereby in the competition experiment run over 168 days, the growth rate of E. curvula was

138

0.03g/day. In Chapter 4, E. curvula monoculture plants grown over 84 days was

0.12g/day. This potentially supports a declining response to elevated CO2 in line with a decline in both available resources and growth rate over time.

In Chapters 4 and 5, resource-limitation was also observed to reduce the biomass enhancement under dry conditions of elevated CO2 in the C3 plant, A. racemosa. The lack of biomass and photosynthetic enhancement was hypothesised to be caused by limited water and thus nutrient uptake (Atwell et al. 1999). There was an observed reduction in percent leaf nitrogen and photosynthesis was down-regulated.

Past research predicts the magnitude of a biomass response to be greater in dry conditions at elevated CO2 compared to wet conditions. This is due to the water conserving effects of elevated CO2. The watering treatments chosen for these experiments however, negated the water- conservation effect under elevated CO2 so physiological responses coupled with the effects of nutrient-limitation on those processes were therefore accentuated.

In summary, the effects of resource-limitation in this thesis caused plant responses to differ from what would be expected based on metabolic pathway alone.

This is a result well supported in the literature. The capacity of the species in this study to overcome such resource-limitation determined further the expression of biomass response to elevated CO2. The importance of resource-availability in the magnitude and direction of plant responses to elevated CO2 should be an important consideration when predictions are made of responses of more complex plant systems to elevated CO2.

7.4 The impact of species phenotypic variability

While there are clear limits to the amount of resource-limitation a plant can

139 overcome through phenotypic variability (for example the intense competition that E. curvula target plants were exposed to in Chapter 3), the growth strategies of a plant can play an added role in determining the amount of biomass a plant can accumulate

(Chapin 2003). A plant that is plastic in its allocation of biomass and use of physiological systems can adapt more effectively to conditions such as elevated CO2

(Teughels et al. 1995; Ryser & Eek 2004).

The phenotypic variability of C3, A. racemosa, and C4, E. curvula, were found to be quite different under the growth conditions used in this thesis. A variety of parameters were examined to gain an understanding of the capacity of plants to modify structure in response to environment. These were: plant height, leaf number, above and below-ground biomass, percent leaf nitrogen, specific leaf area, and additionally, a meta-analysis in Chapter 6 addressing growth rate.

In Chapter 4, C4 grass, E. curvula, demonstrated an increase in the diversion of biomass to the roots possibly to overcome the increased nutrient demands caused by the elevated CO2 conditions. In Chapter 5, E. curvula also showed increased efficiency in the use of nitrogen demonstrated through reduced percent leaf nitrogen under elevated CO2. In addition, it was speculated that the rapid initial growth of the species caused photosynthesis to be most sensitive to increased elevated CO2 very early in development, declining with age. The combination of these 3 responses led to a greater than 25 % enhancement of biomass under elevated CO2.

In contrast, the C3 grass, A. racemosa, showed evidence of limited phenotypic variability with no evidence of altered allocation of biomass, irrespective of resource limitations imposed upon it. Under dry conditions, this led to nutrient-limitation and a lack of biomass enhancement under elevated CO2.

The capacity of plants used in this thesis to adjust growth patterns in response

140 to environment may have played an important role in the level of biomass enhancement observed under elevated CO2 and thus could have been the difference between a significant biomass enhancement and a complete lack of biomass enhancement in these examples of C4 and C3 plants. Such a response could clearly have greater implications for competitive interactions between the two species if it is reflected in more complex plants systems.

The observed impact of both resource-limitation and phenotypic variability on plant responses to elevated CO2 in this thesis is in support of the conclusions gained from the critical analysis of past research carried out in Chapter 6. The meta-analysis in Chapter 6 identified a lack of consistency of biomass enhancements with that predicted based on metabolic pathway alone. While the analysis revealed that this could be a product of repeated sampling from the Poaceae family, expansion of research into other plant families may prove that classifications based on metabolic pathway may be obsolete.

The meta-analysis in Chapter 6 suggested that other drivers of plant responses to elevated CO2 may be more important than metabolic pathway. This is a conclusion supported in this thesis and the literature in general. The possible early developmental responsiveness of E. curvula to elevated CO2 which decreased with age, may be explained by growth rates.

Growth rate is a trait found to be correlated with biomass responses in the meta-analysis, where the analysis revealed a trend of slower growth rates leading to higher biomass enhancements to elevated CO2 conditions. This was however not assumed to be the sole driver of response to elevated [CO2] and comparison of growth rates and BER under elevated [CO2] in this thesis did not support this observation.

Other plants traits such as leaf area and root fraction were also suggested as possible

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contributors to CO2-induced biomass responses in the meta-analysis. Such traits were seen to contribute to the results gained in this thesis.

7.5 Implications for Australian grasslands

Conditions of low nutrients, low rainfall and intense competition, common in

Australian grasslands systems could see elevated atmospheric [CO2] cause significant changes to native ecosystems. Initial studies by Lilley et al. (1997), Bolger (1998),

Ghannoum et al. (2001) and Hely & Roxburgh (2005) have already shown that some

Australian species do not respond as predicted based on their metabolic pathways.

Findings in this thesis also support the significant effect of elevated CO2 on plant on

Australian grass species.

