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The effect of disturbance on plant rarity and ecosystem function

John Wallace Patykowski

B.Env.Sci. (hons)

Submitted in fulfilment of the requirements

for the degree of Doctor of Philosophy

Deakin University

February 2018

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Preface

This thesis is a compilation of my own work. I designed the method for the study

under guidance from my supervisors Dr Maria Gibson, Dr Matt Dell and Dr Tricia

Wevill. Dr Greg Holland and Professor Andrew Bennett provided floristic data for

this project; for all other data, I designed and conducted all fieldwork, and collected

and organised all data. I designed and undertook all analysis associated with this

research, and wrote and revised all thesis chapters. Each data chapter in the thesis

was written as a manuscript for publication; as such, each chapter is self-contained

and some repetition occurs among chapters, particularly within methods. Due to

Deakin University requirements, all references occur at the end of the thesis, rather

than at the conclusion of each chapter. All data chapters have been submitted to

scientific journals; two (Chapters 2 and 4) have been accepted, and two are under

review. Each manuscript was co-authored by my supervisory panel, as well as others

specified below, and they have contributed to the ideas presented in each.

Chapter 2: Patykowski J, Holland GJ, Dell M, Wevill T, Callister K, Bennett AF,

Gibson M (2018) The effect of prescribed burning on plant rarity in a

temperate forest. Ecology and Evolution. DOI: 10.1002/ece3.3771

Chapter 3: Patykowski J, Dell M, Wevill T, Gibson M (2017) When functional

diversity is not driven by rare species: a case study in a temperate

woodland. Manuscript submitted for publication.

Chapter 4: Patykowski J, Dell M, Wevill T, Gibson M (In Press) Rarity and

nutrient acquisition relationships before and after prescribed burning

in an Australian box-ironbark forest. AoB Plants.

Chapter 5: Patykowski J, Dell M, Wevill T, Gibson M (2017) Seasonal

physiological patterns among sympatric trees during water-deficit,

and implications of climate change. Manuscript submitted for

publication.

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Acknowledgements

Thank you to my supervisors Dr Maria Gibson, Dr Matt Dell, and Dr Tricia Wevill

for their tireless efforts over the past few years. Furthermore, thank you to Professor

Andrew Bennett and Dr Shaun Cunningham (deceased), both of whom were on my

supervisory panel at various stages and made valuable contributions to the project. I

would also like to acknowledge the contributions of Dr Greg Holland, Professor

Michael Clarke, and the team involved with the Box-Ironbark Experimental Mosaic

Burning Project, who provided pre- and post-fire floristic data for this study.

Thank you to all family and friends who contributed their support along the way, in

particular my parents, Celine Yap, and all past and present post-graduate students

whom I have had the pleasure of working alongside and sharing an office with over

the past few years. I also would like to thank the Deakin technical staff, in particular

Linda Moon, who always was willing to provide assistance and encouragement.

Thank you to all the volunteers and field assistants who contributed to this project

along the way.

Funding was provided by Deakin University, Deakin University Centre for

Integrative Ecology, and generous grants from the Holsworth Wildlife Research

Endowment Fund, and the Victorian Environmental Assessment Council. This

research was conducted under Department of Environment and Primary Industries

Scientific Research Permit No. 10006826. This study also used data collected as part

of the Box-Ironbark Experimental Mosaic Burning Project, which was funded by the

Australian State Government of Victoria’s Department of Environment, Land, Water

and Planning (North West Region and Project Hawkeye), and Parks Victoria. This

research was conducted under Department of Sustainability and Environment

Scientific Research Permit No. 10005470 (2010), and Department of Environment

and Primary Industries Scientific Research Permit No. 10007003 (2013).

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Contents

Access to thesis i Candidate declaration ii Preface iii Acknowledgements iv List of tables viii List of figures xi Abstract 1

1. Introduction 4

1.1 Defining plant rarity 5 1.2 The functional role of rare species 6 1.3 The effect of disturbance on rare species 9 1.4 Box-ironbark forests 13

1.4.1 Location, geology, and climate 13 1.4.2 Vegetation 14 1.4.3 Disturbance history 14 1.4.4 Nutrient cycling 16

1.5 Thesis aims and structure 18 2. The effect of prescribed burning on plant rarity 21

in a temperate forest 2.1 Introduction 21 2.2 Materials and methods 24 2.2.1 Study area 24

2.2.2 Experimental prescribed burns 26 2.2.3 Pre- and post-burn floristic surveys 28 2.2.4 Categorising species frequency of occurrence 29 2.2.5 Data analysis 29

2.3 Results 32 2.3.1 Effects of prescribed burns on floristic diversity 32 2.3.2 Effects of prescribed burns on floristic 34

composition 2.3.3 Contribution of species life-form and frequency 40

groups to floristic composition 2.4 Discussion 45 2.5 Supporting information for chapter two 50

vi

Contents (contd.)

3. When functional diversity is not driven by rare species: 65

a case study in a temperate woodland 3.1 Introduction 65 3.2 Materials and methods 69

3.2.1 Study site 69 3.2.2 Experimental prescribed burns 70 3.2.3 Measuring species rarity 70 3.2.4 Species functional traits 71 3.2.5 Data analysis 73

3.3 Results 76

3.4 Discussion 84

3.4.1 Effect of prescribed burning on functional 84 diversity

3.4.2 Effect of species loss on functional diversity 86 3.4.3 Limitations and considerations 87

3.5 Conclusion 88 3.6 Supporting information for chapter three 89

4. Rarity and nutrient acquisition relationships before and 91

after prescribed burning in an Australian box-ironbark forest 4.1 Introduction 91 4.2 Materials and methods 94

4.2.1 Study area 94 4.2.2 Species frequency data 95 4.2.3 Leaf collections 97 4.2.4 Soil sampling 98 4.2.5 Nutrient concentration 98 4.2.6 Data analysis 99

4.3 Results 100 4.3.1 Uniqueness of leaf nutrient profiles and rarity 100 4.3.2 Nutrient acquisition strategies and leaf 103 nutrient profiles 4.3.3 Soil nutrients and fire 110

4.4 Discussion 113 4.4.1 Uniqueness of leaf nutrient profiles and rarity 113 4.4.2 Nutrient acquisition strategies and leaf 114 nutrient profiles 4.4.3 Soil nutrients and fire 117

4.5 Conclusion 118 4.6 Supporting information for chapter four 119

vii

Contents (contd.)

5. Seasonal physiological patterns among sympatric trees 126

during water-deficit and implications of climate change 5.1 Introduction 126 5.2 Methods 129

5.2.1 Study area 129 5.2.2 Quantifying species dominance 132 5.2.3 Tree selection for physiological measurements 132 5.2.4 Physiological measurements         133 5.2.5 Data analysis 134

5.3 Results 136 5.3.1 Photosynthesis 136 5.3.2 Transpiration 139 5.3.3 Instantaneous water-use efficiency 141

5.4 Discussion 143 5.4.1 Patterns among species 144 5.4.2 Interactions under a changing climate 145 5.4.3 Functional insurance and global vegetation shift   146

6. Discussion 149

6.1 Effect of disturbance on plant rarity 149 6.2 Contributions of species to ecosystem function 154 6.3 Managing disturbance for ecosystem functionality 156 6.4 Conclusion 159

7. References 160 8. Authorship statement 216

viii

List of tables

2.1 The effect of survey year and burn treatment on 35

the floristic composition of landscapes in a box-ironbark forest.

2.2 The similarity in floristic composition of rare plant 36 species among landscapes assigned to different burn treatments, in 2010 (before prescribed burns), and in 2013 (after burns).

2.3 The contribution of species frequency groups 41 (common, less-common, rare) to the floristic similarity of vegetation within, and dissimilarity among, landscapes surveyed in 2010 and 2013.

2.4 The contribution of rare species in different life-form 43 groups towards floristic similarity of rare species within, and dissimilarity among, landscapes in pre-burn and post-burn surveys.

3.1 Functional dispersion, functional divergence, 77 functional evenness, and functional richness among study plots in a temperate forest in southeastern Australia, with survey year (2010, pre-burn; 2013, post-burn) and prescribed burn treatment as fixed factors.

3.2 Community-weighted mean trait values (maximum 79 height, specific leaf area, leaf nitrogen to phosphorus ratio) among study plots in a temperate forest in southeastern Australia, with survey year (2010, pre-burn; 2013, post-burn) and prescribed burn treatment (unburnt control, autumn burn, spring burn) as fixed factors.

4.1 Leaf nutrient concentrations for box-ironbark species 104 identified as important contributors of sampled nutrients through PCA analysis for senesced leaf nutrient concentration.

4.2 Difference in concentration of nutrients between soil 111 depths and among burn treatments in a box-ironbark forest.

ix

List of tables (contd.)

5.1 Seasonal weather of the Heathcote region in 131

southeastern Australia.

5.2 Seasonal relationship between light and physiological 137 parameters of trees.

S2.1 Permutational analysis of dispersions among plots 50

based on their floristic composition, with year of survey and season of burn as fixed factors.

S2.2 Pairwise comparisons of average dispersion distance 51 (distance-to-centroid, calculated by permutational analysis of dispersions) among plots within different treatment groups (survey year, season of prescribed burn).

