Keeping up with Climate Change: Assessing the Vulnerability of Eucalyptus Species to a Changing Climate in the South-West of Western Australia

Keeping up with climate change: assessing the vulnerability of Eucalyptus species to a changing climate in the south-west of Western Australia

Jason J. Hamer BSc. (Environmental Biology) BSc. (Honours) - First Class Honours

This thesis is presented for the degree of Doctor of Philosophy

The University of Western Australia Faculty of Science School of Plant Biology

2015

Statement of candidate contribution

The research presented in this thesis is an original contribution to the field of Plant Biology. The poetry, hypotheses and experiments presented and discussed in this thesis are my own original ideas and writing. Others who made significant contributions to the research are acknowledged in the section: “Publications arising from this Thesis”. Permission has been obtained from all co-authors to include these publications in this thesis.

This thesis has been completed during the course of enrolment in a Doctorate of Philosophy at The University of Western Australia, and has not been used previously for a degree or diploma at any other institution.

… ……….. Jason J. Hamer 17th August 2015

Michael Renton (coordinating supervisor) 17th August 2015

i Climate Change and the Poor Gum Tree An Australian bush poem by Jason J. Hamer

Today I have a story to tell, Summarising my PhD. This bush poetry has been three years in the making, And it’s about our iconic gum tree.

And if you pay quite close attention, There’ll be something interesting you’ll find. As I only have four beats per line The statistics won’t be a grind!

Now, right around the world you see, There are woodlands in decline. From Europe to America and in Australia, This is surely one big sign.

These trees are starving with closed stomata, And pests are eating them out. They’ve bubbles that grow as xylem collapse, We can only blame the drought.

The effects are worldwide, but they really hit home When they encroach on our diverse hotspot. My poor gum trees, the king of the woods, They are already suffering root rot!

So some modelling was done into species distribution To find to what extent they’re in danger. And as it turns out they are required to shift, With very little luck I’ll wager.

The arid species appear most at risk, With their climate envelope quite narrow. The gum trees just don’t have the dispersal tricks; Their seed can’t fly like an arrow.

“But what am I to do?” said the poor gum tree “I can’t pick up my feet!” “For the climate is shifting and the rain depleting, It’s not just a walk down the street”

So I set out to find how vulnerable he is – My poor, stranded gum tree. I looked high and low, from micro to macro, I worked hard, you won’t disagree.

I looked down at his roots as these are what bind him – It seemed a good place to start. I observed their root architecture, and believe it or not, I could tell the species apart!

Those typically in sand had plenty of roots, Exploring a large lateral section. But the clay-bound species focused all their energy, To grow in a downward direction.

But the problem was they didn’t appear plastic, Clay species couldn’t compete in sand. So if they did move they might not survive, They will probably need a hand.

Next step, I shifted my gaze further up, Studying the branch and leaves. With the anatomy and structure of these components Physiology I could perceive.

Vein length, Huber value and leafing intensity, Conductivity of xylem and stomata. You sure showed me, for what I found Was not what I expected from the data.

Though you're well adapted to current conditions, These are likely to change quite soon. With larger climate ranges it seems more likely It’s the wetter species that’ll attune.

“Where next?” he asks, still standing root bound. “You won’t just leave me here?” “PhD is all done and you are on the run” “Is my fate just to disappear?”

Unfortunately for you your future may rest, In the coal-stained hands of mankind. For the best solution to ensure you survive Is to show our fates intertwined.

For we are more alike than you may think, My brave, good hearted gum tree. We can rhyme all we want but if nothing is done, We have, nowhere else, to flee.

iii Thesis abstract

Under the influence of global climate change, the area experiencing a Mediterranean climate in the south-west of Western Australia – of which a large portion is classified as a biodiversity hotspot – is predicted to decrease by up to 51% by the year 2100. The increase in temperature and decrease in precipitation in this region has already caused large scale health decline in Eucalyptus species in a number of woodlands in more mesic climates. As eucalypts are the dominant overstorey species in many habitat types in the region and provide many ecosystem services, the large scale loss of the species would likely result in significant changes to community composition. Thus, it is necessary to increase our understanding of the vulnerability of Eucalyptus species to a changing climate so that we can better conserve these species and the flora and fauna that depend on them. In this thesis, vulnerability is defined as the combination of a species’ exposure to climate change, its resilience to change and its adaptive capacity. These components of vulnerability are explored in the context of species’ current distribution patterns, as understanding how a species is currently adapted to its current climate can allow us to predict what future distributions may look like. Exposure to climate change was quantified using species distribution modelling techniques to predict the percentage of species’ current distribution that will no longer be suitable for species’ survival. Above and below ground plant functional traits that contribute to both species’ resilience to change and adaptive capacity were then quantified to improve our understanding as to how these species have adapted across an existing aridity gradient.

Results suggest that species in more arid conditions will have a greater proportion of their habitat exposed to unfavourable conditions compared to mesic species, with some predicted to lose all suitable habitat. Though the shallow climate gradients of inland Western Australia result in large distribution ranges for these species, it also means that, in comparison to more mesic species, a given change in climate will result in a larger proportion of their distribution becoming unsuitable. As these species are unlikely to be able to migrate fast enough to keep up with the changing climate, the high level of exposure faced by many species means that species’ sensitivity and adaptive capacity are very important for long term survival. In terms of above ground traits, species in more arid conditions appear to be well adapted to current climate conditions, with important traits showing some degree of plasticity within species. This was observed as a decrease in vessel diameter, increase to sapwood density and leaf mass per area and a decrease in leaf size across an aridity gradient, all of which confer a reduced sensitivity to hydraulic failure in water-limited environments. Though the combination of the above traits resulted in more arid species having lower xylem conductivity, this was balanced by an increase in sapwood area to leaf area ratio, maintaining total stomatal conductance to xylem conductivity ratios across the aridity gradient. However, in a rapidly changing climate, species may not have enough time to adapt and change their average trait values to those of species that replace them iv along the aridity gradient. Even if species could migrate to new locations, seedling root architecture of species typically growing in finer-textured soil (such as many arid Eucalyptus species) may not be suitable for establishment in a coarser-textured soil (more typical of the regions to which they would likely need to migrate). This is because species typically growing in fine-textured soil tend to invest most resources into producing a taproot, with minimal lateral roots produced. This is likely to be a disadvantage in a coarser-textured (a soil with lower-water holding capacity), for which more lateral roots are likely to be needed to be able to explore a large enough soil volume for water. Thus, due to the combination of a high exposure to climate change and local root system adaptation to predominantly fine textured soils, Eucalyptus species in more arid environments are likely to be at greater risk to a changing climate than those living in a more mesic climate. These findings suggest that our research, restoration and conservation efforts should not focus disproportionally on more coastal higher rainfall species which occur conveniently close to human population centres; rather, consideration should be given to those species further inland where the greater impacts may actually occur.

v Publications arising from this thesis

In accordance with The University of Western Australia guidelines, this thesis has been prepared as a series of publications proceeded by a general introduction and followed by a general discussion. As such, it contains published manuscripts and manuscripts prepared for publication, all of which have been co-authored. The bibliographical details of the published work and where it appears in the thesis are outlined below.

Published manuscripts:

Hamer J.J., Veneklaas E.J., Poot P., Mokany K., Renton M. (2015) Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions of Eucalyptus species in South Western Australia. Austral Ecology 40:923- 932. DOI: 10.1111/aec.12274 This publication forms Chapter 2 of this thesis. Jason J Hamer conceived the project and designed the model with Michael Renton. Modelling, statistical analysis, data interpretation and writing of the manuscript were completed by Jason J Hamer. Karel Mokany produced the downscaled current and projected climate variables used in the models. Michael Renton, Erik Veneklaas and Pieter Poot all provided input into the interpretation of data and the preparation of the manuscript.

Hamer J.J., Veneklaas E.J., Renton M., Poot P. (2015) Links between soil texture and root architecture of Eucalyptus species may limit distribution ranges under future climates. Plant and Soil, DOI: 10.1007/s11104-015-2559-5 This publication forms Chapter 4 of this thesis. The project was conceived and designed by all authors. Experimental maintenance, data collection, statistical analysis, data interpretation and writing of the manuscript were completed by Jason Hamer. Pieter Poot, Erik Veneklaas and Michael Renton all provided input into the interpretation of data and the preparation of the manuscript.

Manuscripts prepared for publication:

Hamer J.J., Poot P., Renton M., Price C., Veneklaas E.J. (in prep) Relationships of leaf area to sapwood area ratio with stomatal and xylem traits in south-west Australian Eucalyptus species along an aridity gradient. At the time of submission of this PhD this manuscript is in preparation for submission for publication. It forms Chapter 3 of this thesis. The project was conceived and designed by all authors. Field work, data collection, statistical analysis, data interpretation and writing of the manuscript were completed by Jason Hamer. Erik Veneklaas, Pieter Poot and Michael Renton all provided input into the interpretation of data and the preparation of the manuscript.

Acknowledgements

First and foremost, I would like to thank The University of Western Australia for awarding me the Australia Postgraduate Award, a top-up scholarship and a travel grant which allowed me to pursue my passion for research through a Doctorate of Philosophy. Of course, I would not be able to mention the University without giving my thanks to the School of Plant Biology, not only for the financial support, or for the great research environment, but also for the opportunity to simply get involved and making it feel like I made a difference.

I would like to send a big thanks to the Centre of Excellence for Climate Change, Woodland and Forest Health for not only providing me with an additional top-up scholarship and generous operating grant, but for also inspiring this exciting research project. Thanks also to the Holsworth Wildlife Research Endowment for their financial support of Chapter 3.

To my three supervisors, Michael Renton, Erik Veneklaas and Pieter Poot, thank you. It seems obvious to point out that this wouldn’t have been possible without them. Thank you for your guidance and feedback throughout my PhD, as well as putting up with the occasional question which I hadn’t thought through very far. I would especially like to thank my coordinating supervisor, Michael Renton, who was always just a couple of metres away to answer questions and lend support, advice and yoga. It is with Michael as a role model that I have been able to discover and develop my love for numbers and statistics, and for this I will always be grateful.

Thank you all of my close friends who have been willing to ask “so how is the PhD going?”, knowing very well that they may get an ear full, laughed at, or just be ignored. Even if you were wise enough never to ask, thank you to those friends who kept me distracted. A big thanks to my near constant companions Kenny Png, Felipe Albornoz and Luke Kealley. Finally, a special thanks to Tegan Davies for introducing me to meditation and mindfulness, which certainly helped me throughout my PhD.

Thank you to my family – Megan, Peter and Michael – who have supported me and encouraged me throughout my whole PhD and shown nothing but pride and love, even during my lowest times. Special thanks to my father, Peter, for the assistance and companionship while out in the field, and for introducing me to a new species apparently called Eucalyptus deadus (while looking for a grey barked species). May this new weed species never become dominant.

vii Table of Contents

Statement of candidate contribution i

Climate Change and the Poor Gum Tree (bush poetry) ii

Thesis abstract iv

Publications arising from this thesis vi

Acknowledgements vii

Table of Contents viii

Chapter 1 General Introduction

Climate Change and global tree health decline 1

South-west Australian tree decline 3

Historical and future distribution of Eucalyptus 7

Short and long term adaptation to increasingly arid environments 9

Thesis objective and outline 11

References 14

Chapter 2 Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions of Eucalyptus species in South Western Australia

Preface to Chapter 2 27

Abstract 29

Introduction 30

Methods 31

Results 36

Discussion 37

Acknowledgements 41

References 42

Appendices 51

Chapter 3 Relationships of leaf area to sapwood area ratio with stomatal and xylem traits in south-west Australian Eucalyptus species along an aridity gradient.

Preface to Chapter 3 63

Abstract 65 viii

Introduction 66

Methods 69

Results 74

Discussion 78

Acknowledgements 82

References 83

Appendices 89

Chapter 4 Links between soil texture and root architecture of Eucalyptus species may limit distribution ranges under future climates

Preface to Chapter 4 93

Abstract 95

Introduction 96

Materials and methods 98

Results 102

Discussion 107

Acknowledgements 110

References 111

Chapter 5 General Discussion

Vulnerability framework 117

Exposure 117

Sensitivity 121

Adaptive Capacity 124

Vulnerability and Conclusion 126

Future Work 128

References 129

Appendices Copies of published papers

Chapter 2 (Austral Ecology) 135

Chapter 4 (Plant and Soil) 145

Chapter 1

General Introduction

Climate change and global tree health decline The past three decades have been the hottest on record since 1850, with each decade continuing to be hotter than the last (IPCC 2014). Concentrations of carbon dioxide, methane and nitrous oxide in the atmosphere have increased since the pre-industrial era and are now at concentrations that haven’t been observed in the last 800,000 years (IPCC 2014). The Intergovernmental Panel on Climate Change (IPCC), and the vast majority of scientists, agree that humans’ influence on climate change is clear; this increase in greenhouse gas concentration is the dominant cause of the observed global warming in the past half century (Oreskes 2004; IPCC 2014). Though both the degree to which climate is expected to change and the type of change predicted varies across the globe, the IPCC has predicted that, on average, we are already committed to an increase in temperature of between 0.3 °C and 0.7 °C over the next 20 years due to past emissions (IPCC 2014). This increase in temperature is predicted to also result in an increase in frequency and duration of temperature extremes and heat waves (IPCC 2014). Though the impact of these changes on humans is the most talked about issue of climate change, these changes will also affect plants and animals, potentially even more so.

One of the most visually obvious effect of climate change on our natural environment has been the large-scale tree health decline observed around the globe over the past 50 years (see Allen et al. 2010; Figure 1). Tree health ‘decline’ is most often used to describe trees that are showing signs of deteriorating health of the canopy, loss of canopy, and even outright death of the tree. It has been proposed that drought is acting as a trigger that causes trees that are already stressed to pass a tipping point where they can no longer survive the combined effects of multiple stress factors (McDowell et al. 2008; Allen et al. 2010). Typically, the three main factors which are associated with the observed large scale tree decline are: 1) the cavitation of the hydraulic system; 2) carbon starvation; and 3) increased population density of pests and pathogens (McDowell et al. 2008). Cavitation of the hydraulic system is caused by the water in xylem vessels being held under excessive tension, which allows for more water to be extracted from the soil. This can cause the formation of embolisms – bubbles of air – in the xylem vessels, effectively blocking water movement through the vessels (Tyree and Sperry 1988; Tyree and Sperry 1989). There is an association between a reduction in cavitation risk and a plants’ ability to regulate stomatal opening. However, over extended periods of time, keeping stomata closed to avoid cavitation can result in carbon starvation due to the plants’ inability to gain sufficient carbon dioxide from the air (McDowell 2011; Breshears et al. 2008; Sevanto et al. 2014,

1 although see Anderegg et al. 2012; Sala 2009). The combination of water and carbon stress reduces the ability of the plant to defend against pests and pathogens – some of which may increase in population density due to increased temperatures (Brasier 1996; Rosenzweig et al. 2001; Harvell et al. 2002; Anderson et al. 2004). It is due to these three interrelated factors that we are observing such large scale tree health decline in the recent decades (McDowell et al. 2008; Allen et al. 2010).

Though senescence and mortality are normal phenomena in forests, there has been a marked increase in published reports of significant tree health decline over the last decades (Allen et al. 2010). These reports identified patches of forest showing signs of decline across every continent: Africa (Viljoen 1995; Gonzalez 2001; Lwanga 2003; Foden et al. 2007), America (Jones and Hendershot 1989; Williamson et al. 2000; Suarez et al. 2004; Berg et al. 2006; Kurz et al. 2008), Asia (Khan et al. 1994; Wang et al. 2007), Australasia (Hosking and Hutcheson 1988; Fensham and Holman 1999; Rice et al. 2004; Fensham and Fairfax 2005) and Europe (Vertui and Tagliaferro 1998; Solberg 2004; Tsopelas et al. 2004; Bréda et al. 2006). These cases were not just restricted to arid and semi-arid areas within which water is typically the limiting factor, as occurrences were also observed in tropical forests in Borneo (Aiba and Kitayama 2002; Van Nieuwstadt and Sheil 2005) and the Amazon basin (Phillips et al. 2009). The scale of decline ranged from local patches of decline to areas exceeding 50 million hectares. One area of large-scale tree health decline was not reported in Allen et al. (2010): the decline in Eucalyptus forests in the south-west of Western Australia, covering an estimated 17,000 hectares (Matusick et al. 2012; Matusick et al. 2013).

Figure 1. Figure reproduced from Allen et al. (2010). White dots indicate general locations within which decline of forest health had been reported in the literature up to 2010. The colours depict the main abiotic factors limiting net primary production (Boisvenue and Running 2006).

South-west Australian tree decline The south-west of Western Australia (SWWA; Figure 2) contains one of five Mediterranean climate biodiversity hotspots in the world (Myers et al. 2000). The climate in this area is characterised by hot, dry summers (December to February) and cool, wet winters (June to August), with roughly 80% of the annual precipitation falling between the months of April and October (Bates et al. 2008). The majority of this precipitation is deposited along the coast, with as much as 1200 mm received annually in the southern-most portion of SWWA and dropping off quickly to reach as little as 200 mm further inland (Figure 2; Australian Bureau of Meteorology 2011b). The rapid drop off of rainfall is caused by the Darling Scarp, a hill range (tallest peak of 582 m at Mt. Cooke) that stretches from north of Bindoon (S 31.25°, E 116.30°) to south of Pemberton (S 34.45°, E 116.03°) and causes most of the precipitation to occur along the western and southern coastlines. The southernmost coastline is also the coldest part of the state with an annual average temperature of 15 °C increasing to 23 °C near the northern extent of SWWA (Figure 2; Australian Bureau of Meteorology 2011a).

Figure 2. Extent of the South-West Western Australia study region (SWWA) and south- west Floristic Region (outlined, SWAFR), overlayed with mean annual precipitation.

Mediterranean-type ecosystems are predicted to be at the greatest risk of biodiversity loss in comparison with other terrestrial biomes due to sensitivity to many interacting drivers, including climate change and land use (Sala et al. 2000; Laurance et al. 2011). Indeed, models have predicted that the area experiencing a Mediterranean climate in SWWA will decrease by between 23 to 51% by the year 2100, depending on the climate scenario employed (Klausmeyer and Shaw 2009). Since 1970, this area has decreased in annual precipitation at an approximate rate of 50 mm per decade and increased in temperature at a rate of up to 0.3 °C per decade

3 (Australian Bureau of Meteorology 2015). According to a global climate model developed by Australia’s national science agency CSIRO (CSIRO-Mk3.5; CSIRO 2012) and a moderate change emissions scenario (AB1), it is predicted that in the remainder of the 21st century we can expect a further decrease in precipitation and increase in temperature throughout SWWA. The wettest corner of SWWA is predicted to see a reduction in annual precipitation of up to 200 mm and an increase in annual mean temperature of up to 2.6 °C. The inland region is predicted to decrease in annual precipitation by on average 100 mm and increase in temperature by 5.2 °C. These changes in climate will transform areas previously classified as having a Mediterranean climate into a semi-arid climate (Klausmeyer and Shaw 2009). This has negative implications for the high levels of biodiversity present in this SWWA Mediterranean climate biodiversity hotspot.

The south-west Australian biodiversity hotspot – also known as the South-West Floristic Region (SWAFR) – is home to over 8362 plant species (derived from records listed in Western Australian Herbarium FLORABASE in July 2015; Western Australian Herbarium [2015]). Of these, it is predicted that between 58% are endemic to the region (Mucina et al. 2014). This high level of biodiversity has been attributed to the old, climatically buffered (by the ocean) nature of the landscape, together with the nutrient impoverished soils (Hopper 2009; Lambers et al. 2010). As it has been a long time since catastrophic disturbance (such as glaciations and volcanic eruptions) this has given species time to speciate in this environment, resulting in a high biodiversity (Linder 2008; Sauquet et al. 2009; Lambers et al. 2010). In tandem, the low nutrient availability of the soils makes it hard for one species to dominate in an environment frequented by fires and changes to precipitation (Orians and Milewski 2007; Lambers et al. 2010). Though the south-west Australian biodiversity hotspot originally covered 309,850 km2, only 33,336 km2 (10.8%) remains in its natural state, due to the clearing of land for agriculture, mining and urbanisation (Myers et al. 2000). In some places there is as little as 3% of habitat that is suitable for native species, leaving isolated pockets of remnant vegetation – often Eucalyptus woodlands – between vast areas of agricultural land (Hobbs and Saunders 1993).

The majority of the SWAFR is classified either as tall open forest, open forest, woodland or mallee, all of which feature Eucalyptus species as the dominant overstorey in most habitats (Williams and Brooker 1997). Not including closely related species of the Corymbia and Angophora genera (which are also commonly referred to as eucalypts), there are 830 species of Eucalyptus (The Atlas of Living Australia 2015), only two of which (E. urophylla and E. deglupta ) are native outside of Australia (Doughty 2000). Of the remaining, more than 304 occur in the SWAFR (Western Australian Herbarium 2015). These species range in stature from 60 - 80 m tall trees in the wettest part of SWWA to 3 m tall multi-stemmed trees (mallees) in the more arid inland areas (Figure 3). Eucalyptus species play a major role in the ecological community: providing a range of services such as food and shelter for a number of invertebrates

(Recher et al. 1996; Majer et al. 2007), vertebrates (Whitford and Williams 2002; Radford et al. 2005) and parasitic plants (Norton et al. 1995); altering abiotic conditions under their canopy, allowing understorey species to survive where they may not normally be able (Kirkpatrick 1997); and drawing water and nutrients closer to the surface through hydraulic lift, making it available for species with less expansive root systems (Burgess et al. 1998; Brooksbank et al. 2011b; Brooksbank et al. 2011a; Verboom and Pate 2013). Thus, the loss of these species and the ecosystem services they provide has the potential to significantly change species composition and habitat structure of many SWAFR habitats (Ellison et al. 2005; Manning et al. 2006; Anderegg et al. 2013). It is for this reason, as well as the iconic nature of eucalypts in general, that the recent large scale tree health declines in SWAFR Eucalyptus species are so troubling.

Figure 3. Three Eucalyptus species from across an aridity gradient in the south-west of Western Australia. Left panel: E. albida (8m, White-leaved Mallee) near Corrigin (345 mm precipitation annually). Centre panel: E. marginata (30m, Jarrah) in Jarrahdale State Forest (715 mm precipitation annually). Right panel: E. jacksonii (60m, Red Tingle) in the Tingle State Forest (1140 mm precipitation annually).

During the summer of 2010 and 2011, an estimated 16,515 ha of E. marginata and Corymbia calophylla (Matusick et al. 2013) and a total of 500 ha of E. gomphocephala woodlands (Matusick et al. 2012) suffered from health decline (Figure 4). The symptoms ranged from complete tree death through to crown thinning and chlorosis. Earlier decline events have been reported for both E. wandoo and E. gomphocephala (Hooper and Sivasithamparam 2005; Cai et al. 2010; Brouwers et al. 2012). Much like the drought-related declines reported in Allen et al. (2010), these decline events have been attributed to the increase in temperature and decrease in precipitation in the past few decades (Matusick et al. 2013). Together with these changes to climates, other abiotic factors such as soil depth and characteristics, fragmentation, landscape

5 topography, drainage patterns and local climate conditions have been implicated to affect population declines (Brouwers et al. 2012; Matusick et al. 2012; Brouwers et al. 2013). Most of these factors relate to the ability of the species to access water. For example, plants situated higher in the landscape and on steeper slopes showed greater signs of stress than those in valleys. This was attributed to poor soil water holding capacity of the sandy and stony slopes lowering water availability and the distance from the groundwater table at higher elevations restricting groundwater recharge during summer (Brouwers et al. 2013). This finding is congruent with observations of declining groundwater levels in response to decreased annual precipitation (Petrone et al. 2010; Kinal and Stoneman 2011; Hughes et al. 2012).

Figure 4. Pockets of tree health decline observed in Jarrah (E. marginata) woodland in the south-west of Western Australia. The top panels show varying degrees of canopy collapse in individual Jarrah trees. The bottom two panels show what this decline looks like from the air. Figure adapted from Matusick et al. (2013).

In addition to the climate extremes causing health decline in Eucalyptus species, a number of pathogens have recently become more abundant and are causing large scale Eucalyptus tree death (Shearer 1994). Of particular note in the past century is Phytophthora cinnamomi, a soilborne water mould which causes root rot in susceptible plants (approximately 40% of the native plants, including some Eucalyptus species) restricting their ability to take up water from the soil

(Podger et al. 1965; Shearer and Shea 1987; Shearer and Tippett 1989; Shearer and Smith 2000; Shearer et al. 2007). The other notable pathogen is a group of fungi called cankers (mainly Quambalaria species), which invade the phloem and xylem of stems, killing the tissue and slowly spreading around the tree, decreasing its ability to transport water and nutrients (Smith 1970; Davidson and Tay 1983; Burgess et al. 2006; Pegg et al. 2008; Paap et al. 2008). Future increases in temperature may increase the distribution of both type of pathogens (Brasier 1996; Rosenzweig et al. 2001; Harvell et al. 2002; Anderson et al. 2004) as well as make Eucalyptus trees less resilient to them (Shearer 1994; Allen et al. 2010). Though we are starting to understand why stands of E. gomphocephala, E. marginata and E. wandoo species show signs of decline, our ability to predict to what extent other species of Eucalyptus will experience decline in the future is limited. This thesis contributes to this ability.

Historical and future distribution of Eucalyptus Historically, Eucalyptus species have had different distributions. It is well known that species contract into remnant populations during times of stress and expand their distribution again when suitable conditions arise (Comes and Kadereit 1998; Parmesan 2006; Byrne 2008). The long-term isolation of remnant populations can often lead to speciation due to remnant populations becoming genetically distinct (Levin 1993; Hewitt 1996; Williams and Brooker 1997; Bomblies 2010). It is for this reason that we often see a higher level of diversity in more stressful environments (Nevo 2001), such as arid conditions or nutrient-impoverished soils, both of which occur in the inland of SWWA (Hopper and Gioia 2004). The genetic diversity within E. wandoo populations has allowed Dalmaris et al. (2015) to identify historical patterns of contraction and expansion. They found that this species has been expanding in the recent history from a remnant population near the centre of its distribution. There are also records of E. jacksonii – a species currently surviving in a small pocket of high precipitation in the far south-west corner of SWWA – historically having had a much larger distribution, more like the current distribution of E. diversicolor (Wardell-Johnson and Coates 1996). If this pattern of contraction during times of stress is common for all Eucalyptus species, then one might expect distributions to contract again in the near future due to a rapidly changing climate.

Species distribution modelling is a technique which allows prediction of the future distribution of a species based on knowledge of its current distribution, along with the current climate and expected future climate conditions (Elith et al. 2010; Elith et al. 2011). Our current understanding of future distributions of Eucalyptus species is based on the species distribution modelling in Hughes et al. (1996). In this study, 819 Eucalyptus species from across Australia were modelled using predicted average annual temperature and average annual precipitation in the year 1970. They predicted that, because of the narrow temperature ranges that many species currently occur within, up to 53% of all Australian species (in a median climate warming

7 scenario) would have their distribution completely displaced by a hotter climate than within which they are currently known to survive. A large proportion of these species were found to be SWWA species. Changes to annual precipitation were found to be a smaller threat to species than the change in temperature, with only 35% of all species predicted to be displaced by decreased precipitation in the worst case climate change scenario (Hughes et al. 1996). In many cases, there was no predicted overlap between the current distribution of a species and that predicted to be suitable in the future. If these large shifts in distribution are indeed required, measures such as assisted migration may be necessary to conserve these species. Due to the severity of this prediction it is important to refine these predictions using updated species distribution modelling techniques and climate change predictions. Chapter 2 of this thesis contributes to updating these models.

The migratory ability of a plant species depends on its seed dispersal mechanism. Some plants rely on animals to physically relocate seeds, employing mechanisms such as the indigestible seed of the mistletoe (Reid 1989; Ladley and Kelly 1996), the elaiosome of the acacia (Gallagher et al. 1969; O’Dowd and Gill 1986) or adhesive residue or thorns (Sorensen 1986). Others species, such as many grasses, have adapted appendages such as wings or parachute-like structures to assist in the dispersal of the seed by wind (Greene and Johnson 1993). However, Eucalyptus species have no such mechanisms, with their seed dispersal distance determined by the height of the canopy and the weight of the seed (Cremer 1966; Cremer 1977; Byrne et al. 2008; Rejmánek and Richardson 2011). Modelling studies have predicted that tree species with poor dispersal capabilities and long time to maturity, such as most Eucalyptus species, are most vulnerable to a rapidly changing climate (Zhu et al. 2012; Renton et al. 2013; Renton et al. 2014). Even in an un-fragmented landscape, only an average of 34% of modelled species (including annuals, perennials and trees of varying dispersal abilities) are likely able to migrate fast enough to track the rapidly changing climate (Renton et al. 2012). This percentage is much lower in a highly fragmented landscape such as in the SWAFR. Even if Eucalyptus species were able to migrate to new areas outside of their current distribution – whether by natural or assisted migration – we have a poor understanding of the ability of these species to survive in novel conditions, with acclimatization to their current ecohydrological conditions also potentially affecting distribution patterns into the future. Chapter 3 and 4 of this thesis contributes to our understanding of these adaptations and their relevance to future distribution patterns. However, as emphasised by Hughes et al. (1996) and other species distribution modelling studies (Dormann 2007; Fordham et al. 2012; Dullinger et al. 2012), just because these models predict that part of a population will no longer overlap with the species’ preferred conditions (based on current distribution), does not necessarily mean that the species will immediately become extinct in these areas. Rather, the persistence of these species is determined by their ability to resist and/or acclimatise to the changing climate.

Short and long term adaptation to increasingly arid environments Species have the ability, over both a short and long period of time, to change their morphology and physiology in response to changing climatic conditions (Begg et al. 1980; Turner and Begg 1981). In the short term, plants are able to regulate both hydrostatic pressure and osmotic potential to maintain a positive water status through seasonal changes and short periods of drought. The regulation of stomatal conductance is one of the key mechanisms that can be used to control water potential, with higher stomatal conductance resulting in higher transpiration rates and thus more negative water potentials (Franks 2004). When conditions become such that transpiration would exceed uptake – resulting in a more negative water potential than the plant may be able to support without risking cavitation – stomata can be closed to limit transpiration (Tyree and Sperry 1988; Sack and Holbrook 2006; Drake et al. 2013). As such, species adapted to arid conditions, including Eucalyptus species, tend to have their stomata closed during the hottest and driest period of the day (Schulze et al. 1980; Tenhunen et al. 1981; Tenhunen et al. 1984; Zhang et al. 2013). On a seasonal to interannual timescale, plants may reduce leaf area to sapwood area ratio by shedding leaves during hotter and drier months, reducing both the water loss relative to the supply of water and the amount of biomass requiring that water (Eamus and Prior 2001; Bucci et al. 2005). However, all south-west eucalypts are perennial and there is no documented net loss of leaves during summer months in years with average climatic conditions. Crown decline, such as that observed in SWWA Eucalyptus species in 2011 (Matusick et al. 2012; Matusick et al. 2013), represents exceptional loss of foliage in summers with extreme climatic conditions. These species were unable to support leaves at the top of the canopy due to cavitation caused by the more negative water potentials required to draw water to such a height (Ryan and Yoder 1997; Schäfer et al. 2000; McDowell et al. 2002; Koch et al. 2004). Both reduced transpiration and the loss of leaves come at the cost of a reduced growth rate, thus making long term survival in stressful climates unlikely using these short term strategies alone, especially if co-occurring species are not affected as severely.