The lack of biomass enhancement in C3 grass, A. racemosa, in dry conditions and the significant biomass enhancement of E. curvula at elevated CO2 in both dry and wet conditions implies that under climate change conditions in the field, the invasive species, E. curvula, has a significant competitive advantage compared to native species, A. racemosa. This appears to be due to its heightened phenotypic variability in responses to overcome limitations in resources, a trait not clearly expressed in A. racemosa. Under elevated CO2, where more extreme weather and the frequency of disturbances are likely to increase, plant species possessing heightened ability to accommodate these changes are likely to out-compete their less plastic counter parts. Weed species have been found in other research to be favoured under elevated CO2 conditions as a result of their faster growth rates and phenotypic variability (Ziska & Bunce 1998). Further research will need to be carried out, however, to establish the extent in which the trends observed here will be reflected in the field and additionally with the effects of water conservation expressed.

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Research to date shows convincingly that under elevated CO2, a considerable reduction in stomatal conductance occurs (Farquhar & Sharkey 1982). Under well- watered conditions, this has little impact on biomass accumulation. However, in dry conditions a significant reduction in water loss during photosynthesis can lead to the conservation of soil water and prolonged plant growth (Morison 1993). The effects of water conservation at high CO2 were not tested in this thesis. Therefore, to understand the responses observed here more fully, further experimentation is needed under watering regimes that allow expression of water conserved at elevated CO2.

7.6 Future research

The results found in this thesis confirm a great deal of past research illustrating the highly species-specific nature of plant responses to elevated CO2. When interactions with elevated CO2, resources and species-specific traits are also included, plant responses are variable and difficult to predict.

Areas identified as needing further research in the wider science of plant responses to elevated [CO2] were also identified in this thesis. Australian grasslands are an important commodity, but little is known of how this commodity will be affected by climate change in the next 50 to 100 years. It was identified that C3 and C4

Australian plants did not respond as predicted to elevated CO2 based on the physiological processes alone. There were however, two dominating factors causing the responses observed in this thesis. These were external environmental conditions

(resource-availability) and species-specific traits. Both of these traits have increasingly been demonstrated as having a more significant effect on plant responses at elevated CO2 than was previously thought.

Only two species were observed in this thesis, so in the first instance it is

143 recommended that future research expand to cover multiple-species comparisons. In using multi-species comparisons, it would be possible to obtain more realistic representation of the effects of interference between plants in ecosystems at a given time.

While controlled environments are an excellent starting point for providing mechanistic investigations of plant responses, extension of research into field environments are a necessary step in ‘ground truthing’ results. Temperature-gradient tunnels (TGT), open-top chambers (OTC), and free-air CO2 enrichment (FACE) facilities have been shown to be important devices for scaling up responses from small-scale cabinet studies to the field (Ainsworth & Long 2005). Such facilities allow multi-species comparisons to be made under near-natural light and precipitation conditions. Due to the complexity of the environment in which Australian plant systems exist in, such field-based studies of multi-species responses are necessary in the collection of realistic information.

Despite the need for larger-scale, field-based research on Australian plant species, attention to small-scale plant responses must not be overlooked. Mechanistic approaches provide rare insight into the underlying processes that plants use in responding to a given environment. The challenge for scientists is to incorporate leaf- level, plant-level, and ecosystem-level scales in future experimental protocols.

Through the incorporation of such measurements on an increased number of plant species, significant advances in the predictive capacity of experiments and thus the understanding of the impacts of climate change can be achieved. It is however integral, that clear targeting and effective experimentation is carried out to ensure the best adaptation strategies are developed to prepare for the future impacts of climate change.

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Bibliography

Ackerly, D. D, and Bazzaz, F.A, 1995. Plant-growth and reproduction along CO2 gradients - Nonlinear responses and implications for community change. Global Change Biology, 1: 199-207.

Adam, N. R, Wall, G. W, Kimball, B. A, Pinter, P. J, Lamorte, R. L, Hunsaker, D. J, Adamsen, F. J, Thompson, T, Matthias, A. D, Leavitt, S. W, and Webber, A. N, 2000. Acclimation response of spring wheat in a Free-Air CO2 Enrichment (FACE) atmosphere with variable soil nitrogen regimes. 1. Leaf position and phenology determine acclimation response. Photosynthesis Research, 66: 65-77.

Ainsworth, E, A and Long, S. P, 2005. What have we learned from 15 years of free- air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytologist, 165: 351-372.

Aliscioni, S. S, Giussani, L. M, Zuloaga, F. O, and Kellogg, E. A, 2003. A molecular phylogeny of Panicum (Poaceae: Paniceae): Tests of monophyly and phylogenetic placement within the Panicoideae American Journal of Botany, 90: 796- 821.

Arp, W. J, 1991. Effects of source-sink relations on photosynthetic acclimation to elevated CO2. Plant Cell and Environment, 14: 869-875.

Atwell, B. J, Kreidemann, P. E, and Turnbull, C, (eds) 1999. Plants in action: Adaptation in nature, performance in cultivation. South Yarra, Macmillan Education Australia Pty Ltd.

Australian Bureau of Meteorology Website, 2006. http://www.bom.gov.au/

Barker, N. P, Morton, C. M, and Linder, P. H, 2000. The Danthonieae: Generic composition and relationships in Grasses: Systematics and Evolution, Jacobs S. W. L, Everett, J (eds), Canberra, CSIRO Publishing.

Bassirirad, H, Gutschick, V. P, and Lussenhop, J, 2001. Root system adjustments: regulation of plant nutrient uptake and growth responses to elevated CO2. Oecologia, 126: 305-320.