S2.3 Average (± SE) dispersion distance (distance-to-centroid, 52 calculated by permutational analysis of dispersions) among plots within different treatment groups (survey year, season of prescribed burn).

S2.4 Difference in richness of rare species from different 53 plant life-form groups within autumn-burn, spring-burn and unburnt control plots, before (2010) and after (2013) prescribed burning.

S2.5 List of rare species and the number of plots in which they 54 were detected within a box-ironbark forest before (2010) and after (2013) prescribed burning in different seasons (unburnt, autumn, spring).

S3.1 Comparison of functional originality, functional 89 specialisation, and functional richness after simulations of local species loss in a temperate forest in southeastern Australia.

S4.1 Concentration of macro- and micro-nutrients in fresh 119 and senesced leaves of species growing in a box-ironbark forest in southeastern Australia.

x

List of tables (contd.)

S4.2 The relationship between species rarity, and the 123

uniqueness of leaf nutrient profiles in senesced leaves, and in proportional resorption of nutrients from leaves, for 42 species from a box-ironbark forest in southeastern Australia.

S4.3 Eigenvector scores and percent variation explained by 124 five principal components for the similarity of leaf nutrient profiles, and proportional resorption, among 42 evergreen species in a box-ironbark forest in southeastern Australia.

S4.4 Mean concentrations of nutrients in soil samples from 125 a box-ironbark forest in southeastern Australia, collected three years after landscape-scale experimental prescribed burn treatments in autumn, spring, or left as unburnt reference landscapes.

xi

List of figures

2.1 Typical box-ironbark vegetation within the Heathcote- 25

Graytown-Rushworth forest, with an open canopy dominated by Eucalyptus tricarpa (red ironbark), E. microcarpa (grey box) and a sparse, shrubby understorey.

2.2 Location of study landscapes in the Heathcote-Graytown- 27 Rushworth forest, in southeastern Australia.

2.3 Floristic diversity of study landscapes before and after 33 experimental burns conducted during different seasons in a box-ironbark forest in southeastern Australia.

2.4 Ordination of plots within a box-ironbark forest in 37 southeastern Australia, based on the floristic composition of common, less-common, and rare species before and after experimental prescribed burns conducted during different seasons.

2.5 Ordination of plots within a box-ironbark forest in 38 southeastern Australia, based on the floristic composition of woody perennial, perennial herb or geophyte, and annual herb species before and after experimental prescribed burns conducted during different seasons.

2.6 Richness of rare species per plot in pre-burn and 44 post-burn landscapes, panelled by burn season.

3.1 Functional dispersion, functional divergence, functional 78 evenness and functional richness indices of study plots in a temperate forest in southeastern Australia before and after prescribed burning in different seasons.

3.2 Community-weighted mean values of plant traits 80 (maximum height, specific leaf area, leaf nitrogen to phosphorus ratio, mode month of onset of flowering) in study plots in a temperate forest in southeastern Australia before and after prescribed burning in different seasons.

3.3 Change in functional originality, functional specialisation, 82 and functional richness after simulations of regional species loss in a temperate forest in southeastern Australia.

xii

List of figures (contd.)

3.4 Change in functional originality, functional 83

specialisation, and functional richness after simulations of local species loss in a temperate forest in southeastern Australia.

4.1 Location of study plots within the Heathcote-Graytown- 96 Rushworth forest, southeastern Australia.

4.2 Relationship between species pre-fire frequency and 101 uniqueness of their senesced leaf nutrient profile.

4.3 Uniqueness of leaf nutrient profile and percent change 102 in species frequency score following disturbance.

4.4 PCA ordination of 42 box-ironbark species by similarity 105 of senesced leaf nutrient content for six macro- and four micro-nutrients prior to leaf drop.

4.5 PCA ordination of 42 box-ironbark species by similarity 108 of resorption of six macro- and four micro-nutrients prior to leaf drop.

4.6 Concentration of key macro- and micro-nutrients nutrients 112 in upper and lower soil horizons in a box-ironbark forest in southeastern Australia.

5.1 Seasonal relationship between photosynthetic photo flux 138 density and photosynthetic rate for Eucalyptus species in Heathcote-Graytown-Rushworth forest, southeastern Australia.

5.2 Seasonal relationship between photosynthetic photo flux 140 density and transpiration for Eucalyptus species in Heathcote-Graytown-Rushworth forest, southeastern Australia.

5.3 Seasonal relationship between photosynthetic photo flux 142 density and instantaneous water-use efficiency for Eucalyptus species in Heathcote-Graytown-Rushworth forest, southeastern Australia.

xiii

List of figures (contd.)

S2.1 Similarity of floristic composition in sites within 58

landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S2.2 Similarity of floristic composition of common species in sites within landscapes before and after prescribed burn 59 treatments in autumn, spring or left unburnt as a control.

S2.3 Similarity of floristic composition of less-common species 60 in sites within landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S2.4 Similarity of floristic composition of rare species in sites 61 within landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S2.5 Similarity of floristic composition of woody perennial 62 species in sites within landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S2.6 Similarity of floristic composition of perennial herb and 63 geophyte species in sites within landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S2.7 Similarity of floristic composition of annual herb species 64 in sites within landscapes before and after prescribed burn treatments in autumn, spring or left unburnt as a control.

S3.1 Quality of the functional space based on number of 90 dimensions (PCoA axes) included in its construction. Mean-squared deviation index (mSD) for each number of dimensions included is shown.

1

Abstract

Rare plants can play important and novel functional roles, and mediate the effects of

disturbance on ecosystem processes by providing functional insurance for loss of

common species. As disturbances such as drought and fire increase in frequency and

intensity under a changing climate, some rare species may become threatened or

extinct. Thus, it is imperative to gain better understanding of the roles of rare species,

to facilitate best management practice to protect our vegetation and to understand

whether particular species or groups of species require priority for conservation.

A temperate forest in southeastern Australia was used as a case study to examine the

functional roles of rare species and the effect of disturbance (fire and drought) on

plant community composition and functional diversity. Species composition was

examined among landscapes in the forest before, and three years after, prescribed

burns in autumn (n = 6) or spring (n = 6) and compared to unburnt landscapes

(n = 3). Spring burns promoted the occurrence of annual herbs that were rare in the

landscapes studied but neither spring or autumn burns affected levels of rarity of

woody perennial species.

The effect of prescribed burning on functional diversity indices was compared

among the autumn-burnt, spring-burnt and unburnt study landscapes. Three scenarios

of biodiversity loss (losing rarest species first, losing commonest species first, and

random species loss) were simulated to determine the contribution of rare species to

functional richness, functional specialisation and functional originality of the

landscapes. An increase in functional richness was observed across survey years, but

was not affected by a prescribed burn. Contrary to expectations, first losing the

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commonest species from the community consistently had a larger than expected

effect on reducing functional diversity indices, and losing the rare species first had a

lower effect on functional diversity indices than would be expected with random

species loss. Based on the trait characteristics examined, rare species generally were

similar to the more common species in the community and common species drove

functional diversity.

Despite this, species with low abundance can greatly influence nutrient cycling, an

important ecosystem process for community productivity. To examine whether this

occurred, the nutrient contents of senesced leaves of 42 woody perennials were

examined. These species were divided into five nutrient uptake strategies

(mycorrhizal, N-fixing, carnivorous, hemiparasitic, and those with proteoid roots).

Nutrient profiles then were compared among uptake strategies, and to species’

abundance pre-and post-fire. Soil nutrient status also was measured throughout the

landscape to determine which nutrients were most limiting in the system. Two

hemiparasitic species differed greatly from all other species, their senesced leaves

containing particularly high concentrations of P and K, which are limiting nutrients

in this ecosystem. Both species were rare in the landscape.

Drought is a disturbance likely to increase in intensity and frequency in southeastern

Australia in the future, which will affect survival and competition among canopy

trees and thus ecosystem functioning. Physiological parameters (photosynthesis,

transpiration and water-use-efficiency) of six canopy tree species (three common,

one less common, and two rare within the landscape) were compared during a year of

severe rainfall deficit, as a surrogate for interspecific competition among species

during prolonged drought. One species rare within the landscape maintained

photosynthetic function during periods of predicted stress, exhibiting the highest

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rates of photosynthesis in the community. The second species rare within the

landscape exhibited very low rates of photosynthesis during the study period, and

appeared to cope poorly under drought.

Disturbance can promote the presence of rare species, but characteristics of the

disturbance will promote different lifeform groups that can play specific post

disturbance roles. Several recent studies have found rare species to be most

influential for supporting functional diversity. Whilst these studies examined species-

rich ecosystems (tropical reef fishes, rainforest trees, alpine plants, tropical birds),

based on the functional traits examined in this study, rare species in temperate box-

ironbark forest are often functionally similar to species that are common. However,

some rare species were functionally unique and supported important functional roles,

in current ecosystem processes (i.e. nutrient cycling), and for stabilising ecosystems

in the event of future disturbance (i.e. canopy tree decline under drought). Therefore,

predicting the effect of disturbance on ecosystems requires a greater understanding

than simply disturbance impacts on species composition. Attention to species traits,

and the range and diversity of traits in the ecosystem is important for understanding

how changes in composition will translate to changes in ecosystem processes. Rare

species do not always drive overall functional diversity in an ecosystem, but loss of

those which are functionally unique can lead to loss of important current, or future

functional roles.