Over a longer period of time (beyond seasonal changes), plants can acclimatise to more arid conditions by balancing functional traits that are conducive to more negative water potentials, with traits that reduce vulnerability to cavitation. This is particularly important for evergreen trees in seasonally dry climates, such as the SWWA Eucalyptus species. Indeed, such adaptations can be observed when comparing Eucalyptus species across the environmental gradient in SWWA. Poot & Veneklaas (2013) have shown that species that typically inhabit drier climates have a lower minimum water potential, a greater resistance to cavitation and lower osmotic potentials, the combination of which allows these species to extract proportionally more water from drier soils. These differences in water use strategies between species across an environmental gradient have also been seen in a number of other Eucalyptus species around Australia (White et al. 1996; White et al. 2000; Rice et al. 2004). The contrasting water use strategies of these species are associated with adaptations to both stomata and xylem

9 traits. Stomata regulate aperture to control water loss, restricting transpiration during hot and dry conditions (Tyree and Sperry 1988; Sack and Holbrook 2006; Drake et al. 2013). Species in arid conditions generally have a higher density of smaller stomata (Bosabalidis and Kofidis 2002; Hetherington et al. 2003; Xu and Zhou 2008; Franks et al. 2009). These smaller stomata are thought to be more efficient at regulating aperture over shorter periods of time, allowing the plant to take advantage of short periods of localised conditions suitable for transpiration and quickly closing stomata in more stressful conditions (Tyree and Sperry 1988; Drake et al. 2013). This is particularly important for species in more arid climates, such as the inland SWWA Eucalyptus species, which tend to have their stomata closed for a considerable portion of the day (Schulze et al. 1980; Tenhunen et al. 1981; Tenhunen et al. 1984; Zhang et al. 2013), as reflected by their more negative carbon isotope ratios (Miller et al. 2001; Schulze et al. 2006b; Schulze et al. 2006a; Turner et al. 2008; Turner et al. 2010; Schulze et al. 2014). Xylem vessels in arid species also tend to be narrower and more numerous with thicker, more reinforced walls (Hacke et al. 2001; Hacke et al. 2006). The narrower vessels act to increase the forces drawing water further up the tree, and in combination with the smaller pit membrane pores, increases the vessels’ resilience to cavitation (Tyree and Sperry 1989). In changing both the size and density of stomata and xylem vessels, the plant is also changing its maximum ability to transport water to the leaves (xylem conductivity; Gleason et al. 2012) and the rate at which it can be lost from the leaves (stomatal conductance; Franks & Beerling 2009). The “supply and demand” allometry of trees or branches is often studied through the measurement of leaf area to sapwood area ratio, with species in arid conditions having lower ratios than those in more mesic conditions (Mencuccini and Grace 1995; Kelley et al. 2007; Carter and White 2009; Gotsch et al. 2010). The leaves of these arid species also tend to be smaller (Gibson 1998; Cunningham et al. 1999; Ackerly 2003; Ackerly 2004) and have a higher leaf mass per area (Skarpe 1996; Cunningham et al. 1999; Fonseca et al. 2000; Wright et al. 2001), as has been observed in SWWA Eucalyptus species (Schulze et al. 2006b; Schulze et al. 2006a). These smaller leaves likely benefit these species by having a thinner boundary layer, which assists in temperature regulation via convective cooling (Gibson 1998; Nicotra et al. 2008; Vogel 2009; Yates et al. 2010). Although global trends in plant responses to aridity are well studied, and clear general patterns in traits have been identified, many traits – such as stomata and xylem size and density and leaf area to sapwood area ratio – have yet to be documented for a range of Eucalyptus species. Understanding how Eucalyptus species’ traits have acclimatised to climate conditions, and whether these adaptations are similar to species in other environments globally, can assist in predicting the relative ability of species to persist in a changing climate. Chapter 3 of this thesis contributes to this understanding.

A species’ vulnerability to a changing climate – both in terms of its ability to persist in changing climate conditions, or move to alternative habitats with different soils – is not only determined by above-ground traits; the root system may be just as important. The placement of roots in the

10 soil profile determines the quantity of water to which a plant has access. A species with a deep taproot is able to access water from deeper soil horizons or even from the water table (where present) during the drier seasons, whereas a species with a shallower root system must survive on water held within the soil in the upper horizons of the profile (Schwinning and Ehleringer 2001; Groom 2004; Lambers et al. 2014). It is for this reason that a species on the top of a hill is required to have a longer taproot than the same species in a valley (Groom 2004). Although there is a global database of rooting depths for a large range of species (Canadell et al. 1996; Jackson et al. 1996; Schenk and Jackson 2002; Schenk and Jackson 2005), little is known about Eucalyptus species. This is because of the expansive nature of a typical tree root system, with roots often reaching depth equal or deeper than bedrock, making extraction difficult for scientists. It is assumed that many Eucalyptus trees have a deep rooting system. Indeed studies on E. marginata (a tree found in a broad variety of soil profiles, but typically not deep sand or heavy clay) and E. gomphocephala (a tree commonly found in deep sand), have found roots at depths of 40 m and 15 m respectively (Doley 1967; Lamont and Lange 1976; Carbon et al. 1980; Dell et al. 1983; Stone and Kalisz 1991). Relative allocation to a tap root or lateral roots may also be important in determining a plant’s fitness in different soil profiles. For example E. wandoo , a species commonly found in broad valleys with heavy clay soils, has been reported to have an extensive root mat just below the soil surface (Lamont 1985; Hooper 2009). Thus, it is clear that there are differences in the architecture of the root systems between Eucalyptus species. Indeed, such differences are thought to contribute to varying degrees of health decline between co-occurring Eucalyptus species (Poot and Veneklaas 2013). If root architectures are adapted to different soil profiles and climates, then any change to climate (due to climate change) or soil profile (if migrating to a new habitat) may limit a species’ ability for water uptake. However, the range of different root system architectures and the relationship with climate and soil profiles is currently unknown. Thus, if the vulnerability of Eucalyptus species to climate change is to be assessed, variation in root system architecture needs to be better understood. Chapter 4 of this thesis contributes to this understanding.

Thesis objectives and outline The Western Australian State Centre of Excellence for Climate Change, Woodland and Forest Health was formed in response to the sudden large-scale decline observed in Eucalyptus species in the SWAFR (as outlined above). The main objective of this research centre was to increase our understanding of tree decline, including but not limited to Eucalyptus species, in the SWWA during a time of rapid climate change. Research projects were instigated to assess different aspects of the ecology, pathology and physiology of declining trees, as well as the impacts of this decline on our biodiversity and the implications for conservation, restoration and

11 policy. The Centre of Excellence was also formed to inform and engage with government and the community through mediums such as seminars, workshops, newsletters and citizen science.

The research presented here was part of the program involved in improving the mechanistic understanding of the causes of woodland and forest declines, and assisting in the implementation of adaptive management and control of these declines. Specifically, the overall aim of the research presented in this thesis was to assess the relative vulnerability of a broad range of Eucalyptus species in SWWA to a changing climate. Vulnerability is defined here as the combination of a species’ exposure to unfavourable conditions, their sensitivity to change and their capacity to acclimatise to these conditions.

In terms of this thesis, this can be broken down into four key objectives:

1. Improve our understanding of which factors define a species’ distribution. 2. Determine the exposure of species’ current distribution to unfavourable conditions as a result of climate change. 3. Quantify the variation in traits related to water use (both above and below ground) to increase our knowledge of how sensitive these species are to a changing climate and their capacity to acclimatise to this change. 4. Use the newly acquired knowledge to predict SWWA Eucalyptus distributions in the future, and to identify species most at risk from a changing climate.

This research is presented across the following four chapters:

Chapter 2 – Predictions of future species’ distribution for 16 SWWA Eucalyptus species were modelled to identify those most at risk from a changing climate in terms of exposure to unsuitable conditions, and to predict areas of potential refuge for these species. New modelling methodology, current global climate change predictions, a broader range of environmental variables and a map of cleared land was used to achieve detailed predictions of distribution of SWWA Eucalyptus species. Knowledge gained here can be used to inform conservation and restoration practices as well as guide future research of Eucalyptus species.

Chapter 3 – This chapter builds on our current knowledge of Eucalyptus physiology and morphology as it relates to the acclimatization of species to their local climate. A suite of morphological and physiological traits were analysed for 16 Eucalyptus species across the SWWA aridity gradient. These included traits such as stomatal and xylem vessel traits, sapwood to leaf area ratio, carbon isotope ratio, leaf nitrogen content and other leaf traits.

Chapter 4 – The relationship between root architecture, precipitation and soil texture was explored to build on our understanding of current species’ distributions and to identify possible constraints to future distributions if the species is required to migrate. Using large rhizotrons, both lateral- and tap-root growth were observed over time in juvenile plants of nine Eucalyptus

12 species. Relative biomass and root length allocated to different sections of the soil profile were quantified to identify relationships with the species’ typical growing conditions, both in terms of annual precipitation and soil texture.

Chapter 5 – This chapter summarises the results from the three previous chapters and relates these findings to the larger picture of Eucalyptus vulnerability, discussed in terms of exposure, resilience and adaptive capacity. Areas for future research are also outlined.

13 References

Ackerly DD (2003) Community assembly, niche conservatism, and adaptive evolution in changing environment. Int J Plant Sci 164:S165–S184.

Ackerly DD (2004) Adaptation, niche conservatism, and convergence: comparative studies of leaf evolution in the California chaparral. Am Nat 163:654–671.

Aiba S-I, Kitayama K (2002) Effects of the 1997/1198 El Nino drought on rain forests of Mount Kinabalu, Borneo. J Trop Ecol 18:215–230.

Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg E, Gonzalez P, Fensham RJ, Zhang Z, Castro J, Demidova N, Lim J, Allard G, Running SW, Semerci A, Cobb N (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manage 259:660–684.

Anderegg WRL, Berry JA, Smith DD, Sperry JS, Anderegg LDL, Field CB (2012) The roles of hydraulic and carbon stress in a widespread climate-induced forest die-off. Proc Natl Acad Sci 109:233–237.

Anderegg WRL, Kane JM, Anderegg LDL (2013) Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Chang 3:30–36.

Anderson PK, Cunningham AA, Patel NG, Morales FJ, Epstein PR, Daszak P (2004) Emerging infectious diseases of plants: pathogen pollution, climate change and agrotechnology drivers. Trends Ecol Evol 19:535–544.

Australian Bureau of Meteorology (2011a) Average annual, seasonal and monthly rainfall. http://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp. Accessed 19 May 2015

Australian Bureau of Meteorology (2011b) Average annual & monthly maximum, minimum, & mean temperature. http://www.bom.gov.au/jsp/ncc/climate_averages/temperature/index.jsp. Accessed 19 May 2015

Australian Bureau of Meteorology (2015) Climate change and variability. http://www.bom.gov.au/climate/change/. Accessed 19 May 2015

Bates BC, Hope P, Ryan B, Smith I, Charles S (2008) Key findings from the Indian Ocean Climate Initiative and their impact on policy development in Australia. Clim Change 89:339–354.

Begg JE, Turner NC, Kramer PJ, others (1980) Morphological adaptations of leaves to water stress. Adapt plants to water high Temp Stress 33–42.

Berg EE, Henry JD, Fastie CL, De Volder AD, Matsuoka SM (2006) Spruce beetle outbreaks on the Kenai Peninsula, Alaska, and Kluane National Park and Reserve, Yukon Territory: relationship to summer temperatures and regional differences in disturbance regimes. For Ecol Manage 227:217–232.

Boisvenue C, Running S (2006) Impacts of climate change on natural forest productivity – evidence since the middle of the 20th century. Glob Chang Biol 12:862–882.

Bomblies K (2010) Doomed lovers: mechanisms of isolation and incompatibility in plants. Annu Rev Plant Biol 61:109–124.

Bosabalidis AM, Kofidis G (2002) Comparative effects of drought stress on leaf anatomy of two olive cultivars. Plant Sci 163:375–379.

Brasier CM (1996) Phytophthora cinnamomi and oak decline in southern Europe. Environmental constraints including climate change. Ann For Sci 53:347–358.

Bréda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann For Sci 63:625–644.

Breshears DD, Myers OB, Meyer CW, Barnes FJ, Zou CB, Allen CD, McDowell NG, Pockman WT (2008) Tree die-off in response to global change-type drought: mortality insights from a decade of plant water potential measurements. Front Ecol Environ 7:185–189.

Brooksbank K, Veneklaas EJ, White DA, Carter JL (2011a) The fate of hydraulically redistributed water in a semi-arid zone Eucalyptus species. Tree Physiol 31:649–658.

Brooksbank K, White DA, Veneklaas EJ, Carter JL (2011b) Hydraulic redistribution in Eucalyptus kochii subsp. borealis with variable access to fresh groundwater. Trees 25:735–744.

Brouwers NC, Matusick G, Ruthrof K, Lyons T, Hardy G (2013) Landscape-scale assessment of tree crown dieback following extreme drought and heat in a Mediterranean eucalypt forest ecosystem. Landsc Ecol 28:69–80.

Brouwers NC, Mercer J, Lyons T, Poot P, Veneklaas E, Hardy G (2012) Climate and landscape drivers of tree decline in a Mediterranean ecoregion. Ecol Evol 3:67–79.

Bucci S, Goldstein G, Meinzer F, Franco A, Campanello P, Scholz F (2005) Mechanisms contributing to seasonal homeostasis of minimum leaf water potential and predawn disequilibrium between soil and plant water potential in Neotropical savanna trees. Trees 19:296–304.

Burgess SSO, Adams MA, Turner NC, Ong CK (1998) The redistribution of soil water by tree root systems. Oecologia 115:306–311.

Burgess T, Sakalidis M, Hardy G (2006) Gene flow of the canker pathogen Botryosphaeria australis between Eucalyptus globulus plantations and native eucalypt forests in Western Australia. Austral Ecol 31:559–566.

Byrne M (2008) Evidence for multiple refugia at different time scales during Pleistocene climatic oscillations in southern Australia inferred from phylogeography. Quat Sci Rev 27:2576–2585.

Byrne M, Elliott C, Yates C, Coates D (2008) Maintenance of high pollen dispersal in Eucalyptus wandoo, a dominant tree of the fragmented agricultural region in Western Australia. Conserv Genet 9:97–105.

Cai YF, Barber P, Dell B, O’Brien P, Williams N, Bowen B, Hardy G (2010) Soil bacterial functional diversity is associated with the decline of Eucalyptus gomphocephala. For Ecol Manage 260:1047–1057.

15 Canadell J, Jackson RB, Ehleringer J, Mooney HA, Sala OE, Schulze ED (1996) Maximum rooting depth of vegetation types at the global scale. Oecologia 108:583–595.

Carbon BA, Bartle GA, Murray AM, Macpherson DK (1980) The distribution of root length, and the limits to flow of soil water to roots in a dry sclerophyll forest. For Sci 26:656– 664.

Carter JL, White DA (2009) Plasticity in the Huber value contributes to homeostasis in leaf water relations of a mallee Eucalypt with variation to groundwater depth. Tree Physiol 29:1407–1418.

Comes HP, Kadereit JW (1998) The effect of Quaternary climatic changes on plant distribution and evolution. Trends Plant Sci 3:432–438.

Cremer K (1966) Dissemination of seed from Eucalyptus regnans. Aust For 30:33–37.

Cremer K (1977) Distance of seed dispersal in eucalypts estimated from seed weights. Aust For Res 7:225–228.

CSIRO (2012) OzClim. Available from: http://www.csiro.au/ozclim/home.do.

Cunningham SA, Summerhayes B, Westoby M (1999) Evolutionary divergences in leaf structure and chemistry, comparing rainfall and soil nutrient gradients. Ecol Monogr 69:569–588.

Dalmaris E, Ramalho CE, Poot P, Veneklaas EJ, Byrne M (2015) A climate change context for the decline of a foundation tree species in south-western Australia: insights from phylogeography and species distribution modelling. Ann Bot, DOI: 10.1093/aob/mcv044

Davidson EM, Tay CS (1983) Twig, branch, and upper trunk cankers of Eucalyptus marginata. Plant Dis 67:1285–1287.

Dell B, Bartle JR, Tacey WH (1983) Root occupation and root channels of jarrah forest subsoils. Aust J Bot 31:615–627.

Doley D (1967) Water relations of Eucalyptus marginata Sm. under natural conditions. J Ecol 55:597–614.

Dormann CF (2007) Promising the future? Global change projections of species distributions. Basic Appl Ecol 8:387–397.

Doughty RW (2000) The Eucalyptus: a natural and commercial history of the gum tree. Johns Hopkins University Press, Baltimore

Drake PL, Froend RH, Franks PJ (2013) Smaller, faster stomata: scaling of stomatal size, rate of response, and stomatal conductance. J Exp Bot 64:495–505.

Dullinger S, Gattringer A, Thuiller W, Moser D, Zimmermann NE, Guisan A, Willner W, Plutzar C, Leitner M, Mang T, Caccianiga M, Dirnbock T, Ertl S, Fischer A, Lenoir J, Svenning J-C, Psomas A, Schmatz DR, Silc U, Vittoz P, Hulber K (2012) Extinction debt of high-mountain plants under twenty-first-century climate change. Nat Clim Chang 2:619–622.

Eamus D, Prior L (2001) Ecophysiology of trees of seasonally dry tropics: comparisons among phenologies. Adv Ecol Res 32:113–197.

Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342.

Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57.

Ellison AM, Bank MS, Clinton BD, Colburn EA, Elliott K, Ford CR, Foster DR, Kloeppel BD, Knoepp JD, Lovett GM, Mohan J, Orwig DA, Rodenhouse NL, Sobczak W V, Stinson KA, Stone JK, Swan CM, Thompson J, Von Holle B, Webster JR (2005) Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front Ecol Environ 3:479–486.

Fensham RJ, Fairfax RJ (2005) Preliminary assessment of gidgee (Acacia cambagei) woodland thickening in the Longreach district, Queensland. Rangel J 27 :159–168.

Fensham RRJ, Holman JEJ (1999) Temporal and spatial patterns in drought-related tree dieback in Australian savanna. J Appl Ecol 36:1035–1050.

Foden W, Midgley GF, Hughes G, Bond WJ, Thuiller W, Hoffman MT, Kaleme P, Underhill LG, Rebelo A, Hannah L (2007) A changing climate is eroding the geographical range of the Namib Desert tree Aloe through population declines and dispersal lags. Divers Distrib 13:645–653.

Fonseca CR, Overton JM, Collins B, Westoby M (2000) Shifts in trait-combinations along rainfall and phosphorus gradients. J Ecol 88:964–977.

Fordham DA, Resit Akçakaya H, Araújo MB, Elith J, Keith DA, Pearson R, Auld TD, Mellin C, Morgan JW, Regan TJ, Tozer M, Watts MJ, White M, Wintle BA, Yates CJ, Brook BW (2012) Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? Glob Chang Biol 18:1357– 1371.

Franks PJ (2004) Stomatal control and hydraulic conductance, with special reference to tall trees. Tree Physiol 24:865–878.

Franks PJ, Beerling DJ (2009) Maximum leaf conductance driven by CO2 effects on stomatal size and density over geologic time. Proc Natl Acad Sci 106:10343–10347.

Franks PJ, Drake PL, Beerling DJ (2009) Plasticity in maximum stomatal conductance constrained by negative correlation between stomatal size and density: an analysis using Eucalyptus globulus. Plant Cell Environ 32:1737–1748.

Gallagher CH, Gupta DN, Judah JD (1969) Competition for dispersal in ant-dispersed plants. Med Bull 25:274.

Gibson AC (1998) Photosynthetic organs of desert plants. Bioscience 48:911–920.

Gleason SM, Butler DW, Ziemińska K, Waryszak P, Westoby M (2012) Stem xylem conductivity is key to plant water balance across Australian angiosperm species. Funct Ecol 26:343–352.

17 Gonzalez P (2001) Desertification and a shift of forest species in the West African Sahel. Clim Res 17:217–228.

Gotsch S, Geiger E, Franco A, Goldstein G, Meinzer F, Hoffmann W (2010) Allocation to leaf area and sapwood area affects water relations of co-occurring savanna and forest trees. Oecologia 163:291–301.

Greene DF, Johnson EA (1993) Seed mass and dispersal capacity in wind-dispersed diaspores. Oikos 67:69–74.

Groom PK (2004) Rooting depth and plant water relations explain species distribution patterns within a sandplain landscape. Funct Plant Biol 31:423–428.

Hacke UG, Sperry JS, Pockman WT, Davis SD, McCulloh KA (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461.

Hacke UG, Sperry JS, Wheeler JK, Castro L (2006) Scaling of angiosperm xylem structure with safety and efficiency. Tree Physiol 26:689–701.

Harvell CD, Mitchell CE, Ward JR, Altizer S, Dobson AP, Ostfeld RS, Samuel MD (2002) Climate warming and disease risks for terrestrial and marine biota. Science 296:2158– 2162.

Hetherington AM, Woodward IF, Woodward FI (2003) The role of stomata in sensing and driving environmental change. Nature 424:901–908.

Hewitt GM (1996) Some genetic consequences of ice ages, and their role in divergence and speciation. Biol J Linn Soc 58:247–276.

Hobbs R, Saunders D (1993) Reintegrating Fragmented Landscapes: Towards Sustainable Production and Nature Conservation. Springer-Verlag, New York

Hooper R (2009) The role of stress and factors contributing to the decline of Eucalyptus wandoo (Blakely) in southwestern Australia. University of Western Australia

Hooper RJ, Sivasithamparam K (2005) Characterization of damage and biotic factors associated with the decline of Eucalyptus wandoo in southwest Western Australia. Can J For Res 35:2589–2602.

Hopper S (2009) OCBIL theory: towards an integrated understanding of the evolution, ecology and conservation of biodiversity on old, climatically buffered, infertile landscapes. Plant Soil 322:49–86.

Hopper SD, Gioia P (2004) The southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity. Annu Rev Ecol Evol Syst 35:623–650.

Hosking GP, Hutcheson JA (1988) Mountain beech (Nothofagus solandri var. cliffortioides) decline in the Kaweka Range, North Island, New Zealand. New Zeal J Bot 26:393–400.

Hughes JD, Petrone KC, Silberstein RP (2012) Drought, groundwater storage and stream flow decline in southwestern Australia. Geophys Res Lett 39:L03408.

Hughes L, Cawsey EM, Westoby M (1996) Climatic range sizes of Eucalyptus species in relation to future climate change. Glob Ecol Biogeogr Lett 5:23–29.

IPCC (2014) Summary for Policymakers. In: The Core Writing Team, Pachauri RK, Meyer L (eds) Climate Change 2014 Synthesis Report. IPCC, Geneva, Switzerland, pp 2–26

Jackson RB, Canadell J, Ehleringer JR, Mooney HA, Sala OE, Schulze ED (1996) A global analysis of root distributions for terrestrial biomes. Oecologia 108:389–411.

Jones ARC, Hendershot WH (1989) Maple decline in Quebec: A discussion of possible causes and the use of fertilizers to limit damage. For Chron 65:280–287.

Kelley G, O’Grady AP, Hutley LB, Eamus D (2007) A comparison of tree water use in two contiguous vegetation communities of the seasonally dry tropics of northern Australia: the importance of site water budget to tree hydraulics. Aust. J. Bot. 55:700–708.

Khan JA, Rodgers WA, Johnsingh AJT, Mathur PK (1994) Tree and shrub mortality and debarking by sambar Cervus unicolor (Kerr) in Gir after a drought in Gujarat, India. Biol Conserv 68:149–154.

Kinal J, Stoneman GL (2011) Hydrological impact of two intensities of timber harvest and associated silviculture in the jarrah forest in south-western Australia. J Hydrol 399:108– 120.

Kirkpatrick JB (1997) Vascular plant-eucalypt interactions. In: Williams JE, Woinarski JCZ (eds) Eucalypt Ecology: Individuals to Ecosystms. Cambridge University Press, New York, pp 227–245

Klausmeyer KR, Shaw MR (2009) Climate change, habitat loss, protected areas and the climate adaptation potential of species in mediterranean ecosystems worldwide. PLoS One 4:e6392.

Koch GW, Sillett SC, Jennings GM, Davis SD (2004) The limits to tree height. Nature 428:851–854.

Kurz WA, Dymond CC, Stinson G, Rampley GJ, Neilson ET, Carroll AL, Ebata T, Safranyik L (2008) Mountain pine beetle and forest carbon feedback to climate change. Nature 452:987–990.

Ladley J, Kelly D (1996) Dispersal, germination and survival of New Zealand mistletoes (Loranthaceae): dependence on birds. N Z J Ecol 20:69–79.

Lambers H, Brundrett M, Raven J, Hopper S (2010) Plant mineral nutrition in ancient landscapes: high plant species diversity on infertile soils is linked to functional diversity for nutritional strategies. Plant Soil 334:11–31.

Lambers H, Colmer TD, Hassiotou F, Mitchell PM, Poot P, Shane MW, Veneklaas EJ (2014) Carbon and water relations. In: Lambers H (ed) Plant life on the sandplains in southwest Australia, 1st edn. Western Australia: UWA Publishing, Perth, Australia, pp 129–146

Lamont B (1985) Gradient and zonal analysis of understorey suppression by Eucalyptus wandoo. Vegetatio 63:49–66.

Lamont B, Lange B (1976) “Stalagmiform” roots in limstone caves. New Phytol 76:353–360.

Laurance WF, Dell B, Turton SM, Lawes MJ, Hutley LB, McCallum H, Dale P, Bird M, Hardy G, Prideaux G, Gawne B, McMahon CR, Yu R, Hero J-M, Schwarzkopf L,

19 Krockenberger A, Douglas M, Silvester E, Mahony M, Vella K, Saikia U, Wahren C-H, Xu Z, Smith B, Cocklin C (2011) The 10 Australian ecosystems most vulnerable to tipping points. Biol Conserv 144:1472–1480.

Levin DA (1993) Local speciation in plants: the rule not the exception. Syst Bot 18:197–208.

Linder HP (2008) Plant species radiations: where, when, why? Philos Trans R Soc London B Biol Sci 363:3097–3105.

Lwanga J (2003) Localized tree mortality following the drought of 1999 at Ngogo, Kibale National Park, Uganda. Afr J Ecol 41:194–196.

Majer JD, Brennan KEC, Moir ML (2007) Invertebrates and the restoration of a forest ecosystem: 30 years of research following bauxite mining in Western Australia. Restor Ecol 15:S104–S115.

Manning AD, Fischer J, Lindenmayer DB (2006) Scattered trees are keystone structures – implications for conservation. Biol Conserv 132:311–321.

Matusick G, Ruthrof K, Brouwers NC, Dell B, Hardy G (2013) Sudden forest canopy collapse corresponding with extreme drought and heat in a Mediterranean-type eucalypt forest in southwestern Australia. Eur J For Res 132:1–14.

Matusick G, Ruthrof KX, Hardy J (2012) Drought and heat triggers sudden and severe dieback in a dominant Mediterranean-type woodland species. Open J For 2:183–186.

McDowell N, Barnard H, Bond B, Hinckley T, Hubbard R, Ishii H, Köstner B, Magnani F, Marshall J, Meinzer F, Phillips N, Ryan M, Whitehead D (2002) The relationship between tree height and leaf area: sapwood area ratio. Oecologia 132:12–20.

McDowell N, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry J, West A, Williams DG, Yepez EA (2008) Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol 178:719–739.

McDowell NG (2011) Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol 155:1051–9.

Mencuccini M, Grace J (1995) Climate influences the leaf area/sapwood area ratio in Scots pine. Tree Physiol 15:1–10.

Miller JM, Williams RJ, Farquhar GD (2001) Carbon isotope discrimination by a sequence of Eucalyptus species along a subcontinental rainfall gradient in Australia. Funct Ecol 15:222–232.

Mucina L, Laliberte E, Thiele K, Dodson J, Harvey J (2014) Biogeography of Kwongan: origins, diversity, endemism and vegetation patterns. In: Lambers H (ed) Plant life on the sandplains in southwest Australia: A global biodiversity hotspot. UWA Publishing, Crawley, Western Australia, pp 35–79

Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858.

Nevo E (2001) Evolution of genome–phenome diversity under environmental stress. Proc Natl Acad Sci 98:6233–6240.

Nicotra AB, Cosgrove MJ, Cowling A, Schlichting CD, Jones CS (2008) Leaf shape linked to photosynthetic rates and temperature optima in South African Pelargonium species. Oecologia 154:625–635.

Norton DA, Hobbs RJ, Atkins L (1995) Fragmentation, disturbance, and plant distribution: mistletoes in woodland remnants in the Western Australian wheatbelt. Conserv Biol 9:426–438.

O’Dowd DJ, Gill AM (1986) Seed dispersal syndromes in Australian Acacia. Seed dispersal 87–121.

Oreskes N (2004) The scientific consensus on climate change. Science 306:1686.

Orians GH, Milewski A V (2007) Ecology of Australia: the effects of nutrient-poor soils and intense fires. Biol Rev 82:393–423.

Paap T, Burgess TI, McComb JA, Shearer BL, St J. Hardy GE (2008) Quambalaria species, including Q. coyrecup sp. nov., implicated in canker and shoot blight diseases causing decline of Corymbia species in the southwest of Western Australia. Mycol Res 112:57– 69.

Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669.

Pegg GS, O’Dwyer C, Carnegie a. J, Burgess TI, Wingfield MJ, Drenth a. (2008) Quambalaria species associated with plantation and native eucalypts in Australia. Plant Pathol 57:702–714.

Petrone KC, Hughes JD, Van Niel TG, Silberstein RP (2010) Streamflow decline in southwestern Australia, 1950–2008. Geophys Res Lett 37:L11401.

Phillips OL, Aragão LEOC, Lewis SL, Fisher JB, Lloyd J, López-González G, Malhi Y, Monteagudo A, Peacock J, Quesada CA, van der Heijden G, Almeida S, Amaral I, Arroyo L, Aymard G, Baker TR, Bánki O, Blanc L, Bonal D, Brando P, Chave J, de Oliveira ÁCA, Cardozo ND, Czimczik CI, Feldpausch TR, Freitas MA, Gloor E, Higuchi N, Jiménez E, Lloyd G, Meir P, Mendoza C, Morel A, Neill DA, Nepstad D, Patiño S, Peñuela MC, Prieto A, Ramírez F, Schwarz M, Silva J, Silveira M, Thomas AS, Steege H ter, Stropp J, Vásquez R, Zelazowski P, Dávila EA, Andelman S, Andrade A, Chao K-J, Erwin T, Di Fiore A, Eurídice Honorio C, Keeling H, Killeen TJ, Laurance WF, Cruz AP, Pitman NCA, Vargas PN, Ramírez-Angulo H, Rudas A, Salamão R, Silva N, Terborgh J, Torres-Lezama A (2009) Drought sensitivity of the Amazon rainforest. Science 323:1344–1347.

Podger FD, Doepel RF, Zentmyer GA (1965) Association of Phytophthora cinnamomi with a disease of Eucalyptus marginata forest in Western Australia. Plant Dis Report 49:943– 947.

Poot P, Veneklaas E (2013) Species distribution and crown decline are associated with contrasting water relations in four common sympatric eucalypt species in southwestern Australia. Plant Soil 364:409–423.

21 Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol Conserv 124:317–337.

Recher HF, Majer JD, Ganesh S (1996) Seasonality of canopy invertebrate communities in eucalypt forests of eastern and western Australia. Aust J Ecol 21:64–80.

Reid N (1989) Dispersal of misteltoes by honeyeaters and flowerpeckers: components of seed dispersal quality. Ecology 70:137–145.

Rejmánek M, Richardson D (2011) Eucalypts. In: Simberloff D, Rejmanek M (eds) Encyclopedias of the Natural World: Encyclopedia of Biological Invasions. University of California Press, Oakland, California, pp 203–209

Renton M, Childs S, Standish R, Shackelford N (2013) Plant migration and persistence under climate change in fragmented landscapes: does it depend on the key point of vulnerability within the lifecycle? Ecol Modell 249:50–58.

Renton M, Shackelford N, Standish RJ (2012) Habitat restoration will help some functional plant types persist under climate change in fragmented landscapes. Glob Chang Biol 18:2057–2070.

Renton M, Shackelford N, Standish RJ (2014) How will climate variability interact with long- term climate change to affect the persistence of plant species in fragmented landscapes? Environ Conserv 41:110–121.

Rice K, Matzner S, Byer W, Brown J (2004) Patterns of tree dieback in Queensland, Australia: the importance of drought stress and the role of resistance to cavitation. Oecologia 139:190–198.

Rosenzweig C, Iglesias A, Yang XB, Epstein P, Chivian E (2001) Climate change and extreme weather events; implications for food production, plant diseases, and pests. Glob Chang Hum Heal 2:90–104.

Ryan MG, Yoder BJ (1997) Hydraulic limits to tree height and tree growth. Bioscience 47:235– 242.

Sack L, Holbrook NM (2006) Leaf hydraulics. Annu Rev Plant Biol 57:361–381.

Sala A (2009) Lack of direct evidence for the carbon-starvation hypothesis to explain drought- induced mortality in trees. Proc Natl Acad Sci 106:E68–E68.

Sala OE, Stuart Chapin F, III, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, Poff NL, Sykes MT, Walker BH, Walker M, Wall DH (2000) Global biodiversity scenarios for the year 2100. Science 287:1770–1774.

Sauquet H, Weston PH, Anderson CL, Barker NP, Cantrill DJ, Mast AR, Savolainen V (2009) Contrasted patterns of hyperdiversification in Mediterranean hotspots. Proc Natl Acad Sci 106 :221–225.

Schäfer KVR, Oren R, Tenhunen JD (2000) The effect of tree height on crown level stomatal conductance. Plant Cell Environ 23:365–375.

Schenk HJ, Jackson RB (2002) The global biogeography of roots. Ecol Monogr 72:311–328.

Schenk HJ, Jackson RB (2005) Mapping the global distribution of deep roots in relation to climate and soil characteristics. Geoderma 126:129–140.

Schulze E, Turner N, Nicolle D, Schumacher J (2006b) Leaf and wood carbon isotope ratios, specific leaf areas and wood growth of Eucalyptus species across a rainfall gradient in Australia. Tree Physiol 26:479–92.

Schulze E, Turner N, Nicolle D, Schumacher J (2006a) Species differences in carbon isotope ratios, specific leaf area and nitrogen concentrations in leaves of Eucalyptus growing in a common garden compared with along an aridity gradient. Physiol Plant 127:434–444.

Schulze E-D, Hall AE, Lange OL, Evenari M, Kappen L, Buschbom U (1980) Long-term effects of drought on wild and cultivated plants in the Negev desert. II. Diurnal patterns of net photosynthesis and daily carbon gain. Oecologia 45:19–25.