Bazzaz, F.A, 1990. The response of natural ecosystems to the rising global CO2 levels. Annual Review of Ecology and Systematics, 21:167-196.

Bazzaz, F.A, and McConnaughay, K. D. M, 1992. Plant-plant interactions in elevated CO2 environments. Australian Journal of Ecology, 40: 547-563

Bengtson, J, Fagerstrom, T, and Rydin, H, 1994. Competition and coexistence in plant-communities. Trends in Ecology & Evolution, 9:146-150.

Bloom, A. J, Chapin, F. S, and Money, H. A, 1985. Resource-limitation in plants- An economic analogy. Annual Review of Ecology and Systematics, 16: 363-392.

145

Bolger, T. P, Lilley, J. M, Gifford, R. M, and Donnelly, J. R, 1997. Growth response of Australian temperate pasture species to CO2 enrichment. In ‘Proceedings of the XVIII International Grassland Congress’, 9: 19-20.

Callaway, R. M, Brooker, R. W, Choler, P, Kikvidze Z, Lortie C. J, Michalet R, Paolini L, Pugnaire F. I, Newingham B, Aschehoug E. T, Armas C, Kikodze D, and Cook B. J, 2002. Positive interactions among alpine plants increase with stress. Nature, 417:844-848.

Campbell, B. D, Laing, W. A, Greer, D. H, Crush, J. R, Clark, H, Williamson, D. Y, and Given, M. D. J, 1995. Variation in grassland populations and species and the implications for community responses to elevated CO2. Journal of Biogeography, 22: 315-322.

Carlson, R. W and Bazzaz, F. A, 1980. The effects of elevated CO2 concentrations on growth, photosynthesis, transpiration and water-use efficiency of plants. Environmental and climatic impacts on coal utilization. Academic Press. New York. 609-622.

Chapin, F. S, 2003. Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Annals of Botany, 91: 455-463.

Chase, M. W, Soltis, D. E, Olmstead, R. G, Morgan, D, Les, D. H, Mishler, B. D, Duvall, M. R, Price, R. A, Hills, H. G, Qiu, Y. L, Kron, K. A, Rettig, J. H, Conti, E, Palmer, J. D, Manhart, J. R, Sytsma, K. J, Micheals, H. J, Kress, W. J, Karol, K. G, Clark, W. D, Hedren, M, Gaut, B. S, Jansen, R. K, Kim, K. J, Wimpee, C. F, Smith, J. F, Funier, G. R, Strauss, S. H, Xiang, Q. Y, Plunkett, G. M, Soltis, P. S, Swensen, S. M, Williams, S. E, Gadek, P. A, Quinn, C. J, Eguiarte, L. E, Golenberg, E, Learn, G. H, Graham, S. W, Barrett, S. C. H, Dayanandan , S, and Albert, V. A, 1993. Phylogenetics of seed plants - An analysis of nucleotide-sequences from the plastid gene RBCL. Annals of the Missouri Botanical Garden, 80: 528-580.

Coleman, J. S, and Bazzaz F. A, 1992. Effects of CO2 and temperature on growth and resource use of co-occurring C3 and C4 annuals. Ecological Publications of the Ecological Society of America, 73:1244-1259.

Connolly, J, Wayne, P, and Bazzaz, F. A, 2001. Interspecific competition in plants: How well do current methods answer fundamental questions? American Naturalist, 157:107-125.

Cotrufo, M. F, Ineson, P, and Scott, A, 1998. Elevated CO2 reduces the nitrogen concentration of plant tissues. Global Change Biology, 4:43-54.

Daepp, M, Nosberger, J, and Luscher, A, 2001. Nitrogen Fertilization and Developmental Stage Alter the Response of Lolium Perenne to Elevated CO2. New Phytologist, 150:347-358.

De Graaff, M. A, Van Groenigen, K. J, Six, J, Hungate, B, and Van Kessel, C. 2006. Interactions between plant growth and soil nutrient cycling under elevated CO2:

146 a meta-analysis. Global Change Biology, 12: 2077-2091.

Derner, J. D, Johnson H. B, Kimball, B. A, Pinter, P. J, Polley, H. W, Tischler, C. R, Boutton, T. W, Lamorte, R. L, Wall, G. W, Adam, N. R, Leavitt, S. W, Ottman, M. J, Matthias, A. D, Brooks, T. J, 2003. Above- and below-ground responses of C-3-C-4 species mixtures to elevated CO2 and soil water-availability. Global Change Biology, 9:452-460.

Dodd, M. E, Silvertown, J, and Chase, M. W, 1999, Phylogenetic analysis of trait evolution and species diversity variation among angiosperm families. Evolution, 53: 732-744. Drake, B. G, Gonz Agravelez-Meler, M. A, and Long, S. P, 1997. More efficient plants: a consequence of rising atmospheric CO2? Annual Review of Plant Physiology and Plant Molecular Biology, 48:609-639.

Dukes J. S, Chiariello N. R, Cleland E. E, Moore L. A, Shaw M. R, Thayer S, Tobeck T, Mooney H. A, Field C. B, 2005. Responses of grassland production to single and multiple global environmental changes. Plos Biology, 3: 1829-1837.

Dukes, J. S, and Mooney, H. A, 1999. Does global change increase the success of biological invaders? Trends in Ecology & Evolution, 14:135-139.

Eddy, D, Mallinson, D, Rehwinkle, R, and Sharp, S, 1998. Grassland flora: A field guide for the Southern Tablelands (NSW and ACT). NCP, Canberra.