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Chapter 1 Introduction

Rare plant species, those with low abundance or limited distribution (Rabinowitz

1981; Gaston 1994), can act as keystone species (Marsh et al. 2000) or have

profound influences on ecosystem function (Naeem et al. 2012). Some rare species

can play unique or important ecosystem roles (Mouillot et al. 2013a; Leitão et al.

2016) and provide ecological and functional insurance against the loss of common

species in the event of environmental disturbance (Jain et al. 2014). Global

biodiversity is declining rapidly (Dirzo and Raven 2003), and rare species may be

more prone to extinction than their common counterparts (Van Calster et al. 2008).

Biodiversity erosion can, thus, have profound consequences on ecosystem dynamics

and biogeochemical processes (Naeem et al. 2012).

A number of studies provide compelling evidence to suggest that rare plants play

important ecosystem roles, collectively and singularly (Hector et al. 2001a; Lyons

and Schwartz 2001), and over different spatial and temporal scales (Lyons et al.

2005). It is concerning that, despite this evidence, there is little detailed research

exploring the ecosystem roles of rare plants, aside from those which are iconic or

economically important (Small 2011). Rare plants deserve greater attention than they

currently receive (Loreau et al. 2001; Mouillot et al. 2013a); this study examines the

effect of disturbance on plant rarity, and the roles of species in ecosystem

functioning.

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1.1 Defining plant rarity

Rarity encompasses a diverse range of spatial and temporal phenomena (Kunin and

Gaston 1993), so development of a unified definition for biological research is

difficult (Gaston 1994; Zlobin 2012). Most species within an ecosystem are either

infrequent or rare (Gaston 1994) depending on the spatial and temporal scale being

considered (Levine and Rees 2004; Nield et al. 2009). For example, populations can

fluctuate in size due to climate or season (Levine and Rees 2004), because of

disturbances such as fire (Nield et al. 2009) or drought (Levine et al. 2008), or at

different stages of community succession (van der Maarel 1993; Nield et al. 2009;

Gabrielová et al. 2013). Rabinowitz (1981) described a widely accepted system for

classifying species rarity at the broadest scale, utilising three dichotomised attributes;

geographic range (large/small), habitat specificity (wide/narrow) and local

population size (large, dominant somewhere/small, non-dominant). Seven of these

eight possible combinations are regarded as an indication or degree of rarity, while

only one (large geographic range, wide habitat specificity and large local population

size) suggests commonness (Rabinowitz 1981). However, when exploring the

functional role of species it is often necessary to consider rarity at a finer scale

because a geographically widespread species may be rare within an ecosystem.

Applying an objective method to define plant rarity remains challenging, with a

variety of methods employed in recent studies (Drever et al. 2012). Often an

arbitrary proportion of species in an assemblage is regarded as rare. For example,

Gaston (1994) proposed that the lower quartile of species in a frequency distribution

could be regarded as rare, and the upper quartile as common. Adaptations of this

include taking the lower two (Pérez-Quesada and Brazeiro 2012), or lower three

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quartiles to define rare species (Siqueira et al. 2012). Rarity has also been defined as

broadly as the lower 90% of species in terms of total abundance in a community

(Soliveres et al. 2016), or as all species with <5% of the maximum observed

abundance in a community (Mouillot et al. 2013a). Rarity can also be treated as a

continuum, where species are ranked from commonest to rarest, with no attempt to

provide discrete classification of species (Leitão et al. 2016). Clearly, defining

species rarity is contextual and justified by the geographic scale being considered,

the type of ecosystem or organism of interest, and the motivation or objective of

applying such classification.

1.2 The functional role of rare species

There is usually some similarity between the functional roles of common and rare

species in any ecosystem. Thus, the effect that losing rare species would have on

ecosystem functioning could be considered minimal due to their low abundance

within communities (Grime 1998). Indeed, Schwartz et al. (2000) determined that

dominant species were more influential than overall species richness for maximising

the stability of ecosystem functions and services. However, others subsequently

argued the contrary (Hector et al. 2001b). Many recent, compelling studies clearly

implicate species diversity as an important driver for maintaining overall ecosystem

stability (Tilman et al. 2006; Van Ruijven and Berendse 2010; Hooper et al. 2012;

de Mazancourt et al. 2013), and ecosystem functions (Lyons and Schwartz 2001;

Hooper et al. 2005; Lyons et al. 2005; Hector and Bagchi 2007) and services (Isbell

et al. 2011), such as productivity (Craine et al. 2003; Tilman et al. 2012), soil

resource and nutrient dynamics (Marsh et al. 2000; Pohl et al. 2009; Lamb et al.

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2010; Eisenhauer et al. 2011a; Eisenhauer et al. 2011b), water retention and filtration

(Rixen and Mulder 2005) and invasion resistance (Hector et al. 2001a; Lyons and

Schwartz 2001; Wilsey and Polley 2002; Van Ruijven et al. 2003). This diversity-

stability hypothesis is well demonstrated in a Queensland savannah rangeland

grazing experiment (Walker et al. 1999). After grazing reduced the dominance of

common species, rare species that were functionally similar to the common species

were released from competition and became more abundant. The rare species thus

stabilised ecosystem processes such as productivity by providing functional

insurance against the loss of common species, minimising long-term changes to

ecosystem properties (Walker et al. 1999).

More recently, it has been recognised that species diversity is not the sole driver of

stability in ecosystems. Species diversity promotes functional diversity (Lohbeck et

al. 2011), which in turn drives ecosystem stability (Lavorel et al. 2013). Functional

diversity considers the range of functional traits possessed by plants in a

community—the ecological adaptations that govern the way a plant responds to and

influences its surrounding environment—including all aspects of a species

morphology, physiology, biochemistry, regeneration and demography (Weiher et al.

1999; Lavorel et al. 2007; Pérez-Harguindeguy et al. 2013).

Functional trait diversity is important for ecosystem stability (Diaz and Cabido 2001)

as different functional traits contribute to different ecosystem processes, and can

complement or be synergistic with the functional traits of other species (Finn et al.

2013). Trait diversity among species can lead to a greater range of responses to

disturbance and environmental change, increasing community resistance and

resilience; trait redundancy among species can provide insurance against impacts to

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ecosystem functioning in the event of biodiversity erosion (Carvalho et al. 2013;

Mori et al. 2013; Pillar et al. 2013).

In some communities, rare species have been found to possess functional trait

combinations that are distinctly different to those of common species (Mouillot et al.

2013a), which were more likely to possess convergent, functionally redundant traits

(Richardson et al. 2012). Notably, Mouillot et al. (2013a) investigated the functional

traits of species in relation to their local and regional abundance in high-diversity

ecosystems. They found that the rarest species were consistently the most

functionally unique; 32 and 89% of locally and regionally rare alpine plants,

respectively, supported vulnerable functions and 55% of tropical tree species that

were likely to support vulnerable functions were represented by only one occurrence

in their data set.

Marsh et al. (2000) demonstrated that Equisetum spp. play a keystone role in nutrient

cycling in cold-temperate Alaskan shrub wetlands; their deep-rooting habit allowed

access to nutrients otherwise unavailable to the rest of the community. Whilst

representing 5% of the above and below ground biomass—hence its consideration as

a rare species—the plants contributed 29 and 39% of the P and K, respectively, in

annual community foliage litterfall, and 55, 41, and 75% of the P, K and Ca inputs

into soil nutrient pools, respectively, over a two year period. Essentially acting as a

nutrient pump, Equisetum spp. improved access to nutrients for the rest of the

community, resulting in higher net primary productivity in an area where nutrients

would otherwise be a limiting factor.

Although some rare species may be functionally distinct, and some can play keystone

roles, Jain et al. (2014) noted that rare species generally contribute little to functional

9

diversity due to their low mean abundance. However, their unique or functionally

redundant characteristics may collectively be important if their abundance increased,

for instance, in the event of disturbance or environmental change. Indeed, through

sheer weight in numbers, common species are considered to contribute most to

ecosystem function (Gaston 2011). However, the capacity at which ecosystem

processes operate can be enhanced greatly by rare species as a collective. Ecosystem

functioning in grassland plots was explored by Soliveres et al. (2016), who found

that 50% of the maximum capacity of any ecosystem function was maintained by

10% of species—the most common—that comprised 80% of all individuals sampled

in the community. The addition of the remaining 90% of species (20% of the

individual plants in the community), however, allowed ecosystem functions to

operate at >90% of the maximum capacity observed in any plot, highlighting the

collective importance of the rarer species. Furthermore, Leitão et al. (2016)

demonstrated that the sequential collective loss of the rarest species in an ecosystem

disproportionately reduced functional diversity compared to the loss of common

species, and species loss at random.

1.3 Effect of disturbance on rare species

Most species in an ecosystem will, at some stage, be perceived as rare or uncommon,

due to spatial and temporal fluctuations in their abundance resulting from changing

resource availability (Smith and Huston 1990; White et al. 2010), climatic variability

(Kreyling et al. 2011; Letten et al. 2013), and stochastic disturbance regimes (Turner

et al. 1998; Miller et al. 2011).