Schulze ED, Nicolle D, Boerner A, Lauerer M, Aas G, Schulze I (2014) Stable carbon and nitrogen isotope ratios of Eucalyptus and Acacia species along a seasonal rainfall gradient in Western Australia. Trees 28:1125–1135.

Schwinning S, Ehleringer JR (2001) Water use trade-offs and optimal adaptations to pulse- driven arid ecosystems. J Ecol 89:464–480.

Sevanto S, McDowell N, Dickman L, Pangle R, Pockman W (2014) How do trees die? A test of the hydraulic failure and carbon starvation hypotheses. Plant Cell Environ 37:153–161.

Shearer B, Smith I (2000) Disease of eucalypts caused by soilborne species of Phytophthora and Pythium. In: Keane P, Kile G, Podger F, Brown B (eds) Diseases and pathogens of eucalypts. CSIRO Publishing, Melbourne, pp 259–291

Shearer B, Tippett J (1989) Research Bulletin 3. Jarrah dieback: the dynamics and management of Phytophthora cinnamomi in the jarrah (Eucalyptus marginata) forest of southwestern Australia. Perth

Shearer BL (1994) The major plant pathogens occurring in native ecosystems of south-western Australia. J R Soc West Aust 77:113–122.

Shearer BL, Crane CE, Barrett S, Cochrane A (2007) Phytophthora cinnamomi invasion, a major threatening process to conservation of flora diversity in the South-west Botanical Province of Western Australia. Aust. J. Bot. 55:225–238.

Shearer BL, Shea SR (1987) Variation in seasonal population fluctuations of Phytophthora cinnamomi within and between infected Eucalyptus marginata sites of southwestern Australia. For Ecol Manage 21:209–230.

Skarpe C (1996) Plant functional types and climate in a southern African savanna. J Veg Sci 7:397–404.

Smith WC (1970) Stem canker disease of red flowering Gums. J Agric 11:33–39.

Solberg S (2004) Summer drought: a driver for crown condition and mortality of Norway spruce in Norway. For Pathol 34:93–104.

Sorensen AE (1986) Seed dispersal by adhesion. Annu Rev Ecol Syst 17:443–463.

Stone E, Kalisz P (1991) On the maximum extent of tree roots. For Ecol Manage 46:59–102.

23 Suarez ML, Ghermandi L, Kitzberger T (2004) Factors predisposing episodic drought induced tree mortality in Nothofagus–site, climatic sensitivity and growth trends. J Ecol 92:954– 966.

Tenhunen JD, Lange OL, Braun M (1981) Midday stomatal closure in Mediterranean type sclerophylls under simulated habitat conditions in an environmental chamber. Oecologia 50:5–11.

Tenhunen JD, Lange OL, Gebel J, Beyschlag W, Weber JA (1984) Changes in photosynthetic

capacity, carboxylation efficiency, and CO2 compensation point associated with midday

stomatal closure and midday depression of net CO2 exchange of leaves of Quercus suber. Planta 162:193–203.

The Atlas of Living Australia (2015) Eucalyptus. http://bie.ala.org.au/species/Eucalyptus. Accessed 17 Jun 2015

Tsopelas P, Angelopoulos A, Economou A, Soulioti N (2004) Mistletoe (Viscum album) in the fir forest of Mount Parnis, Greece. For Ecol Manage 202:59–65.

Turner N, Begg J (1981) Plant-water relations and adaptation to stress. Plant Soil 58:97–131.

Turner NC, Schulze ED, Nicolle D, Kuhlmann I (2010) Growth in two common gardens reveals species by environment interaction in carbon isotope discrimination of Eucalyptus. Tree Physiol 30:741–7.

Turner NC, Schulze E-D, Nicolle D, Schumacher J, Kuhlmann I (2008) Annual rainfall does not directly determine the carbon isotope ratio of leaves of Eucalyptus species. Physiol Plant 132:440–5.

Tyree MT, Sperry JS (1989) Vulnerability of xylem to cavitation and embolism. Annu Rev Plant Biol 40:19–36.

Tyree MT, Sperry JS (1988) Do woody plants operate near the point of catastrophic xylem dysfunction caused by dynamic water stress? Anwers from a model. Plant Physiol 88:574–580.

Van Nieuwstadt M, Sheil D (2005) Drought, fire and tree survival in a Borneo rain forest, East Kalimantan, Indonesia. J Ecol 93:191–201.

Verboom WH, Pate JS (2013) Exploring the biological dimension to pedogenesis with emphasis on the ecosystems, soils and landscapes of southwestern Australia. Geoderma 211– 212:154–183.

Vertui F, Tagliaferro F (1998) Scots pine (Pinus sylvestris L.) die-back by unknown causes in the Aosta Valley, Italy. Chemosphere 36:1061–1065.

Viljoen A (1995) The influence of the 1991/92 drought on the woody vegetation of the Kruger National Park. Koedoe 38:85–97.

Vogel S (2009) Leaves in the lowest and highest winds: temperature, force and shape. New Phytol 183:13–26.

Wang HB, Zhang Z, Kong XB, Liu S, Shen Z (2007) Preliminary deduction of potential distribution and alternative hosts of invasive pest, Dendroctonus valens (Coleoptera: Scolytidae). Sci Silvae Sin 143:71–76.

Wardell-Johnson G, Coates D (1996) Links to the past: local endemism in four species of forest eucalypts in southwestern Australia. In: Hopper SD, Chappill J, Harvey M, George A (eds) Gondwanan heritage: Past, present and future of the Western Australian Biota. Surrey Beatty and Sons, Chipping Norton, pp 137–154

Western Australian Herbarium (2015) Florabase - The Western Australia Flora. https://florabase.dpaw.wa.gov.au. Accessed 2 Jul 2015

White DA, Beadle CL, Worledge D (1996) Leaf water relations of Eucalyptus globulus ssp. globulus and E. nitens: seasonal, drought and species effects. Tree Physiol 16 :469–476.

White DA, Turner NC, Galbraith JH (2000) Leaf water relations and stomatal behavior of four allopatric Eucalyptus species planted in Mediterranean southwestern Australia. Tree Physiol 20:1157–1165.

Whitford KR, Williams MR (2002) Hollows in jarrah (Eucalyptus marginata) and marri (Corymbia calophylla) trees: II. Selecting trees to retain for hollow dependent fauna. For Ecol Manage 160:215–232.

Williams JE, Brooker MIH (1997) Eucalypts: an introduction. In: Williams JE, Woinarski JCZ (eds) Eucalypt Ecology: Individuals to Ecosystms. Cambridge University Press, New York, pp 1–15

Williamson GB, Laurance WF, Oliveira AA, Delamônica P, Gascon C, Lovejoy TE, Pohl L (2000) Amazonian tree mortality during the 1997 El Niño drought. Conserv Biol 14:1538–1542.

Wright IJ, Reich PB, Westoby M (2001) Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct Ecol 15:423–434.

Xu Z, Zhou G (2008) Responses of leaf stomatal density to water status and its relationship with photosynthesis in a grass. J Exp Bot 59:3317–3325.

Yates MJ, Anthony Verboom G, Rebelo AG, Cramer MD (2010) Ecophysiological significance of leaf size variation in Proteaceae from the Cape Floristic Region. Funct Ecol 24:485– 492.

Zhang Y-J, Meinzer FC, Qi J-H, Goldstein G, Cao K-F (2013) Midday stomatal conductance is more related to stem rather than leaf water status in subtropical deciduous and evergreen broadleaf trees. Plant Cell Environ 36:149–158.

Zhu K, Woodall CW, Clark JS (2012) Failure to migrate: lack of tree range expansion in response to climate change. Glob Chang Biol 18:1042–1052.

26 Chapter 2

Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions of Eucalyptus species in South Western Australia

Preface to Chapter 2 This chapter focuses on determining the extent to which 16 Eucalyptus species, chosen to represent the wide range of Eucalyptus species found in south-west Australia, will be exposed to a changing climate in the year 2100. Three non-linear climate change scenarios are employed to cover a broad spectrum of potential climate change scenarios. The modelling for this chapter was completed in 2012, when the species distribution model MAXENT was the most popular and widely used model in the literature. Although it is still commonly used at the time of publication of this paper (2015), there have been many advances in this rapidly moving field. Many of these new models are starting to take into account species’ sensitivity and adaptive capacity to improve model predictions. In this study, we focus only on the potential exposure of a species to climate change, relying on the other chapters presented in this thesis to assess other components of vulnerability. The benefit of assessing species’ exposure and species’ sensitivity separately is that we avoid compounding potential errors into our model by including too many assumptions. My hope is that, when we have a better understanding of Eucalyptus sensitivity and adaptive capacity through quantitative research, we can incorporate this knowledge with new species distribution modelling techniques.

Regolith layer showing the complexity of edaphic conditions across SWWA

Shallow environmental gradients put inland species at risk: insights and implications from predicting future distributions of Eucalyptus species in South Western Australia.

Hamer JJ,1,2* Veneklaas EJ1,2, Poot P,1,2 Mokany K3, Renton M, 1,2. 1 School of Plant Biology (M090), The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia (Email: [email protected]) 2 Centre of Excellence for Climate Change, Woodland and Forest Health, Crawley, Western Australia, 6009, Australia 3 CSIRO Ecosystem Sciences, PO Box 1700, Canberra, ACT 2601, Australia

Abstract  Background and Aims: As climate changes, tree decline in Mediterranean-type ecosystems is increasing worldwide, often due to decreased effective precipitation and increased drought- and heat-stress, and has recently been observed in coastal species of the iconic Eucalyptus (Myrtaceae) genus in the biodiversity hotspot of south-west Western Australia. We aimed to investigate how this drought-related decline is likely to continue in future.  Methods: We used species distribution modelling techniques to generate broad-scale predictions of future distribution patterns under three distinct projected climate change scenarios.  Results: In a moderate climate change scenario, suitable habitat for all species was predicted to decrease by, on average, 73% by the year 2100, with most receding into southern areas of their current distribution. Although the most severe Eucalyptus declines in south-west Western Australia have been observed in near-coastal regions, our predictions suggest that inland species are at greater risk from climate change, with six inland species predicted to lose 95% of their suitable habitat in a moderate change scenario.  Conclusions: This is due to the shallow environmental gradients of inland regions causing larger spatial shifts of environmental envelopes, which is likely to be relevant in many regions of the world. The knowledge gained suggests that future research and conservation efforts in south-west Western Australia and elsewhere should avoid focussing disproportionately on coastal regions for reasons of convenience and proximity to population centres, and properly address the inland region where the biggest future impacts may occur.

Keywords: Climate Change, Land use, Fragmentation, SDM, Species distribution model, Substrate, True Absences, Variable selection.

29 Introduction Past greenhouse gas emissions will continue to influence the global climate for at least the next century (Intergovernmental Panel on Climate Change 2013a), with high confidence that such climate change will increase global extinction rates (Intergovernmental Panel on Climate Change 2013b), with potentially as much as 37% of the Earth’s species predicted to be extinct by 2050 (Thomas et al. 2004). Already we are observing changes in vegetation structure worldwide, with large-scale tree mortality and major shifts in species distributions (Phillips et al. 2009; Allen et al. 2010; Huang and Anderegg 2012). Mediterranean-type ecosystems are likely to be especially affected, as most climate models consistently predict them to become both drier and hotter in the coming century (Intergovernmental Panel on Climate Change 2013a). For example, in south-west Western Australia, the area experiencing a Mediterranean climate, which is classified as a biodiversity hotspot (Myers et al. 2000), is predicted to decrease by up to 51% by the year 2100, with this area becoming more arid (Klausmeyer and Shaw 2009). This region has already experienced a 14% drop in winter rainfall and a 0.4 °C increase in mean annual temperature since the mid-1970s (Bates et al. 2008), resulting in crown decline in several of the major Eucalyptus (Myrtaceae) species (Brouwers et al. 2013; Evans and Lyons 2013; Evans et al. 2013). By 2070, it is predicted that, for 25% to 73% of Australian Eucalyptus species (exact figures depend on the climate prediction employed), the climate will have changed to the extent that their entire current distribution will no longer experience a suitable climate (Hughes et al. 1996). Large-scale loss of dominant overstorey Eucalyptus and the valuable ecosystem services they provide, such as the alteration of abiotic conditions below the canopy (Kirkpatrick 1997) and the provision of hollows for nesting sites (Whitford and Williams 2002; Radford et al. 2005), will likely result in the alteration of many native habitats. It is therefore important for future conservation and management of not only Eucalyptus species, but also for many co-existing species, to understand how the distribution of this genus will alter in the future.

Species distribution models (SDMs) have been widely used to predict species future distributions under varying climate change scenarios (Elith et al. 2006; Elith and Graham 2009). The prediction process involves training the SDM using the associations between known species presences, absences and current environmental variables to create an environmental envelope for the species. This model is then applied to a novel set of environmental values, based on predicted future climate scenarios, to produce a prediction of the species future distribution (Elith et al. 2011). This type of modelling has become increasingly popular over the past two decades, with a wide variety of techniques becoming available (Yee and Mitchell 1991; Carpenter et al. 1993; Phillips et al. 2006; Latimer et al. 2006; Elith et al. 2008; Engler and Guisan 2009; Pollock et al. 2014; Booth et al. 2014). Many SDM software packages have now reached the stage that one does not need to understand the underlying model or assumptions to be able to make predictions. Furthermore, very little input is required, with some

30 packages only needing a dataset of presence locations and any number of environmental variables. Although this means that predictions can be made quickly and easily, researchers may become less aware of the importance of proper model construction and evaluation and how these affect the results. For example, many studies have shown how the selection of predictor variables during model development can have a significant effect on model performance and predictions (Thuiller et al. 2004; Coudun et al. 2006; Yates et al. 2010; Syphard and Franklin 2010; Meier et al. 2010; Austin and Van Niel 2011; Synes and Osborne 2011; Williams et al. 2012; Sheppard 2013; Braunisch et al. 2013). This is just one factor which should be considered while constructing SDMs, with a number of reviews documenting this and other limitations and assumptions associated with this type of modelling (Araújo and Guisan 2006; Elith et al. 2006; Thuiller et al. 2008; Elith et al. 2010; Elith et al. 2011; Sillero 2011; Franklin 2012; Araújo and Peterson 2012; Warren 2012; Wisz et al. 2013).

In this study we rigorously constructed a SDM to predict the current and future distributions of Eucalyptus species in the south-west of Western Australia. Our main objective was to determine the degree to which a wide range of different species are at risk from a changing climate and identify any species of particular concern, to inform the management and conservation of the iconic Eucalyptus and associated habitats.

Methods

Study region and species The south-west corner of Western Australia (Figure 1a) was used for this study because it contains one of only five Mediterranean climate biodiversity hotspots in the world (Myers et al. 2000), and it is already undergoing substantial changes in climate (Bates et al. 2008). The region features a strong annual precipitation gradient ranging from 700 – 1200 mm along the coast, down to 200 mm further inland (Appendix 1). The annual mean temperature ranges from 15 °C at the southern edge up to 23 °C at the northern extent of our study area. However, global climate models predict that much of this Mediterranean region will become arid in the future (IPCC 2007; Klausmeyer and Shaw 2009). The wettest corner of the study region will likely see a decrease in annual precipitation anywhere between 40 – 400 mm and an increase in annual mean temperature between 1 – 4 °C by the year 2100. The annual precipitation received in the inland region is predicted to change by between 20 mm (wetter) and -100 mm (drier), with temperature rising by between 1.5 – 5 °C.

Figure 1. Extent of study area (113.0° S, -36.0° E to 125.0° S, -26.0° E) with the boundary of the Southwest Floristic Region outlined and overlayed with important model data: a) shows all occurrence records for the 16 selected Eucalyptus species (•) and all known absence locations (×) use in the model; b) depicts remnant vegetation in green (shaded), with cleared land not available to native plant life in white.

The south-west of Western Australia has also been extensively cleared for urbanisation and agriculture (Figure 1b). A greater proportion of natural habitat has been removed in the inland region for agricultural purposes compared to that in the higher precipitation regions along the coast where most urban centres are located. This has resulted in a highly fragmented landscape of remnant vegetation in the inland region, with often 5 – 20 km between natural reserves.

The Eucalyptus genus is comprised of a broad range of species that dominates the overstorey in most ecosystems of the region, ranging from tall forests in high rainfall coastal ecosystems, to open mallee woodlands in drier inland areas. This genus was chosen for this study due to the recently observed large scale tree mortality in a number of species in the wetter coastal region, close to population centres (Hooper and Sivasithamparam 2005; Brouwers et al. 2012; Brouwers et al. 2013; Matusick et al. 2013; Poot and Veneklaas 2013; Evans et al. 2013). It is therefore important to know whether this is a restricted phenomenon, or whether we need to expand our monitoring and conservation efforts inland to less local populations.

For this study, sixteen Eucalyptus species were chosen to represent a variety of growth forms, soil and climate preferences, landscape positions and habitat ranges (Figure 1, Appendix 1 – 3). Presence records for all species were obtained from the Western Australian Herbarium, as well as a comprehensive flora survey of the inland area (Figure 1a; Gibson et al. 2004).

Climate variables Climate variables were chosen from the BIOCLIM dataset (Xu & Hutchinson 2010), which consists of 35 variables describing temperature, precipitation, radiation and soil moisture. BIOCLIMs soil moisture index variables are based on a simple water balance model, assuming 32 a ‘standard’ soil type; deviations from true soil moisture are expected for specific cases, such as in shallow soils. To account for the range of climate change possibilities, we considered a range of global climate models and emission scenarios from OZClim (CSIRO 2012). Three model/scenario combinations were chosen to represent low (GCM = INM-CM3.0, emission scenario = B1), moderate (GCM = CSIRO-Mk3.5, emission scenario = AB1) and severe (GCM = CCR:MIROC-M, emission scenario = A1Fl) climate change in south-west Western Australia in the year 2100 (Appendix 1). These three scenarios are not intended to represent a linear or temporal change in climate, rather they were chosen to represent three contrasting possibilities of how climate might change in south-west Western Australia. Predictions were extrapolated over a spatial grid of the study region and statistically downscaled to 250 m grid resolution using ANUCLIM v6.1 (Xu & Hutchinson 2010) to match the resolution of other predictor variables.

Predicting species distributions In this study we were mainly interested in regional-scale changes in distribution caused by climate change rather than fine-scale predictions. Therefore we opted for one of the more well- established, widely available and accessible SDM techniques: MAXENT (version 3.3.3k; Phillips et al. 2006). Unless otherwise specified, only two settings were changed from the MAXENT default: 1) the model was refitted five times (instead of only once) using cross validation to obtain the average prediction, and 2) the probability threshold for presence/absence prediction was calculated using the maximum training sensitivity plus specificity method, which sets the threshold value to that which maximises the number of correctly identified presences and absences (Liu et al. 2005; Jiménez-Valverde and Lobo 2007; Bean et al. 2012). The threshold was applied to the average of the five maximum-likelihood maps produced by MAXENT to create a predicted presence/absence map for each species. Using this map, the total area of suitable habitat was calculated by multiplying the cell size (250m2) by the total number of presence cells. A general approximation of the distance and direction that each species environmental envelope shifted in future climate change scenarios was calculated by measuring changes in the centroid of the species suitable habitat. This was calculated as the centre point of the distribution as judged by the minimum and maximum longitude and latitude. Though this resulted in the centroid being slightly outside the distribution for some species along the coast, the shift in centroids still appeared to be a good representative of that of the shift in predicted distribution. The importance of each variable to the species distribution was assessed using Permutation Importance scores provided by standard MAXENT output.

Models were evaluated using both the area under the receiver operating characteristic curve (AUC) statistic and the True Skill Statistic (TSS). Regardless of the criticisms against using AUC (Lobo et al. 2008; Jiménez-Valverde 2012), it has still been reported here due to its

33 common use in the literature. Though the Kappa statistic is commonly used in the literature, it was not appropriate for use in our study due to the high prevalence of absence data compared to presence data, due to the inclusion of both true absences and pseudo absences (McPherson et al. 2004; Allouche et al. 2006). Instead, TSS, as described in Allouche et al. (2006), was calculated due to its independence from prevalence to supplement AUC for model evaluation.

Variable pre-selection The choice of predictor variables plays a major role in determining the accuracy of predictions, especially when extrapolating into the future (Peterson and Nakazawa 2008; Synes and Osborne 2011; Sheppard 2013). It can be argued that variable selection should be based on factors that are known to affect growth and distribution (Austin and Van Niel 2011), as this should increase the realism of predictions (Thuiller et al. 2004; Coudun et al. 2006; Yates et al. 2010; Syphard and Franklin 2010; Meier et al. 2010). Therefore, in this study we used variables that were most likely to have a direct effect on species survival, and therefore distribution (‘biologically important’ variables), that also minimised correlation between predictor variables. The maximum and minimum temperature throughout the year (bioclim4 and bioclim5 respectively), as well as the mean temperature of the warmest and coldest quarter (bioclim10 and bioclim11) were selected as these are well-known to affect plant metabolism and hydraulics (Osmond et al. 1987; Lambers et al. 2008; Pereira et al. 2010) and have been positively correlated with tree health decline in some of our study species (Evans and Lyons 2013). Annual precipitation (bioclim12) and the total precipitation in the wettest and driest quarter (bioclim16 and bioclim17) were chosen to represent the availability of soil water. Finally, mean soil moisture index of the highest and lowest quarter (bioclim32 and bioclim33) were included to represent the interaction between precipitation and evaporation.

Though Austin and Van Niel (2011) suggest the use of radiation to represent the amount of light available to the plants, it is not considered a limiting factor for plant growth in south-west Western Australia (Churkina and Running 1998) due to the flat landscape and low cloud levels throughout the year, especially for Eucalyptus trees which are generally the primary overstorey species. There were also technical issues with the radiation predictions for future climate change scenarios (Harwood et al. 2013). For these reasons, radiation variables were not included.

Preliminary analysis of the novelty of future climates was undertaken using multivariate environmental similarity surfaces described in Elith et al. (2010). We found that, using the climate variables described above, the majority of the study region’s climate is not predicted to become novel in any of the three future climate scenarios considered here (Appendix 4). Furthermore, even in the most extreme scenario, our species distribution models consistently did not extrapolate into novel environments, avoiding issues caused by this (Elith et al. 2010; Sheppard 2013).

We aimed to further improve the potential explanatory power of our predictor variables by adding soil attributes the model. Soil attributes have been shown to affect Eucalyptus distributions (Parsons 1994) and have been linked with recent tree health decline in drought conditions (Brouwers et al. 2013). Furthermore, soils tend to have a higher clay content further inland in south-west Western Australia than coastal areas which may contribute to defining species distributions. As continuous, high-resolution maps of soil variables over the study region were not available, as a surrogate we tested the use of available surface substrate variables: regolith (1:500,000; Marnham and Morris 2003), surface geology (1:1,000,000; Stewart et al. 2008) and the weighted solum depth to bedrock (McKenzie et al. 2000). All three variables were converted to a 0.0025° raster for consistency with the climate layers’ resolution. Within the study region, the regolith layer, which broadly classifies soils by their geological origin, contained nine classes of soil, while the surface geology, a much finer classification of soil origin, consisted of 317 classes.

True absences Most SDM techniques have been developed to deal with presence-only data (Elith et al. 2006), or where pseudo-absences are randomly generated across the landscape, since true absence data are often lacking. Nonetheless, it is expected that true absences should improve model validity and that of predicted distributions (Anderson 2003; Chefaoui and Lobo 2008; VanDerWal et al. 2009; Lobo et al. 2010). True absence data for each of the 16 species were obtained from surveys of 1500 sites in the inland region of the south-west of Western Australia (Figure 1a; Gibson et al., 2004). Due to the thorough nature of these surveys, it was possible to determine in which plots each of the 16 target species were absent. However, true absence data are missing from the coastal and eastern regions which were not surveyed. To account for this, both the true absences and presences were weighted by a factor of three (through duplication) and then combined with 10000 pseudo absences randomly generated from across the region. The MAXENT setting “remove duplicated records” was turned off due to the method used to weight known data.

Post processing Previous models have shown that without assistance, tree species in south-west Western Australia are unlikely to be able to migrate far or fast enough to keep pace with the rate at which the climate is changing (Renton et al. 2012; Renton et al. 2013; Renton et al. 2014). Although it is interesting to look at the potential distribution patterns with infinite dispersal, it is more realistic to assume zero migration for the study species due to Eucalyptus’ poor seed dispersal (Cremer 1966; Cremer 1977; Byrne et al. 2008). This also has the added benefit of not requiring the model to extrapolate to new climates, decreasing the predictive uncertainty of our results (Elith et al. 2010). Therefore, we adjusted model outputs to better represent the migratory abilities of Eucalyptus species, with predictions under zero dispersal computed as the overlap between the current and future distributions of the raw output ‘infinite dispersal’ model.

35 In addition to the limited area that the species can disperse to, much of the study region, particularly that of the inland region, has been cleared for human use (Figure 1b) and it is unreasonable to assume that the study species will be able to inhabit every location predicted by the model. Within the same study area, Banksia species distribution predictions have previously been shown to be substantially restricted by habitat fragmentation (Yates et al. 2010). Therefore, in this step we continued post-processing the model predictions to more accurately display the habitat available to the study species in both the zero-dispersal and infinite-dispersal models. This was accomplished by computing the overlap between the model outputs and a map of native remnant vegetation (Figure 1b; Shepherd 2003).

Results Inland species were generally more negatively affected by a changing climate than coastal species, contrary to current observations, however none of the species did particularly well in the severe scenario (Figure 2). All inland species lost over 95% of suitable habitat, with the exception being E. albida, which only lost 65%. On the other hand, coastal species only lost 50-75%, with one exception being E. diversicolor which lost 95.7%. The intermediate species showed a range of responses, with E. accedens retaining the most suitable habitat (only 31.4% lost) and E. caesia being the only species to completely lose all suitable habitat in the moderate climate change scenario. AUC scores for all species were above 0.89, and TSS were all above 0.72 (Appendix 5), indicating a good fit for all species. Presence/absence prediction maps for all species can be found in Supplementary material (Appendix 3).

The mean soil moisture index of the wettest quarter was the most important in defining species distribution, with a Permutation Importance score of greater than 10% for 11 out of the 16 species (Appendix 6). Precipitation in the driest quarter was also important for inland species, with a score of more than 10% for six of out seven inland species. It was less important to coastal species, with only one out of the six having a score above 10%. Overall, mean temperature in the coldest quarter and mean soil moisture index in the driest quarter were not as important, with at most only one species scoring more than 10% for these variables, and none above 20%.

Out of the three substrate variables, only for E. longicornis did any of the variables (surface geology) receive a Permutation Importance score exceeding 10%. However, the effect of including these substrate variables can be seen in most species (when compared to models not presented here that did not include these variables), with the edges of the range being more refined for both the current and future predicted distribution maps.

Figure 2. Predicted change in suitable habitat of the 16 study species (sorted high to low precipitation, left to right) under three climate change scenarios (low, moderate and high change) for both infinite and zero dispersal models, restricting available habitat to existing remnant vegetation. Coastal species are indicated by a solid circle before the name, intermediate species with no symbol, and a solid triangle indicating inland species.

Assuming zero dispersal had a big effect on predicted distribution area, decreasing the suitable area for all species in all climate scenarios. With species no longer able to find suitable habitat outside their current distribution, the direction and distance of species distribution shifts also changed (Figure 3), with inland species affected more than coastal species. In the moderate and severe scenario, dry region species could no longer disperse to wetter areas in the south-west, and were forced to retreat into smaller populations in a south-easterly direction.

Furthermore, limiting species distribution to areas of remnant vegetation significantly reduced the predicted current distribution by 52.65% on average over the 16 species. Species most affected were E. accedens, E. albida, E. caesia, E. gardneri and E. wandoo , each losing more than 65% of their suitable habitat in the current climate.

Discussion

Predictions under Climate Change Our models predict that the distribution range for the majority of Eucalyptus species will suffer large reductions in all climate change scenarios. As expected, there were clear differences among the three climate change scenarios, with the severe scenario resulting in none of the species retaining more than 10% of their existing habitat, and five species losing all currently suitable habitat (future environmental envelopes do not overlap with current distributions). This

37 does not necessarily mean these five species will go extinct in such a scenario, as it is possible that these species will migrate to new suitable areas or continue to survive in areas they currently inhabit because they have broader fundamental environmental tolerances than the realised responses modelled here; and/or by adapting to the new climates; and/or by declining into isolated patches of suitable environment at a finer scale than is modelled in this study (Fordham et al. 2012). Nonetheless, our results indicate that many Eucalyptus species in the south-west of Western Australia are at high risk of declines in tree health, large range contraction, and possible extinction.

Figure 3. Predicted shifts in distribution assuming infinite dispersal and zero dispersal under low, moderate and severe climate change scenarios. Species are coloured according to their precipitation preference, where blue (darkest, first six species) indicates the wetter zone species, orange (lightest, the following three species) the mid-range species and red (mid-tone, last seven species) the dry zone species. Symbols represent the centroid of the current distribution while arrows point to the centroid of the future distribution. The number in the bottom right corner of the map indicates how many species were predicted to have no suitable habitat remaining in each scenario. No symbols or arrows were drawn for these species.

Our results indicate inland species may be at particular risk, with the zero dispersal models predicting that coastal species will retain a greater proportion of currently suitable habitat in the future than inland species. This is because coastal species wider predicted environmental envelopes, which encompass a larger range of climatic conditions, allow these species to retract into small populations, generally at the southern range edge, without having to migrate. Although the reasonably uniform climate conditions of inland areas allow species to have wide

38 distributions in the current climate, it also makes the area more sensitive to change. Where climate gradients are shallower, similar shifts in climate will require species to migrate larger distances to stay within suitable conditions. This prediction is consistent with that made by Hughes et al. (1996) for many south-west Western Australian Eucalyptus species, but is inconsistent with current observations of population decline (Hooper and Sivasithamparam 2005; Brouwers et al. 2012; Brouwers et al. 2013; Matusick et al. 2013; Poot and Veneklaas 2013; Evans et al. 2013). This could be explained by an observational bias towards habitats local to population centres, which are generally near the coast, causing declines in health in these areas to be more prominent in the literature. However, it is also possible that inland species may be more resilient to change than coastal species and have not begun to show signs of stress yet.

The infinite dispersal projections suggest that, where migration is possible, both inland species and coastal species will generally need to migrate in a southerly direction. In fact, migration may be the only option for some inland species, as their entire current habitat may become unsuitable. This was the case for E. gardneri, E. salmonophloia and E. wandoo under the severe scenario, as well as E. caesia in both moderate and severe scenarios. The case for E. gomphocephala was slightly different, with this species predicted to increase its total distribution in all infinite dispersal climate change scenarios. This was due to the habitat south of the species’ current distribution becoming suitable at a faster rate than the northern extent of its distribution becoming unsuitable. However, due to Eucalyptus species limited dispersal capacity (Cremer 1966; Cremer 1977; Byrne et al. 2008; Rejmánek and Richardson 2011) and long time to maturity, it is unlikely that any of these species will be able to migrate fast enough to keep up with the steadily changing climate (Renton et al. 2012; Renton et al. 2013; Renton et al. 2014). However, these infinite dispersal predictions do allow for the identifying of potential areas of refuge for species in the future if assisted migration becomes a necessity (Vitt et al. 2010).

It is already been shown that current distribution patterns of Banksia species in Western Australia are affected by landscape fragmentation caused by urbanisation and agriculture (Yates et al. 2010). Similarly for Eucalyptus, species which were most affected by restricting distribution to remnant vegetation were those with distributions mostly within the extensively fragmented inland agricultural region, E. accedens , E. albida, E. caesia, E. gardneri and E. wandoo. This fragmented landscape also restricts species migration (Renton et al. 2012; Renton et al. 2013; Renton et al. 2014) and, coupled with poor dispersal (Cremer 1966; Cremer 1977; Byrne et al. 2008; Rejmánek and Richardson 2011), makes the natural migration of inland species through the fragmented inland agricultural area very unlikely. Furthermore, the predicted shift in distribution for many of the inland species places their future distribution mostly within the highly fragmented agricultural area of the inland region, drastically reducing

39 the suitable habitat for these species. In conjunction with shallow climate gradients, this is one of the main reasons that inland species appear to be at greater risk from a changing climate than coastal species.

Knowing which variables best define species distributions is important for conservation monitoring and can provide an early warning system for at-risk species. Of the biologically important variables selected, it was clear that precipitation and soil moisture index were more important to the study species than temperature. The most important variable in the model for the majority of species was mean soil moisture index of the wettest quarter, despite the fact that this variable does not take into account soil hydraulics and the effects of storage capacity on soil moisture content (Xu and Hutchinson 2011). Its importance for the majority of species suggests that precipitation alone is not as important as the interaction between precipitation and evaporation in defining species distributions. As such, developing a more realistic variable of soil moisture which can take into account varying soil types across the landscape would increase the biological relevance of species distribution modelling for these species. Interestingly, precipitation in the driest quarter was considered important for defining distributions of inland species where this variable had low values, but also in the wettest area of the state, where it was considered the main contributor for E. jacksonii. As this is the single most important variable for this species, it suggests that the species roots may not grow deep enough to tap into ground water reserves during a dry summer. This merits further glasshouse and field studies, as a better understanding of these climatic limitations will assist in the future conservation of this species.