Ehleringer, J. R, and Monson, 1993. Evolutionary and ecological aspects of photosynthetic pathway variation. Annual Review of Ecology and Systematics, 24: 411-439.

Ehleringer, J. R, Cerling, T. E, and Helliker, B. R, 1997. C-4 Photosynthesis, atmospheric CO2 and climate. Oecologia, 112:285-299.

Ellsworth, D. S, Reich, P. B, Naumburg, E. S, Koch, G.W, Kubiske, M. E, and Smith S.D, 2004. Photosynthesis, carboxylation and leaf nitrogen responses of 16 species to elevated pCO2 across four free-air CO2 enrichment experiments in forest, grassland and desert. Global Change Biology, 10:2121-2138.

Eurobodalla Shire Council website, 2005. http://www.esc.nsw.gov.au/Weeds/Sheets/grasses/G%20African%20lovegrass.htm

Farquhar, F. D, and Sharkey, T. D, 1982. Stomatal conductance and photosynthesis. Annual Review of Plant Physiology, 33:317-45.

Felsenstein, J. 1992. Phylogenies from gene-frequencies – A statistical problem. Systematic zoology, 34: 300-311 1985.

Garland, T, Harvey, P. H, and Ives, A. R, 1992. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Systematic Biology, 41:18-32.

147

Ghannoum, O, and Conroy, J. P, 1998. Nitrogen deficiency precludes a growth response to CO2 enrichment in C-3 and C-4 Panicum grasses. Australian Journal of Plant Physiology, 25:627-636.

Ghannoum, O, Siebke, K, Von Caemmerer S, and Conroy, J. P, 1998. The photosynthesis of young Panicum C-4 leaves is not C-3-like. Plant Cell and Environment, 21:1123-1131.

Ghannoum O, Von Caemmerer S, and Conroy J. P, 2001. Carbon and Water Economy of Australian NAD-ME and NADP-ME C-4 Grasses. Australian Journal of Plant Physiology, 28:213-223.

Ghannoum, O, Von Caemmerer, S, and Conroy, J. P, 2001. Plant water-use efficiency of 17 Australian NAD-ME and NADP-ME C-4 Grasses at ambient and elevated CO2 partial pressure. Australian Journal of Plant Physiology, 28:1207-1217.

Ghannoum, O, Von Caemmerer, S, Barlow, E. W. R, and Conroy, J. P, 1997. The effect of CO2 enrichment and irradiance on the growth, morphology and gas exchange of a C-3 (Panicum laxum) and a C-4 (Panicum antidotale) grass. Australian Journal of Plant Physiology, 24: 227-237.

Ghannoum, O, Von Caemmerer, S, Ziska, L.H, and Conroy, J. P, 2000. The growth response of C-4 plants to rising atmospheric CO2 partial pressure: a reassessment. Plant Cell and Environment, 23:931-942.

Gibson, D, J, Connolly, J, Hartnett, D, C and Weidenhamer, J, D, 1999. Designs for greenhouse studies of interactions between plants. Journal of Ecology, 87:1-16.

Gifford, R. M, 1979. Growth and yield of CO2 enriched wheat under water-limited conditions. Australian Journal of Plant Physiology, 79:367-378.

Gifford, R. M, 2004. The CO2 fertilising effect - Does it occur in the real world? New Phytologist, 163: 221-225.

Goldberg, D. E, 1996. Competitive ability: definitions, contingency and correlated traits. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 351:1377-1385.

Goldberg, D. E, and Landa, K, 1991, Competitive effect and response: hierarchies and correlated traits in early stages of competition. Journal of Ecology, 75:1013-1030.

Goldberg, D. E, Werner, P. A, 1983. Equivalence of competitors in plant communities: A null hypothesis and a field experimental approach. American Journal of Botany, 70:1098-1104.

Grace, J. B, 1995. On measurement of plant competition intensity. Ecology, 76:305- 308.

Grime J. P, 1977. Evidence for the existence of 3 primary strategies in plants and its relevance to ecological and evolutionary theory. American Naturalist, 111 (982): 1169-1194.

148

Gurevitch J, Scheiner, S. M, and Fox, G. A, 2002. The ecology of plants. Sinaur Associates, Sunderland MA.

Hanley, M. E, Trofimov, S, and Taylor, G, 2004. Species-level effects more important than functional group-level responses to elevated CO2: evidence from simulated turves. Functional Ecology, 18:304-313.

Harper J. L, 1977. Population Biology of Plants. London, Academic Press.

Hastwell G. T, and Panetta F. D, 2005. Can differential responses to nutrients explain the success of environmental weeds? Journal of Vegetation Science 16: 77-84.

Hatch, M. D, 1987. C-4 photosynthesis- A unique blend of modified biochemistry, anatomy and ultrastructure. Biochimica Et Biophysica Acta, 895:81-106.

Hely, S. E. L, and Roxburgh S. H, 2005. The interactive effects of elevated CO2, temperature and initial size on growth and competition between a native C3 and an invasive C3 grass. Plant Ecology, 177: 85-98.

Hewitt, E. J, 1966. Sand and water culture methods in the study of plant nutrition. CAB, Bucks.

Hikosaka, K, Onoda, Y, Kinugasa, T, Kinugasa, T, Nagashima, H, Anten, N. P. R, and Hirose, T, 2005. Plant responses to elevated CO2 concentration at different scales: leaf, whole plant, canopy, and population Ecological Research, 20: 243-253.