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Climate change is a disturbance occurring on a global scale, causing fluctuations in

weather that are likely to result in more frequent and intense drought in many parts of

the world (Smith et al. 2009; Collins et al. 2013), and an increase in the intensity and

extent of wildfires (Moritz et al. 2012; Batllori et al. 2013). Fire is a globally-

relevant disturbance (Krawchuk et al. 2009), and is well documented in the

Australian landscape (Bradstock et al. 2012); many plants are adapted to fire regimes

of various intensity, frequency, patterns of fuel consumption, and spatial and

temporal occurrence (Bowman et al. 2012). Based on their general response

following fire, species can be categorised as either obligate seeders (plant is killed by

fire and is dependent upon post-fire seedling establishment), obligate resprouters

(may vegetatively recover after fire, but lacks fire-cued seed germination), or

facultative seeders (may vegetatively recover after fire and has fire-cued seed

germination) (Bond and Midgley 2001; Keeley 2012). The way a plant species

responds following fire can have serious implications on its presence in the

landscape at any point in time and, thus, its contributions to ecosystem functioning.

Plants that can resprout after fire generally require minimal seedling regeneration to

maintain population size, although fire may lead to the mortality of some individuals

(Vesk et al. 2004), and some senescence may occur during inter-fire periods (Tucker

and Cadotte 2013). Thus, although a degree of recruitment during inter-fire periods is

required for populations of obligate resprouter species to exist in stable abundance,

the main factors influencing their persistence in a community are the magnitude of

the disturbance (fire intensity and residence time) (Vesk et al. 2004), resource

availability (Clarke et al. 2013) and the frequency of fire intervals. Over-frequent

burning may weaken the resprouting capacity of a species due to depletion of its

stored energy reserves (Canadell and López-Soria 1998; Vilà-Cabrera et al. 2008),

11

although some species have a stronger resprouting capacity than others (Vesk et al.

2004).

Obligate and facultative seeders from fire prone regions often utilise the effects of

fire as a disturbance to trigger a pulse of germination from otherwise dormant seed

stored within the soil seed bank (Auld 1986; Auld and O'Connell 1991; Auld et al.

2007; Paula and Pausas 2008; Auld and Ooi 2009), which may have remained viable

from several decades to hundreds of years (Baskin and Baskin 1998). Fire-stimulated

germination can occur as a result of heat shock, through heat fracturing or

desiccating the seed coat, making it permeable to water and stimulating the embryo

(Baskin and Baskin 1998), through heat directly stimulating the embryo, or through

denaturing seed coat inhibitors, which may otherwise promote seed dormancy

(Keeley and Fotheringham 2000). Obligate seeders are wholly dependent on

successful post-fire recruitment to replace all adult plants killed, and to replace

senescent individuals (Verdú 2000).

Maintenance of fire regimes as they would have ‘naturally’ occurred would result in

spatial and temporal fluctuations in species abundance, but overall long-term stability

in communities (Mori 2011). Anthropogenic changes to disturbance regimes can

threaten species persistence; an increase in fire frequency can rapidly deplete species

which fail to reach reproductive maturity during inter-fire periods (Bradstock 2008).

A decrease in fire frequency could result in senescence of species that require fire as

a cue for germination, causing a species to appear rare (Nield et al. 2009; Duff et al.

2013) or absent from the community (although a viable soil seed bank may remain)

(Davies et al. 2013). As such, a fire can temporarily cause a species perceived as

uncommon to become relatively abundant, or can stimulate germination of species

that never have been seen in a particular area, although they may have persisted for a

12

long time within the soil seed bank (Coates et al. 2004; Just and Beardsell 2011;

Tolsma et al. 2012b; Davies et al. 2013).

Resprouters have a lower evolutionary pressure to recruit en masse following fire,

whereas obligate seeders show a distinct recruitment pulse after fire. This increase in

abundance of obligate seeders can lead to their temporal dominance in the years

following fire (Paula and Pausas 2008), provided conditions for recruitment are

favourable. As obligate seeders thin out due to natural attrition, slow-growing

resprouters increase in size and cover and may again dominate (Duff et al. 2013).

Many species show a clear change in abundance in response to different fire regimes

(Penman et al. 2009; Duff et al. 2013), but overall a linear decrease in species

diversity may be observed in fire-prone ecosystems as canopy cover increases with

increasing time since fire (Specht and Specht 1989). Temporal changes to the

abundance of a single rare species, or rare species collectively, can profoundly affect

the suite of plant functional traits in the community and thus influence ecosystem

function, although this is yet to be explored in the literature.

Similarly, fluctuations in weather and the timing and duration of drought can

influence recruitment patterns and competition among species, and among species’

functional or structural groups (Matías et al. 2012). Some plants are more tolerant to

drought than others (McDowell et al. 2008) and, so, the distribution of plant

communities may change in the future (Allen et al. 2010). Levine and Rees (2004)

modelled environmental fluctuations in an invaded Californian grassland and found

that climatic variability benefited rare forbs, where, during unfavourable growing

periods, the forb was given temporal release from competition with otherwise

dominant exotic grasses. Climate variability such as the timing of rainfall events also

can affect germination of species (Levine et al. 2008), thus temporal rarity. Changing

13

species composition because of climate-mediated competition and recruitment, as

with fire, can affect the functional diversity of ecosystems, but effects on functional

processes are yet to be explored in the literature.

1.4 Box-ironbark forests

1.4.1 Location, geology, and climate

Box-ironbark forests are a major component of temperate woodlands found

throughout the central and south-central regions of Victoria, southeastern Australia,

formerly occupying approximately three million hectares prior to European

settlement (Environment Conservation Council [ECC] 2001). The underlying soils

are typically characterised by peneplains and low, gently undulating hills, ranging

between 150–300 m in elevation (Douglas and Ferguson 1988; ECC 1997). Soils are

typically stony and shallow or skeletal clay loams of low fertility, derived from

Lower Palaeozoic sandstone and slates, interbedded with quartz reefs (Mikhail 1976;

Douglas and Ferguson 1988). 

Victoria’s box-ironbark forests have a relatively uniform, temperate climate, with

mean annual rainfall ranging from 400 mm in the north-west to 700 mm in the

southeast (Bureau of Meteorology 2016). Heathcote (the region where this study was

undertaken) averages 594 mm of rainfall annually; August is the wettest month

(69 mm) and February the driest (31 mm) (Heathcote weather station, station ID:

088029; Bureau of Meteorology 2016). Temperatures in Heathcote are coolest in

July (12 °C); January and February are the warmest months with mean maximum

daily temperatures reaching 28–29 ºC, although consecutive days above 35 ºC are

14

common (Redesdale weather station, station ID: 088051; Bureau of Meteorology

2016).

1.4.2 Vegetation

Most extant areas of box-ironbark forest have regenerated following extensive

clearing for mining and agriculture (ECC 2001). They are dry sclerophyll forests

characterised by an open overstorey to 20 m tall, generally dominated by red

ironbark Eucalyptus tricarpa  (L.A.S.Johnson) L.A.S.Johnson & K.D.Hill and grey

box Eucalyptus microcarpa (Maiden) Maiden (Department of Environment and

Primary Industries; DEPI 2013). The mid storey is dense to open with small trees or

shrubs and the ground-layer ranges from sparse to well-developed, with a range of

herbs and grasses (Calder and Calder 2002; DEPI 2013). Primary productivity in

these forests is low compared to that of other dry sclerophyll forests (Grierson et al.

1992), attributable to the relatively low rainfall and nutrient-poor soils which

characterise the region (Mikhail 1976; ECC 1997).

1.4.3 Disturbance history

As much as 83% of Victoria’s box-ironbark forests have been cleared since

European settlement, with an estimated 496 000 ha remaining (ECC 2001). Initial

clearing occurred in the 1830s for grazing and cropping (Muir et al. 1995), and in the

1850s after gold was discovered in the region (Newman 1961). Increased regional

activity and local population growth placed increased pressure on the land, with

much of the remaining box-ironbark forest cleared to expand agriculture and to

utilise timber resources (ECC 2001). The expansion of mining, logging and

15

agriculture altered much of the original forest and often the topsoil was completely

removed; many soils are now degraded from alluvial mining, topsoil extraction,

grazing and erosion (Orscheg and Enright 2011).

Few fire ecology studies have been undertaken within central Victoria’s box-

ironbark forests and debate surrounds the fire-dependency of this system (Tolsma et

al. 2007). There is little direct evidence to suggest the type of fire regime that

naturally occurred in box-ironbark forests (Tolsma et al. 2007) although, generally,

they are not considered fire-prone (Calder and Calder 2002). Fuel hazard levels are

usually low, even after decades without fire (Chatto 1996; Bennett et al. 2012) and

the open structure of the tree and shrub layer (Muir et al. 1995) reduces connectivity

between vegetation, thus reducing potential spread of wildfire. Lightning was

probably the main ignition source of naturally occurring fires within box-ironbark

forests prior to European settlement; owing to low levels of surface fuel (Chatto

1996), fires would have been small and of low intensity with long intervals between

them. Deliberately lit fires are now the most common type of fire, with most being

relatively small in area (<5.0 ha) (Department of Sustainability and Environment;

DSE 2003).