Both soil characteristics and geological features have been shown to influence many species distributions (Fensham and Kirkpatrick 1992; Gibson et al. 2004; Coudun et al. 2006; Keith 2011) and have even been linked to crown decline in E. marginata during drought (Brouwers et al. 2013). However, our results suggest that, for the majority of species, surface substrate did not have as strong an influence on distribution as expected. Though some visible refinement was observed at the edges of predicted distributions, the variables did not have as much of an effect on the interior of the predicted distributions. This could be caused by a combination of the coarse resolution of data layers, the inaccuracy and bias of survey data and the broad-scale approach employed in this study, resulting in less accurate substrate information for some of the presence locations. It is also possible that the available substrate variables are not related to the distributions of Eucalyptus species, and that more relevant soil variables are required (e.g. soil particle size and water holding capacity).

Randomly placed pseudo absences without the inclusion of true absence data appeared to provide enough information for model fitting and did not leave much room for improvement. This indicates how well-suited MAXENT is for dealing with presence-only data. Though a significant (though minor) decrease in area was observed in predicted current distributions when

40 true absences were included, the changes in future predictions did not follow any particular trend, with distributions both under current and future conditions remaining in the same geographical vicinity. The consistent changes to predicted distributions under the current climate were thought to be an artefact caused by the increase in density of absences compared to presences.

Model Limitations Though we have predicted regional-scale changes in Eucalyptus distribution into the future, there is still much room for future refinement. Recently developed, more advanced modelling techniques can improve confidence in fine-scale predictions, which can be used for identifying key areas of habitat for conservation or restoration. Some of these techniques would require further adjustment of MAXENT parameters (Merow et al. 2013), testing of survey bias and imperfect detection (Syfert et al. 2013; Lahoz-Monfort et al. 2014; Dorazio 2014), and even employing new models to combine multiple species into a single model to better represent biotic interactions (Pollock et al. 2014). Furthermore, we feel that finding more relevant, higher resolution soil layers and more expansive field surveys can further improve the predictions. As the simple soil moisture index variable proved to be such a useful predictor, we believe that even better indicators of available soil water, accounting for soil depth and texture as well as rainfall and evaporation, would be even more valuable. Research is also needed to better understand how Eucalyptus species may adapt and persist at the lagging edges of their shifting distributions, as well as colonise new areas at the leading edges. This will likely depend on species ecophysiological tolerance to change (Dawson et al. 2011) and their interactions with other species (Pollock et al. 2014). Such developments promise to lead to better predictions of future distributions.

Acknowledgments This project uses species presence records from the Western Australian Herbarium, as well as survey data collected by Dr. Neil Gibson from the Department of Parks and Wildlife. This project was funded by the School of Plant Biology, University of Western Australia, as well as the Centre of Excellence for Climate Change, Woodland and Forest Health. I would also like to thank anonymous reviewers for their valuable comments on the manuscript.

41 References

Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232.

Anderson RP (2003) Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. J Biogeogr 30:591–605.

Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688.

Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539.

Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8.

Bates BC, Hope P, Ryan B, Smith I, Charles S (2008) Key findings from the Indian Ocean Climate Initiative and their impact on policy development in Australia. Clim Change 89:339–354.

Bean WT, Stafford R, Brashares JS (2012) The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography (Cop) 35:250–258.

Booth TH, Nix HA, Busby JR, Hutchinson MF (2014) bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Divers Distrib 20:1–9.

Braunisch V, Coppes J, Arlettaz R, Suchant R, Schmid H, Bollmann K (2013) Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography (Cop) 36:971–983.

Brouwers NC, Mercer J, Lyons T, Poot P, Veneklaas E, Hardy G (2012) Climate and landscape drivers of tree decline in a Mediterranean ecoregion. Ecol Evol 3:67–79.

Byrne M, Elliott CP, Yates CJ, Coates DJ (2008) Maintenance of high pollen dispersal in Eucalyptus wandoo, a dominant tree of the fragmented agricultural region in Western Australia. Conserv Genet 9:97–105.

Carpenter G, Gillison AN, Winter J (1993) DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodivers Conserv 2:667–680.

Chefaoui RM, Lobo JM (2008) Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol Modell 210:478–486.

Churkina G, Running SW (1998) Contrasting Climatic Controls on the Estimated Productivity of Global Terrestrial Biomes. Ecosystems 1:206–215.

Coudun C, Gégout J, Piedallu C, Rameau J (2006) Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. J Biogeogr 33:1750–1763.

Cremer K (1966) Dissemination of seed from Eucalyptus regnans. Aust For 30:33–37.

Cremer K (1977) Distance of seed dispersal in eucalypts estimated from seed weights. Aust For Res 7:225–228.

CSIRO (2012) OzClim. Available from: http://www.csiro.au/ozclim/home.do.

Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conservation in a changing climate. Science (80- ) 332:53–8.

Dorazio RM (2014) Accounting for imperfect detection and survey bias in statistical analysis of presence-only data. Glob Ecol Biogeogr 23:1472–1484.

Elith J, Graham C, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LR, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Townsend Peterson A, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE

43 (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop) 29:129–151.

Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography (Cop) 32:66–77.

Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342.

Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813.

Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57.

Engler R, Guisan A (2009) MigClim: Predicting plant distribution and dispersal in a changing climate. Divers Distrib 15:590–601.

Evans B, Lyons T (2013) Bioclimatic Extremes Drive Forest Mortality in Southwest, Western Australia. Climate 1:28–52.

Evans B, Stone C, Barber P (2013) Linking a decade of forest decline in the south-west of Western Australia to bioclimatic change. Aust For 76:164–172.

Fensham RJ, Kirkpatrick JB (1992) Soil Characteristics and Tree Species Distribution in the Savannah of Melville Island, Norther Territory. Aust J Bot 40:311–333.

Franklin J (2012) Back of the envelope: climate change and species distribution modelling. Bull Br Ecol Soc 43:28–30.

Gibson AN, Keighery GJ, Lyons MN, Webb A (2004) Terrestrial flora and vegetation of the Western Australian wheatbelt. Rec West Aust Museum Suppl 67:139–189.

Harwood T, Williams KJ, Ferrier S (2013) Generation of spatially downscaled climate change predictions for Australia. A report to the Department of Environment, Water, Heritage and the Arts. CSIRO Climate Adaptation Flagship, Canberra, p 12

Hooper RJ, Sivasithamparam K (2005) Characterization of damage and biotic factors associated with the decline of Eucalyptus wandoo in southwest Western Australia. Can J For Res 35:2589–2602.

Huang C, Anderegg WRL (2012) Large drought-induced aboveground live biomass losses in southern Rocky Mountain aspen forests. Glob Chang Biol 18:1016–1027.

Hughes L, Cawsey EM, Westoby M (1996) Climatic range sizes of Eucalyptus species in relation to future climate change. Glob Ecol Biogeogr Lett 5:23–29.

Intergovernmental Panel on Climate Change (2013a) Long-term Climate Change: Projections, Commitments and Irreversibility.

Intergovernmental Panel on Climate Change (2013b) Terrestrial and Inland Water Systems.

IPCC (2007) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) IPCC Fourth Assessment Report: Climate Change 2007. Cambridge University Press, Cambridge, UK, p 976

Jiménez-Valverde A (2012) Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob Ecol Biogeogr 21:498–507.

Jiménez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either–or presence–absence. Acta Oecologica 31:361–369.

Keith DA (2011) Relationships between geodiversity and vegetation in southeastern Australia. Proc Linn Soc New South Wales 132:5–26.

Kirkpatrick JB (1997) Vascular plant-eucalypt interactions. In: Williams JE, Woinarski JCZ (eds) Eucalypt Ecology: Individuals to Ecosystms. Cambridge University Press, New York, pp 227–245

Klausmeyer KR, Shaw MR (2009) Climate change, habitat loss, protected areas and the climate adaptation potential of species in mediterranean ecosystems worldwide. PLoS One 4:e6392.

Lahoz-Monfort JJ, Guillera-Arroita G, Wintle BA (2014) Imperfect detection impacts the performance of species distribution models. Glob Ecol Biogeogr 23:504–515.

45 Lambers H, Chapin FS, Pons TL (2008) Plant Physiological Ecology, 2nd edn. Springer Science+Business Media, New York

Latimer AM, Wu S, Gelfand AE, Silander Jr. JA (2006) Building Statistical Models To Analyze Species Distributions. Ecol Appl 16:33–50.

Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop) 28:385–393.

Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography (Cop) 33:103–114.

Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151.

Marnham J, Morris P (2003) A seamless digital regolith map of Western Australia: a potential resource for mineral exploration and environmental management. Geol Surv Annu Rev 3:27–33.

McKenzie NJ, Jacquier DW, Ashton LJ, Cresswell HP (2000) Estimation of soil properties using the Atlas of Australian Soils. CSIRO Land and Water

McPherson JM, Jetz W, Rogers DJ (2004) The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J Appl Ecol 41:811– 823.

Meier ES, Kienast F, Pearman PB, Svenning J-C, Thuiller W, Araújo MB, Guisan A, Zimmermann NE (2010) Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography (Cop) 33:1038–1048.

Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography (Cop) 36:1058– 1069.

Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858.

Osmond CB, Austin MP, Berry JA, Billings WD, Boyer JS, Dacey JWH, Nobel PS, Smith SD, Winner WE (1987) Stress Physiology and the Distribution of Plants. Bioscience 37:38–48.

Parsons R (1994) Eucalyptus scrubs and shrublands. Australian Vegetation. pp 291–320

Pereira HM, Leadley PW, Proença V, Alkemade R, Scharlemann JPW, Fernandez-Manjarrés JF, Araújo MB, Balvanera P, Biggs R, Cheung WWL, Chini L, Cooper HD, Gilman EL, Guénette S, Hurtt GC, Huntington HP, Mace GM, Oberdorff T, Revenga C, Rodrigues P, Scholes RJ, Sumaila UR, Walpole M (2010) Scenarios for Global Biodiversity in the 21st Century. Science (80- ) 330:1496–1501.

Peterson AT, Nakazawa Y (2008) Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. Glob Ecol Biogeogr 17:135– 144.

Phillips S, Anderson RP, Schapire R (2006) Maximum entropy modeling of species geographic distributions. Ecol Modell 190:231–259.

Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol Evol 5:397–406.

Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol Conserv 124:317–337.

47 Rejmánek M, Richardson D (2011) Eucalypts. In: Simberloff D, Rejmanek M (eds) Encyclopedias of the Natural World: Encyclopedia of Biological Invasions. University of California Press, Oakland, California, pp 203–209

Renton M, Shackelford N, Standish RJ (2012) Habitat restoration will help some functional plant types persist under climate change in fragmented landscapes. Glob Chang Biol 18:2057–2070.

Shepherd D (2003) Implementation of the National Vegetation System model in Western Australia. Milestone 6 Report. Final report on the implementation of the National Vegetation System model in Western Australia.

Sheppard CS (2013) How does selection of climate variables affect predictions of species distributions? A case study of three new weeds in New Zealand. Weed Res 53:259–268.

Sillero N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol Modell 222:1343–1346.

Stewart AJ, Sweet IP, Needham RS, Raymond OL, Whitaker AJ, Liu SF, Phillips D, Retter AJ, Connolly DP, Stewart G (2008) Surface geology of Australia 1:1,000,000 scale, Western Australia. Canberra: The Commonwealth of Australia

Syfert MM, Smith MJ, Coomes DA (2013) The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models. PLoS One 8:e55158.

Synes NW, Osborne PE (2011) Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change. Glob Ecol Biogeogr 20:904–914.

Syphard AD, Franklin J (2010) Species traits affect the performance of species distribution models for plants in southern California. J Veg Sci 21:177–189.

Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Townsend Peterson A, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–148.

Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152.

Thuiller W, Araújo MB, Lavorel S (2004) Do we need land-cover data to model species distributions in Europe? J Biogeogr 31:353–361.

VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecol Modell 220:589–594.

Vitt P, Havens K, Kramer AT, Sollenberger D, Yates E (2010) Assisted migration of plants: Changes in latitudes, changes in attitudes. Biol Conserv 143:18–27.

Warren DL (2012) In defense of “niche modeling.” Trends Ecol & Evol 27:497–500.

Williams KJ, Belbin L, Austin MP, Stein JL, Ferrier S (2012) Which environmental variables should I use in my biodiversity model? Int J Geogr Inf Sci 26:2009–2047.

Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes J-A, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson M-C, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning J-C (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30.

Xu T, Hutchinson M (2011) ANUCLIM 6.0 User Guide. Australian National University, Fenner School of Environment and Society, Canberra

49 Yates CJ, McNeill A, Elith J, Midgley GF (2010) Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Divers Distrib 16:187–201.

Yee TW, Mitchell ND (1991) Generalized additive models in plant ecology. J Veg Sci 2:587– 602.

Appendices

Appendix S1: Mean annual precipitation (bioclim #12; Xu & Hutchinson 2010) for current and future climate scenarios applied in this study: (a) current climate, (b) low climate change (GCM = INM-CM3.0, emission scenario = B1), (c) moderate climate change (GCM = CSIRO-Mk3.5, emission scenario = AB1) and (d) severe climate change (GCM = CCR:MIROC-M, emission scenario = A1Fl).

51 Appendix S2. The 16 Eucalyptus species modelled in this study and their key characteristics.

Rainfall group/ Number of Precipitation Special habitat Species Habit geographical presence range (mm) requirements position samples E. accedens tree 350 – 700 (450) mid / mid 162 E. albida mallee 325 – 550 (225) dry / inland 143 E. caesia mallee 300 – 475 (175) mid / inland Granite outcrop 55 E. diversicolor tree 750 – 1250 (500) wet / coastal 78 E. gardneri mallet 325 – 475 (150) dry / inland 76 E. gomphocephala tree 575 – 900 (325) wet / coastal 82 E. incrassata mallee 275 – 500 (225) mid /mid 431 E. jacksonii tree 1050 – 1250 (200) wet / coastal 26 E. longicornis tree 250 – 450 (200) dry / inland 230 E. marginata tree 500 – 1200 (700) wet / coastal 186 E. megacarpa tree 850 – 1250 (400) wet / coastal 86 E. patens tree 500 – 1250 (750) wet / coastal 133 E. salicola tree 275 – 325 (150) dry / inland Salt lake 119 E. salmonophloia tree 200 – 450 (250) dry / inland 351 E. salubris mallee 200 – 400 (200) dry / inland 358 E. wandoo tree 325 – 850 (525) mid / mid 123

Appendix S3.1. Presence/absence predictions for Eucalyptus accedens in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.2. Presence/absence predictions for Eucalyptus albida in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.3. Presence/absence predictions for Eucalyptus caesia in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.4. Presence/absence predictions for Eucalyptus diversicolor in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.5. Presence/absence predictions for Eucalyptus gardneri in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.6. Presence/absence predictions for Eucalyptus gomphocephala in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.7. Presence/absence predictions for Eucalyptus incrassata in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.8. Presence/absence predictions for Eucalyptus jacksonii in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.9. Presence/absence predictions for Eucalyptus longicornis in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.10. Presence/absence predictions for Eucalyptus marginata in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.11. Presence/absence predictions for Eucalyptus megacarpa in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.12. Presence/absence predictions for Eucalyptus patens in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.13. Presence/absence predictions for Eucalyptus salicola in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.14. Presence/absence predictions for Eucalyptus salmonophloia in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.15. Presence/absence predictions for Eucalyptus salubris in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.16. Presence/absence predictions for Eucalyptus wandoo in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S3.17. Presence/absence predictions for Corymbia calophylla in low (a), moderate (b) and severe (c) climate change scenario, assuming no fragmentation. Areas in red represent the predicted current distribution that will no longer be suitable for the species in the future in the future, areas in blue are predicted to become suitable but will be unreachable in a zero dispersal scenario, whereas the area in gold indicates the area which is predicted to remain suitable in the future and is a part of the species current distribution. Current distribution is the combination of red and gold areas.

Appendix S4. A comparison of climate change scenarios using multivariate environmental similarity surfaces (Elith et al. 2010). Red indicates novel environments that do not exist in the study region in the current climate and blue indicates climates that are very similar, with more intense colours indicating more extreme values.

Appendix S5. Average (n = 5) AUC (area under the receiver operating characteristic curve) statistic and TSS (True Skill Statistic) for each species model. Higher values indicate a better fit to current distributions.

AUC TSS E. accedens 0.975 0.941 E. albida 0.965 0.874 E. caesia 0.990 0.955 E. diversicolor 0.991 0.970 E. gardneri 0.978 0.935 E. gomphocephala 0.992 0.977 E. incrassata 0.914 0.784 E. jacksonii 0.998 0.943 E. longicornis 0.917 0.732 E. marginata 0.974 0.926 E. megacarpa 0.991 0.948 E. patens 0.979 0.940 E. salicola 0.950 0.825 E. salmonophloia 0.890 0.724 E. salubris 0.902 0.733 E. wandoo 0.961 0.895

E. accedens 5.06 1.65 3.82 4.88 1.71 5.62 8.45 0.22 1.44 1.80 1.81 63.53

E. albida 1.84 0.63 3.02 1.85 8.60 1.06 1.76 2.34 1.59 11.27 13.54 52.51

E. caesia 3.91 1.76 3.92 0.30 6.51 0.32 2.53 9.83 1.82 21.80 29.23 18.08

E. diversicolor 4.09 3.83 1.16 0.88 1.00 1.81 0.53 5.53 4.54 27.67 38.54 10.41

E. gardneri 9.08 4.56 0.56 2.72 3.52 6.75 1.13 5.21 2.38 1.18 36.42 26.50

E. gomphocephala 2.3 1.18 3.19 2.84 1.51 6.79 0.00 0.06 0.62 27.25 26.32 27.93

E. incrassata 6.05 5.65 1.97 9.94 2.94 4.28 5.15 0.99 13.26 16.87 13.80 19.09

E. jacksonii 0.3 0.02 0.00 0.00 0.02 4.09 0.00 0.07 0.07 0.01 0.01 95.41

E. longicornis 9.36 1.46 1.95 7.03 0.20 3.38 13.21 13.86 13.62 10.57 12.69 12.66

E. marginata 1.38 0.94 2.20 0.64 4.64 4.98 0.62 0.48 1.64 1.12 41.72 39.64

E. megacarpa 5.03 1.24 3.01 0.91 0.78 3.37 2.11 0.26 3.25 4.29 2.41 73.34

E. patens 1.34 0.68 0.98 0.29 1.38 0.42 0.98 0.27 2.89 2.09 0.38 88.31

E. salicola 3.05 7.35 2.13 0.64 0.10 5.04 0.58 12.99 20.87 11.31 19.94 16.01

E. salmonophloia 8.44 5.19 6.48 2.09 9.83 1.94 2.53 4.37 4.86 27.34 16.01 10.93

E. salubris 3.58 4.51 6.47 0.30 2.37 9.02 4.85 14.70 11.31 12.78 11.19 18.92

E. wandoo 4.74 3.71 5.45 2.05 5.35 6.66 8.75 1.58 5.85 1.90 41.01 12.96

Permutation Importance(%) of variables training for each species in model.final the

precipitation

Appendix Max temperature of warm period temperatureMin of cold period Mean temperature of warmest quarter Mean temperature of coldest quarter Annual Precipitation of wettest quarter Precipitation of dry quarter Mean Moisture index wet quarter Mean Moisture index driest quarter Soil depth Surface geology Regolith

62 Chapter 3

Relationships of leaf area to sapwood area ratio with stomatal and xylem traits in south-west Australian Eucalyptus species along an aridity gradient

Preface to Chapter 3 With respect to the overall thesis objective, this chapter is aimed at quantifying the variability in above-ground functional traits between and within species across an environmental gradient. This will allow us to better understand how species have adapted to the climate to produce their current distribution (realised niche), as well as starting to quantify the extent to which species are sensitive to a changing climate and how much adaptive capacity there may be in key functional traits. In conjunction with knowledge gained from Chapter 2, this knowledge can be used to better predict how populations exposed to climate change are likely to change in the future.

Comparison of size and density of xylem vessels (left) and stomata (right) at 100x magnification.

Relationships of leaf area to sapwood area ratio with stomatal and xylem traits in south-west Australian Eucalyptus species along an aridity gradient

Hamer JJ,1,2* Poot P,1,2 Renton M, 1,2 Price C,3 Veneklaas EJ1,2. 1 School of Plant Biology (M090), The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia (Email: [email protected]) 2 Centre of Excellence for Climate Change, Woodland and Forest Health, Crawley, Western Australia, 6009, Australia 3 National Institute of Mathematical and Biological Synthesis (NIMBioS), University of Tennessee, Knoxville, TN,37996, USA

Abstract  Background and Aims: Species living in water limited environments typically have a lower leaf area to sapwood area ratio, suggesting a greater capacity to supply water relative to the leaf area. We aimed to investigate this balance, given that current theory suggests that the ratio between stomatal conductance and stem conductivity may be the inverse of leaf area to sapwood area.  Methods: We collected a range of functional traits relating to stem hydraulics and leaf gas exchange from Eucalyptus species across 30 sites along an aridity gradient in the south-west of Western Australia. These included traits such as leaf and sapwood area per branch, stomatal size and density, xylem area and density, sapwood specific density, leaf mass per area, nitrogen content per area, carbon isotope ratio, vein density and seasonal osmotic potential.  Key Results: Species growing in more arid conditions had lower leaf area to sapwood area ratios yet higher maximum stomatal conductance to stem conductivity ratio, resulting in the invariance of total stomatal conductance to total stem conductance ratio across the gradient. These species had a higher density of narrower vessels, a lower density of larger stomata which tended to be closed more often, and lower osmotic potentials, allowing them to operate at lower water potentials in an environment with higher vapour pressure deficit.  Conclusions: Because it does not consider stomatal conductance and stem conductivity, leaf area to sapwood area ratio does not take into account an important component in the balance between supply and demand of water. The Eucalyptus species studied were well adapted to the aridity gradient; however, in a rapidly changing climate these adaptations may be insufficient to allow the species to persist in their current distribution.

Keywords: Huber value, LA:SA, LMA, Physiology, SLA, Stomata density, Stomata size, Xylem Density, Xylem size.

65 Introduction Ensuring that water lost from leaves is balanced by a sufficient supply through stems is vital for any plant. This is particularly true for evergreen species living in seasonally dry environments where vapour pressure deficit (VPD) is high and soil water availability is low, as is the case for Eucalyptus species in the south-west of Western Australia (SWWA). When the amount of water lost from the leaves exceeds the amount supplied at any given time, the plant risks hydraulic failure in the form of loss of leaf turgor and stem cavitation – the formation of embolisms in xylem vessels which then block the flow of water to the leaves (Tyree and Sperry 1989; Meinzer et al. 2001). The leaf area to sapwood area ratio reflects this balance between demand and supply (Addington et al. 2006; Carter and White 2009; Gotsch et al. 2010), with species living in arid environments typically having a lower leaf area to sapwood area ratio to accommodate stronger demands and weaker supplies in these climates (Mencuccini and Grace 1995; Addington et al. 2006; Gotsch et al. 2010; Gleason et al. 2012; Westoby et al. 2012). However, this ratio does not take into account the specific capacities of stems and leaves to transport and transpire water.

The morphologically-defined theoretical maximal stomatal conductance is a standard measure of the capacity of the leaf surface to support water and CO2 diffusion when comparing between species (Franks et al. 2012). A species’ theoretical maximal stomatal conductance is dependent on the density and physical size of stomata (Hetherington et al. 2003; Franks et al. 2009; Franks and Beerling 2009b; Brodribb et al. 2013). There is a strong negative relationship between the size and density of stomata, with species with smaller stomata at a higher density typically having a higher conductance (Franks et al. 2009; Franks and Beerling 2009a; Franks and Beerling 2009b; Lammertsma et al. 2011). It is often reported that species in water-limited environments have smaller stomata in higher densities (Aasamaa et al. 2001; Bosabalidis and Kofidis 2002; Hetherington et al. 2003; Xu and Zhou 2008; Franks et al. 2009), suggesting that they have a higher maximum stomatal conductance. Nevertheless, values of stomatal conductance measured under favourable conditions in arid systems are not necessarily higher than those in more mesic systems (Körner 1995). The reason for this discrepancy is unclear; it is possible that the stomatal size-density relationship is somewhat different for arid species, or that arid species’ maximum operating conductance is at a lower percentage of their maximum theoretical conductance, compared to mesic species. It is hypothesised that the smaller stomata of arid species can regulate stomatal aperture faster, allowing the species to increase conductance during short time periods that favour photosynthesis but not transpiration, thereby increasing water use efficiency (Tenhunen et al. 1981; Tenhunen et al. 1984; Tyree and Sperry 1988; Drake et al. 2013). Carbon isotope ratios have provided ample evidence for extended periods of reduced conductance in arid species, for example in Eucalyptus species across an aridity gradient in the SWWA (Schulze et al. 2006b; Schulze et al. 2006a; Turner et al. 2008;

Turner et al. 2010; Schulze et al. 2014). However, there is little research showing how stomatal traits and conductance vary across a broad range of Eucalyptus species along an aridity gradient.

Maximal specific stem conductivity is determined by the density and radius of xylem vessels within the sapwood (Gleason et al. 2012). Like stomatal conductance, actual stem conductivity is often lower than its theoretical and operational maximum, due to cavitation and variation in pit membrane permeability (Sperry et al. 1988; Tyree and Sperry 1989; Pammenter and Van der Willigen 1998). In arid environments, tissue water potentials are generally lower than in mesic environments, partly due to lower soil water potentials, and partly due to greater tissue dehydration, which enables larger within-plant hydraulic gradients providing greater access to tightly bound soil water. The greater tolerance of these high tensions tends to be associated with narrower xylem vessels at a higher density (Hacke et al. 2001; Tyree and Zimmermann 2002; Hacke et al. 2006). These narrow vessels – along with smaller pit membrane pores, thicker xylem walls and higher sapwood densities – confer a stronger resistance of the hydraulic system to cavitation (Hacke et al. 2001; Tyree and Zimmermann 2002; Hacke et al. 2006). However, these narrower vessels greatly reduce maximal stem conductivity, as compensatory increases in vessel density are generally insufficient, and thus may compromise water transport capacity and productivity under adequate water supply (Zanne et al. 2010; Gleason et al. 2012).

Based on current observations on stomata and xylem size and density across an aridity gradient in woody species, it seems unlikely that the ratio between stomatal conductance and xylem conductivity would mirror that of the leaf area to sapwood area ratio. Rather, with increasing aridity the more numerous but smaller stomata (associated with a higher leaf stomatal conductance), combined with the more numerous but much smaller xylem vessels (associated with a reduced stem conductivity) may lead to the opposite trend, Therefore, we hypothesise that the scaling of total stomatal conductance (specific conductance multiplied by total leaf area of a branch) and total stem conductance (specific conductivity multiplied by the total sapwood area supporting the branch) will be similar between mesic and arid environments.

Few studies have combined measurements of morphological and physiological traits relevant to plant water relations within a range of dominant congeneric species across an aridity gradient. Eucalyptus species across a strong aridity gradient in the south-west of Western Australia provide an ideal model to further enhance our understanding of species’ adaptations to aridity and trait interrelationships and trade-offs. Previous studies on Eucalyptus species have shown that they are adapted similarly to other woody species in an increasingly arid environment. They tend to have lower leaf area to sapwood area ratios (Thomas et al. 2004; Carter and White 2009; Gleason et al. 2012), smaller leaves, a higher leaf mass per area (LMA), higher leaf nitrogen per area, less negative δ13C (Schulze et al. 1998; Schulze et al. 2006b; Schulze et al. 2006a; Turner et al. 2008; Turner et al. 2010; Schulze et al. 2014) and are able to reach more negative water potentials (Poot and Veneklaas 2013) in increasingly arid climates, all traits associated with

67 increased water use efficiency in dry environments. However, we still lack an understanding as to how stomatal conductance and stem conductivity vary across the genus and along an aridity gradient, and how these are related to the above traits. Over the past decade many Eucalyptus species in the south-west Australian biodiversity hotspot have shown episodes of drought- related health decline (Hooper and Sivasithamparam 2005; Brouwers et al. 2012; Brouwers et al. 2013; Matusick et al. 2013; Poot and Veneklaas 2013), with models predicting further tree decline if the climate continues to become more arid (Hughes et al. 1996; Hamer et al. 2015a; Chapter 2 of this thesis). However, most research on functional traits for these species has been conducted on individual species (often plantation species). Thus, this study aimed to improve our understanding of how Eucalyptus species are adapted to an arid environment both between and within species (and how this might affect species’ responses to a changing climate) as well as providing insights into the relationship between leaf area to sapwood area ratio, stomatal conductance and stem conductivity.

Box 1: Abbreviations

Abbreviation Variable name Units 2 AS Size of stoma µm 2 AX Hydraulically weighted xylem vessel cross- µm sectional area δ13C Carbon-13 isotope ratio ‰ -2 DS Density of stomata (both leaf sides) per projected mm leaf area -2 DX Density of xylem vessels per cross-sectional mm sapwood area -2 -1 gs Stomatal conductance (leaf-area based, maximum) mol m s -1 gs*LA Total branch stomatal conductance mol s -2 -1 KX Stem hydraulic conductivity (Stem cross-sectional- kg m s area based, maximum) -1 KX*SA Total stem hydraulic conductivity kg s LA/SA Leaf area to sapwood area ratio mm2 mm-2 LDMC Leaf dry matter content g g-1 LMA Leaf mass per area g m-2 -2 Narea Nitrogen per projected leaf area g m VLA Vein length per leaf area mm-1

ΨπS Leaf osmotic potential at full hydration in summer MPa

ΨπW Leaf osmotic potential at full hydration in winter MPa

Methods

Study region and species The south-west West Australian climate features a strong precipitation gradient ranging from 1250 mm annually in areas close to the south-western coast, to less than 300 mm further inland (Figure 1). The vegetation changes from tall forest to low heath with sparse trees, with different species of Eucalyptus occupying different sections of this gradient.

A total of 15 Eucalyptus species were selected to represent a broad range of soil preferences, precipitation ranges and growth habits of the Eucalyptus genus in the south-west of Western Australia. Between one and three sites were selected from across each species’ distribution, with more sites sampled for species with distributions encompassing a broader range of climates (Figure 1 and Table 1). Only one species was sampled at each site as sites were chosen to represent a typical population of the area where the study species was the dominant eucalypt. Each site was visited in both late summer (February and March) and late winter (July and August) of 2013, with morphological characteristics measured during the winter months and osmotic potential measured during both winter and summer, as detailed below.

Figure 1. A map of south-west Western Australia (SWWA) showing the approximate location of each study site and the annual aridity index. Although sites are grouped by locality here for convenience, only one species was sampled per site. Table 1 lists the species sampled at each location on this map.

For each site a number of environmental variables were also determined. The total precipitation received at each site in the previous year (point data, observed at the nearest weather station) was compared to the average annual precipitation from 1961 to 1990 (GIS data; Australian Bureau of Meteorology 2011). As there was no significant difference between these measures of

69 previous rainfall (p = 0.318) the 30-year average was used for further climate calculations. Average annual precipitation was divided by the 30-year average mean point potential evapotranspiration (Australian Bureau of Meteorology 2012) to calculate the annual aridity index for each site (Middleton and Thomas 1992). We considered a number of other explanatory climate variables for our analyses, but found that these variables were highly correlated with annual aridity index (annual precipitation, vapour pressure deficit, relative humidity, average maximum temperature and evapotranspiration, r > 0.79 in all cases), or showed little variation across the gradient (summer precipitation and summer aridity index).

Besides climate, soil texture may also contribute to Eucalyptus species’ distribution (Chapter 4 of this thesis; Hamer et al. 2015b), partly due to its effect on water retention properties (Saxton et al. 1986; Poot and Veneklaas 2013). We calculated the clay content of the top 2 m of soil for each site from CSIRO’s Soil and Landscape Grid of Australia database (Bishop et al. 1999). These data were not available for E. caesia – a granite outcrop endemic species – due to the absence of soil data on rock outcrops.

Table 1. Eucalyptus species sampled, their typical growth habit, preferred growing conditions in terms of rainfall and special habitat requirements and the collection locality for each species as shown in Figure 1. Although sites are grouped by locality here for convenience, only one species was sampled per site.