Hilu, K. W, Alice, L. A, and Liang, H. P, 1999. Phylogeny of Poaceae inferred from matK sequences, Annals of the Missouri Botanical Garden, 86: 835-851.

Hilu, K.W, and Alice, LA, 2000, Phylogenetic relationships in subfamily Chloridoideae (Poaceae) based on matK sequences: A preliminary assessment in Grasses: Systematics and Evolution, Jacobs, S. W. L, and Everett, J (eds), Canberra, CSIRO Publishing.

Hoorens, B, Aerts, R, and Stroetenga, M, 2003. Is there a trade-off between the plant’s growth response to elevated CO2 and subsequent litter decomposability? Oikos, 103:17-30.

Houghton, J. T, Ding, Y, Griggs, D. J, Noguer, M, and Van der Linden, P. J, 2001. Climate Change 2001: The Scientific Basis: Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The Edinburgh Building Shaftesbury Road Cambridge England, Cambridge University Press UK.

Hughes, L, 2003. Climate change and Australia: trends, projections and impacts. Austral Ecology, 28:423-443.

Kimball, B. A, 1983. CO2 and agricultural yield: An assemblage and analysis of 430 prior observations. Agronomy Journal, 75:779-788.

Kirkegaard, J. A, Howe, G. N, and Mele, P.M 1999. Enhanced accumulation of

149 mineral-N following canola. Australian Journal of Experimental Agriculture, 39:587- 593.

Körner, C. 2006. Plant CO2 responses: an issue of definition, time and resource supply. New Phytologist, 172: 393-411.

Leadley, P. W, Niklaus, P. A, Stocker, R, and Korner, C, 1999. A field study of the effects of elevated CO2 on plant biomass and community structure in a calcareous grassland. Oecologia, 118:39-49.

Lecain, D. R, Morgan, J. A, Mosier, A. R, and Nelson, J. A, 2003. Soil and plant water relations determine photosynthetic responses of C-3 and C-4 grasses in a semi- arid ecosystem under elevated CO2. Annals of Botany, 92:41-52.

Lilley, J. M, Bolger, T. P, and Gifford, R. M, 2001. Productivity of Trifolium subterranean and Phalaris aquatica under warmer, high CO2 conditions. New Phytologist, 150:371-383.

Lutze, J. L, and Gifford, R. M, 1998. Acquisition and allocation of carbon and nitrogen by Danthonia richardsonii in response to restricted nitrogen supply and CO2 enrichment. Plant Cell and Environment, 21:1133-1141.

Lutze, J.L, and Gifford, R. M, 2000. Response to CO2 and nitrogen supply over four years of growth. Global Change Biology, 6:1-12.

Gómez-Martíne, R and Culham, A, 2000, Phylogeny of the subfamily Panicoideae with emphasis on the tribe Paniceae: Evidence from the trnL-F cpDNA region in Grasses: Systematics and Evolution. Jacobs, S.W.L, and Everett, J. (eds) Canberra, CSIRO publishing.

Maestre F. T, and Reynolds J. F, 2006. Spatial heterogeneity in soil nutrient supply modulates nutrient and biomass responses to multiple global change drivers in model grassland communities. Global Change Biology, 12: 2431-2441.

Markham, J. H, and Chanway, C. P, 1996. Measuring plant neighbour effects. Functional Ecology, 10:548-549.

McCarthy, J. J, Canziani, O. F, and Leary, N. A, 2001. Climate change 2001: impacts, adaptation & vulnerability. Cambridge University Press. David J. Cambridge.

McConnaughay, K. D. M, and Coleman, J. S, 1999. Biomass allocation in Plants: ontogeny or optimality? A test along three resource gradients. Ecology, 80:2581- 2593.

Mcdonald, E. P, Erickson, J. E, and Kruger E. L, 2002. Can decreased transpiration limit plant nitrogen acquisition in elevated CO2? Functional Plant Biology, 29:1115-1120.

Moore, B. D, Cheng, S. H, Sims, D, and Seemann, J. R, 1999. The biochemical and molecular basis for photosynthetic acclimation to elevated atmospheric CO2. Plant Cell and Environment, 22:567-582.

150

Morgan, J. A, Lecain, D. R, Mosier, A. R, Milchunas, D. G, 2001. Elevated CO2 enhances water relations and productivity and affects gas exchange in C-3 and C-4 grasses of the Colorado shortgrass steppe. Global Change Biology, 7:451-466.

Morison, J. I. L, 1993. Response of Plants to CO2 under water-limited conditions. Vegetatio, 104:193-209.

Navas, M. L, Garnier, E, Austin, M. P, and Gifford, R. M, 1999. Effect of competition on the responses of grasses and legumes to elevated atmospheric CO2 along a nitrogen gradient: Differences between isolated plants, monocultures and multi-species mixtures. New Phytologist, 143:323-331.

Niklas, K. J and Enquist, B. J, 2001. Invariant scaling relations for interspecific plant biomass production rates and body size. Proceedings of the National Academy of Sciences, 98:2922-2927.

Niklas, K. J and Enquist, B. J, 2002 – Canonical rules for plant organ biomass partitioning and annual production. American Journal of Botany, 89: 812-819.

Oksanen, L, Sammul, M and Magi, M, 2006. On the indices of plant-plant comeptition and their pitfalls. Oikos, 112:149-155.

Owensby, C. E, Coyne, P. I, Ham, J. M, Auen, L. M, and Knapp, A. K, 1999. Biomass production in a tall-grass prairie ecosystem exposed to ambient and elevated CO2. Ecological Applications, 3:644-653.