Most box-ironbark species exhibit some form of adaptation to fire, such as a heat-

stimulated pulse of germination or the ability to resprout (Brown et al. 2003;

Orscheg and Enright 2011). Species also show adaptations to infrequent fire

intervals, such as production of non-dormant seed and releasing a portion of soil-

stored seed from dormancy during inter-fire periods, to allow for recruitment in the

absence of fire (Orscheg and Enright 2011). Biodiversity may be maintained through

an appropriate fire regime (Tolsma et al. 2010). Based on vital attribute data of the

box-ironbark flora (method of persistence, primary juvenile period, longevity and

16

pattern of post-fire establishment), Cheal (2010) recommended that minimum

tolerable fire intervals were 12 years for patchy fires of low intensity and 30 years for

wildfires, with a maximum tolerable fire interval of 150 years. In situ experiments on

E. tricarpa indicate germination may be dependent on a combination of fire and

optimum rainfall events (Orscheg et al. 2011). A decline was observed in all species

of a box-ironbark community which germinated after experimental fires, due to poor

rainfall over the six months that followed (Meers and Adams 2003).

1.4.4 Nutrient cycling

Water and nutrients are key limiting factors for plant growth in box-ironbark forests.

Whilst climatic factors such as drought greatly influence productivity (Bennett et al.

2013), cycling and conservation of nutrients also are critically important for

functional stability and productivity. Understanding nutrient cycling dynamics and

the functional role plants play is vital, particularly where remnant or degraded areas

have been affected by past disturbances such as loss of topsoil, vegetation clearing

and altered fire regimes. Given the importance of nutrient conservation in this

depauperate system, it is surprising that very little research towards plant-soil-

nutrient dynamics has been undertaken. In E. tricarpa and E. microcarpa dominated

forests, soil carbon (C) to nitrogen (N) ratios are high (21.1 and 16.3, respectively)

and thus competition between soil microbes and mycorrhizal tree roots for N is high

(Adams and Attiwill 1986). Although this results in little loss of N from the system

through leaching of soils, the system has poor resilience to loss of N, as any that is

lost is not easily replaced.

17

Release of nutrient rich ash into the soil following fire can lead to an ephemeral

increase in nutrient availability (Certini 2005; Yusiharni and Gilkes 2012) and

temporal post-fire increases in soil nitrogen compounds are common throughout

Australian ecosystems (Weston and Attiwill 1990; May and Attiwill 2003). Many

compounds are readily water-soluble and easily lost through leaching or erosion if

not immobilised in cells (Thomas et al. 1999), although underlying soil profiles can

retain elements leached from above (Soto and Diaz-Fierros 1993). Rapid uptake and

immobilisation of N by regrowing vegetation is an important nutrient conservation

strategy to minimise losses in oligotrophic systems (Boerner 1982; Adams and

Attiwill 1986; Weston and Attiwill 1996). Further, the proliferation of N-fixing

species post-fire seems to have a noticeable impact in quickly replacing lost N

(Vitousek et al. 1982; Weston and Attiwill 1990; May and Attiwill 2003). The role,

importance and abundance of both rare and common N-immobilising and N-fixing

species in box-ironbark forests may be vital for recovery of soil N, especially if there

are losses of N from erosive processes or leaching after fire. Currently, this has not

been investigated. As N is a limiting nutrient for plant productivity, it is possible that

common or rare N-fixing species play a keystone role in replacing any post-fire

losses.

The cycling of nutrients that may limit plant productivity, other than N, has not been

investigated within box-ironbark forest. Phosphorus (P) is a limiting nutrient in

Australian soils (Attiwill and May 2001; Cramer 2010) and likely so within box-

ironbark forests. Increases to the availability of P in the topsoil occurs following fire

and through the breakdown of organic matter. The ability of species to capture

available P before it precipitates into less-available forms is important for continued

cycling of P where inputs are minimal. Some species have the ability to form root

18

clusters and mycorrhizal associations; adaptations that improve the capability of a

species to acquire soil P (Specht 1981; Lambers et al. 2006; Smith et al. 2011).

Many proteaceous species, and at least several Australian Acacia, have adaptations

that enable them to access and utilise the organic P fraction in limited amounts (He et

al. 2012). Such species may be important contributors to soil inorganic P due to their

ability to acquire generally unavailable organic P and deposit inorganic forms back

into the soil through litterfall. It is important to understand if any species play this

role within the box-ironbark system.

Fire-induced changes to the availability of other soil nutrients are generally less

pronounced and more ephemeral than that of soil N and P. Potassium (K), calcium

(Ca), sulphur (S) and magnesium (Mg) generally increase in availability after fire,

although this is often temporal (Adams and Boyle 1980; Khanna and Raison 1986;

Tomkins et al. 1991; Oswald et al. 1998). The effect of fire on other micronutrients,

such as manganese (Mn), copper (Cu), zinc (Zn), and boron (B) is poorly understood.

It is presumed that temporal increases also occur (Certini 2005). Post-fire availability

of these nutrients, and how species influence their cycling, need to be addressed.

1.5 Thesis aims and structure

The aim of this thesis was to investigate the effects of disturbance on plant rarity, and

how changes in plant rarity might translate into changes in ecosystem functioning.

In chapter two, the effect of prescribed burning as a disturbance on the frequency of

occurrence of vascular plants was investigated in a box-ironbark forest in

southeastern Australia. Plant species were grouped by life-form (woody perennial,

perennial herb or annual geophyte, annual herb) and rarity in the landscape prior to

19

burning (common, less-common, rare). The relative effect of a prescribed burn (low-

cover autumn burn or high-cover spring burn) was compared among species in

different life-form and frequency groups, as well as the effects of drought-breaking

rain that occurred at the time of initial floristic surveys.

In chapter three, the effect of disturbance on functional diversity was examined in a

box-ironbark forest in southeastern Australia. Four functional diversity indices

(functional divergence, functional dispersion, functional evenness, and functional

richness) were calculated based on functional traits relating to key structural,

physiological and phenological attributes of plants governing their influence on the

broader ecosystem (maximum height, specific leaf area, leaf nitrogen to phosphorus

ratio, and month of onset of flowering). The effect of a prescribed burn and drought-

breaking rain on functional diversity indices was then compared among landscapes

before and after burning. Species loss was then simulated under three scenarios—

losing rarest species first, losing commonest species first, and losing species

randomly (null model)—and the effect of local and regional species loss on

functional diversity (functional richness, functional originality and functional

specialisation) was measured. The contributions of rare and common species towards

driving functional diversity in the ecosystem was determined, thus which are most

important for primary ecosystem function.

In chapter four, the contributions of species to nutrient cycling was examined by

measuring the nutrient content of senesced leaves and rates of resorption of six

macro- and four micro-nutrients among woody perennial species in a box-ironbark

forest. The uniqueness of leaf nutrient profiles was compared among different

nutrient uptake strategies (mycorrhizal, N-fixing, carnivorous, hemiparasitic,

20

proteoid roots). The relationship between rarity and uniqueness of leaf nutrient

profiles, and how this relationship changes following fire, was explored.

In chapter five, the physiological performance of six canopy trees was examined

within a box-ironbark ecosystem, during a year of severe water deficit (considered as

a surrogate for drought under climate change). Likely competitive interactions under

a changing climate were inferred by comparing physiological parameters among rare

and common canopy species, across four seasons. How this may translate into

ecosystem function in the future, and the role of rare species, is discussed.

In chapter six, all four data chapters are synthesised and an overview of the major

findings from this research is provided. The importance of managing disturbance to

maintain rare species presence is highlighted, and the contributions of rare species

towards ecosystem functioning is discussed based on the results of this study.

Findings from this research in a temperate ecosystem are compared to similar studies

in ecosystems around the word, and suggestions for future research are provided.

21

Chapter 2 The effect of prescribed burning on plant rarity in a

temperate forest

The published version of this chapter can be found at:

Patykowski J, Holland GJ, Dell M, Wevill T, Callister K, Bennett AF, Gibson M (2018) The effect of prescribed burning on plant rarity in a temperate forest. Ecology and Evolution. DOI: 10.1002/ece3.3771

2.1 Introduction

Managing disturbance regimes in ecosystems is crucial for avoiding the loss of rare

and endemic species and homogenising species composition (McKinney and

Lockwood 1999). Although ecosystem processes often are maintained primarily by a

low-proportion of the total species—the dominants—in any given community

(Schwartz et al. 2000; Smith and Knapp 2003; Sasaki and Lauenroth 2011), it is

increasingly evident that rare species can play unique (Mouillot et al. 2013a; Jain et

al. 2014; Leitão et al. 2016) or keystone roles (Marsh et al. 2000), or collectively

make important contributions to ecosystem function over different temporal or

spatial scales (Lyons and Schwartz 2001; Lyons et al. 2005; Allan et al. 2011).

Consequently, the loss of rare species—those with low abundance or limited

distribution (Rabinowitz 1981; Gaston 1994)—can result in a reduction in the

functional trait diversity of communities (Leitão et al. 2016). The loss of

physiological, morphological and phenological attributes that determine how species

respond to environmental factors (Pérez-Harguindeguy et al. 2013) can render

ecosystems more vulnerable to collapse (MacDougall et al. 2013).