Annual Growth Rainfall Special habitat Location Species collected precipitation Habit group requirements number range (mm) E. accedens W.Fitzg. tree 350 – 700 mid 1, 2, 3 E. albida Maiden & Blakely mallee 325 – 550 dry 4 E. caesia Benth. mallee 300 – 475 mid Granite outcrop 5, 6 E. gardneri Maiden mallet 325 – 475 dry 7 E. gomphocephala DC. tree 575 – 900 wet 8, 9 E. incrassata Labill. mallee 275 – 500 mid 10 E. jacksonii Maiden tree 1050 – 1250 wet 11 E. longicornis (F.Muell.) Maiden tree 250 – 450 dry 12, 13 E. marginata Sm. tree 500 – 1200 wet 14, 15, 16 E. megacarpa F.Muell. tree 850 – 1250 wet 17, 18 E. salicola Brooker tree 275 – 325 dry Saline flats 19 E. salmonophloia F.Muell. tree 200 – 450 dry 10, 13 E. salubris F.Muell. mallee 200 – 400 dry 12, 13 E. wandoo Blakely tree 325 – 850 mid 4, 20, 21

Terminal branch traits Five representative trees were selected from the population of target species at each site, with five sun-lit (50% or greater of the day spent in full sun) terminal branches cut from the outer canopy of each tree. A terminal branch is defined here as the segment of branch and associated leaves from the apical meristem to the first branching point (Westoby and Wright 2003). Generally, these branches had one annual cohort (the most recent) of mature leaves. Branches that had missing leaves (as judged by the presence of leaf scars), signs of herbivory, abundant flowering, budding or new growth (incompletely expanded leaves) were avoided.

From each of these branches the total leaf area (with petioles removed) was measured by digitally scanning the fresh leaves and measuring projected area in ImageJ (Schneider et al. 2012). As the amount of pith varied greatly between replicates from the same tree, measurements of stem diameter using callipers in the field did not give an accurate representation of actual sapwood area. Thus, the actual sapwood area (excluding both bark and pith) was measured from a cross section from the stem base of one replicate terminal branch per tree. Branch leaf area for that branch and actual sapwood area were then used to calculate leaf area to sapwood area ratio (LA/SA; mm2 mm-2).

Leaf traits The total fresh mass of leaves per terminal branch was measured in the field during the winter season, and in conjunction with the total dry mass (after drying in a 70 °C oven for 48 hours) was used to determine the total leaf dry matter content (LDMC; g g-1). Total leaf dry mass for each of the 25 terminal branches per site was divided by the total leaf area (as measured for LA/SA) to give leaf mass per area (LMA; g m-2) of each branch. Digital leaf scans used for the calculation of LA/SA were also used to determine the average area per leaf for each of the terminal branches. Average leaf lamina thickness was measured from the middle section of each fully expanded leaf, avoiding the mid-rib, using digital callipers, and averaged per branch. These data were all measured for all 25 branches per site, and then averaged to the tree level resulting in five replicate values per site.

Stomatal traits were measured for five replicate leaves per site – one typical (healthy, sun-lit with average morphology) leaf per tree. Sections 1 cm by 2 cm in area were cut from the leaf and soaked in 4% NaClO until the epidermis could be easily peeled from both the abaxial and adaxial sides of the leaf. Species with epidermises that could not be removed using this method (E. incrassata, E. jacksonii and E. megacarpa) were instead soaked in a 1:1 solution of 30%

H2O2 and 80% ethanol in a 60 °C water bath for two days, with the solution changed each day. Cuticles were then stained with safranin before being digitally photographed at 100x magnification. Stomata from the image were counted for each side of the leaf, and then -2 combined to obtain the density of stomata per projected leaf area (DS; mm ). Two species had

71 stomata only on the abaxial side: E. marginata and E. jacksonii. Stomatal dimensions were measured for 10 stomata per image and averaged between sides where stomata were present on both sides. Pore length, single guard cell width and total length were measured using ImageJ 2 (Schneider et al. 2012). Stomatal size (AS; µm ) was calculated by multiplying the total stoma length by double the guard cell width (Drake et al. 2013). The theoretical maximum stomatal conductance for water vapour for each side was calculated as in Franks et al. (2009):

푑 × 퐷 × 푎푚푎푥 푔푠 = 휋 푎 (Equation 1) 푣 × 푤 + × √ 푚푎푥 ( 2 휋 ) where d is the diffusivity of water in air (0.249x10-4 m2 s-1), D is the stomatal density of a single -2 2 side of the leaf (m ), amax is the mean theoretical maximal stomatal pore area (m ) calculated as a circle with the pore length (m) as the diameter, v is the molar volume of air (0.0244 m3 mol-1), w is the width of a single guard cell (m) used to represent the depth of the stoma and π is the mathematical constant. Values for both sides of the leaf were summed to give the stomatal -2 -1 conductance per projected leaf area (gS; mol m s ). Total stomatal conductance per terminal branch (gS*LA) was calculated as the product of gS and the total leaf area of that branch.

Vein density was also measured for one leaf per tree. Lamina sections 1.5 by 1.5 cm in size were cut from the centre of each leaf, avoiding the mid-rib where possible. These were left in 10% NaOH until the whole section was bleached (between 2 to 12 weeks), at which time they were stained with 10% safranin. Epidermises were removed, along with as much mesophyll as possible, before two fields of view per leaf were digitally photographed at 40x magnification. Images were then traced by hand and converted to a binary network before being analysed for total vein length per area (VLA; mm-1) using Leaf GUI (Price et al. 2011), with the final value being the average of the two images per leaf.

Total Nitrogen concentration per leaf dry mass (mg g-1) and the carbon isotope ratio of the leaf (δ13C) were analysed for a single leaf from the same branch as the leaf used for stomata traits and vein length. A continuous flow system consisting of a Delta V Plus mass spectrometer connected with a Thermo Flush 1112 via Conflo IV (Thermo-Finnigan/Germany) was used for all samples, with multipoint normalization to reduce raw values to the international scale (Coplen et al. 2006; Skrzypek and Paul 2006; Skrzypek 2013). Total Nitrogen per projected -2 area of leaf (Narea; g m ) was calculated as the product of Nitrogen per mass and LMA.

Osmotic potential of leaves of each terminal branch (five per tree) was determined from samples collected in both winter and summer. A circular hole-puncher was used to cut 10 samples (95 mm2) from a number of sun-lit leaves on each branch, which were then placed in a cryovial and immediately frozen in liquid Nitrogen. Osmotic potential was determined for the sap extracted from these samples from each terminal branch, after thawing, psychrometrically

(Psypro with C-52 sample chambers, Wescor Inc., Logan, Utah). This was then multiplied by the known Relative Water Content (RWC; see below) leaves of each tree to produce the osmotic potential at 100% RWC for each of the summer (ΨπS; MPa) and winter (ΨπW; MPa) samples.

Osmotic adjustment was calculated as the ΨπS minus ΨπW (Merchant 2014). RWC was determined using 20 sun-lit leaves from adjacent branches. These leaves were weighed without their petioles for fresh mass immediately after collection. They were then placed between saturated paper towels in a dark box for between 12 and 24 hours (a time period previously determined to be sufficient for saturation of the leaf without oversaturation occurring) before being weighed for saturated mass (Ryser et al. 2008). They were then dried in an oven at 70 °C for 48 hours prior to measuring dry mass. Leaf fresh, saturated and dry mass were used to calculate RWC. Data were averaged per tree.

Sapwood traits Cross sections from the base of one terminal branch per tree were taken to determine stem xylem traits. Stem sections were hand-sectioned and stained with toluidine blue to increase the contrast between xylem vessels and the surrounding tissue. Multiple digital images were taken at 100x magnification and stitched together using the MosaicJ plugin for ImageJ (Schneider et al. 2012). Both the total wood cross-sectional area and the non-conductive pith were manually outlined and measured in ImageJ with actual sapwood area calculated as the total wood minus the pith area. An approximately 1 cm length of stem (with bark removed) was then removed from the base terminal branch, measured for length, then dried in a 70 °C oven for 48 hours for the measurement of dry mass. The dry mass of this sample was divided by the total wood volume, calculated as the product of the length of the sample and wood cross-sectional area (with bark removed) to determine the stem specific density (g mm-3). Xylem vessels were manually coloured on cross sectional images to enable the conversion of the image to binary data. The area of every xylem vessel was then measured in ImageJ (Schneider et al. 2012) and used to calculate the theoretical conductance of each vessel (Ki), according to Hagen- Poiseuille’s law:

휋 × 푟4 퐾 = (Equation 2) 푖 8 × ɳ × 퐿 where r is the radius of a xylem vessel (m), ɳ is the viscosity of water at 20 °C (1.002 mPa s) and L is length of the vessel (m), in this case set to 1 to arrive at an estimate per unit length. These single-vessel conductances were then summed and divided by the cross sectional area 2 -2 -1 sampled to determine the conductivity per m (KX; kg m s ). Total xylem conductivity of the branch (KX*SA) was calculated as the product of KX and the total sapwood area (not including -2 pith) of that individual branch. Xylem vessel density (DX; mm ) was calculated as the number of xylem vessels per sapwood area. The vessel lumen fraction was calculated as the sum of

73 xylem vessel areas divided by the sapwood area. Hydraulically weighted average xylem vessel 2 size (AX; µm ) was determined by calculating the average conductance of all vessels, then back- calculating using equation 2 to find the vessel area corresponding with that average conductivity.

Statistical analysis Mixed effect models, using the 'nlme' R package (Pinheiro et al. 2013; R Development Core Team 2014), were employed for all analyses. We fitted models including annual aridity index (used to represent several correlated climate variables) in interaction with clay content as fixed effects, and site as a random effect to avoid pseudo-replication, and then simplified the model based on AIC. In all cases, the interaction was not significant (p > 0.1) and was removed during model simplification. E. caesia was not included in analyses involving clay content due to soil data unavailability. Random effects were used to account for spatial autocorrelation in the data. In the first set of models we identified trends within the Eucalyptus genus (among species) across an environmental gradient by including species as a random effect. In a second set of analyses used to identify trends within species, site was nested within species as the random effect. In a third set of analyses, we used mixed effects models to examine relationships between pairs of plant traits, including site as the only random effect.

Of the explanatory variables, annual aridity index was log-transformed prior to analysis to ameliorate skewness in the data, while clay content was not transformed. All functional trait variables except for osmotic adjustment were log-transformed as either the variable was a ratio, or we expected a power law relationship (allometric scaling) between variables rather than linear relationships. However, all descriptive statistics presented summarize untransformed data.

Results Overall, most traits conformed to expected trends across an aridity gradient, both within and among species. Mean values of all traits presented here for each study site can be found in Appendix S1, S2 and S3, with the significance of explanatory variables summarised in Table 2. Both among and within species, we observed a significant decrease in LA/SA with increasing aridity (Table 2, Figure 2a). Similarly, species native to drier regions had a higher LMA, a higher LDMC, a higher Narea, and smaller leaves (Table 2). There was no significant relationship between VLA and aridity or clay content (p > 0.613 in all cases), average individual leaf size (p = 0.145) or LMA (p = 0.220).

There was a marginal tendency for the theoretical maximal stomatal conductance (gS) to decrease with increasing aridity (Table 2; Figure 3a). Species’ gS was positively related to stomatal density (DS; p < 0.001) – with a two-fold increase in DS corresponding to a 1.54-fold

increase in gS – but was not significantly related to stomatal size (AS; p = 0.267). Species in more arid climates had a lower DS (Table 2; Figure 3b), and a larger AS (p = 0.001; Figure 3c). These two traits were strongly negatively related (p < 0.001) with every two-fold increase in density resulting in 1.53 times smaller stomata. Species in more arid conditions also tended to have significantly less negative δ13C values in comparison to more mesic species (Table 2,

Figure 3d), indicating generally lower leaf internal CO2 concentrations, suggestive of lower operating stomatal conductance due to partial stomatal closure. A two-fold increase in LMA was associated with a 1.48-fold decrease in DS (p = 0.001), a 1.57-fold increase in AS (p < 0.001) and a 1.07-fold decrease in δ13C (becoming less negative; p = 0.003).

Table 2. Results of mixed-effect models used to identify relationships between in each of the key functional traits along a climate gradient, both within and among species. Aridity index is the ratio between annual rainfall and annual potential evapotranspiration. Clay content is the percentage clay in soils to a depth of 2 m. The table shows p values for each explanatory variable where this variable was retained in the model.

Among species Within species Aridity Clay Aridity Clay Trait index content index content

LA/SA < 0.001 N.S. 0.010 N.S.

(gs*LA)/(KX*SA) N.S. N.S. N.S. N.S. Lamina thickness 0.017 N.S. 0.046 N.S.

LDMC < 0.001 N.S. <0.001 N.S. Average individual leaf size < 0.001 0.003 <0.001 0.007

LMA < 0.001 N.S. 0.005 N.S.

Narea 0.016 N.S. 0.066 N.S. Summer osmotic potential < 0.001 N.S. < 0.001 N.S.

Winter osmotic potential < 0.001 N.S. < 0.001 N.S.

Osmotic adjustment N.S. 0.003 N.S. 0.031 Stem specific density 0.025 N.S. N.S. N.S.

Theoretical max stomatal conductance (gs) 0.078 N.S. 0.093 N.S.

Stomata density (DS) 0.007 0.019 0.020 0.040

Stomata size (AS) 0.001 N.S. 0.056 N.S. Vessel lumen fraction 0.051 N.S. N.S. N.S.

VLA N.S. N.S. N.S. N.S.

Maximal xylem conductivity (KX) 0.004 N.S. 0.018 N.S.

Xylem density (DX) 0.047 N.S. 0.060 N.S.

Xylem size (AX) < 0.001 N.S. 0.001 N.S. 13 δ C < 0.001 N.S. < 0.001 N.S. N.S. = removed during model simplification (p > 0.1)

75 There was a significant decrease in theoretical maximal stem conductivity (KX) with increasing aridity (Table 2; Figure 4a). Species’ KX increased 2.51-fold for every doubling in hydraulically weighted average vessel size (AX; p < 0.001) and 2.61-fold for every doubling in vessel lumen fraction (p < 0.001), but there was no relationship with xylem density (p = 0.886). With increasing aridity, an increase in DX was observed (Table 2, Figure 4b) while both AX (Figure 4c) and vessel lumen fraction tended to decrease (Table 2). There was a strong negative relationship between DX and AX (p < 0.001) with a 1.44-fold decease in AX for a two-fold increase in DX. Stem specific sapwood density was not related to either AX (p = 0.240) or DX (p = 0.246), but decreased 1.08-fold for every doubling of vessel lumen fraction and 1.06-fold for every two-fold increase in aridity (Table 2, Figure 4d).

Figure 2. The relationship between a) leaf area to sapwood area ratio (LA/SA) and b) total

stomatal conductance to total xylem conductivity ratio ([gs*LA]/[KX*SA]) across an aridity gradient (low aridity index indicates a drier climate). Each point () represents a single tree (with five trees per site), while each cross (×) is the average value for each site. Regression lines (where significant) are based on the best-fit model for among species comparisons, with site as the random effect. Dotted lines represent 95% confidence intervals.

There was a significant negative relationship between gS/KX and LA/SA (p = 0.005) with E. caesia the only species that did not conform. Although total stomatal conductance to total xylem conductivity ratio ([gS*LA]/[Kx*SA]) was found to be positively related to LA/SA across all species (p < 0.001), this appeared to be driven by the two sites of E. caesia species which had both a much lower (gS*LA)/(Kx*SA) and LA/SA in comparison to all other species. There was no significant relationship between (gS*LA)/(Kx*SA) across the aridity gradient (p = 0.493; Figure 2b).

Leaf osmotic potentials were more negative for arid species (see Table 2 for statistics), with ΨπS and ΨπW for the most arid species approximately 1 MPa lower than those of the most mesic species. No osmotic adjustment was observed between summer and winter across an aridity gradient (p = 0.357), however, species occurring on soils with lower clay content had more negative osmotic potentials in summer compared with values determined in winter (Table 2).

The inclusion of clay content generally did not significantly improve the predictive power for most traits studied here, with the exception of leaf size (decreasing with clay content, Table 2), and AS (increasing with clay content, Table 2). In general, within-species trends of traits varying with climate, matched trends found among species. There were no traits that varied significantly within species that did not vary significantly across species. The only traits that varied across species but not within species were stem specific density (p = 0.318) and vessel lumen fraction (p = 0.486); all other traits that varied across species showed some evidence of plasticity within species, albeit not as strongly as among species (Table 2).

Figure 3. The relationship between a) theoretical maximal stomatal conductance (gs),

b) stomatal density per projected area (DS), c) stomatal size (AS) and d) carbon isotope ratio (δ13C) across an aridity gradient (low aridity index indicates a drier climate). Each point () represents a single tree (with five trees per species per site), while each cross (×) is the average value for each site. Regression lines are based on the best-fit model for among- species comparisons, with site as the random effect. Dotted lines represent 95% confidence intervals.

Figure 4. The relationship between a) stem conductivity (KX), b) xylem vessel density

(DX), c) hydraulically weighted xylem vessel size (AX) and d) stem specific density across an aridity gradient (low aridity index indicates a drier climate). Each point () represents a single tree (with five trees per site), while each cross (×) is the average value for each site. Regression lines are based on the best-fit model for among species comparisons, with site as the random effect. Dotted lines represent 95% confidence intervals.

Discussion

Balancing supply, demand and safety Clear trends in architectural, morphological, anatomical and physiological traits were apparent within the Eucalyptus genus along the aridity gradient. Within species trends were generally in a similar direction to those among species, indicating either genetic differences or phenotypic plasticity across a species’ distribution range. Most traits did conform to global trends, such as the increase in LMA and decrease in leaf size with aridity (Cunningham et al. 1999; Fonseca et al. 2000; Wright et al. 2001). Similarly, SWWA Eucalyptus species from more arid climates had lower LA/SA than more mesic species, as observed for other woody species along aridity gradients around the world (Mencuccini and Grace 1995; Thomas et al. 2004; Addington et al. 2006; Carter and White 2009; Gotsch et al. 2010; Gleason et al. 2012; Westoby et al. 2012).

However, the negative relationship between gS/KX and LA/SA, and the absence of a strong relationship between total stomatal conductance to total xylem conductivity ratio

([gS*LA]/[Kx*SA]) and LA/SA supports the hypothesis that LA/SA may not be a reliable indicator of hydraulic demand and supply.

Species in increasingly arid environments tend to have a higher density of small stomata (Gindel 1969; Quarrie and Jones 1977; Clifford et al. 1995; Bosabalidis and Kofidis 2002; Xu and Zhou 2008). Theoretically this would lead to a higher maximum stomatal conductance (Franks et al. 2009; Franks and Beerling 2009a; Lammertsma et al. 2011) and a more rapid regulation of conductance in response to daily changes in local climate conditions, thereby increasing water use efficiency and the safety of the hydraulic system (Tyree and Sperry 1988; Drake et al. 2013). We observed the opposite trend both among and within species, with a lower density of larger stomata (rather than a high density of small stomata) more common in Eucalyptus species from more arid conditions. This was not only opposite to other studies of arid species, but also to studies on individual species of Eucalyptus (James and Bell 1995;

Franks et al. 2009). The decreased density of stomata resulted in marginally lower maximum gS in more arid species. However, operating conductance will likely be much lower than this, as operating stomatal conductance has been shown to be lower in species living in arid conditions than those in a mesic climate (Körner 1995), even though transpiration is often greater, likely due to increased vapour pressure deficit (Burgess 2006). This explains why Eucalyptus species of arid environments both here and in other studies (Schulze et al. 2006b; Schulze et al. 2006a; Turner et al. 2008; Turner et al. 2010; Schulze et al. 2014) have a higher δ13C, indicating relatively more closed stomata for a greater portion of the day, limiting stomatal conductance. If these species do not often open their stomata other than at dawn and dusk, and thus avoid the need to quickly regulate aperture, this may explain why smaller stomata have not evolved in these species.

Given that arid Eucalyptus species would often need to operate at a more negative water potential during the summer months, to access tightly bound water in the drying soils, these species must reduce their risk of cavitation. Lower osmotic potentials during both summer and winter observed in more arid species lowers the turgor loss point, conferring a greater tolerance to lower water potentials (Barlett et al. 2012; Merchant 2014), as has previously been observed in other Eucalyptus species (Merchant et al. 2007). However, unlike other Eucalyptus studies (Myers and Neales 1986; Fan et al. 1994; White et al. 1996), osmotic adjustment was not observed between seasons across anywhere along the aridity gradient. This may suggest a relatively non-stressful summer, a stressful winter, or the presence of solutes accumulated during the previous summer present in the winter measurement. The exception to this was E. gomphocephala plants on soils with a much lower clay content, which may have needed to

79 reduce their osmotic potential during summer to lower the turgor loss point and thus keep their stomata open for longer.

In addition to lower osmotic potentials, species from more arid climates also had narrower xylem vessels. This physiology is common in arid species as it allows for more negative water potentials to be reached, and also correlating with traits known to increase cavitation resistance

(Hacke et al. 2001; Hacke et al. 2006). However, as a consequence of decreasing vessel size, KX was also significantly decreased, limiting the maximum capacity to supply leaves with water.

Although the density of vessels increased, KX could not be maintained without a much larger increase in both density and vessel lumen fraction due to KX scaling with the fourth-power of vessel radius (Tyree and Zimmermann 2002; Gleason et al. 2012). It was this significant decrease in KX while gS was only marginally lower in more arid climates that resulted in the negative relationship between gS/KX and LA/SA. Having a much higher gS/KX may put the plants at greater risk of cavitation given the higher vapour pressure deficit in more arid environments. This may explain the higher δ13C observed in these species, suggesting that their stomata are proportionally more closed for a greater period of the day in comparison to mesic species, limiting operating conductance.

Osmotic potential and xylem vessel size are not the only traits that these Eucalyptus species employ to decrease their vulnerability to more arid conditions. Stem specific density – a trait commonly correlated with cavitation resistance (Hacke et al. 2001; Meinzer 2003; Markesteijn et al. 2011) – did not increase with aridity within species, suggesting that there is limited phenotypic plasticity for this trait. LMA was higher both within and among species in more arid environments, due to increased leaf thickness and a higher leaf mass density. These leaves were also smaller, reducing the boundary layer of air around the leaf (Gibson 1998; Nicotra et al. 2008; Vogel 2009; Yates et al. 2010), allowing for quicker evaporative cooling and gas exchange during the short periods when their stomata are open. High LMA in these smaller leaves decreases the transpiring leaf area per mass or volume of leaf (Poorter et al. 2009), increasing hydraulic safety, but also reducing CO2 exchange capacity and increasing internal shading of photosynthetic cells (Terashima et al. 2001; Hassiotou et al. 2009). This, along with increased cost of high LMA leaves results in an overall reduction in plant growth rate.

Implications in a changing climate Our findings show clear trends in functional traits, presumably indicating acclimation of Eucalyptus species in SWWA to the existing aridity gradient. However, a rapidly changing climate will expose all species to more arid conditions than currently experienced (Chapter 2 of this thesis; Hamer et al. 2015a). The period of optimal climate for growth will likely become shorter in a climate scenario where winter precipitation is predicted to decrease (reducing water reserves) and temperature increases (shifting the ideal growth period earlier), requiring the

80 species to close their stomata more often. This may lead to both reduced growth rate and increased risk of carbon starvation. Furthermore, the driest and hottest periods of summer are likely to become more severe, increasing the rate of cavitation with trees, regardless of climate, operating close to hydraulic failure (Tyree and Sperry 1988; Choat et al. 2012). If species are unable to adapt or acclimatise to these rapidly changing conditions, species may not be able to persist in their current distribution.

Paleo-distribution modelling for one of our widespread study species – E. wandoo – indicates that this species has retracted into smaller remnant populations of suitable climate conditions during stressful times (Dalmaris et al. 2015). There is also evidence to suggest that E. jacksonii, a species from the wet south-coast region, was once widespread but now has contracted into a small region with a suitable climate from which it has not been able to expand again (Wardell- Johnson and Coates 1996). If we assume that these responses are typical, it is likely that retractions will also be a common feature of Eucalyptus species in the future. Indeed, this has been predicted by Hamer et al. (2015a; Chapter 2 of this thesis), who found that in a moderate climate change scenario, the climate envelope would decrease by, on average, 73% by 2100 for all Eucalyptus species modelled (which include all species studied here). However, these predictions of decline are based on the assumption that the species’ competitiveness and their ability to withstand stress will not change at all. Whilst significant genetic changes may not occur fast enough in a rapidly changing climate, phenotypic plasticity may reduce the severity of future stress to an unknown extent.

In this study we observed that some traits vary within a species across the aridity gradient, such as stomatal density, xylem vessel density and size, leaf size, LDMC, LMA and osmotic potential. It is likely that species will respond to the current warming and drying of the climate by changing trait values in the direction of species currently native to warmer and drier climates, through a combination of phenotypic plasticity and evolutionary change (Byrne et al. 2013; Steane et al. 2014; McLean et al. 2014). The one exception to this is stomatal size and density. Currently, more-arid species are able to transpire throughout the day during summer (if only at a low operating conductance; Burgess, 2006) presumably due to the availability of soil-bound water recharged during winter. However, if future water availability decreases, these species may be required to further limit conductance through stomatal closure, leading to carbon starvation.

Recent species distribution modelling has shown that the overlap between future suitable distributions of arid and mesic Eucalyptus species is relatively small, suggesting that mesic species may not need to compete with species already adapted to drier conditions (Chapter 2 of this thesis; Hamer et al. 2015a). For arid species, it is unclear to what extent these traits can change further, as we have sampled species which are already close to the extreme end of the aridity gradient within which Eucalyptus species are known to survive. Thus, we may see a

81 contraction of Eucalyptus dominated ecosystems in the driest inland areas. It may be that the strategy employed by trees such as Eucalyptus may not be viable in more arid conditions, meaning the only option for these species is to retract into refugia – a scenario which does not look promising, considering the degree of habitat fragmentation in south-west Australia (Chapter 2; Hamer et al. 2015a).

Acknowledgments We would like to acknowledge our funding bodies, the Centre of Excellence for Climate Change, Woodland and Forest Health as well as the Holsworth Wildlife Research Endowment for providing the funds necessary for this research to happen. We would also like to thank the tireless volunteers who made this research possible with their assistance both out in the field and in the lab.

References

Aasamaa K, Sõber A, Rahi M (2001) Leaf anatomical characteristics associated with shoot hydraulic conductance, stomatal conductance and stomatal sensitivity to changes of leaf water status in temperate deciduous trees. Funct Plant Biol 28:765–774.

Addington RN, Donovan LA, Mitchell RJ, Vose JM, Pecot SD, Jack SB, Hacke UG, Sperry JS, Oren R (2006) Adjustments in hydraulic architecture of Pinus palustris maintain similar stomatal conductance in xeric and mesic habitats. Plant Cell Environ 29:535–545.

Australian Bureau of Meteorology (2011) Average annual, seasonal and monthly rainfall. http://www.bom.gov.au/jsp/ncc/climate_averages/rainfall/index.jsp. Accessed 19 May 2015

Australian Bureau of Meteorology (2012) Mean monthly and mean annual evapotranspiration data. http://www.bom.gov.au/jsp/ncc/climate_averages/evapotranspiration/index.jsp? Accessed 19 May 2015

Barlett MK, Scoffoni C, Ardy R, Zhang Y, Sun S, Cao K, Sack L (2012) Rapid determination of comparative drought tolerance traits: using an osmometer to predict turgor loss point. Methods Ecol Evol 3:880–888.

Bishop TFA, McBratney AB, Laslett GM (1999) Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91:27–45.

Bosabalidis AM, Kofidis G (2002) Comparative effects of drought stress on leaf anatomy of two olive cultivars. Plant Sci 163:375–379.

Brodribb TJ, Jordan GJ, Carpenter RJ (2013) Unified changes in cell size permit coordinated leaf evolution. New Phytol 199:559–570.

Brouwers NC, Mercer J, Lyons T, Poot P, Veneklaas E, Hardy G (2012) Climate and landscape drivers of tree decline in a Mediterranean ecoregion. Ecol Evol 3:67–79.

Burgess SSO (2006) Measuring transpiration responses to summer precipitation in a Mediterranean climate: a simple screening tool for identifying plant water-use strategies. Physiol Plant 127:404–412.

Byrne M, Prober S, McLean L, Steane D, Stock W, Potts B, Vaillancourt R (2013) Adaptation to climate in widespread Eucalypt species. National Climate Change Adaptation Research Family, Gold Cost

Carter JL, White DA (2009) Plasticity in the Huber value contributes to homeostasis in leaf water relations of a mallee Eucalypt with variation to groundwater depth. Tree Physiol 29:1407–1418.

83 Wright IJ, Zanne AE (2012) Global convergence in the vulnerability of forests to drought. Nature 491:752–755.

Clifford SC, Black CR, Roberts JA, Stronach IM, Singleton-Jones PR, Mohamed AD, Azam-

Ali SN (1995) The effect of elevated atmospheric CO2 and drought on stomatal frequency in groundnut (Arachis hypogaea (L.)). J Exp Bot 46:847–852.

Coplen T, Brand W, Gehre M, Groning M, Meijer H, Toman B, Verkouteren R (2006) New guidelines for δ13C measurements. Anal Chem 78:2439–2441.

Cunningham SA, Summerhayes B, Westoby M (1999) Evolutionary divergences in leaf structure and chemistry, comparing rainfall and soil nutrient gradients. Ecol Monogr 69:569–588.

Drake PL, Froend RH, Franks PJ (2013) Smaller, faster stomata: scaling of stomatal size, rate of response, and stomatal conductance. J Exp Bot 64:495–505.

Fan S, Blake TJ, Blumwald E (1994) The relative contribution of elastic and osmotic adjustments to turgor maintenance of woody species. Physiol Plant 90:408–413.

Fonseca CR, Overton JM, Collins B, Westoby M (2000) Shifts in trait-combinations along rainfall and phosphorus gradients. J Ecol 88:964–977.

Franks P, Beerling D (2009a) CO2-forced evolution of plant gas exchange capacity and water- use efficiency over the Phanerozoic. Geobiology 7:227–236.

Franks PJ, Beerling DJ (2009b) Maximum leaf conductance driven by CO2 effects on stomatal size and density over geologic time. Proc Natl Acad Sci 106:10343–10347.

Franks PJ, Leitch IJ, Ruszala EM, Hetherington AM, Beerling DJ (2012) Physiological

framework for adaptation of stomata to CO2 from glacial to future concentrations. Philos Trans R Soc B Biol Sci 367:537–546.

Gibson AC (1998) Photosynthetic organs of desert plants. Bioscience 48:911–920.

Gindel I (1969) Stomatal number and size as related to soil moisture in tree xerophytes in Israel. Ecology 50:263–267.

Gleason SM, Butler DW, Ziemińska K, Waryszak P, Westoby M (2012) Stem xylem conductivity is key to plant water balance across Australian angiosperm species. Funct Ecol 26:343–352.

Hacke UG, Sperry JS, Pockman WT, Davis SD, McCulloh KA (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461.

Hacke UG, Sperry JS, Wheeler JK, Castro L (2006) Scaling of angiosperm xylem structure with safety and efficiency. Tree Physiol 26:689–701.

Hamer JJ, Veneklaas EJ, Poot P, Mokany K, Renton M (2015a) Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions of Eucalyptus species in South Western Australia. Austral Ecol, DOI: http://dx.doi.org/10.1111/aec.12274

Hamer JJ, Veneklaas EJ, Renton M, Poot P (2015b) Links between soil texture and root architecture of Eucalyptus species may limit distribution ranges under future climates. Plant Soil, DOI: 10.1007/s11104-015-2559-5

Hassiotou F, Ludwig M, Renton M, Veneklaas EJ, Evans JR (2009) Influence of leaf dry mass

per area, CO2, and irradiance on mesophyll conductance in sclerophylls. J Exp Bot 60:2303–2314.

Hetherington AM, Woodward IF, Woodward FI (2003) The role of stomata in sensing and driving environmental change. Nature 424:901–908.

Hooper RJ, Sivasithamparam K (2005) Characterization of damage and biotic factors associated with the decline of Eucalyptus wandoo in southwest Western Australia. Can J For Res 35:2589–2602.

Hughes L, Cawsey EM, Westoby M (1996) Climatic range sizes of Eucalyptus species in relation to future climate change. Glob Ecol Biogeogr Lett 5:23–29.

James SA, Bell DT (1995) Morphology and anatomy of leaves of Eucalyptus camaldulensis clones: variation between geographically separated locations. Aust J Bot 43:415–433.

Körner C (1995) Leaf diffusive conductances in the major vegetation types of the globe. In: Schulze E-D, Caldwell MM (eds) Ecophysiology of Photosynthesis. Springer, pp 463– 490

Lammertsma EI, Boer HJ de, Dekker SC, Dilcher DL, Lotter AF, Wagner-Cremer F (2011)

Global CO2 rise leads to reduced maximum stomatal conductance in Florida vegetation. Proc Natl Acad Sci 108:4035–4040.

Markesteijn L, Poorter L, Paz H, Sack L, Bongers F (2011) Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ 34:137–148.

McLean EH, Prober SM, Stock WD, Steane DA, Potts BM, Vaillancourt RE, Byrne M (2014) Plasticity of functional traits varies clinally along a rainfall gradient in Eucalyptus tricarpa. Plant Cell Environ 37:1440–1451.

85 Meinzer F (2003) Functional convergence in plant responses to the environment. Oecologia 134:1–11.

Meinzer FC, Clearwater MJ, Goldstein G (2001) Water transport in trees: current perspectives, new insights and some controversies. Environ Exp Bot 45:239–262.