Pearcy, R. W, and Ehleringer, J, 1984. Comparative ecophysiology of C3 and C4 plants. Plant, Cell and Environment, 7:1-13.

Pittock, B, 2003. Climate change: An Australian guide to the science and potential impacts. Australian Greenhouse Office, Canberra.

Polley, H. W, H. B, Johnson, H. S, Mayeux, D. A, Brown, and J. W. C, White, 1996. Leaf and plant water-use efficiency of C4 species grown at glacial to elevated CO2 concentrations. International Journal of Plant Sciences, 157:164-170.

Poorter, H, 1993. Interspecific variation in the growth-response of plants to an elevated ambient CO2 concentration. Vegetatio, 104:77-97.

Poorter, H, and Nagel, O, 2000. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Australian Journal of Plant Physiology 27: 595-607.

Poorter, H, and Navas, M. L, 2003. Plant growth and competition at elevated CO2: on winners, losers and functional groups. New Phytologist, 157:175-198.

Purvis, A, and Rambaut, A, 1995. Comparative-analysis by independent contrasts (CAIC) - An Apple Macintosh application for analyzing comparative data. Computer Applications in Biosciences, 11: 247-251.

Read J. J, Morgan J. A, Chatterton N. J, and Harrison P. A, 1997, Gas exchange,

151

carbohydrate and nitrogen concentrations in leaves of Pascopyrum smithii (C3) and Bouteloua gracilis (C4) at different carbon dioxide concentrations and temperatures. Annals of Botany. 79:197-206.

Rogers, H. H, Bingham, G. E, Cure, J. D, Smith, J. M, and Surano, K. A, 1983. Responses of selected plant-species to elevated carbon-dioxide in the field. Journal of Environmental Quality, 12: 569-574.

Rotter, R, and Van de Geijn, S. C. 1999, Climate change effects on plant growth, crop yield and livestock Climatic Change, 43: 651-681.

Rudmann, S. G, Milham, P. J, and Conroy, J. P, 2002. Influence of high CO2 partial pressure on nitrogen use efficiency of the C-4 grasses Panicum Coloratum and Cenchrus Ciliaris. Annals of Botany, 89: 799-799.

Ryser, P, and Eek, L, 2000. Consequences of phenotypic plasticity Vs. interspecific differences in leaf and root traits for acquisition of aboveground and belowground resources. American Journal of Botany, 87:402-411.

Sage, R. F, 2004. The evolution of C4 photosynthesis. New Phytologist, 161: 341- 370.

Sage, R. F, Sharkey, T. D, and Seemann, J. R, 1989. Acclimation of photosynthesis to elevated CO2 in 5 C-3 species. Plant Physiology, 89:890-896.

Seneweera, S, Ghannoum, O, and Conroy, J. P, 2001. Root and shoot factors contribute to the effect of drought on photosynthesis and growth of the C-4 grass Panicum Coloratum at elevated CO2 partial pressures. Australian Journal of Plant Physiology, 28:451-460.

Shaw, R. M, Zavaleta, E. S, Chiariello, N. R, Cleland, E. E, Mooney, H. A, and Field, C. B. 2002. Grassland responses to global environmental changes suppressed by elevated CO2, Science. 298: 1987-1990.

Soinit, N, and Patterson, D. T, 1984. Responses of C4 grasses to atmospheric CO2 enrichment. I. Effect of irradiance. Oecologia, 65:30-34.

Soreng, R. J, and Davis, J. I, 2000, Phylogenetic structure in Poaceae subfamily Pooideae as inferred from molecular and morphological characters: Misclassification versus reticulation in Grasses: Systematics and Evolution, Jacobs, S. W. L, and Everett, J (eds), Canberra, CSIRO Publishing.

Soussana, J. F, Teyssonneyre, F, Picon-Cochard, and C, Dawson, L, 2005. A trade-off between nitrogen uptake and use increases responsiveness to elevated CO2 in infrequently cut mixed C3 grasses. New Phytologist, 166:217-230.

Stewart, J, and Potvin, C, 1996. Effects of elevated CO2 on an artificial grassland community: Competition, invasion and neighbourhood growth. Functional Ecology, 10:157-166.

Stock, W. D, Ludwig, F, Morrow, C, Midgely, G. F, Wand, S. J. E, Allsopp, N,

152

and Bell, T. L, 2005. Long term effects of elevated atmospheric CO2 on species composition and productivity of southern African C4 dominated grassland in the vicinity of a CO2 exhalation. Plant Ecology, 178 : 211-224.

Teughels, H, Nijs, I, Vanhecke, P, and Impens, I, 1995. Competition in a global change environment: the importance of different plant traits for competitive success. Journal of Biogeography, 22:297-305.

Thompson, K, Hodgson, J. G, Grime, J. P, and Burke, M. J. W, 2001. Plant traits and temporal scale: Evidence from a 5-year invasion experiment using native species. Journal of Ecology, 89:1054-1060.

Tilman, D, 1988. Plant strategies and the dynamics and structure of plant communities. Princeton University Press, Princeton, NJ.

Tremmel, D. C, and Patterson, D. T, 1993. Responses of soy bean and five weeds to CO2 enrichment under two temperature regimes. Canadian Journal of Plant Science, 73: 1249-1260.

Waller, D. M, 1981. Neighbourhood competition in several violet populations. Grass and Forage Science, 51:116-122.