Climate change is predicted to bring larger fluctuations in weather patterns, including

more severe drought (Smith et al. 2009), and an associated increase in the intensity,

22

duration, and extent of wildfires in many parts of the world (Moritz et al. 2012;

Batllori et al. 2013). In fire-prone ecosystems, prescribed burning often is used as a

management tool to reduce fuel loads and protect human life and assets from wildfire

(Penman et al. 2011). As a large-scale disturbance process, the effects of prescribed

burning need to be understood at the landscape scale; but the difficulty of carrying

out manipulative experiments at this scale means that such knowledge is limited

(Driscoll et al. 2010), especially for rare species.

Multiple aspects of a fire regime can influence the status of plant species. The burn

season (timing) affects the response of species and the trajectory of community

recovery because of interspecific differences in phenology, life-history, and

physiology (Gill 1975; Drewa et al. 2002). The extent, intensity, and duration of a

fire also strongly affect how plants respond, with these properties being heavily

influenced by the season of burn, weather preceding the burn, and fuel-load

characteristics (Bradstock et al. 2012). In temperate and mediterranean-type

climates, ambient temperatures are lower and the vegetation wetter in autumn, winter

and spring, resulting in less intense fires that typically burn a lower proportion of the

vegetation than fires occurring in summer (Sullivan et al. 2012). Consequently,

prescribed burns generally are conducted in autumn and spring when fires are easier

to control. Burning in autumn may produce less intense burns than spring, reducing

pressure on species that resprout after disturbance. However, soil temperatures in an

autumn burn may not reach levels needed to break morphological or physiological

dormancy in soil-stored seed, and thus the response of rare species present only in

the soil seed bank may be lower in autumn than spring.

In this study, we used a landscape-scale, manipulative experiment in a box-ironbark

eucalypt forest in southeastern Australia to examine the short-term effects of

23

prescribed burning on the plant community. Our specific focus was on the response

of species that were rare prior to vegetation being burnt (recorded in <5% of study

plots), and the life-form groups (woody perennials, perennial herbs or geophytes,

annual herbs) to which these species belonged. As the fire-responses of a species

must be in context with the responses of other species within a system, the relative

influence of a prescribed burn on all plant species was examined. Box-ironbark

forests are not prone to regular widespread fire (Calder and Calder 2002), nor are

they dependent on fire for their regeneration (Cheal 2010). However, many plants

found in box-ironbark forests are fire-adapted, either successfully resprouting after

fire (Morgan 1999), having fire-cued seed germination (Tolsma et al. 2007), or both.

Some species may require infrequent fire for sufficient recruitment events to

maintain viable populations, without which a species may appear rare or absent

(Nield et al. 2009; Duff et al. 2013), although a viable soil seed-bank may remain

(Davies et al. 2013).

Severe rainfall deficit affected southeastern Australia in a period known as the

Millennium Drought between 1997 and 2009 (Gergis et al. 2012). This drought

negatively affected recruitment (Meers and Adams 2003) and vegetation structure

(Bennett et al. 2013) in box-ironbark forests. The drought broke in early 2010, at the

time of initial floristic surveys. Owing to the inclusion of control landscapes, we

were able to compare the effect of rainfall versus the effect of prescribed fire and

rainfall on plant recruitment. Specifically, we asked:

Does prescribed burning increase floristic diversity in excess of rainfall-

driven, background recruitment?

Does prescribed burning influence the floristic composition of sites?

Are there different responses from species assigned to different life-form

24

groups (woody perennials, perennial herbs or geophytes, annual herbs) or

frequency groups (rare, less-common, common)?

Are the contributions of rare, less-common, and common species to the

floristic composition of the vegetation influenced by prescribed burning?

2.2 Materials and methods

2.2.1 Study area

Surveys were conducted in the Heathcote-Graytown-Rushworth forest, the largest

remaining box-ironbark forest in Victoria, Australia, covering an area of approx.

40,000 ha (Environment Conservation Council; ECC 2001). These forests were

extensively cleared for deep lead alluvial mining and agriculture in the mid-1800s

and, whilst regenerating over the last century, have been subject to ongoing selective

logging (Newman 1961; Muir et al. 1995). The dry, sclerophyll forest is

characterised by an open eucalypt overstorey up to 20 m tall, with a sparse to well-

developed understorey of small trees and shrubs, and a range of herbs and grasses

(Fig. 2.1). The dominant canopy species are Eucalyptus tricarpa (L.A.S.Johnson)

L.A.S.Johnson & K.D.Hill (red ironbark) and Eucalyptus microcarpa (Maiden)

Maiden (grey box), with Eucalyptus polyanthemos Schauer (red box) and Eucalyptus

macrorhyncha F.Muell. ex Benth. (red stringybark) occurring on drier slopes. Soils

are typically stony and shallow or skeletal clay loams of low fertility, derived from

Lower Palaeozoic sandstone and slates, interbedded with quartz reefs (Mikhail 1976;

Douglas and Ferguson 1988). The region is characterised by peneplains and low,

gently undulating hills, ranging in elevation between 150–300 m (Douglas and

Ferguson 1988).

25

Fig. 2.1. Typical box-ironbark vegetation within the Heathcote-Graytown-Rushworth

forest, with an open canopy dominated by Eucalyptus tricarpa (red ironbark),

E. microcarpa (grey box) and a sparse, shrubby understorey (J. Patykowski).

26

The climate is temperate; mean daily maximum temperatures are greatest in January

(~29 °C) and coolest in July (~12 °C) (Bureau of Meteorology 2015; Redesdale

weather station, station ID: 088051). Mean annual rainfall is ~594 mm; August is the

wettest month (~69 mm) and February the driest (~31 mm) (Bureau of Meteorology

2015; Heathcote weather station, station ID: 088029). During the Millennium

Drought (1997–2009), mean annual rainfall was 480 mm, compared with 869 mm

for 2010–2011 when the drought broke (Bureau of Meteorology 2015).

2.2.2 Experimental prescribed burns

Data were collected from 15 study landscapes (Fig. 2.2) as part of a broader study

examining the effects of experimental prescribed burns on a range of ecological

attributes at a landscape-scale (Holland et al. 2017). Study landscapes were

~70–120 ha in area, separated by >500 m, and bounded by management tracks to

enable prescribed burns to be contained. None of the study landscapes had

experienced burning for at least 30 years, and areas recently subjected to timber

harvesting (approx. <20 years) were avoided.

The study design involved three treatment groups: landscapes to be burned in autumn

(n=6), landscapes to be burned in spring (n=6), and unburned reference landscapes to

serve as experimental controls (n=3). Burn treatment was randomly assigned to the

landscape. Prescribed burns were conducted by fire management agencies

(Department of Environment, Land, Water and Planning, and Parks Victoria) in

autumn (late February – April) and spring (October – November) of 2011. The mean

burn cover achieved for autumn-burn landscapes was 26% (range 22–51%) and for

spring-burned landscapes 69% (range 52–89%) (Holland et al. 2017).

27

Fig. 2.2. Location of study landscapes in the Heathcote-Graytown-Rushworth forest, in southeastern Australia.

■ unburnt reference (control), ● autumn burn, ▲ spring burn.

28

2.2.3 Pre- and post-burn floristic surveys

Eight permanent 20 m × 20 m plots were established in each study landscape to

monitor vegetation composition (total n = 120). In each plot, five 1 m × 1 m quadrats

were established at fixed locations and surveyed for plant species presence/absence

(total n = 600). These data were used to determine the frequency of occurrence for all

native vascular plant species per 20 m × 20 m plot. Species that occurred within, or

had foliage overhanging all five quadrats were given a frequency score of 1; species

occurring within, or had foliage overhanging four, three or two quadrats received a

score of 0.8, 0.6 or 0.4, respectively. Species occurring within, or had foliage

overhanging a single quadrat, or the 20 m × 20 m plot (but not in a quadrat), were

given a frequency score of 0.2.

Pre-burn surveys were conducted in each of the study landscapes during spring

(Sept - Oct) of 2010. This was just after the Millennium Drought broke; rainfall from

Jan - Sept of 2010 was 597 mm, with 979 mm falling by the end of 2010 (nearly

double the long-term average). Annual rainfall was above-average in 2011 (759

mm), and below-average in 2012 and 2013 (518 and 521 mm, respectively; Bureau

of Meteorology 2015). All study landscapes were resurveyed in spring 2013, which

allowed time for species to germinate in the case of a delayed germination response

to fire (Ooi et al. 2004) and to become established. This both aided species

identification and affirmed their contribution to ecosystem function (i.e. germinants

that failed to successfully recruit would make little, if any, functional contribution).

Plants that could not be identified to species level, or recognised as a distinct species,

were not included for analysis. Species in the family Poaceae, Orchidaceae, and

genus Arthropodium R. Br. were excluded as many species within each family and

29

genus have similar leaf characteristics, and often lacked reproductive material

necessary for identification to a species level. Species were categorised into life-form

groups: woody perennials, perennial herbs or geophytes, or annual species.

2.2.4 Categorising species frequency of occurrence

Species were categorised into groups based on their presence among 20 m x 20 m

study plots in 2010, prior to burning. Species present in ≤5% of plots (six or fewer

plots) were classified as ‘rare’, regardless of their frequency score, as they had a

limited distribution within the study area. Species were considered ‘common’ if

present in ≥50% of the plots and their mean frequency score was ≥0.25 within the

plots they occupied: this ensured species classified as common were both widespread

and relatively abundant. Species present in 5–50% of the plots were classified as

‘less-common’.