Mencuccini M, Grace J (1995) Climate influences the leaf area/sapwood area ratio in Scots pine. Tree Physiol 15:1–10.

Merchant A (2014) The regulation of osmotic potential in trees. In: Tausz M, Grulke N (eds) Trees in a Changing Environment. Springer Netherlands, Netherlands, pp 83–97

Merchant A, Callister A, Arndt S, Tausz M, Adams M (2007) Contrasting physiological responses of six Eucalyptus species to water deficit. Ann Bot 100:1507–15.

Middleton N, Thomas DSG (1992) World atlas of desertification, 1st edn. United Nations Environment Programme (UNEP) Edward Arnold, London, UK

Myers BA, Neales TF (1986) Osmotic adjustment, induced by drought, in seedlings of three Eucalyptus species. Funct Plant Biol 13:597–603.

Nicotra AB, Cosgrove MJ, Cowling A, Schlichting CD, Jones CS (2008) Leaf shape linked to photosynthetic rates and temperature optima in South African Pelargonium species. Oecologia 154:625–635.

Pammenter NW, Van der Willigen C (1998) A mathematical and statistical analysis of the curves illustrating vulnerability of xylem to cavitation. Tree Physiol 18:589–593.

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2013) nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-108.

Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol 182:565–588.

Price CA, Symonova O, Mileyko Y, Hilley T, Weitz JS (2011) Leaf extraction and analysis framework graphical user interface: segmenting and analysing the structure of leaf veins and areoles. Plant Physiol 155:236–245.

Quarrie SA, Jones HG (1977) Effects of abscisic acid and water stress on development and morphology of wheat. J Exp Bot 28:192–203.

R Development Core Team (2014) R: A Language and Environment for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/.

Ryser P, Bernardi J, Merla A (2008) Determination of leaf fresh mass after storage between moist paper towels: constraints and reliability of the method. J Exp Bot 59 :2461–2467.

Saxton KE, Rawls WJ, Romberger JS, Papendick RI (1986) Estimating generalized soil-water characteristics from texture. 1031–1036.

Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675.

Schulze ED, Williams RJ, Farquhar GD, Schulze W, Langridge J, Miller JM, Walker BH (1998) Carbon and nitrogen isotope discrimination and nitrogen nutrition of trees along a rainfall gradient in northern Australia. Funct Plant Biol 25:413–425.

Skrzypek G (2013) Normalization procedures and reference material selection in stable HCNOS isotope analyses – an overview. Anal Bioanal Chem 405:2815–2823.

Skrzypek G, Paul D (2006) δ13C Analyses of calcium carbonate: comparison between the GasBench and elemental analyzer techniques. Rapid Commun Mass Spectrom 20:2915–2920.

Sperry JS, Donnelly JR, Tyree MT (1988) A method for measuring hydraulic conductivity and embolism in xylem. Plant Cell Environ 11:35–40.

Steane DA, Potts BM, McLean E, Prober SM, Stock WD, Vaillancourt RE, Byrne M (2014) Genome-wide scans detect adaptation to aridity in a widespread forest tree species. Mol Ecol 23:2500–2513.

Tenhunen JD, Lange OL, Braun M (1981) Midday stomatal closure in Mediterranean type sclerophylls under simulated habitat conditions in an environmental chamber. Oecologia 50:5–11.

Tenhunen JD, Lange OL, Gebel J, Beyschlag W, Weber JA (1984) Changes in photosynthetic

capacity, carboxylation efficiency, and CO2 compensation point associated with midday

stomatal closure and midday depression of net CO2 exchange of leaves of Quercus suber. Planta 162:193–203.

Terashima I, Miyazawa S-I, Hanba YT (2001) Why are sun leaves thicker than shade leaves? —

consideration based on analyses of CO2 diffusion in the leaf. J Plant Res 114:93–105.

Thomas DS, Montagu KD, Conroy JP (2004) Changes in wood density of Eucalyptus camaldulensis due to temperature—the physiological link between water viscosity and wood anatomy. For Ecol Manage 193:157–165.

Turner NC, Schulze ED, Nicolle D, Kuhlmann I (2010) Growth in two common gardens reveals species by environment interaction in carbon isotope discrimination of Eucalyptus. Tree Physiol 30:741–7.

87 Turner NC, Schulze E-D, Nicolle D, Schumacher J, Kuhlmann I (2008) Annual rainfall does not directly determine the carbon isotope ratio of leaves of Eucalyptus species. Physiol Plant 132:440–5.

Tyree MT, Sperry JS (1989) Vulnerability of xylem to cavitation and embolism. Annu Rev Plant Biol 40:19–36.

Tyree MT, Sperry JS (1988) Do woody plants operate near the point of catastrophic xylem dysfunction caused by dynamic water stress? Anwers from a model. Plant Physiol 88:574–580.

Tyree MT, Zimmermann MH (2002) Xylem Structure and the Ascent of Sap. Springer, New York, NY.

Vogel S (2009) Leaves in the lowest and highest winds: temperature, force and shape. New Phytol 183:13–26.

Westoby M, Cornwell WK, Falster DS (2012) An evolutionary attractor model for sapwood cross section in relation to leaf area. J Theor Biol 303:98–109.

Westoby M, Wright I (2003) The leaf size – twig size spectrum and its relationship to other important spectra of variation among species. Oecologia 135:621–628.

White DA, Beadle CL, Worledge D (1996) Leaf water relations of Eucalyptus globulus ssp. globulus and E. nitens: seasonal, drought and species effects. Tree Physiol 16 :469–476.

Xu Z, Zhou G (2008) Responses of leaf stomatal density to water status and its relationship with photosynthesis in a grass. J Exp Bot 59:3317–3325.

Yates MJ, Anthony Verboom G, Rebelo AG, Cramer MD (2010) Ecophysiological significance of leaf size variation in Proteaceae from the Cape Floristic Region. Funct Ecol 24:485– 492.

Zanne AE, Westoby M, Falster DS, Ackerly DD, Loarie SR, Arnold SEJ, Coomes DA (2010) Angiosperm wood structure: global patterns in vessel anatomy and their relation to wood density and potential conductivity. Am J Bot 97:207–215.

Appendicies

0.41

‰) (

C 30.730.41 ± 29.260.28 ± 28.700.25 ± 28.450.49 ± 29.420.37 ± 29.350.17 ± 27.980.34 ± 28.180.38 ± 28.260.43 ± 27.710.59 ± 30.930.32 ± 27.030.41 ± 25.350.49 ± 30.97 ± 30.190.36 ± 28.890.21 ± 30.800.27 ± 32.160.34 ± 28.530.36 ± 29.340.37 ± 25.420.26 ± 27.880.41 ± 25.360.24 ± 29.670.37 ± 29.320.47 ± 26.670.38 ± ------13 δ

.33

) 2 - mm 96.43 ± 3.95 ( 220.049.25 ± 108.077.09 ± 101.539.77 ± 112.906.40 ± 191.587.08 ± 157.746.36 ± 125.522.61 ± 111.417.16 ± 116.438 ± 209.558.82 ±

S 222.2130.58 ± 140.8617.87 ± 144.5210.41 ± 177.8119.76 ± 283.6013.15 ± 272.7818.55 ± 194.4317.89 ± 111.9914.00 ± 221.0238.49 ± 213.0011.65 ± 253.9114.66 ± 155.1212.81 ± 196.9824.31 ± 168.2413.33 ± 150.0010.15 ± D

) 2 µm (

211.4 ± 36.2 381.4 ± 24.3 331.1 ± 12.4 310.0 ± 14.9 387.7 ± 19.2 411.3 ± 20.9 240.0 ± 27.9 196.3 ± 21.2 208.7 ± 16.5 714.2 ± 71.4 315.9 ± 30.2 409.9 ± 20.3 372.9 ± 25.5 218.9 ± 50.7 190.5 ± 10.8 186.3 ± 13.6 225.1 ± 11.3 175.1 ± 11.6 314.6 ± 26.2 387.0 ± 24.5 324.9 ± 12.7 400.2 ± 40.0 364.0 ± 46.4 282.4 ± 23.5 298.3 ± 33.3 326.7 ± 38.6 A

) 1 - s

0.25 2 -

3.99± 0.43 3.62± 0.27 4.06± 0.35 5.65± 0.36 2.60± 0.18 3.40± 0.24 2.58± 0.24 4.74± 0.31 4.73± 0.31 3.66± 0.29 4.33± 0.63 3.48± 0.23 3.13± 0.39 4.07± 0.09 4.65± 0.73 3.54± 0.19 3.45± 0.09 3.60± 0.27 4.29± 3.90± 0.21 2.95± 0.18 3.49± 0.21 4.38± 0.39 3.33± 0.10 4.21± 0.26 5.00± 0.53 s mol m g ( and stomatal traits.

36.32 30.72

) 2 - no pith; mm 292.224.18 ± 2 525.3719.71 ± 433.2831.42 ± 562.1856.68 ± 455.6122.97 ± 368.1019.48 ± 437.1822.98 ± 393.9134.51 ± 691.1565.98 ± 644.46 ± 615.1465.89 ± 422.7829.52 ± 438.0023.49 ± 582.0667.30 ± 746.6058.01 ± 703.8654.64 ± 549.8933.73 ± 425.7217.31 ± 518.6565.52 ± 507.7734.86 ± 511.7324.16 ± 388.3121.29 ± 557.5371.74 ± 481.6437.84 ± 542.1149.05 ± 518.05 ± LA/SA ( LA/SA mm

) 13 *SA) ± 2.29

x 10 branch plantfunctional traits

1 - 4.04± 0.76 7.40± 0.83 9.07± 1.46 14.38 ± 2.72 13.86 ± 3.50 19.10 ± 4.67 16.40 ± 0.93 14.56 ± 1.78 27.26 ± 6.38 19.60 ± 4.55 12.97 ± 1.90 16.67 ± 3.94 18.34 ± 4.12 14.13 ± 3.86 18.10 ± 2.52 18.13 ± 3.79 14.81 15.60 ± 1.35 13.50 ± 1.75 22.08 ± 7.35 12.24 ± 2.72 21.82 ± 2.49 23.28 ± 3.68 16.30 ± 1.46 15.90 ± 0.36 20.34 ± 3.68 *LA)/(K ol kg S m (g (

0 0 Aridity Index 0.46 0.3 0.22 0.19 0.25 0.17 0.19 0.55 0.53 0.24 0.83 0.23 0.16 0.64 0.42 0.3 0.81 0.66 0.17 0.23 0.16 0.22 0.16 0.55 0.42 0.19 transformed data for whole

wandoo Appendices Appendix Species E. accedens E. albida E. caesia E. gardneri E. gomphocephala E. incrassata E. jacksonii E. longicornis E. marginata E. megacarpa E. salicola E. salmonophloia E. salubris E.

3 0.22 - g mm 6.30± 0.36 7.09± 0.13 6.38± 0.23 7.07± 0.44 5.89± 0.42 6.64± 0.19 8.06± 0.34 6.48± 0.27 6.87± 7.81± 0.19 6.83± 0.13 7.64± 0.42 6.95± 0.16 6.27± 0.20 6.56± 0.20 6.33± 0.24 6.80± 0.28 6.52± 0.39 7.79± 0.15 8.06± 0.51 7.50± 0.26 9.29± 0.33 7.99± 0.35 6.95± 0.46 7.04± 0.22 6.53± 0.08 Specific

) 4 10 Stem Density ( x

0.47

9.94± 1.08 7.88± 0.49 8.20± 1.01 9.02± 0.88 8.42± 0.82 6.79± 0.73 7.68± 1.19 9.97± 0.60 9.86± 0.48 7.61± 0.75 8.55± 1.32 6.32± 9.03± 0.85 9.18± 0.94 11.89 ± 0.74 10.23 ± 1.37 10.93 ± 1.08 13.44 ± 1.20 11.41 ± 0.67 10.09 ± 1.65 11.38 ± 1.29 11.11 ± 1.12 13.02 ± 0.63 10.87 ± 1.22 10.08 ± 0.67 10.11 ± 1.07 Vessel Vessel Lumen Fraction

) 2 479.4777.40 ± 410.7146.20 ± 395.9339.13 ± 424.2651.39 ± 531.3047.28 ± 405.0528.17 ± 263.3322.38 ± 415.3332.08 ± 409.6150.30 ± 348.9226.69 ± 521.8218.65 ± 338.4342.39 ± 325.1946.93 ± 578.7136.44 ± 444.8543.70 ± 359.3446.28 ± 462.0639.32 ± 403.4336.12 ± 374.6123.74 ± 354.0740.78 ± 384.1948.05 ± 261.8218.82 ± 312.2344.95 ± 394.4744.64 ± 405.4721.87 ± 309.6719.96 ± µm (

X A

1.72 35.95

) 2 - mm ( 296.1746.74 ± 278.32 ± 286.8244.99 ± 228.4330.87 ± 231.9929.96 ± 346.0814.88 ± 354.5355.21 ± 298.1820.30 ± 248.2334.08 ± 269.9722.81 ± 209.5820.77 ± 243.9124.50 ± 275.3635.72 ± 207.2023.02 ± 290.6539.27 ± 463.55108.3 ± 256.7326.99 ± 281.9040.96 ± 330.684 ± 261.1129.05 ± 271.5440.49 ± 281.8013.79 ± 383.0186.60 ± 277.9921.26 ± 280.8221.95 ± 360.9118.16 ±

X D

1 - s

2 -

) kg m 9 - 2.70± 0.39 1.92± 0.30 2.10± 0.44 1.62± 0.14 2.83± 0.39 2.61± 0.37 1.03± 0.17 1.77± 0.33 2.27± 0.27 1.42± 0.23 2.59± 0.49 1.23± 0.27 1.28± 0.37 2.36± 0.31 3.06± 0.45 2.43± 0.31 1.91± 0.21 2.41± 0.36 1.94± 0.13 1.38± 0.24 1.77± 0.41 0.82± 0.10 1.34± 0.11 1.56± 0.24 2.02± 0.21 1.89± 0.38 ( X 10 K x

0 0 Aridity Index 0.46 0.3 0.22 0.19 0.25 0.17 0.19 0.55 0.53 0.24 0.83 0.23 0.16 0.64 0.42 0.3 0.81 0.66 0.17 0.23 0.16 0.22 0.16 0.55 0.42 0.19 transformed data for xylem vessel traits and specific sapwood density.

Appendix Species E. accedens E. albida E. caesia E. gardneri E. gomphocephala E. incrassata E. jacksonii E. longicornis E. marginata E. megacarpa E. salicola E. salmonophloia E. salubris E. wandoo

0.05 πW Ψ

πS 0.25 ± 0.18 0.39 ± 0.20 0.04 ± 0.15 0.22± 0.15 0.19 ± 0.14 0.01± 0.13 0.29 ± 0.36 ± 0.13 0.25 ± 0.14 0.21 ± 0.35 0.06 ± 0.13 0.38± 0.08 0.20± 0.18 0.13± 0.07 0.04 ± 0.17 0.08 ± 0.18 0.18± 0.10 0.09 ± 0.07 0.02 ± 0.21 0.21± 0.08 0.36± 0.10 0.28± 0.27 0.27 ± 0.18 0.01± 0.16 0.03± 0.13 0.08 ± 0.25 ------Ψ

MPa) (

πW 1.65 ± 0.07 1.83 ± 0.27 1.86 ± 0.11 2.13 ± 0.16 1.46 ± 0.07 1.50 ± 0.02 1.45 ± 0.09 1.22 ± 0.05 1.23 ± 0.06 1.89 ± 0.23 1.24 ± 0.12 2.22 ± 0.09 2.19 ± 0.06 1.16 ± 0.11 1.48 ± 0.10 1.72 ± 0.18 1.25 ± 0.06 0.88 ± 0.04 2.23 ± 0.18 1.91 ± 0.12 2.32 ± 0.07 2.20 ± 0.17 2.02 ± 0.18 1.56 ± 0.17 1.76 ± 0.09 2.06 ± 0.22 ------Ψ

0.06 MPa) (

πS 1.90 ± 0.12 2.22 ± 0.23 1.90 ± 0.13 1.91 ± 0.11 1.65 ± 0.10 1.49 ± 0.13 1.74 ± 0.09 1.57 ± 0.15 1.48 ± 0.07 2.10 ± 0.16 1.29 ± 1.84 ± 0.04 2.00 ± 0.19 1.04 ± 0.07 1.53 ± 0.08 1.79 ± 0.13 1.07 ± 0.04 0.97 ± 0.06 2.26 ± 0.12 1.70 ± 0.10 1.95 ± 0.05 1.92 ± 0.15 2.29 ± 0.22 1.55 ± 0.02 1.73 ± 0.13 2.14 ± 0.22 ------Ψ

- 0.30 mm ± 0.64

8.77± 0.55 9.05± 0.21 9.08± 9.59± 0.24 9.07± 0.25 9.89± 0.36 9.91± 0.31 7.73± 0.21 8.31± 0.34 9.59± 0.64 8.94± 0.38 9.66± 0.25 8.80± 0.19 8.41± 0.27 8.17± 0.29 9.63± 0.12 9.14 8.71± 0.59 8.81± 0.34 ) 10.12 ± 0.21 11.15 ± 0.51 10.01 ± 0.88 10.60 ± 0.70 10.03 ± 0.24 12.74 ± 0.50 10.13 ± 0.21 VLA VLA ( 1

)

± 0.21 2

- area g m 1.56± 0.14 1.21± 0.15 1.61± 0.14 2.06± 0.19 2.90± 0.55 1.87± 0.33 1.25± 0.16 1.99± 0.40 1.81± 0.06 2.00± 0.32 1.31± 0.07 1.69± 0.17 2.11± 0.26 1.03± 0.10 0.86± 0.05 0.88± 0.08 1.38 1.15± 0.04 2.09± 0.12 2.04± 0.31 2.48± 0.07 1.31± 0.15 2.12± 0.25 1.91± 0.19 1.23± 0.26 2.40± 0.23 N (

) ± 0.00 1 -

g g 0.53± 0.01 0.53± 0.01 0.53± 0.02 0.55± 0.00 0.52± 0.01 0.52± 0.01 0.51± 0.00 0.47± 0.01 0.49± 0.01 0.51 0.47± 0.01 0.53± 0.01 0.51± 0.01 0.45± 0.00 0.50± 0.01 0.52± 0.01 0.48± 0.01 0.41± 0.01 0.53± 0.00 0.55± 0.02 0.52± 0.01 0.52± 0.01 0.53± 0.01 0.49± 0.01 0.51± 0.00 0.55± 0.01 LDMC (

0.01

0.39± 0.01 0.44± 0.02 0.43± 0.01 0.38± 0.01 0.43± 0.00 0.47± 0.01 0.44± 0.02 0.37± 0.01 0.37± 0.02 0.68± 0.01 0.28± 0.00 0.43± 0.01 0.40± 0.01 0.30± 0.01 0.33± 0.01 0.33± 0.01 0.45± 0.01 0.37± 0.01 0.41± 0.38± 0.01 0.40± 0.02 0.50± 0.01 0.40± 0.01 0.40± 0.01 0.39± 0.01 0.46± 0.02 Leaf thickness (mm)

± 0.06 ± 0.20 ± 0.09 ± 0.04 ± 0.03 ± 0.08 ± 0.10 ± 0.10 ± 0.20 ± 0.10 ± 0.02 ± 0.02 ± 0.04 ± 0.06 ± 0.03 ± 0.06 ± 0.05 ± 0.05 ± 0.07 ± 0.05 ± 0.10 ± 0.06 ± 0.20 ± 0.08 ± 0.06 ± 0.10 ±

) 2 .04 .36 .28 .48 .00 .00 .36 .40 .00 .16 .00 .40 - 53 00 74 58 02 47 36 82 08 56 59 23 2 3 309.80 2 2 280.96 307.56 2 207.80 428.96 1 294.88 265.52 1 1 2 285.40 187.80 258.96 261.92 2 373.58 2 251.76 271.84 3 g m LMA LMA (

2.67

) 2 130 13.14 ± 48.24 ± 1.72 76.24 ± 4.19 71.65 ± 4.76 44.88 ± 2.22 75.90 ± 4.57 54.27 ± 5.31 49.68 ± 6.11 97.27 ± 6.88 38.42 ± 44.52 ± 3.10 62.14 ± 1.42 38.26 ± 2.98 84.82 ± 8.34 105.158.57 ± 118.949.06 ± 138.915.23 ± 188.1 ± 15.13 151.748.77 ± 85.94 ± 13.84 105.3211.56 ± 118.1813.03 ± 159.7415.22 ± 165.3512.19 ± 132.9214.79 ± 105.2210.01 ± Leaf Size (mm

Aridity Index 0.46 0.30 0.22 0.19 0.25 0.17 0.19 0.55 0.53 0.24 0.83 0.23 0.16 0.64 0.42 0.30 0.81 0.66 0.17 0.23 0.16 0.22 0.16 0.55 0.42 0.19 transformed data for plantfunctional traits relating to leaf andmorphology physiology as well as seasonal osmotic potential -

Species E. accedens E. albida E. caesia E. gardneri E. gomphocephala E. incrassata E. jacksonii E. longicornis E. marginata E. megacarpa E. salicola E. salmonophloia E. salubris E. wandoo Appendix

92 Chapter 4

Links between soil texture and root achitecture of Eucalyptus species may limit distribution ranges under future climates

Preface to Chapter 4 Similar to Chapter 3, the objective of this chapter is to build on our understanding of species sensitivity to a changing climate, as well as starting to explore potential adaptive capacity, but in this case the focus is on studying the root architecture of juvenile plants. As plants are most vulnerable to a drying climate during early establishment (when they don’t have such large root systems accessing soil water held at depth), it is important to understand how seedling root architecture is adapted to different soil types and climate. The knowledge gained is relevant to evaluating species’ ability to either migrate to different soil types in an effort to stay within their climate envelope, or persist in the same soil they are adapted to, but in unfavourable climate conditions.

A rhizotron with the Perspex pane removed showing the comparison between above and below ground biomass for a Eucalyptus albida seedling. Note the small above ground growth compared to the very deep root system with minimal lateral roots.

Links between soil texture and root architecture of Eucalyptus species may limit distribution ranges under future climates

Hamer JJ,1,2*, Veneklaas EJ1,2, Renton M 1,2, Poot P1,2 1 School of Plant Biology (M090), The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia (Email: [email protected]) 2 Centre of Excellence for Climate Change, Woodland and Forest Health, Crawley, Western Australia, 6009, Australia

Abstract  Background and Aims: Root traits adapted to specific environmental conditions may impose a disadvantage to species when grown outside of their preferred habitat. Here we aim to identify whether Eucalyptus species typically considered as ‘less specialised’ may have root architectures that are adapted to either climate or soil texture, potentially limiting their current and future distribution.  Methods: We compared the root architecture and biomass allocation of nine Eucalyptus species from a biodiversity hotspot. Plants were grown from seed in 60 cm deep, 40 cm wide rhizotrons filled with sandy soil, with roots observed over time and measured at harvest.  Results: Species that typically grow in coarse-textured soil tended to have more lateral roots exploring a greater soil volume compared to those typically found in fine-textured soils. Root distribution also varied with annual precipitation, with species typically found in dry climates – especially those preferring fine-textured soil – having fewer roots in the topsoil.  Conclusions: Differences between root architecture and biomass allocation of juvenile Eucalyptus species were observed, suggesting adaptation to the environment that the species typically grow in. As these differences were expressed in a common substrate, phenotypic plasticity did not override genetic differences, making them vulnerable to a rapidly changing climate.

Keywords: Biomass allocation, Climate, Climate change, Lateral roots, Precipitation, Rhizotron, Root distribution, Tap root.

Introduction Insights from past records, current observations and future predictions are all in agreement that climate change will result in large scale range shifts of species distributions (Hughes 2000; McCarty 2001; Walther et al. 2002; Parmesan and Yohe 2003; Burrows et al. 2014). Plant species which are not able to track their shifting climate envelope will be required to adapt to changing conditions (Hoffmann and Sgro 2011), recede into refugia or otherwise become extinct. Observations of such changes have already been documented, with tree populations no longer in a suitable climate suffering from health decline (Allen et al. 2010), and others migrating to higher elevations (Pauli et al. 2012). Whether species persist where they are or migrate to new locations, it is likely that they will encounter novel environments, differing from where they have typically grown in terms of the climate or soil characteristics.

The resilience of plant species subject to environmental change is typically estimated by determining responses to environmental factors such as temperature or water availability, e.g. the hydraulic safety margins of stems and leaves (Blackman et al. 2010; Choat et al. 2012). However, belowground traits are generally overlooked. How much water or nutrients a plant can access is partly determined by the placement of roots in the soil profile (Hutchings et al. 2003; Hodge 2004). Specialised rooting architecture or root morphology has been documented for a number of species in unique habitats. Some examples of this are denser, thicker roots (lower specific root length) required to penetrate compacted soils (Tracy et al. 2012), adventitious roots with higher porosity developed by those in a waterlogged environment (Poot and Lambers 2003a) or the exploratory roots adapted to finding wet cracks in shallow soil granite outcrops (Poot et al. 2012; Renton and Poot 2014). Such specialised traits may infer a limited plasticity which would reduce these species’ competitiveness against unspecialised species in an environment other than that which they have adapted to (Poot and Lambers 2003b). This would limit their realised distribution range and thus make them more vulnerable to a changing climate.

Even in more common environments different species possess different root architecture, depending on their functional group and landscape position (Jackson et al. 1996; Schenk and Jackson 2002a; Schenk and Jackson 2005). Such differences can be observed in evergreen species in seasonally dry climates due to the need to access reserves of water during the dry periods (Schwinning and Ehleringer 2001; Groom 2004; Lambers et al. 2014). These species typically either invest in many shallow soil roots to capture what little rain does fall, or deep tap roots to access water from the ground-water table (Schwinning and Ehleringer 2001; Groom 2004; Lambers et al. 2014). Soil texture has also been related to soil and plant water relations (Schulze et al. 2006; Turner et al. 2010; Poot and Veneklaas 2013) with species required to reach lower minimum water potential to be able to access tightly bound water typical of fine- textured soils. This has implications for the shoot to root ratio, root traits and root system

96 architecture, as the placement of roots in the soil profile and the proportion of biomass allocated to them can determine the strategy the plant employs during drier periods (Davis and Mooney 1986; Groom 2004; Fensham and Fairfax 2007; Westoby et al. 2012; Poot and Veneklaas 2013). Typically, those with deeper root systems that access water stored lower down in the soil profile – a strategy common in desert trees (Canadell et al. 1996) and in coarse-textured soil (Jackson et al. 2000) – do not need to be able to reach such negative water potentials and can keep their stomata open for longer compared to those with shallower root systems in a similar environment (Davis and Mooney 1986; Groom 2004; Fensham and Fairfax 2007; Mitchell et al. 2009). As the water use strategy employed by a species is partly constrained by its root distribution, this may hamper a species’ ability to establish in a changing climate, or in novel soil conditions if the species is forced to shift distribution in response to climate change. Thus, it is important to document how root distribution through the soil profile varies between species and to determine to what extent they are associated with a species’ local climate and soil type.

Species of the genus Eucalyptus form the dominant overstorey genus in many habitats in the south-west Australian biodiversity hotspot. Of all the Mediterranean climates, the south-west Australian region is likely to experience the largest change in climate, with 49% of the Mediterranean climate region predicted to become semi-arid/arid by the year 2100 (Klausmeyer and Shaw 2009). Already we have started to observe crown decline and tree death for a number of Eucalyptus species caused by the recent decade of droughts (Hooper and Sivasithamparam 2005; Brouwers et al. 2012; Brouwers et al. 2013; Matusick et al. 2013; Poot and Veneklaas 2013), with models suggesting a further decline in health in the future, and inland species being most at risk (Hamer et al. in press). Due to its high diversity over a large geographic range, Eucalyptus is an ideal genus for studying the variation in root traits between prominent species across environmental gradients. Nine species, representing the diversity along climate and soil gradients of the region, were grown in a common soil type in rhizotrons for six months to observe root growth and architecture over time. We hypothesised that species would differ in terms of their biomass allocation to different components of their root system (e.g. lateral roots vs tap root), reflecting the environmental conditions of their native habitat. More specifically, we expected to observe species from drier climates investing a higher percentage of biomass into developing a taproot to reach soil moisture at depth. On the other hand, species from wetter climates would invest in more lateral roots near the surface to capture the more frequent precipitation during juvenile development. Apart from local climate, root architectures may be influenced by local soil profiles (including hard clay pans) and local soil texture as these are known to heavily affect plant water availability. From this perspective we would expect that species that typically grow in fine-textured soils would invest proportionally less biomass into taproots versus lateral roots, compared to species from coarse-textured soils. Though results presented here are based on juvenile root architecture rather than those of adults, the initial root

97 architecture employed by seedlings in the first year of development is vital to the success of species establishment in novel conditions.

Methods

Study area and species classification The south-west of Western Australia is a Mediterranean climate biodiversity hotspot, with over 250 Eucalyptus species, half of which are endemic. The region features a strong precipitation gradient, ranging from over 1200 mm annually in the south-west corner, to less than 250 mm in the in-land region, with Eucalyptus species dominating the overstorey in most forest and woodland habitats along this gradient (Figure 1). Within the same region there are contrasts in soil texture, including coastal and inland sands, loamy hillsides, and clay-rich flats. Nine Eucalyptus species were selected for this study (Table 1) to represent the wide range of species found within this biodiversity hotspot, based precipitation and soil preference. Seed was sourced from a provenance of each species representing an average rainfall for their distribution.

Figure 1. Map of Western Australia, showing the study region – as defined by the combined total distribution range of all study species (dashed line) – overlayed with annual precipitation.

Species were classified according to both their preferred climate and the soil type they typically occur in (Table 1). ‘Wet’ species typically receive between 500 and 1200 mm of precipitation annually), ‘dry’ species receive 250 to 500 mm annually and the ‘midrange’ species fall in the middle of this gradient with 325 to 850 mm annual precipitation, surviving both in higher rainfall zones and into the drier inland habitats, but not in the extremes of each. Each species’ soil classification was determined by referring to expert knowledge, herbarium records and regolith datasets (Marnham and Morris 2003; Stewart et al. 2008). The classification was based

98 on texture in the top metre of soil, as the focus of the experiment was on early seedling fitness post germination. Two species (E. caesia and E. salicola) were excluded from the soil and climate classifications as their soil characteristics have a strong atypical influence on water availability. Eucalyptus caesia is a granite outcrop endemic and its root system architecture has been found to be specialised for finding cracks in the underlying rock to access deeper stored water (Poot et al. 2012). Eucalyptus salicola occurs around salt lakes and salt pans. The roots of this species may be adapted to high salt concentrations in the soil, confounding the effect of soil and water availability on root traits.

Table 1. Brief descriptions of the nine study species.

Species Annual precipitation (mm) Soil preference Seed mass (mg  SD)

E. salmonophloia Dry (200 – 450) Fine 0.21  0.01

E. longicornis Dry (250 – 450) Fine 0.47  0.01

E. gardneri Dry (325 – 475) Intermediate 0.61  0.02

E. albida Dry (325 – 550) Coarse 0.78  0.04

E. wandoo Mid (325 – 850) Fine 0.43  0.01

E. gomphocephala Wet (575 – 900) Coarse 1.57  0.02

E. marginata Wet (500 – 1200) Intermediate 16.36  0.63

E. salicola *Dry (220 – 340) Specialised 0.23  0.01

E. caesia *Dry (300 – 460) Specialised 3.54  0.15 * Due to the specialised nature of the soil these species typically inhabit, annual precipitation is not a good representative of water availability, and therefore these species were analysed separately.

Experimental setup and measurements To monitor and observe seedling root growth and architecture over time, large rhizotrons (external dimensions: 60 cm deep, 40 cm wide and 3 cm thick) were constructed (Figure 2), with clear Perspex on one side to allow for weekly observations of the developing root system. The rhizotrons were tilted on a 30 degree angle from the vertical to promote root growth along the Perspex. The experiment was conducted in a glasshouse environment from June to November 2013. The temperature reached an average maximum of 25 °C during the day and an average minimum of 15 °C for the majority of the experiment. Atmospheric humidity was not monitored, but was typically close to outdoor conditions in these glasshouses. Both temperature and humidity were favourable for growth of these species. The rhizotrons were randomised around the glasshouse every other week. Each species was grown from seed and five individuals from each species were transplanted into individual rhizotrons once their cotyledons were fully expanded. To reduce intra-species variation, seedlings of similar size and condition were chosen

99 for transplantation. Each species was germinated and grown in a pasteurised red sandy soil (95% sand, 4% clay, 1% silt) local to Gingin, Western Australia. We acknowledge that pasteurising the soil could have a large effect on results, given the potential influence of mycorrhizal networks on eucalypt root development (Janos et al. 2013), but it is necessary for standardization when comparing multiple species in the same soil. This soil type was chosen as it is unlikely to cause restrictions to root growth. The soil was evenly packed by pouring soil into the rhizotrons in layers and applying equal pressure to each layer before the next was added, creating an average bulk density of 1.46 kg L-1 ± 0.05 (SD). The rhizotrons were given a fixed amount of water per day, sufficient to maintain a high moisture level throughout the rhizotrons (666 ml of water per day, ~11% of total soil volume). Holes at the bottom of the rhizotron allowed for excess water to drain. Due to low nutrients in the soil itself, 100 ml of a nutrient solution (0.3 mg L-1 Nitrogen, 0.035 mg L-1 Phosphorus, 0.175 mg L-1 Potassium) was added to each rhizotron every second week. The non-water-limited and non-nutrient-limited condition was chosen to ensure unimpeded root growth, allowing the observation of inherent tendencies of species in terms of root growth.