Wand, S. J. E, and Midgley, G. F, 2004. Effects of atmospheric CO2 concentration and defoliation on the growth of Themeda triandra. Grass and Forage Science 51:215-226.

Wand, S. J. E, Midgley, G. F, Jones, M. H, and Curtis, P. S, 1999. Responses of wild C4 and C3 grass (Poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of current theories and perceptions. Global Change Biology, 5:723- 741.

Wand, S. J. E, Midgley, G. F, and Stock, W. D, 2001. Growth responses to elevated CO2 in NADP-ME, NAD-ME and PCKC4 grasses and a C-3 grass from South Africa. Australian Journal of Plant Physiology, 28:13-25.

Watson, R. W, Mcdonald, W. J, and Bourke, C. A, 2000. Phalaris pastures. Agfacts P2.5.1, 13. 2000. NSW Agriculture.

Weiner, J, 1982. A neighbourhood model of annual plant interference. Ecology, 63:1237-1241.

Weltzin, J. F, Belote, R. T, and Sanders, N. J, 2003. Biological invaders in a greenhouse world: will elevated CO2 fuel plant invasions? Frontiers in Ecology and the Environment, 1:146-153.

Wilson, J. R, 1975. Influence of temperature and nitrogen on growth, photosynthesis and accumulation of non-structural carbohydrate in tropical grass, Panicum maximum var. trichoglume. Australian Journal of Agricultural Research, 26:127-136.

Wilson, J. B, 1999, Guilds, functional types and ecological groups Oikos, 86: 507-522.

153

Williamson, R, and Faithfull, I, 1998. African Lovegrass. http://www.dse.vic.gov.au/dpi/nreninf.nsf/LinkView/7B0E5487CE3B2BD3CA256B CF000AD556ECC844336D72F0634A256DEA00293F8A.

Winslow, J. C, Hunt, E. R, Piper, S. C, 2003.The influence of seasonal water- availability on global C-3 versus C-4 grassland biomass and its implications for climate change research Ecological Modeling, 163: 153-173.

Wolfe, D. W, Gifford, R. M, Hilbert, D, and Luo, Y. Q, 1998. Integration of photosynthetic acclimation to CO2 at the whole-plant level. Global Change Biology, 4:879-893.

Wong, S. C, and Osmond, C. B, 1991. Elevated atmospheric partial-pressure of CO2 and plant-growth. 3- interactions between Triticum-Aestivum (C3) and Echinochloa- Frumentacea (C4) during growth in mixed culture under different CO2, N-nutrition and irradiance treatments, with emphasis on belowground responses estimated using the delta-C-13 value of root biomass. Australian Journal of Plant Physiology, 18:137- 152.

Woodrow, I. E, 1994. Optimal acclimation of the C-3 photosynthetic system under enhanced CO2. Photosynthesis Research, 39:401-412.

Wullschleger, S. D, Tschaplinski, T. J, and Norby, R. J, 2002. Plant water relations at elevated CO2- Implications for water-limited environments. Plant Cell and Environment, 25:319-331.

Xu, D. Q, Gifford, R. M, and Chow, W. S, 1994. Photosynthetic acclimation in pea and soybean to high atmospheric CO2 partial-pressure. Plant Physiology, 106:661- 671.

Zak, D. R, Pregitzer, K. S, King, J. S, and Holmes, W. E, 2000. Elevated atmospheric CO2, fine roots and the response of soil micro-organisms: A review and hypothesis. New Phytologist, 147:201-222.

Zangerl, A. R and Bazzaz, F. A, 1984. The response of plants to elevated CO2. Oecologia, 62:412-417.

Zens, M. S, and Webb, C. O, 2002. Sizing up the shape of life, Science, 295:1475- 1476.

Ziska, L. H, and Bunce, J. A, 1997. Influence of increasing carbon dioxide concentration on the photosynthetic and growth stimulation of selected C-4 crops and weeds. Photosynthesis Research, 54:99-208.

Ziska, L. H, Sicher, R. C, and Bunce, J. A, 1999. The impact of elevated carbon dioxide on the growth and gas exchange of three C-4 species differing in CO2 leak rates. Physiologia Plantarum, 105:74-80.

Zhang W, 2000. Phylogeny of the grass family (Poaceae) from rpl16 intron sequence data. Molecular phylogenetics and evolution, 15: 135-146.

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Appendix 1 Species list, metabolic pathway and references for species used in meta-analysis.