2.2.5 Data analysis

First, to examine whether there was an effect of survey year and burn treatment on

overall floristic diversity at the landscape scale, we calculated the effective number

of species for each study plot (the exponential of Shannon H’ diversity index; Hill

1973), using PRIMER ver. 7 (PRIMER-E Ltd, Plymouth, UK). Differences among

treatments were compared using permutational analysis of variance (PERMANOVA)

of Euclidean distances between study plots (two-way design with year and burn

treatment as fixed effects). We used the PERMANOVA+ package in PRIMER for

these tests, and set an a priori significance level of α = 0.05.

30

Second, we used a two-way PERMANOVA to test the effect of survey year and burn

treatment (fixed effects), and their interaction, on floristic composition. We included

‘landscape’ as a random factor in the analysis, nested within burn treatment, to

account for repeated sampling of landscapes through time. A significant interaction

effect would indicate that changes between survey years differed between the burn

treatments. These tests were undertaken for the floristic community as a whole, and

separately for each plant life-form (i.e. woody perennials, perennial herbs or

geophytes, annual herbs) and frequency group (i.e. rare, less-common, common).

Similarity matrices of floristic composition among plots were first created by using

the Bray-Curtis index on untransformed frequency data. When creating similarity

matrices for annual herbs and rare species, a ‘dummy species’ was added as present

in each plot to cope with a prevalence of absences (Clarke et al. 2006). Type III

(partial) sums of squares was used and floristic data were permuted 9999 times under

a reduced model. Permuting floristic composition within treatments assumes that the

observation units are exchangeable under a true null hypothesis, and breaks down

any temporal or spatial correlation, if present (Anderson 2001).

Where there was a significant interaction between survey year and burn treatment,

pair-wise comparisons were made between each burn treatment and year

combination using PERMANOVA, thus identifying where differences between

treatment groups occurred. We also compared species richness for each life-form

group between survey years, within each burn treatment, using Wilcoxon signed-

rank tests for paired, non-parametric data.

An assumption of PERMANOVA is that dispersion of samples within treatment

groups is homogenous among treatment groups (Anderson 2006); we tested this

using the PERMDISP routine in the PERMANOVA+ package in PRIMER

31

(Anderson et al. 2008). Where differences in dispersion were detected, unconstrained

ordination plots (non-metric multidimensional scaling; nMDS) were generated to

determine if differences detected by PERMANOVA were likely caused by

differences in grouping among treatments, as an effect of dispersion among

treatments, or a combination of both (Anderson et al. 2008).

Third, we determined the similarity in floristic composition of study landscapes

within survey years, and the dissimilarity of floristic composition between survey

years, by using the one-way Similarity Percentages (SIMPER) routine with the Bray-

Curtis similarity index, performed in PRIMER. The collective percentage

contribution of rare, less-common and common species in each life-form group was

calculated within and between survey years.

Finally, we used the SIMPER routine, with rare species only, to determine the

contribution of each life-form group to the similarity in floristic composition of rare

species in landscapes within each burn treatment, for each survey year. We then

calculated the contribution of each life-form group to the dissimilarity in floristic

composition of rare species between burn treatments for each survey year.

Non-metric multidimensional scaling based on Bray-Curtis similarity between sites

was used to construct 2-dimensional ordination diagrams displaying the location of

study plots within each treatment group relative to one-another, with 500 restarts per

plot.

32

2.3 Results

One hundred and twenty-one vascular plant species were identified during the study

(not including species in the families Poaceae, Orchidaceae, or genus Arthropodium),

with a total of 98 species in 2010 and 118 species in 2013. There were 11 common,

48 less-common and 39 rare species identified in the pre-burn 2010 survey. Three

rare species recorded in 2010 were not recorded in 2013, and 23 rare species were

recorded in 2013 that were not recorded in 2010.

2.3.1 Effects of prescribed burns on floristic diversity

Floristic diversity (effective number of species) differed between 2010 and 2013

(pseudo F(1,2) = 31.75, P < 0.001); pre-burn landscapes (2010) had a lower floristic

diversity than post-burn landscapes (2013; Fig. 2.3). Floristic diversity did not differ

among burn treatments (pseudo F(1,2) = 1.74, P = 0.17) and there was no interaction

between burn treatment and survey year (pseudo F(1,2) = 0.08, P = 0.91). Dispersion

among treatment groups was homogenous (F(5, 234) = 0.45, P = 0.82).

33

Fig. 2.3. Mean (±1.96 SE) floristic diversity (effective number of species;

exponential of Shannon H’ diversity index) of study landscapes before (2010) and

after (2013) experimental burns conducted during different seasons in a box-ironbark

forest in southeastern Australia; ■ unburnt reference (control), ● autumn burn, ▲

spring burn.

34

2.3.2 Effects of prescribed burns on floristic composition

PERMANOVA analysis indicated that overall floristic composition differed between

2010 and 2013, as did the composition of plant life-form and frequency of

occurrence groups (Table 2.1). The effect of year appeared to be strongest for rare

species and annual herbs; for all other groups, there was a high degree of overlap in

floristic composition between survey years (Figs. 2.4 and 2.5). Burn treatment

generally did not affect floristic composition (Table 2.1). There was a random effect

of landscape; study plots within some landscapes displayed a degree of aggregation

within the multidimensional space, based on their floristic composition (Figs. S2.1–

S2.7). A significant interaction between survey year and burn treatment occurred

only for the composition of rare species (pseudo F(1,2) = 2.0, P = 0.004). Thus, rare

species was the only group to show a clear effect of burn treatment on floristic

composition, after taking into account differences between survey years. The floristic

composition of rare species in 2010 (pre-burning) did not differ among landscapes.

However, in 2013, after the prescribed burns, the composition of rare plants in the

spring-burn landscapes (more extensive burn cover) differed from that of autumn-

burn landscapes, and separation of spring-burn from unburnt control landscapes

occurred (Table 2.2; Fig. 2.4).

35

Table 2.1. PERMANOVA results for the effect of survey year (2010, 2013) and burn treatment (unburnt control, autumn, spring) on the floristic

composition of landscapes (LS, included as a random factor, nested within burn treatment). Comparisons are provided with respect to plant

species’ life-form and frequency groups.

Group   Year    Burn    LS(Burn)    Year*Burn    Year*LS(Burn) 

  Pseudo‐F  P    Pseudo‐F  P    Pseudo‐F  P    Pseudo‐F  P    Pseudo‐F  P 

   All species    12.01  <0.001    0.71  0.854    5.77  <0.001    1.02  0.443    0.79  0.981 

 

Life‐form 

    

    

    

    

    

   Woody perennials    16.19  <0.001    0.56  0.929    6.08  <0.001    1.23  0.294    0.57  0.999 

   Perennial herbs or geophytes    6.23  <0.001    0.93  0.526    5.36  <0.001    0.87  0.573    1.06  0.339 

   Annual herbs    12.10  <0.001    0.57  0.738    3.95  <0.001    1.00  0.427    1.19  0.188 

 

Frequency 

    

    

    

    

    

   Common    6.75  0.002    0.54  0.870    4.93  <0.001    0.60  0.701    0.48  0.999 

   Less‐common    11.91  <0.001    0.72  0.845    6.18  <0.001    1.13  0.344    0.89  0.805 

   Rare    13.46  <0.001    1.22  0.221    3.72  <0.001    1.97  0.034    1.19  0.062 

36  

Table 2.2. Pairwise PERMANOVA comparisons of the similarity in floristic

composition of rare plant species among landscapes assigned to different burn

treatments, in 2010 (before prescribed burns), and in 2013 (after burns).

Pairwise comparison  

t  P 

2010 surveys     

   Control, Autumn  1.08  0.239 

   Control, Spring  0.89  0.545 

   Autumn, Spring  1.14  0.223 

2013 surveys     

   Control, Autumn  0.82  0.845 

   Control, Spring  1.41  0.095 

   Autumn, Spring  1.37  0.049 

37  

Fig. 2.4. Ordination (nMDS) of plots within a box-ironbark forest in southeastern Australia, based on the floristic composition of common (C),

less-common (L), and rare (R) species before (2010; open symbols) and after (2013, closed symbols) experimental prescribed burns conducted

during different seasons; ■ unburnt reference (control), ● autumn burn, ▲ spring burn.

38

Fig. 2.5. Ordination (nMDS) of plots within a box-ironbark forest in southeastern Australia, based on the floristic composition of woody

perennial (WP), perennial herb or geophyte (PH), and annual herb (AH) species before (2010; open symbols) and after (2013, closed symbols)

experimental prescribed burns conducted during different seasons; ■ unburnt reference (control), ● autumn burn, ▲ spring burn.

39  

Dispersion among treatment groups was homogenous between 2010 and 2013 for

overall floristic composition, woody perennials, perennial herbs and common species

groups (Table S2.1). Dispersion among plots also was homogenous among all burn

treatments, except when including only less-common species (Table S2.1).