Figure 2. Illustration of the rhizotron setup. Dotted lines show where the soil was sectioned at harvest. Coloured soil sections correspond with the three variables derived from PCA analysis, with blue representing the topsoil sections (top row), red the centre sections (middle column) and yellow the lower-lateral sections (sections other than the topsoil and centre). The topsoil and centre variable both include the middle top row section. 100

Each week, photographs were taken of the developing root systems. These were used to calculate vertical and horizontal root growth using ImageJ (Schneider et al. 2012). Each individual replicate was harvested when its roots first hit the bottom of the rhizotron, in an effort to reduce intra-species variation in seedling development (note that vertical and total biomass growth rate was also assessed). From seed sowing to harvest, this took anywhere from five to 16 weeks, depending on the species. At harvest, the Perspex was removed and the soil cut into a matrix of five horizontal by six vertical sections – as in Figure 2 – prior to the roots being washed clean. For each section, all roots were scanned at 600 dpi for measurements of root length and diameter using WinRhizo (Regent Instruments Inc., Canada). Root diameter results are not presented as there was no significant difference between soil sections or between species (with the exception of E. caesia), most likely due to the early stage of development and the bulking of roots into sections. The roots were then dried for over 48 hours at 70°C prior to measuring dry mass. Above ground growth was divided into stem and leaves with average leaf thickness and sapwood diameter at ground level measured using digital callipers. Leaves were scanned at 300 dpi and total leaf area was calculated using ImageJ (Schneider et al. 2012) before drying them at 70°C and weighing dry mass.

Statistical analysis Root biomass and length for each rhizotron section (Figure 2) were standardised by converting to a percentage of the total root biomass or root length of that replicate, in order to assess root distribution independent of total biomass. Analysis of the overall two-dimensional spatial distribution of roots throughout the profile was conducted using Generalised Additive Models (GAMs) with a smoother function of the x and y position (Figure 2) of each section as the predictor. This was done using the ‘gam’ function in the mgcv package (Wood 2011) within the R software environment (R Development Core Team 2014). Overall differences in spatial distributions between species were tested by fitting models with and without the species as a factor variable multiplying the smoother, using the ‘by’ argument in the ‘gam’ function, and then testing whether the model with the factor variable was significantly better than the model without. Subsequent comparisons between pairs of species were done by comparing models with and without species as a factor multiplying the smoother using only the data for the two species in question. Here and in other analyses, pairwise comparisons were only made if the global test indicated significant differences; in such cases further pairwise p-values were not adjusted for multiple comparisons, but left for direct interpretation by the reader. Further analyses of individual whole-plant characteristics and root distribution variables excluded the ‘specialised’ species E. caesia and E. salicola, which were analysed separately.

Variables summarising the spatial root distribution of each replicate were selected using Principal Component Analyses (PCA) with root length or mass percentage within each

101 individual section as the variables, and replicates as the observations. Based on the weightings of each section on the principal components, this analysis showed that three variables could be used to adequately describe the spatial distribution of roots, both in terms of root length and mass: the percentage of root biomass or length in 1) the topsoil (the top row of the rhizotron), 2) the centre column, and 3) the lower-lateral sections (which weren’t in the centre column or top row of the rhizotron; Figure 2). These three variables were used for subsequent analyses. In addition to these three variables, vertical growth rates were calculated by measuring the distance the main taproot grew from when it first hit the Perspex to when it reached the bottom of the rhizotron and dividing this distance by the number of days this took to happen.

Three linear models were fitted for each of the three root distribution variables mentioned above and each of the four key functional traits related to growth rate and supply and demand of water: leaf mass per area (LMA), specific root length (SRL), shoot to root ratio (S:R) and leaf area to root length ratio (LA:RL). The explanatory variables considered in these three models were respectively 1) species, 2) average annual precipitation of the species’ distribution (Bioclim #12; Xu and Hutchinson 2011), soil type (fine, intermediate or coarse), and the two-way interaction between them, and 3) whole plant biomass, average species seed mass, and the two- way interaction between them. Using winter (wettest months) and summer (driest months) precipitation as additional explanatory variables was decided against as winter precipitation was highly correlated with annual precipitation (R=0.99, p < 0.001) and summer precipitation only varied by 16 mm between the highest and the lowest ranking species, these two variables were not considered in the second model. For the latter two models, successive F-tests (ɑ = 0.05) were used to remove explanatory variables that did not significantly contribute to the model. A random effect for species was then added to this simplified model, using the ‘lme’ function in the ‘nlme’ package (Pinheiro et al. 2013) and the fitted mixed-effects model was compared to the model without the random effect, using an F test at a = 0.05. In all cases, the mixed-effects model was not found to be significantly better than the simpler model, and was therefore not used for further analysis.

Results

Whole Plant Characteristics Average whole-plant dry mass at harvest differed greatly between species (p < 0.001) but seedlings of larger species did not reach deeper soil layers faster. Eucalyptus gardneri plants – the smallest at harvest – reached the bottom of the containers at a 5.8-fold lower mass than E. marginata – the largest plants. Total plant dry mass was strongly correlated with log- transformed seed mass (R2 = 0.68, p < 0.001), with the species with the largest seed – E. marginata – having a 78-fold greater seed mass than that of the smallest species –

102

E. longicornis. Log-transformed seed mass was positively correlated with annual precipitation (R2 = 0.70, p = 0.019). Consequently, species from wetter regions had both higher seed mass and achieved a greater whole-plant dry mass, but did not reach the bottom of the Perspex walls (at 60 cm depth) consistently faster than considerably smaller inland species (Figure 3a).

Figure 3: Individual species’ data for a) vertical root growth rate, b) leaf mass per area, c) specific root length and d) leaf area to root length ratio. The box indicates median and interquartile range, while the bars indicate the full range of data. Species are ordered from lowest to highest annual precipitation, and coloured according to their soil-texture classification. Superscript letters above each boxplot represent groups according to non- adjusted multiple comparisons between species i.e. species with the same letter did not differ significantly. Where superscript is not present, the overall model was not significant. Though specialised species are presented here, they were not included in the analyses for whole-plant characteristics.

103

Although there were differences between species in terms of their shoot to root ratio (log- transformed, p = 0.020), there were no clear trends across either a soil or precipitation gradient. This was caused by E. marginata having a significantly higher ratio (1.75) than most other species (on average 1.43). Species from the wetter regions (E. marginata and E. gomphocephala) had a higher leaf area to root length ratio (LA:RL) than those from drier regions (regression of LA:RL versus annual precipitation: R2 = 0.44, p < 0.001, Figure 3d).

Species typically growing in coarse-textured soil had a lower leaf mass per area (LMA) than those from intermediate and fine-textured soils (p < 0.001 in both cases; Figure 3b). Species also differed in specific root length (SRL, p = 0.014; Figure 3c), with species from drier sites having a higher SRL (regression of SRL versus annual precipitation: R2 = 0.22, p = 0.004).

Spatial Root Distribution Data for both root length and root dry mass were analysed, however, results were similar for both, as they were strongly correlated (r = 0.97). Thus, unless otherwise specified, data presented here are for root length only.

The spatial distribution of roots varied among species (p < 0.001), with differences found between some, but not all species (Figure 4). Variation within species was not correlated with total dry biomass.

Species differed in the percentage of root length allocated to the central column (p = 0.045; Figure 5), with species from coarse-textured soil having a significantly lower percentage of roots in the centre (on average 65%) than those from intermediate or fine-textured soil (84%, p = 0.035 and 83%, p = 0.023 respectively). The percentage of root length allocated to the central column was also significantly correlated with total plant biomass (R2 = 0.21, p = 0.002), with species with greater biomass typically having proportionally less roots in the centre, with E. marginata the exception to this.

Species also differed in the relative allocation of root length to the top soil (p < 0.001; Figure 5). The two species from the driest sites (E. salmonophloia and E. longicornis) allocated significantly less roots to the top 10 cm of the soil than other species (35.1% and 23.9% as compared to an average of 44.8% for all other species). These were also the only species which invested significantly more root length between 20-30 cm than 10-20 cm (p = 0.022; Figure 5).

The lower-lateral sections of the rhizotrons contained, on average, only 12.2% of the total root length, even though they represented 52% of the total soil volume. In terms of the distribution of roots in the lower-lateral section of the rhizotron, E. gomphocephala was the only species that significantly differed from other non-specialised species (p = 0.017; Figure 5), with 22.8% of roots distributed in these sections compared to the average of 12.2% across all species.

104

Eucalyptus salicola – a salt tolerant species – was found to be similar to E. gardneri, E. gomphocephala, E. marginata and E. wandoo in terms of overall root length distribution. Though significantly different from E. salmonophloia – a species with similar above-ground traits and similar climate envelopes – it did not appear to have highly specialised root architecture.

Figure 4. Perspective surfaces showing generalised additive models (GAM) fitted over the percentage of root length distribution throughout the rhizotron for each of the study species. Root distribution was measured to a depth of 60 cm, and horizontal spread of 20 cm each side from centre as in Figure 2. Superscript letters on species names indicate grouping of species according to non-adjusted multiple comparisons using GAM i.e. species with the same letter did not differ significantly.

105

Figure 5. Individual species’ data for a) percentage root length in the centre, b) topsoil and c) lower-lateral regions. Plot d) shows the difference in root length between a depth of 10 – 20 cm and 20 – 30 cm. The box indicates median and interquartile range, while the bars indicate the full range of data. Species are ordered from lowest to highest annual precipitation, and coloured according to their soil-texture classification. Superscript letters above each boxplot represent groups according to non-adjusted multiple comparisons between species i.e. species with the same letter did not differ significantly. Though specialised species are presented here, they were not included in the analyses for these variables in text.

In contrast to E. salicola, the granite outcrop endemic Eucalyptus caesia differed from all species other than E. gomphocephala in terms of the overall root distribution. Seedlings of E. caesia had a significantly lower percentage of roots in the centre (53%) and a significantly higher percentage in the lower-lateral sections (32%) compared to most of the other species. These differences were due to the absence of a central taproot and lower order lateral roots exploring in multiple directions. High order finer laterals were only found in the topsoil section

106 of the rhizotron, with thick lateral roots found exclusively lower in the profile. Roots of E. caesia also displayed a distinctive foraging behaviour, with roots repeatedly touching the Perspex briefly before growing back into the soil, remaining no further than 1 cm from the Perspex at all times.

Discussion

Whole plant characteristics Although there were large differences in total biomass between species, those from drier regions with on average lower seed mass managed to reach the same soil depth at a similar rate as species with up to a 5.8-fold higher total biomass. This suggests that some of the smaller species from drier climates allocated a greater proportion of resources into developing their taproot in comparison to the larger species from higher precipitation areas. This was as hypothesised, as trees from drier environments are often deep rooted, as they rely on water found lower in the profile during dry periods (Canadell et al. 1996). That neither vertical growth rate nor shoot to root ratios scaled with the large differences in total biomass suggests that species differ in how they allocate biomass between the taproot and lateral roots during juvenile development.

There was large variation amongst the replicates of species in many of the traits we measured resulting in few clear relationships with any of our predictor variables. Typically, we would expect LMA to increase with aridity (Schulze et al. 1998; Wright et al. 2004) and shoot to root ratio to decrease with aridity (Zimmer and Grose 1958; Schulze et al. 1996; Bai et al. 2010). The large variability in LMA (measured at whole-plant level) is likely due to the large proportion of non-fully expanded leaves in these young plants. We also observed variation in root architecture within species, despite trying to reduce this variation by harvesting each replicate when the taproot reached the bottom. This variation is likely to be caused by a combination of genetic variation and small differences in seed size between plants. Subtle heterogeneity in soil characteristics and water availability in the rhizotrons may also have played a role. Nonetheless, even with the observed variation within species, some general trends in root architecture could still be observed.

Root distribution Our study showed that species in the same genus significantly differed in root system architecture, and that some of these differences were associated with species soil preference or climate. Species typically found in a coarse-textured soil invested a greater proportion of their biomass beyond the central sections of the rhizotron and thus were able to explore a larger volume of soil with lateral roots. These species also had heavier seeds and a higher biomass at the end of the experiment, so even though they did not grow downwards at a faster rate than

107 other species, they invested a greater proportion of resources into lateral roots, increasing their surface area to access a larger pool of water and nutrients for future development and survival through the driest months. The species with the heaviest seeds and greatest biomass – E. marginata, growing on intermediate-textured soils – did not follow this same trend, supporting the conclusion that the soil type a species typically grows in may influence lateral root development.

The influence of preferred climate on root distribution and architecture was not as clear as that of soil. Although there was a general trend between the percentage of roots in the topsoil and the annual precipitation at the species’ natural habitat, this was driven by two species typically found in a dry, fine-textured environment: E. longicornis and E. salmonophloia. Both species invested few roots in the topsoil and were the only species with a slightly higher percentage of root mass deeper in the profile. This was only observed in root length and not root dry mass, indicating that the roots further down in the profile were finer roots. Though this trend could be visually observed in all dry climate species (Figure 4), it was only significant for the two aforementioned species typically growing in fine-textured soil, suggesting a potential interaction between climate and soil texture. In comparison, most other species in this study had a continually decreasing percentage of roots as the depth increased – the expected model based on global compilations of root data (Schulze et al. 1996; Schenk and Jackson 2002b). This allocation of fine roots below the topsoil can be observed in several desert species and is thought to be a strategy for avoiding the drying out of topsoil between rainfall events (Nobel 1991).

The many lateral roots in the topsoil of E. albida – a dry-climate, coarse-textured species – may also be caused by an interaction between soil and the drier climate. This species had a higher proportion of roots outside of the centre sections much like the other coarse texture species, but it allocated more of these roots into the topsoil, suggesting that another strategy may be being employed. As this species typically germinates in a coarser-textured soil, we suggest that these lateral roots were the first priority of the species, with the goal of gathering nutrients and water from a large area of soil – much like the coarse-textured species in a wetter climate – before quickly switching to tap root development. We speculate that the finer roots developed initially in the topsoil may be temporary roots and are likely to dry out during the drier months, with the plant becoming more reliant on the taproot and lateral roots produced lower in the profile at a later stage in development. This seasonal root development strategy in response to drought conditions has been also observed in adult woody trees in a similar seasonally dry environments (Peek et al. 2006; Kitajima et al. 2010). Such diversity in strategies to survive dry conditions is common in stressful environments, with speciation increased in these areas (Nevo 2001).

The granite outcrop endemic – E. caesia – differed from most of the other species in their spatial root distribution. Juveniles of this species did not develop a tap root and produced many

108 relatively thick lateral roots that explored a large portion of the rhizotron, with the majority of finer roots confined to the topsoil. This root foraging strategy resembles that observed for other woody perennial species in shallow-soil ironstone habitats (Poot and Lambers 2008) and granite outcrops (Poot et al. 2012). In both these studies shallow-soil endemics explored a greater region of soil than common deeper-soil congeners. Subsequent modelling of this strategy confirmed that deeper rather than shallow lateral branching enhanced chances of accessing water sources in the underlying rock (Renton and Poot 2014). Thus, the different architecture and foraging of E. caesia roots compared to a range of other Eucalyptus species from a variety of deeper soil environments, again confirms that shallow-soil habitats are unique and have selected for specialised root architecture. The behaviour of this species’ roots as they explored the environment – repeatedly touching and moving away from the surface – is a new discovery. We hypothesise that, by repeatedly touching the surface of an impenetrable object, then moving slightly away from it, the roots maximise the surface area in contact with soil moisture, while still tracking the surface of a rock with potential cracks.

In contrast to shallow-soil habitats we found no evidence for specialised root-system architecture for the salt-tolerant species, E. salicola. This species did not differ in root placement from the majority of other species in this study and was most similar to those species in intermediate soils and/or mid-range precipitation. Though other studies have shown that halophytes tend to have higher shoot to root ratios (Nibau et al. 2008), this was not observed in E. salicola. This suggests that physiological adaptations to access water in these soils such has low osmotic potentials are more important than root morphological adaptations.

Implication for species distribution In this study we used a well-watered, coarse-textured soil with ample nutrients for growth to ensure unimpeded root growth and observe the inherent tendencies of species in terms of the spatial distribution of their roots. However, rooting systems have been shown to be quite plastic (Fitter 1994; Berntson et al. 1995; Poot and Lambers 2003a; Hodge 2004; Schiffers et al. 2011), and thus it is likely that our study species can also acclimate their root system morphology to different climate conditions and soil types. Nonetheless, in this study we have shown that species adapted to dry conditions and fine-textured soil (most different to the conditions represented in this experiment) did not produce a rooting system similar to those species typically found in a wetter climate and coarse textured soil of the coastal region (most similar to experimental conditions). If we assume that the architecture produced by the species from coarse-textured soil and wet climates (the conditions represented in this experiment) is the ideal or optimal architecture for this environment, then we can conclude that the dry-climate, fine- textured soil species were not plastic enough to achieve this ideal architecture for the conditions they were actually grown in within a single growing season – the time Mediterranean climate

109 species have to develop their root system before the dry summer droughts. Nonetheless, imposing the roots of these species to a range of more limiting soil textures, nutrient and water availabilities may reveal additional differences in root architecture that may influence their ability to survive under changing conditions.

Species with a specialised rooting strategy such as those endemic to shallow soil habitats are unlikely to compete as effectively with other less specialised species when outside of their preferred habitat (Poot and Lambers 2003b). Although the species in this study are considered ‘less specialised’, differences in root architecture during seedling establishment of species from different habitats suggest some degree of local adaptation/specialisation. This may hamper species’ competitive ability while establishing in different environmental conditions, whether this is while recruiting new seedlings into their current habitat which may have undergone a change in climate, or while migrating into different soil types in an attempt to keep up with the shifting environmental envelope. This has important implications for species distribution models, reinforcing the idea that soil should be an important variable to consider. Together with the knowledge of species-specific root architecture gained in this study, a reciprocal transfer experiment similar to that conducted in Poot & Lambers (2003b) would assist in understanding how the cost of having a root architecture that is adapted to a specific set of conditions is likely to restrict a species’ realised niche.

Conclusion We have presented evidence that species commonly considered as ‘less specialised’ have different root architectures depending on the environment (and particularly the soil-texture) they typically grow in. These different architectures are likely to play a role in defining the realised distribution of species both in the past and into the future in a changing climate. However, further research needs to be conducted to increase our understanding of the root distribution strategies suggested here and to better understand the implication of these strategies. This research has provided a foundation for such future studies, with this line of research not only valuable to Eucalyptus species conservation, but also to species distribution modelling and species management on a global scale.

Acknowledgments We thank the Centre of Excellence for Climate Change, Woodland and Forest Health for funding this experiment and The Ecological Society of Australia for sponsoring my travel to present this research at their conference in 2014. We also thank previous reviewers for their valuable feedback. This work would not have been possible without Ray Scott and his ability to take whatever equipment our minds can dream up, and make it a reality.

110

References

Bai Y, Zhang W, Jia X, Wang N, Zhou L, Xu S, Wang G (2010) Variation in root:shoot ratios induced the differences between above and belowground mass–density relationships along an aridity gradient. Acta Oecologica 36:393–395.

Berntson G, Farnsworth E, Bazzaz F (1995) Allocation, within and between organs, and the dynamics of root length changes in two birch species. Oecologia 101:439–447.

Blackman CJ, Brodribb TJ, Jordan GJ (2010) Leaf hydraulic vulnerability is related to conduit dimensions and drought resistance across a diverse range of woody angiosperms. New Phytol 188:1113–1123.

Brouwers NC, Mercer J, Lyons T, Poot P, Veneklaas E, Hardy G (2012) Climate and landscape drivers of tree decline in a Mediterranean ecoregion. Ecol Evol 3:67–79.

Burrows MT, Schoeman DS, Richardson AJ, Molinos JG, Hoffmann A, Buckley LB, Moore PJ, Brown CJ, Bruno JF, Duarte CM, Halpern BS, Hoegh-Guldberg O, Kappel C V, Kiessling W, O’Connor MI, Pandolfi JM, Parmesan C, Sydeman WJ, Ferrier S, Williams KJ, Poloczanska ES (2014) Geographical limits to species-range shifts are suggested by climate velocity. Nature 507:492–495.

Canadell J, Jackson RB, Ehleringer J, Mooney HA, Sala OE, Schulze ED (1996) Maximum rooting depth of vegetation types at the global scale. Oecologia 108:583–595.

Choat B, Jansen S, Brodribb TJ, Cochard H, Delzon S, Bhaskar R, Bucci SJ, Feild TS, Gleason SM, Hacke UG, Jacobsen AL, Lens F, Maherali H, Martínez-Vilalta J, Mayr S, Mencuccini M, Mitchell PJ, Nardini A, Pittermann J, Pratt RB, Sperry JS, Westoby M, Wright IJ, Zanne AE (2012) Global convergence in the vulnerability of forests to drought. Nature 491:752–755.

111

Davis SD, Mooney HA (1986) Water use patterns of four co-occurring chaparral shrubs. Oecologia 70:172–177.

Fensham R, Fairfax R (2007) Drought-related tree death of savanna eucalypts: Species susceptibility, soil conditions and root architecture. J Veg Sci 18:71–80.

Fitter AH (1994) Architecture and biomass allocation as components of the plastic response of root systems to soil heterogeneity. In: Caldwell MM, Pearcy RW (eds) Exploitation of environmental heterogeneity by plants: ecophysiological processes above- and belowground. San Diego: Academic Press, pp 305–323

Groom PK (2004) Rooting depth and plant water relations explain species distribution patterns within a sandplain landscape. Funct Plant Biol 31:423–428.

Hamer JJ, Veneklaas EJ, Poot P, Mokany K, Renton M Shallow environmental gradients put inland species at risk: general insights and implications from robust climate change projections for Eucalyptus species in South Western Australia.

Hodge A (2004) The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytol 162:9–24.

Hoffmann AA, Sgro CM (2011) Climate change and evolutionary adaptation. Nature 470:479– 485.

Hooper RJ, Sivasithamparam K (2005) Characterization of damage and biotic factors associated with the decline of Eucalyptus wandoo in southwest Western Australia. Can J For Res 35:2589–2602.

Hughes L (2000) Biological consequences of global warming: is the signal already apparent? Trends Ecol Evol 15:56–61.

Hutchings MJ, John EA, Wijesinghe DK (2003) Toward understanding the consequences of soil heterogeneity for plant populations and communities. Ecology 84:2322–2334.

Jackson RB, Canadell J, Ehleringer JR, Mooney HA, Sala OE, Schulze ED (1996) A global analysis of root distributions for terrestrial biomes. Oecologia 108:389–411.

Jackson RB, Sperry JS, Dawson TE (2000) Root water uptake and transport: using physiological processes in global predictions. Trends Plant Sci 5:482–488.

112

Janos DP, Scott J, Aristizabal C, Bowman DMJS (2013) Arbuscular-mycorrhizal networks inhibit Eucalyptus tetrodonta seedlings in rain forest soil microcosms. Plos One:8.

Kitajima K, Anderson KE, Allen MF (2010) Effect of soil temperature and soil water content on fine root turnover rate in a California mixed conifer ecosystem. J Geophys Res Biogeosciences 115:G04032.

Klausmeyer KR, Shaw MR (2009) Climate change, habitat loss, protected areas and the climate adaptation potential of species in mediterranean ecosystems worldwide. PLoS One 4:e6392.

Marnham J, Morris P (2003) A seamless digital regolith map of Western Australia: a potential resource for mineral exploration and environmental management. Geol Surv Annu Rev 3:27–33.

McCarty JP (2001) Ecological Consequences of Recent Climate Change. Conserv Biol 15:320– 331.

Mitchell PJ, Veneklaas E, Lambers H, Burgess SSO (2009) Partitioning of evapotranspiration in a semi-arid eucalypt woodland in south-western Australia. Agric For Meteorol 149:25–37.

Nevo E (2001) Evolution of genome–phenome diversity under environmental stress. Proc Natl Acad Sci 98:6233–6240.

Nibau C, Gibbs DJ, Coates JC (2008) Branching out in new directions: the control of root architecture by lateral root formation. New Phytol 179:595–614.

Nobel PS (1991) Ecophysiology of roots of desert plants, with special emphasis on agaves and cacti. In: Waisel Y, Eshel A, Kafkafi U (eds) Plant roots: the hidden half. Marcel Dekker, New York, pp 823–844

Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42.

113

Pauli H, Gottfried M, Dullinger S, Abdaladze O, Akhalkatsi M, Alonso JLB, Coldea G, Dick J, Erschbamer B, Calzado RF, Ghosn D, Holten JI, Kanka R, Kazakis G, Kollár J, Larsson P, Moiseev P, Moiseev D, Molau U, Mesa JM, Nagy L, Pelino G, Puşcaş M, Rossi G, Stanisci A, Syverhuset AO, Theurillat J-P, Tomaselli M, Unterluggauer P, Villar L, Vittoz P, Grabherr G (2012) Recent plant diversity changes on Europe’s mountain summits. Science (80- ) 336:353–355.

Peek MS, Leffler AJ, Hipps L, Ivans S, Ryel RJ, Caldwell MM (2006) Root turnover and relocation in the soil profile in response to seasonal soil water variation in a natural stand of Utah juniper (Juniperus osteosperma). Tree Physiol 26:1469–1476.

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2013) nlme: Linear and Nonlinear Mixed Effects Models.

Poot P, Hopper SD, van Diggelen JMH (2012) Exploring rock fissures: does a specialized root morphology explain endemism on granite outcrops? Ann Bot 110:291–300.

Poot P, Lambers H (2003a) Growth responses to waterlogging and drainage of woody Hakea (Proteaceae) seedlings, originating from contrasting habitats in south-western Australia. Plant Soil 253:57–70.

Poot P, Lambers H (2003b) Are trade-offs in allocation pattern and root morphology related to species abundance? A congeneric comparison between rare and common species in the south-western Australian flora. J Ecol 91:58–67.

Poot P, Lambers H (2008) Shallow-soil endemics: adaptive advantages and constraints of a specialized root-system morphology. New Phytol 178:371–381.

R Development Core Team (2014) R: A Language and Environment for Statistical Computing. Vienna, Austria

Renton M, Poot P (2014) Simulation of the evolution of root water foraging strategies in dry and shallow soils. Ann Bot

Schenk HJ, Jackson RB (2002a) Rooting depths, lateral root spreads and below-ground/above- ground allometries of plants in water-limited ecosystems. J Ecol 90:480–494.

114

Schenk HJ, Jackson RB (2005) Mapping the global distribution of deep roots in relation to climate and soil characteristics. Geoderma 126:129–140.

Schenk HJ, Jackson RB (2002b) The global biogeography of roots. Ecol Monogr 72:311–328.

Schiffers K, Tielbörger K, Tietjen B, Jeltsch F (2011) Root plasticity buffers competition among plants: theory meets experimental data. Ecology 92:610–620.

Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675.

Schulze E, Turner N, Nicolle D, Schumacher J (2006) Leaf and wood carbon isotope ratios, specific leaf areas and wood growth of Eucalyptus species across a rainfall gradient in Australia. Tree Physiol 26:479–92.

Schulze ED, Mooney HA, Sala OE, Jobbagy E, Buchmann N, Bauer G, Canadell J, Jackson RB, Loreti J, Oesterheld M, Ehleringer JR (1996) Rooting depth, water availability, and vegetation cover along an aridity gradient in Patagonia. Oecologia 108:503–511.

Schwinning S, Ehleringer JR (2001) Water use trade-offs and optimal adaptations to pulse- driven arid ecosystems. J Ecol 89:464–480.

Tracy SR, Black CR, Roberts JA, Sturrock C, Mairhofer S, Craigon J, Mooney SJ (2012) Quantifying the impact of soil compaction on root system architecture in tomato (Solanum lycopersicum) by X-ray micro-computed tomography. Ann Bot 110:511–519.

Turner NC, Schulze ED, Nicolle D, Kuhlmann I (2010) Growth in two common gardens reveals species by environment interaction in carbon isotope discrimination of Eucalyptus. Tree Physiol 30:741–7.

Walther G-R, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin J-M, Hoegh- Guldberg O, Bairlein F (2002) Ecological responses to recent climate change. Nature 416:389–395.

115

Westoby M, Cornwell WK, Falster DS (2012) An evolutionary attractor model for sapwood cross section in relation to leaf area. J Theor Biol 303:98–109.

Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc 73:3–36.

Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, Navas M-L, Niinemets U, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature 428:821-7.

Xu T, Hutchinson M (2011) ANUCLIM 6.0 User Guide. Australian National University, Fenner School of Environment and Society, Canberra

Zimmer WJ, Grose RJ (1958) Root systems and root/shoot ratios of seedlings of some Victorian Eucalypts. Aust For 22:13–18.

116 Chapter 5

General Discussion

Vulnerability framework This thesis assessed the vulnerability of Eucalyptus species in the south-west of Western Australia to a changing climate. This was approached by studying the distribution of a broad range of species across an aridity gradient, as the current distribution of species (their realised niche) can provide valuable insights into how they have adapted to their environment. Such knowledge can be used to better interpret species distribution models, and thus provide more mechanistic predictions of species' vulnerability to a rapidly changing climate. The discussion presented here is based on the framework laid out by Dawson et al. (2011). In this framework, the vulnerability of a species is determined by the exposure of that species to novel environmental conditions, the species’ sensitivity to such a change and the ability of the species to adapt or respond to the change (Figure 1). Exposure is defined as the rate and magnitude of change to the environment experienced by the species and the total percentage of the distribution that is predicted to experience unfavourable conditions. Sensitivity is the degree to which the persistence and fitness of a species is dependent on the climate. Adaptive capacity is the ability of the species to adapt to the change in environment by either migrating to more suitable regions (i.e. decreasing exposure) or through short (plasticity/acclimatisation) or long term (adaptation) changes to plant structure and function to allow the species to persist in unfavourable conditions (i.e. decreasing sensitivity). Chapter 2 of this thesis focused on quantifying the exposure of Eucalyptus species to a changing climate, while Chapter 3 and 4 studied how species have adapted to their local environmental conditions in terms of the above and below ground traits that contribute to species’ sensitivity to change and their adaptive capacity. The knowledge gained through the assessment of Eucalyptus species’ vulnerability to climate change is then used to identify at-risk species and predict potential future impacts to these species. Such predictions are valuable for the conservation and restoration of south-west West Australian (SWWA) Eucalyptus species, and the biodiversity that rely on them, while also providing valuable insights for Eucalyptus species in general across Australia, making it a good case-study for climate change impacts in general.

Exposure to climate change Species distribution modelling techniques have advanced over the past two decades, with the most recent developments aimed at incorporating migratory ability and species’ sensitivity into the models. Such models aim to predict actual distribution into the future rather than just the

117 exposure of a species’ distribution to climate change. However, to do so requires detailed knowledge – about factors such as biotic interactions, species’ sensitivity and adaptive capacity – which can be difficult to compile for studies focusing on multiple species (Pearson and Dawson 2003; Guisan and Thuiller 2005; Thuiller et al. 2008). In situations where there are large knowledge gaps, it may be more appropriate to study each aspect of vulnerability individually, as in this study, rather than compounding potential inaccuracies into a single model.

Figure 1. The vulnerability of a species as defined by the combination of exposure to a changing climate, the species’ sensitivity to an increasingly arid environment and the species’ adaptive capacity. Figure modified from Dawson et al. (2011).

In Chapter 2 of this thesis (Hamer et al. 2015a), broad scale exposures of 16 SWWA Eucalyptus species to a changing climate were modelled. The model predicted that all species, regardless of distribution size, current climate conditions or geographic location, will suffer from some degree of exposure to unsuitable conditions by the year 2100. In a moderate climate change scenario, the average area exposed to unfavourable climate was 73%. However, there were large differences between species, with the more arid (inland) species predicted to have over 95% of their current distribution exposed to unsuitable conditions in comparison to an average of 53% across all mesic (more coastal) species. Such large decreases in distribution are not uncommon predictions (Thomas et al. 2004; Malcolm et al. 2006; Hamann and Wang 2006; Gómez-Mendoza and Arriaga 2007; Thuiller et al. 2011; Warren et al. 2013; Moritz and Agudo 2013). Studies have predicted that 57% of currently widespread species will have greater than 50% of their distribution exposed to conditions outside of their current climate envelope by the

118 year 2080 (Warren et al. 2013), and between 18 to 35% of all species committed to extinction by 2050 (Thomas et al. 2004).