Plant type Species Pathway Author

Grass Festuca arundinacea C3 Soussana et al. (2005) Grass Lolium perenne C3 Grass Holcus lanatus C3 Grass Echinochloa crusgalli C4 Soinit & Patterson (1984) Grass Digitaria sanguinalis C4 Grass Eleusine indica C4 Grass Setaria faberi C4 Weed Amaranthus retroflexus C4 Ziska & Bunce (1997) Weed Echinochloa crus-galli C4 Weed Panicum dichotomiflorum C4 Weed Setaria faberi C4 Weed Setaria Viridus C4 Weed Sorghum halapenese C4 Crop Amaranthus hypochondriacus C4 Crop Amaranthus hypochondriacus C4 Crop Saccharum officinarum C4 Crop Sorghum bicolor C4 Crop Zeas mays C4 Grass Abutilon thoephrasti C3 Coleman & Bazzaz (1992) Grass Amaranthus retorflexus C4 Grass Panicum Laxum C3 Ghannoum & Conroy (1998) Grass Panicum coloratum C4 Grass Panicum antidotale C4 Grass Agrostis capillaris C3 Campbell et al. (1995) Grass Bromus willdenowii C3 Grass Cichorium intybus C3 Grass Dactylis glomerata C3 Grass Digitaria sanguinalis C4 Grass Festuca arundinacea C3 Grass Lolium multiflorum C3 Grass Lolium perenne C3 Grass Phalaris aquatica C3 Grass Paspalum dilitatum C4 Grass Trifolium dubium C3 Grass Trifolium repens C3 Grass Trifolium subterraneum C3 Grass Pascopyrum Smithii C3 Read et al. (1997) Grass Bouteloua gracilis C4 Grass Lolium perenne C3 Casella et al. (1996) Grass Trifolium Subterraneum C3 Lilley et al. (2001) Grass Phalaris aquatica C3 Grass Danthonia richardsonii C3 Navas et al. (1999) Grass Lotus pedunculatus ? Grass Phalaris aquatica C3 Grass Trifolium repens C3

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Plant type Species Pathway Author

Grass Themeda Triandra C4 Wand & Midgley (2004) Grass Bouteloua eriopoda C4 Grass Eragrostis lehmanniana C4 Wheat Triticum aestivum C3 Derner et al. (2003) Grass Avena barbata ? Hu et al. (2005) Grass Avena fatua ? Wheat Triticum aestivum C3 Wu et al. (2004) Grass Dichanthium sericeum C4 Ghannoum et al. (2001) Grass Panicum coloratum C4 Grass Leptochloa dubia. C4 Grass Pennisetum clandestinum. C4 Grass Pennisetum alopecuroides C4 Grass Dichanthium aristatum. C4 Grass Astrebla squarrosa C4 Grass Panicum decompositum. C4 Grass Astrebla pectinata. C4 Grass Eragrostis superba C4 Grass Cenchrus ciliaris C4 Grass Cynodon dactylon C4 Grass Eleusine coracana C4 Grass Bothriochloa biloba C4 Grass Digitaria brownii C4 Grass Astrebla lappacea C4 Shrub Larrea tridentata C3 Orbist & Arnone (2003) Grass Andropogon appendiculatus C4 Wand et al. (2001) Grass Digitaria natalensis C4 Grass Themeda Triandra C4 Grass Eragrostis curvula C4 Grass Eragrostis racemosa C4 Grass Sporobolus pyramidalis C4 Grass Melinis repens C4 Grass Alloteropsis semialata C3 Grass Gossypium hirsutum C4 Reddy & Zhao (2005) Grass Schizachyrium scoparium C4 Polley et al. (1996) Shrub Atriplex canescens C4 Herb Flaveria trinervia C4 Ziska et al. (1999) Grass Panicum maximum C4 Sedge Carex rostrata C3 Hoorens et al. (2003) Grass Calamagrostis ? Herb Vicia lathyroides C3 Grass Molinia caerulea C3 Herb Aster Pilosus C3 Marks & Strain (1989) Grass Andropogon virginicus C4 Grass Agropyron smithii C3 Volin & Reich (1996) Grass Bouteloua curtipendula C4 Tree Populus tremuloides C3 Grass Briza subaristata C3 Wilsey et al. (1997) Grass Panicum millioides C3/C4

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Plant type Species Pathway Author

Grass Paspalum dilatum C4 Grass Digitaria macroblephara C4 Grass Sporobolus kentrophyllus C4 Grass Themeda triandra C4 Grass Agropyron caninum C3 Grass Festuca idahoensis C3 Grass Stipa occidentalis C3 Grass Eragrostis curvula C4 Hely et al. (Unpublished) Grass Triticum aestivum C3 Grass Austrodanthonia racemosa C3 Grass Vulpia myuros C3 Hely & Roxburgh (2005) Grass Austrodanthonia eriantha C3

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Appendix 2 Source material used for the reconstruction of evolutionary relationships of species within families used in the comparative analyses. All evolutionary relationships were reconstructed using published molecular phylogenies. Evolutionary relationships across plant families were obtained from a composite phylogeny of the families of flowering plants (Chase et al. 1993; Dodd et al 1999).

Family Phylogenetic reconstruction (or subfamily)

Asteraceae Phylogenetic relationships of the genera and species included are not resolved. The five genera included were coded as a polytomy in the analysis. We assumed Centauria is a monophyletic genus and coded the two Centauria species as a dichotomous branch.

Amaranthaceae Two species from this family were included in this study and were coded as a dichotomous branch.

Chenopodaceae Two species from this family were included in this study and were coded as a dichotomous branch.

Cyperaceae One species from this family was included in this study, no reconstruction was required.

Poaceae Phylogenetic relationships of the five subfamilies were obtained from Hilu et al. (1999) and Zhang (2000)

Arundinoideae Phylogenetic relationships among the species and genera were obtained from Hilu et al. (1999) and Barker et al. (2000).

Bambusoideae Phylogenetic relationships of the four species (representing four separate genera) are not resolved. The four genera were coded as a polytomy in the analysis.

Chloridoideae Phylogenetic relationships among the species and genera were obtained from Hilu & Alice (2000).

Panicoideae Phylogenetic relationships among the species and genera were obtained from Gomez Martinez & Culham (2000) and Aliscioni et al. (2003).

Pooideae Phylogenetic relationships among the species and genera were obtained from Soreng & Davis (2000).

Salicaceae One species from this family was included in this study, no reconstruction was required.