The findings presented here are similar to previous predictions for SWWA Eucalyptus species (Hughes et al. 1996; Butt et al. 2013). Although predictions for individual species were not presented, Hughes et al. (1996) also identified SWWA Eucalyptus species as being particularly exposed to a changing climate, and suggested that average annual temperature would be the main driving factor. However, the study presented here (Chapter 2) – which uses more recent species distribution modelling techniques – found that water availability was most important for defining a species’ distribution, whereas temperature was not as important as originally expected. Regardless, in both cases, such large exposures of species to unsuitable climates were attributed to small climate envelopes. Though many species in more arid conditions currently have large distributions, this is likely in part due to the shallow environmental gradients (very gradual changes to climate over a long geographic distance) present in the relatively flat landscape of inland SWWA. Due to these shallow gradients, only a small change in climate is required to expose a large portion of the more arid species’ distributions to unsuitable climate, while the same change in more mesic conditions would not have very much effect. Take for example two species, E. longicornis, found in areas with 250 - 450 mm annual precipitation, and E. marginata , with 500 - 1200 mm (Figure 2). In a moderate climate change scenario (see Chapter 2), it is expected that there will be a maximum decrease in annual precipitation along the coast by the year 2100 of roughly 200 mm, in comparison to 100 mm further inland. This 100 mm decrease will expose ~50% of E. longicornis’ climatic envelope, but only ~30% of E. marginata’s envelope is exposed even with the greater 200 mm decrease (Figure 2). This example is simplified in comparison to the actual species distribution model as it only takes into account one variable, with many other variables contributing to a species’ distribution and therefore exposure to climate change (see Chapter 2). To make matters worse for these more arid species, the area most likely to be climatically suitable for arid species in the future is also the most fragmented landscape in SWWA, further reducing these species’ potential distribution and ability to move across the landscape (Renton et al. 2013) and thus further increasing their exposure to future climate change.

Given the high levels of exposure of more arid species to a changing climate, one might expect that it would be these species that show the first signs of stress. This does not appear to be the case, with records of large scale tree health decline of species in the more mesic climates (Brouwers et al. 2012; Matusick et al. 2012; Brouwers et al. 2013; Matusick et al. 2013) and little evidence of stress in more arid species. This is consistent with Choat et al’s (2012) finding that plant safety margins are largely independent of annual precipitation. As discussed in Brouwers et al. (2013), the patchy decline observed in these mesic species is likely due to local

119

Figure 2. Distribution of a coastal (mesic) and inland (more arid) Eucalyptus species and the potential exposure of their climate envelope to a predicted 200 mm and 100 mm decline in annual precipitation respectively (based on a moderate climate change scenario). The top panel shows the current distribution of the mesic species E. marginata (×) and the more arid species E. longicornis () with respect to annual precipitation. The bottom panel shows hypothetical climate envelopes for E. marginata (blue) and E. longicornis (red) along the annual precipitation gradient. The shaded sections show how much the predicted decrease in annual precipitation will expose the current distributions to unfavourable climate conditions. water availability, with species or populations on tops of hills (further away from the water- table), or on slopes (lower water retention in soil) and those near rock outcrops (shallower soils) more stressed. Although the species distribution models created in this study attempt to take these factors into consideration by including variables such as depth to bedrock, soil texture and elevation, they were not able to predict such local patches of decline. This is because microhabitat conditions and environmental buffering require a much higher resolution of data than is appropriate for use, given the very coarse (1°) resolution of global climate model predictions (Araújo and Guisan 2006; Dormann 2007; Austin and Van Niel 2011; Wisz et al. 2013) and the relative inaccuracy of herbarium GPS records (Visscher 2006; Miller et al. 2007). Due to such small scale variation in climate and soils, landscapes are likely to have both habitats that are less benign and habitats that are more benign than the larger-scale average conditions.

120 As a consequence, models may either underestimate or overestimate impacts (Dormann 2007; Araújo and Peterson 2012). Regardless, such fine scale predictions of exposure alone do not explain the lack of stress observed in more arid conditions.

Sensitivity A species is as sensitive to change as the most sensitive part of its life history. This is likely during seedling establishment for Eucalyptus species. Juvenile Eucalyptus seedlings are generally only successful in establishing after periods of disturbance which causes adult trees to die or lose their canopy (Wellington and Noble 1985a; Wellington and Noble 1985b; Yates et al. 1993; Gill 1997). This is because adult eucalypts have the competitive advantage due to expansive root system and the dominance of the overstorey. In an increasingly arid environment these disturbances can be triggered by drought-induced tree mortality and canopy opening. Unlike fire, after which Eucalyptus trees typically re-sprout (Gill 1997; Wardell-Johnson 2000), drought can kill trees, leaving gaps for juveniles to establish. However, in a changing climate post-drought wet periods may become less frequent – limiting the potential recruitment events – and shorter – reducing the potential period of recovery and the amount of water available to plants in the next dry period. Thus, the ability of juveniles to establish in increasingly arid conditions and survive through drought is important in determining the sensitivity of a population.

As suggested in Chapter 4 of this thesis (Hamer et al. 2015b), different root architectures of juvenile Eucalyptus species may have evolved to survive in different combinations of soil texture and annual precipitation (although these claims are made with caution, given the low replication in the study). Species typically growing in more mesic climates in a coarser soil tended to invest in a greater proportion of lateral roots which explore a large volume of soil (Figure 3a). Even though water availability is higher, coarser-textured soils typical of these climates do not hold as much water (Saxton et al. 1986), thus more roots are required to explore a larger volume of soil to access sufficient water. The abundance of roots in the topsoil also would allow the species to access a larger pool of nutrients, which, in a well-watered soil, may increase growth rates, both below and above ground. In comparison, species in more arid conditions tended to invest the majority of their resources into producing a taproot (Figure 3b), with presumably fewer lateral roots being required as a consequence of the greater water- holding capacity of finer textured soils (Saxton et al. 1986). The goal of this strategy is likely to reach as deep as possible in a short time, as water availability during the summer months is typically greater deeper in the soil profile. This deep rooted architecture can be observed in many adults trees living in semi-arid arid conditions around the world (Canadell et al. 1996). If the climate would become more arid, species currently living in more mesic conditions may be more sensitive to the decrease in water due to their tendency to invest heavily in lateral roots

121 near the surface (where water availability is lowest during summer), rather than prioritising taproot growth to reach greater depths.

Figure 3. Artistic impressions of the two dimensional root architecture for two typical Eucalyptus seedlings. a) depicts E. gomphocephala, a mesic species typically growing in a coarse-textured soil while b) depicts E. salmonophloia, a more arid species that typically grows in a fine-textured soil. Note the use of lateral roots by E. gomphocephala to explore the whole region of the rhizotron, in comparison to E. salmonophloia which invested most of its energy into the taproot, producing few lateral roots only in the topsoil and further down in the profile.

For adult Eucalyptus trees, sensitivity to an increasingly arid climate is in part related to the ability to access water held at more negative water potentials in the soil, and to become more water use efficient. It is known that plants, regardless of local climate conditions, operate near the point of catastrophic xylem dysfunction – the point when cavitation in xylem vessels causes increased tension in other vessels, generating embolisms in a runaway cycle – in order to maximise growth (Tyree and Sperry 1988; Choat et al. 2012). For this thesis I aimed to study a wide range of Eucalyptus species in a common garden to determine intrinsic differences in turgor loss points and predawn water potentials. Unfortunately, due to the extended period of time required to establish these plants and unexpected variability in local environmental conditions at the time of sampling, the results of this experiment could not be included in this thesis. However, field osmotic potentials gathered from 15 species across an aridity gradient (Chapter 3) still provided valuable insights into how species are acclimatised to environmental conditions. Across all species, trees occurring on sites in more arid conditions had a more negative osmotic potential at full hydration, indicating a lower turgor loss point for these trees (Barlett et al. 2012). This allows the plant to access soil-bound water at more negative water

122 potentials. This trend was also observed within species, suggesting either acclimatisation within species to the different climate conditions, or plasticity within a species. Unlike other studies of Eucalyptus species (Myers and Neales 1986; Fan et al. 1994; White et al. 1996), there was no osmotic adjustment observed between seasons across the aridity gradient in our study. This may suggest that winter precipitation sufficiently refilled soil-bound water reserves to allow the plants to operate without the need for even lower water potentials in the drier summer. However, in an increasingly arid climate where winter recharge of water reserves may decrease, future reductions in osmotic potential (becoming more negative), whether through osmotic adjustment or through generating constitutively lower values, may be required for survival, especially during summer periods (Myers and Neales 1986; White et al. 2000).

In addition to the changes in osmotic potential, species are also able to increase their water use efficiency through the regulation of stomatal opening. Previous studies on SWWA Eucalyptus species have shown that species in more arid conditions tend to be more water use efficient, as judged by increases to leaf δ13C (Schulze et al. 2006; Turner et al. 2008; Turner et al. 2010). We observed the same tendency in our study (Chapter 3). This suggests that these more arid species operate at a significantly lower conductance for greater portions of the day or of the year to avoid water loss during periods when water loss is most likely to cause hydraulic failure. This lower operating conductance is particularly important in the more arid regions as a higher vapour pressure deficit results in a greater amount of water lost for a given stomatal conductance, in comparison to mesic climates. This explains how Eucalyptus species in more arid conditions have both higher δ13C while still transpiring at a greater rate than species in a less stressful environment (Burgess 2006).

While a higher stomatal conductance is generally associated with higher density, smaller stomata allow plants to quickly regulate stomatal aperture (Drake et al. 2013). This combination of traits has also been observed in a number of species living in water-limited environments around the world (Schulze et al. 1980; Tenhunen et al. 1981; Tenhunen et al. 1984; Zhang et al. 2013). The ability to rapidly regulate stomatal conductance would be most beneficial in climates where short-lived periods of reduced transpiration allow for increased conductance and thus photosynthesis, leading to increased water use efficiency. However, the opposite trend was observed in the species studied here (Chapter 3), with larger stomata at lower densities in more arid climates both within and among species. This suggests that rapid stomatal regulation may be less important to these species than for other arid species, potentially due to the ability of many of these species to access ground-water during the summer periods, allowing them to be less conservative with transpiration, as seen in Burgess (2006). However, this strategy may make juvenile plants in these conditions – which have not yet developed a large enough root system to access ground-water – more sensitive to climate change due to the limited recharging of soil water during winter.

123 Adaptive capacity Species’ with good migratory abilities can track their environmental envelope, decreasing their sensitivity to local climate conditions and avoiding the need to adapt or acclimatise to the changing climate. This is of particular importance for Eucalyptus species that are predicted to have a very high degree of exposure to a changing climate, such as those in more arid conditions as examined in this study. However, other studies have shown that the migratory ability of tree species is particularly poor compared to other perennial and annual plants (Renton et al. 2013), especially in fragmented landscapes such as that of inland SWWA (Renton et al. 2012). This is due to the long time to maturity of trees (including Eucalyptus species) and their lack of specialised seed dispersal mechanisms (Cremer 1977; Yates et al. 1995; Byrne et al. 2008; Rejmánek and Richardson 2011). Even if the landscape was not fragmented, the climate is changing at such a rapid rate that tree species such as eucalypts would still be unable to track their climate envelope without assistance (Renton et al. 2013).

If Eucalyptus species were able to track their climate envelope to new locations – with or without human assistance – they may still encounter novel environmental conditions in the form of different soil textures. As previously discussed, drying conditions may have different consequences for water availability, as it is dependent on soil texture which may influence optimal root architecture (Chapter 3). Likewise, if species would germinate in a habitat with a different soil type than they are adapted to, root architecture may also limit seedling recruitment success. This limitation would likely be most relevant to more arid species further inland, most of which are adapted to a fine-textured soil, unlike the coarser soils on the coastal plain. In this case, proportionally lower investment in lateral roots in this coarser-textured soil may depress the growth rates of seedlings of these more inland species, making them less competitive compared to locally adapted more mesic species (Eucalyptus or other). Furthermore, the investment in mechanisms contributing to their drought tolerance may hamper plant productivity (and thus competitive ability) on these more coastal habitats as their ability to generate more negative water potentials will provide little reward, because most water is loosely bound in coarser-textured soils (Saxton et al. 1986). Thus, if assisted migration is to be considered as an option to conserve particularly exposed Eucalyptus species (Lunt et al. 2013), soil texture should be taken into account when choosing new habitat locations.

Given the poor dispersal ability of Eucalyptus species (Cremer 1966; Cremer 1977; Byrne et al. 2008; Rejmánek and Richardson 2011), it appears that the continued survival of these species may require persistence in areas considered unfavourable in future climate change scenarios (Byrne et al. 2013). In current conditions, periods of high stress that result in tree health decline (such as partial canopy collapse) are often followed by periods during which populations can recover to some extent before the next period of stress. However, in future years as the climate continues to change, these periods of respite will likely become less frequent and shorter in

124 length, giving species less time to recover. Therefore, a species’ capacity to adapt (either through phenotypic plasticity or genetic adaptation) may be vital for their continued survival.

In Chapter 4 of this thesis, the importance of root system plasticity and seedling root architecture was discussed. The observed differences in architecture between species in a common environment is likely a reflection of adaptation of the root system to the local climate and the soil profile conditions within which species typically grow. However, the large variation in root architecture that was observed even within species when grown in a homogeneous environment suggests that root system variability may be an important aspect of a species’ ecology, presumably enabling individuals to be successful in a range of local soil conditions. Thus, although I was able to identify differences suggesting adaption to a particular climate or soil type, the genetic diversity and/or plasticity present may provide evolutionary potential for a larger range of conditions. If root architecture is to adapt to an increasingly arid climate, the resulting architecture would likely feature fewer lateral roots in the topsoil within which moisture retention is lowest, as was observed in Eucalyptus species from more arid conditions (Chapter 4) and other arid species from around the world (Nobel 1991).

Chapter 3 presented how traits governing species’ sensitivity to change have acclimatised across an aridity gradient between species and also identified traits that vary within a species across an environmental gradient through plasticity or adaptation. These changes occurred in the same direction within species as they did between species, indicating a common strategy to survive increasingly arid conditions. Of the traits that varied within species, some have previously been shown to be plastic (McLean et al. 2014), while others are known to vary within a canopy of a single plant in response to light conditions (Sack et al. 2006). These plastic traits include: leaf size, leaf mass per area, leaf area to sapwood area ratio and stomatal size and density. It should thus only take the production of new branches to alter these traits (dependent on the level of phenotypic plasticity), increasing water use efficiency and reducing sensitivity to the changing climate. As more arid species have, to date, been successful with larger stomata, we may not expect to see much of a change in this trait. Under current climatic conditions it appears that these species are able to remain transpiring throughout the day at, on average, a much lower operating conductance. This is presumably because there is enough soil-bound water stored in the typically finer-textured soil after winter precipitation to support species which can reach the negative water potentials required to obtain it (Burgess 2006). We might only expect to see a shift to smaller stomata if plants were rarely able to reach the larger maximum conductance in future climate conditions, thus making smaller stomata potentially more cost effective.

However, if stomata size were to change in the future, they would likely become smaller due to the benefits of quick stomatal regulation increasing water use efficiency. There is not as much evidence for other traits, such as xylem vessel diameter and density, to change over a short

125 period of time. However, that they were observed to vary both between and within species in this study and in others (Hacke et al. 2001; Tyree and Zimmermann 2002; Hacke et al. 2006) indicates that these traits are plastic and evolve over time..

Given the presence of Eucalyptus species in the most arid regions of Australia (The Atlas of Living Australia 2015; Western Australian Herbarium 2015), it is likely that most species have the ability to acclimatise to more arid conditions, given enough time. Although the change in dominance from Eucalyptus to Acacia below 250 mm mean annual rainfall (Martin 2006; Department of the Environment and Water Resources 2007) is thought to be related to the inability of Eucalyptus species to survive more arid conditions (Barlow 1981; Johnson and Burrows 1981; Pryor and Johnson 1981), evidence suggests that it may be more related to the sensitivity of eucalypts to colder minimum winter temperatures (Bowman and Connors 1996; Choat et al. 2011). Given that temperatures are predicted to increase in the majority of climate change scenarios in the coming century (IPCC 2014), one may expect to observe an increase in Eucalyptus species’ distribution, assuming they can acclimatise to reduced precipitation. However, knowledge of historical distributions suggest that species tend to contract to refugia rather than adapt to changing climate conditions (Wardell-Johnson and Coates 1996; Byrne 2008; Dalmaris et al. 2015). Furthermore, due to the long time to maturity of Eucalyptus species, the reliance on disturbance for recruitment to be successful, and with high levels of fragmentation (thus population isolation), it seems unlikely that successful acclimatisation will have time to occur in a rapidly changing climate.

Vulnerability and Conclusion By assessing the potential exposure of Eucalyptus species in the SWWA to a changing climate, as well as studying how traits relating to species’ sensitivity and adaptive capacity vary across an aridity gradient, our understanding of the species’ vulnerability to climate change has been improved. It is well known that species operate near catastrophic hydraulic failure, regardless of the climate conditions (Tyree and Sperry 1988; Choat et al. 2012). With a predicted decrease in winter precipitation across the SWWA, soil recharge will decrease, resulting in a faster depletion of soil water during spring and early summer. This is likely to result in a reduction of annual growth and would require plants to reduce stomatal conductance for longer during the peak of summer to avoid hydraulic failure. However, this puts the plants at risk of carbon starvation due to the limited gas exchange. This may eventually lead to an increase in canopy decline and eventually mortality.

Although current literature suggests that it is the mesic species along the coast of SWWA that are suffering the most from a changing climate (Hooper and Sivasithamparam 2005; Cai et al. 2010; Brouwers et al. 2012; Matusick et al. 2012; Matusick et al. 2013), the research presented

126 in this thesis suggests that the inland, more arid, species are more vulnerable. Not only will these species be most exposed to unfavourable conditions due to shallow climate gradients (Chapter 2), but models predict that there will be very limited areas that will have suitable climate remaining by the year 2100, in part due to the extensive fragmentation of the inland SWWA landscape (Chapter 2). Even if these species are able to naturally migrate, root architectures that have evolved in fine-textured soils may make it difficult for them to colonise the coarser-textured soils (Chapter 4) which are more common closer to the coast, to which these species would likely need to migrate (Chapter 2). Although these more arid species are currently well equipped to survive in summer conditions (Chapter 3), large stomata which cannot be quickly regulated may make the species more sensitive in the future.

In comparison to more arid species, mesic species along the coast appear less vulnerable to a changing climate. Although current observations suggest that some mesic species may be relatively more sensitive to the changing climate than others (e.g. E. marginata, E. wandoo and E. gomphocephala; Brouwers et al. 2012; Brouwers et al. 2013; Matusick et al. 2013; Matusick et al. 2012; Cai et al. 2010; Hooper & Sivasithamparam 2005; Poot & Veneklaas 2013), large environmental envelopes mean that most species (with the exception of E. diversicolor and E. ja cksonii) will have areas that they can contract to, due to proportionally lower levels of exposure along the coast where environmental gradients are steeper (Chapter 2). In other mesic ecosystems around the world, species have already starting migrating to higher elevations in response to a drying climate (Pauli et al. 1996; Davis and Shaw 2001; Walther et al. 2005; Wilson et al. 2005; Pauli et al. 2007; Gehrig-Fasel et al. 2007; Pauli et al. 2012). Unfortunately for SWWA species, the elevation gradient is not large enough to provide sufficient refugia, given the degree to which climate is predicted to change. Rather, species’ climate envelopes appear to be shifting in a southerly direction, towards the pole. Changes to precipitation in these more southern areas of refuge may still require the species to acclimatise to change. However, given that these areas are already within the species’ current distribution, genetic exchange with populations relatively nearby– which are already more adapted to slightly more arid conditions – (and thus adaption) is more likely (Chapter 3). The most effective long term conservation of these species may thus be the seeding of known areas of refuge (on the more mesic end of the distribution) with seed from populations from the more arid end of the distribution to aid evolutionary change and adaptation (Aitken and Whitlock 2013; McLean et al. 2014). This same strategy can be employed when restoring habitats after mining leases or other disturbances to ensure the long term survival of the restored areas. However, local adaptations to different abiotic conditions and biotic interactions and the possibility of outbreeding depression should also be considered with this approach (Chapter 4).

The actual exposure to, and thus impact of, climate change on both coastal and inland species will depend on the degree and rate at which the climate in the SWWA changes. This, in turn,

127 depends on how quickly humans respond to this global threat by reducing greenhouse gas emissions and increasing carbon sequestration in sinks such as forests. It is possible to slow down climate change if global action is taken, with this being the most effective strategy to minimise the exposure (and thus threat) of not only Eucalyptus species in the SWWA and the biodiversity that depends on them, but to flora and fauna around the world.

Future work The research presented in this thesis is an important contribution towards understanding the vulnerability of Eucalyptus species to a changing climate, and it has also laid the foundation for future research. Chapter 4 of this thesis demonstrated that juvenile plants of species along an aridity gradient have quantifiably different root architectures, which seem to be adapted to the soil texture and water availability at the places where the species typically grow. The next step in this avenue of research would be to determine whether architecture varies within species along the gradient, and to quantify how plastic they are to differing soil textures and water availability. Chapter 3 also opened new avenues of research with the finding that, unlike other species growing in a water-limited environment, Eucalyptus species in more arid conditions tend to have larger stomata in a lower density. Future research would be to determine why this is the case, by analysing the phylogeny of SWWA eucalypts in relation to stomatal size and density. It would also be important to determine seasonal operating stomatal conductance and xylem conductivity and relate this to the calculated maximal capacities reported here to better understand how these species have adapted or acclimatised to their climate, and how they relate to the commonly measured leaf area to sapwood area ratio. More work pairing data presented here with detailed ecophysiological measurements (such as pre-dawn and mid-day osmotic potentials and xylem vulnerability curves) would be beneficial to a better understanding of eucalyptus sensitivity. The information gathered in this thesis and in future research can contribute to better models of future species’ distribution. A model which incorporates a mechanistic water balance component spatially over a landscape would allow predictions to go beyond predicting just exposure to climate change, allowing for the inclusion of population sensitivity to a changing climate into the future distribution predictions. This future work would build on our understanding and help conserve the iconic Eucalyptus gum trees in the south-west Australian biodiversity hotspot.

128 References

Aitken SN, Whitlock MC (2013) Assisted gene flow to facilitate local adaptation to climate change. Annu Rev Ecol Evol Syst 44:367–388.

Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688.

Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539.

Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8.

Barlow BA (1981) The Australian flora: its origins and evolution. In: Barlow BA, Clifford HT, George A., Kanis A, McCusker A (eds) Flora of Australia Volume 1 - Introduction, 1st edn. Bureau of Flora and Fauna, Australian Government Publishing Service, Canberra, pp 25–75

Bowman DMJS, Connors GT (1996) Does low temperature cause the dominance of Acacia on the central Australian mountains? Evidence from a latitudinal gradient from 11° to 26° South in the Northern Territory, Australia. J Biogeogr 23:245–256.

Brouwers NC, Mercer J, Lyons T, Poot P, Veneklaas E, Hardy G (2012) Climate and landscape drivers of tree decline in a Mediterranean ecoregion. Ecol Evol 3:67–79.

Butt N, Pollock LJ, McAlpine DR (2013) Eucalypts face increasing climate stress. Ecol Evol 3:5011-5022

Byrne M (2008) Evidence for multiple refugia at different time scales during Pleistocene climatic oscillations in southern Australia inferred from phylogeography. Quat Sci Rev 27:2576–2585.

Byrne M, Prober S, McLean L, Steane D, Stock W, Potts B, Vaillancourt R (2013) Adaptation to climate in widespread Eucalypt species. Gold Cost

129 Canadell J, Jackson RB, Ehleringer J, Mooney HA, Sala OE, Schulze ED (1996) Maximum rooting depth of vegetation types at the global scale. Oecologia 108:583–595.

Choat B, Medek DE, Stuart SA, Pasquet-Kok J, Egerton JJG, Salari H, Sack L, Ball MC (2011) Xylem traits mediate a trade-off between resistance to freeze–thaw-induced embolism and photosynthetic capacity in overwintering evergreens. New Phytol 191:996–1005.

Cremer K (1977) Distance of seed dispersal in eucalypts estimated from seed weights. Aust For Res 7:225–228.

Cremer K (1966) Dissemination of seed from Eucalyptus regnans. Aust For 30:33–37.

Davis MB, Shaw RG (2001) Range shifts and adaptive responses to quaternary climate change. Science 292:673–679.

Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conservation in a changing climate. Science 332:53–8.

Department of the Environment and Water Resources (2007) Australia’s major vegetation groups, 2007. Canberra, ACT

Dormann CF (2007) Promising the future? Global change projections of species distributions. Basic Appl Ecol 8:387–397.

Drake PL, Froend RH, Franks PJ (2013) Smaller, faster stomata: scaling of stomatal size, rate of response, and stomatal conductance. J Exp Bot 64:495–505.

Fan S, Blake TJ, Blumwald E (1994) The relative contribution of elastic and osmotic adjustments to turgor maintenance of woody species. Physiol Plant 90:408–413.

Gehrig-Fasel J, Guisan A, Zimmermann NE (2007) Tree line shifts in the Swiss Alps: climate change or land abandonment? J Veg Sci 18:571–582.

Gill M (1997) Eucalypts and fires: interdependent or independent. In: Williams J, Woinarski J (eds) Eucalypt Ecology: Individuals to Ecosystms. Cambridge University Press, New York, pp 151–167

Gómez-Mendoza L, Arriaga L (2007) Modeling the effect of climate change on the distribution of oak and pine species of Mexico. Conserv Biol 21:1545–1555.

Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009.

130 Hacke UG, Sperry JS, Pockman WT, Davis SD, McCulloh KA (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461.

Hacke UG, Sperry JS, Wheeler JK, Castro L (2006) Scaling of angiosperm xylem structure with safety and efficiency. Tree Physiol 26:689–701.

Hamann A, Wang T (2006) Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87:2773–2786.

Hooper RJ, Sivasithamparam K (2005) Characterization of damage and biotic factors associated with the decline of Eucalyptus wandoo in southwest Western Australia. Can J For Res 35:2589–2602.

Hughes L, Cawsey EM, Westoby M (1996) Climatic range sizes of Eucalyptus species in relation to future climate change. Glob Ecol Biogeogr Lett 5:23–29.

IPCC (2014) Summary for Policymakers. In: The Core Writing Team, Pachauri RK, Meyer L (eds) Climate Change 2014 Synthesis Report. IPCC, Geneva, Switzerland, pp 2–26

Johnson RW, Burrows WH (1981) Acacia open forests, woodlands and shrublands. In: Groves RH (ed) Australian vegetation. Cambridge University Press, Cambridge, pp 198–226

Lunt ID, Byrne M, Hellmann JJ, Mitchell NJ, Garnett ST, Hayward MW, Martin TG, McDonald-Maddden E, Williams SE, Zander KK (2013) Using assisted colonisation to conserve biodiversity and restore ecosystem function under climate change. Biol Conserv 157:172–177.

Malcolm J, Liu C, Neilson R, Hansen L, Hannah L (2006) Global warming and extinctions of endemic species from biodiversity hotspots. Conserv Biol 20:538–548.

Martin HA (2006) Cenozoic climatic change and the development of the arid vegetation in Australia. J Arid Environ 66:533–563.

Matusick G, Ruthrof KX, Hardy J (2012) Drought and heat triggers sudden and severe dieback in a dominant Mediterranean-type woodland species. Open J For 2:183–186.

131 Miller BP, Enright NJ, Lamont BB (2007) Record error and range contraction, real and imagined, in the restricted shrub Banksia hookeriana in south-western Australia. Divers Distrib 13:406–417.

Moritz C, Agudo R (2013) The future of species under climate change: resilience or decline? Science 341:504–508.

Myers BA, Neales TF (1986) Osmotic adjustment, induced by drought, in seedlings of three Eucalyptus species. Funct Plant Biol 13:597–603.

Pauli H, Gottfried M, Grabherr G (1996) Effects of climate change on mountain ecosystems – upward shifting of alpine plants. World Resour Rev 8:382–390.

Pauli H, Gottfried M, Reiter K, Klettner C, Grabherr G (2007) Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994–2004) at the GLORIA* master site Schrankogel, Tyrol, Austria. Glob Chang Biol 13:147–156.

Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371.

Pryor L., Johnson LAS (1981) Eucalyptus, the universal Australia. In: Keast A (ed) Ecological biogeography of Australia. Springer Netherlands, Netherlands, pp 501–536

Renton M, Shackelford N, Standish RJ (2012) Habitat restoration will help some functional plant types persist under climate change in fragmented landscapes. Glob Chang Biol 18:2057–2070.

Sack L, Melcher PJ, Liu WH, Middleton E, Pardee T (2006) How strong is intracanopy leaf plasticity in temperate deciduous trees? Am J Bot 93:829–839.

Saxton KE, Rawls WJ, Romberger JS, Papendick RI (1986) Estimating generalized soil-water characteristics from texture. 1031–1036.

132 Schulze E, Turner N, Nicolle D, Schumacher J (2006) Species differences in carbon isotope ratios, specific leaf area and nitrogen concentrations in leaves of Eucalyptus growing in a common garden compared with along an aridity gradient. Physiol Plant 127:434–444.

Tenhunen JD, Lange OL, Braun M (1981) Midday stomatal closure in Mediterranean type sclerophylls under simulated habitat conditions in an environmental chamber. Oecologia 50:5–11.

Tenhunen JD, Lange OL, Gebel J, Beyschlag W, Weber JA (1984) Changes in photosynthetic capacity, carboxylation efficiency, and CO2 compensation point associated with midday stomatal closure and midday depression of net CO2 exchange of leaves of Quercus suber. Planta 162:193–203.

The Atlas of Living Australia (2015) Eucalyptus. http://bie.ala.org.au/species/Eucalyptus. Accessed 17 Jun 2015

Thuiller W, Lavergne S, Roquet C, Boulangeat I, Lafourcade B, Araujo MB (2011) Consequences of climate change on the tree of life in Europe. Nature 470:531–534.

Turner NC, Schulze ED, Nicolle D, Kuhlmann I (2010) Growth in two common gardens reveals species by environment interaction in carbon isotope discrimination of Eucalyptus. Tree Physiol 30:741–7.

Turner NC, Schulze E-D, Nicolle D, Schumacher J, Kuhlmann I (2008) Annual rainfall does not directly determine the carbon isotope ratio of leaves of Eucalyptus species. Physiol Plant 132:440–5.

Tyree MT, Sperry JS (1988) Do woody plants operate near the point of catastrophic xylem dysfunction caused by dynamic water stress? Anwers from a model. Plant Physiol 88:574– 580.

Tyree MT, Zimmermann MH (2002) Xylem Structure and the Ascent of Sap. Springer, New York, NY.

Visscher DR (2006) GPS measurement error and resource selection functions in a fragmented landscape. Ecography (Cop) 29:458–464.

Walther G-R, Beißner S, Burga CA (2005) Trends in the upward shift of alpine plants. J Veg Sci 16:541–548.

Wardell-Johnson G, Coates D (1996) Links to the past: local endemism in four species of forest eucalypts in southwestern Australia. In: Hopper SD, Chappill J, Harvey M, George A (eds)

133 Gondwanan heritage: Past, present and future of the Western Australian Biota. Surrey Beatty and Sons, Chipping Norton, pp 137–154

Wardell-Johnson GW (2000) Responses of forest eucalypts to moderate and high intensity fire in the Tingle Mosaic, south-western Australia: comparisons between locally endemic and regionally distributed species. Austral Ecol 25:409–421.

Warren R, VanDerWal J, Price J, Welbergen JA, Atkinson I, Ramirez-Villegas J, Osborn TJ, Jarvis A, Shoo LP, Williams SE, Lowe J (2013) Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat Clim Chang 3:678–682.

Wellington AB, Noble IR (1985a) Post-fire recruitment and mortality in a population of the mallee Eucalyptus incrassata in semi-arid, south-eastern Australia. J Ecol 73:645–656.

Wellington AB, Noble IR (1985b) Seed dynamics and factors limiting recruitment of the mallee Eucalyptus incrassata in semi-arid, south-eastern Australia. J Ecol 73:657–666.

Western Australian Herbarium (2015) Florabase - The Western Australia Flora. https://florabase.dpaw.wa.gov.au. Accessed 2 Jul 2015

White DA, Beadle CL, Worledge D (1996) Leaf water relations of Eucalyptus globulus ssp. globulus and E. nitens: seasonal, drought and species effects. Tree Physiol 16 :469–476.

White DA, Turner NC, Galbraith JH (2000) Leaf water relations and stomatal behavior of four allopatric Eucalyptus species planted in Mediterranean southwestern Australia. Tree Physiol 20:1157–1165.

Wilson RJ, Gutiérrez D, Gutiérrez J, Martínez D, Agudo R, Monserrat VJ (2005) Changes to the elevational limits and extent of species ranges associated with climate change. Ecol Lett 8:1138–1146.

Yates C, Hobbs R, Bell R (1993) Landscape-scale disturbances and regeneration in semi-arid woodlands of southwestern Australia. Pacific Conserv Biol 1:214–221.

Yates CJ, Taplin R, Hobbs RJ, Bell RW (1995) Factors limiting the recruitment of Eucalyptus salmonophloia in remnant woodlands .II. Post-dispersal seed predation and soil seed reserves. Aust J Bot 43:145–155.

134