In the annual herbs group, dispersion of plots differed between 2010 and 2013

(F(1, 238) = 10.52, P = 0.005), and between plots pre- and post-burn in the autumn

treatment group (t = 3.29, P = 0.003; Table S2). The nMDS ordination (Fig. 2.5)

indicated separation of autumn and spring burn treatment groups between years, and

an effect of dispersion (the average distance between autumn plots increased in 2013;

Table S2.3), i.e. floristic composition changed between survey years and there was

an effect of dispersion (autumn-burn plots became less similar in 2013).

In the less-common frequency group, dispersion differed between years

(F(1, 238) = 13.74, P < 0.001), between burn treatments (F(1,238) = 5.01, P = 0.010),

and between control plots and autumn/spring plots in 2010 (t = 3.37, P = 0.003

and t = 2.79, P = 0.01, respectively; Table S2.2). Dispersion of control plots also

differed between survey years (t = 3.29, P = 0.003), as did spring-burned plots

(t = 3.29, P = 0.003). The nMDS ordination (Fig. 2.4) displayed no clear change in

the clustering of treatment groups, suggesting the composition of less-common

species did not change markedly between survey years, but plots became more

similar in 2013 (Table S2.3).

For rare species, dispersion differed among plots between survey years, but did not

differ among treatments within survey years (Table S2.2). Clustering of burn

treatment groups in the nMDS ordination (Fig. 2.4), and average distance between

40

plots in each burn treatment group (Table S2.3), indicated that burn treatment groups

became more dissimilar in 2013, and also changed in composition.

2.3.3 Contribution of species life-form and frequency groups to floristic

composition

Based on SIMPER analysis, common species generally contributed most to the

floristic similarity of the vegetation among study landscapes in each survey year

when combined across burn treatments; the contribution of rare species was

relatively minor compared with that of common and less-common species (Table

2.3). However, for each life-form group, the contribution made by common species

to similarity among landscapes was lower in 2013 (post-burn) than 2010 (pre-burn),

with a corresponding increase in the contribution of less-common and rare species.

This trend was most pronounced for the ‘annual herbs’ life-form group (Table 2.3).

In relation to the dissimilarity among landscapes between years, rare species of

annual herbs made a substantial contribution to differentiating landscapes between

the 2010 and 2013 surveys; 22% of the dissimilarity in annual herbs was explained

by rare species, while 33.6% was explained by common species.

41

Table 2.3. The contribution (%) of species frequency groups (SIMPER analysis),

within each life-form group, to the floristic similarity of vegetation within (sim%),

and dissimilarity among (dis%), landscapes surveyed in 2010 and 2013

(C = common, L = less-common, R = rare), combined across burn treatments.

Contributions of the three rarity groups to overall (dis)similarity sum to 100%.

Survey year All species  Woody perennials 

Relative contribution (%)  Relative contribution (%) 

  sim%  C  L  R  sim%  C  L  R 2010  47.1  62.3  37.6  0.1  46.2  42.7  57.2  0.1 

2013  47.7  52.1  46.8  1.1  46.2  32.9  66.4  0.7 

  dis%        dis%       

2010, 2013  54.3  33.4  63.6  3.0  55.5  25.2  68.8  6.0 

 

 

Perennial herbs or geophytes  Annual herbs 

Relative contribution (%)  Relative contribution (%) 

  sim%  C  L  R  sim%  C  L  R 2010  50.5  80.3  19.6  0.1  23.0  84.8  14.7  0.5 

2013  53.2  73.8  25.5  0.7  18.8  22.4  67.1  10.5 

  dis%        dis%       

2010, 2013  49.4  45.3  47.6  7.1  84.9  33.6  42.1  22.3 

42

There was a low level of similarity in the composition of rare species within

landscapes grouped by burn treatment (Table 2.4). In unburnt control landscapes, the

similarity in floristic composition of rare species was 1.8% in 2010 and 5.0% in

2013. The contribution of rare ‘woody perennial’ species to this similarity increased

from 12.4% (pre-burn) to 70.9% (post-burn) across the survey years (Table 2.4), as

did the richness of rare woody perennials in these unburnt landscapes (Fig. 2.6, Table

S2.4). For autumn-burn landscapes, the relative contributions of each plant life-form

group to the floristic similarity of landscapes did not change markedly between 2010

and 2013, although the mean richness of rare species increased across all life-form

groups (Fig. 2.6, Table S2.4). For spring-burn landscapes, floristic similarity of rare

species was greatest in 2013 (12% similarity) compared with 2010 (1.8%). Most of

the similarity in rare species among spring-burn landscapes in 2010 was explained by

woody perennials (56%); whereas in 2013 it was explained by the combined

contribution of perennial herb and annual herb species (40.9 and 40%, respectively;

Table 2.4); with both of the latter increasing in species richness from 2010 to 2013

(Fig. 2.6, Table S2.4).

There was a very high level of dissimilarity in the floristic composition of rare

species between burn treatments, in both survey years (Table 2.4). Between unburnt

(control) and spring-burn landscapes, and unburnt and autumn-burn landscapes, the

contributions to dissimilarity of rare species by each life-form group were roughly

comparable across the survey years. Autumn-burn and spring-burn landscapes

differed more in terms of annual herb composition in 2013 than in 2010 (32.6 to

18.8%, respectively).

43

Table 2.4. The contribution (%) of rare species in each life-form group towards

floristic similarity of rare species within (sim%), and dissimilarity (dis%) among,

landscapes in pre-burn (2010) and post-burn (2013) surveys (WP = woody

perennials, PH = perennial herbs or geophytes, AH = annual herbs). Contributions of

the three life-form groups to overall (dis)similarity sum to 100%.

Treatment group 

Life‐form group  

Relative contribution (%) 

sim%  WP  PH  AH 

Within 2010 surveys         

    Control  1.8  12.4  43.5  44.1 

    Autumn  1.2  44.0  45.6  10.4 

    Spring  1.8  55.9  24.1  20.0 

Within 2013 surveys         

    Control  5.0  70.9  13.2  15.9 

    Autumn  6.1  53.8  33.7  12.5 

    Spring  12.0  20.0  40.4  39.7 

Pairwise comparison 

Life‐form group 

Relative contribution (%) 

dis%  WP  PH  AH 

Within 2010 surveys         

   Control, Autumn  99.3  34.2  36.5  29.3 

   Control, Spring  99.0  38.1  31.5  30.4 

   Autumn, Spring  98.9  44.8  36.4  18.8 

Within 2013 surveys         

    Control,  Autumn  95.7  49.1  28.3  22.6 

    Control, Spring  95.6  39.3  29.5  31.2 

    Autumn, Spring  94.2  35.5  31.9  32.6 

44

Fig. 2.6. Mean (±1.96 SE) richness of rare species per plot in pre-burn (● 2010) and

post-burn (▲ 2013) landscapes, panelled by burn season. AH = annual herb, PH =

perennial herbs or geophytes, WP = woody perennials. Wilcoxon signed ranks tests

were used to compare the richness of each life-form group between survey years (full

results of tests are provided in Table S2.4); * P <0.05, ** P <0.01, *** P <0.001.

45

2.4 Discussion

Fire did not affect the overall floristic composition or diversity (effective number of

species) of landscapes differently to the rainfall-driven recruitment occurring in

landscapes where fire was absent. Fire is known to promote the recruitment of many

box-ironbark genera (Bell 1999; Penman et al. 2009) and, so, it was expected that

burnt landscapes would be differentiated from unburnt landscapes by species

recruiting as a consequence of fire. However, some species from these forests have

seed, or a portion of seed, which will break dormancy and germinate in the absence

of fire (Orscheg and Enright 2011). Thus, it is likely that the strongest driver of

species recruitment between 2010 and 2013 was the markedly above-average rainfall

acting as a cue for germination and enabling successful recruitment of non-dormant

seed. Drought-conditions preceding the survey were suboptimal for rainfall-driven

germination and recruitment (Meers and Adams 2003), and populations of species in

the community were either stable or declining leading up to 2010 (Bennett et al.

2013). A soil seed-bank would have developed over the years preceding the survey

when environmental triggers for germination were lacking. Aside from loss of seed

due to predation, it is likely that adequate propagules of many species were available

(Andersen 1989; Comino et al. 2004) and awaiting environmental cues such as

rainfall for germination.

Burn treatment did not affect the overall floristic composition of plots differently to

sites that were unburnt. Many woody perennial and geophyte species in the

community have the capacity to resprout following disturbance (Morgan 1999; Cheal

2010) and this would have strongly influenced similarity among landscapes and

survey years. The intensity and residence time of a fire affects the ability of plants to

resist fire and protect resprouting organs (Clarke et al. 2013). A fuel-reduction burn

46

in autumn or spring is unlikely to reach the lethal temperatures and residence time

necessary to kill the dominant eucalypts (Wesolowski et al. 2014), particularly where

fires are actively managed to be patchy. It is likely that many other resprouting

species would be able to survive such fires. Species that resprout are generally poor

recruiters from seed following disturbance compared to species that are obligate

seeders (Paula and Pausas 2008). With most species surviving a fire, the overall post-

fire species composition of dry sclerophyll forest is predominantly determined by the

prior community (Purdie and Slatyer 1976; Christensen et al. 1981).

Prescribed burns affected the type of life-forms of rare species present in a box-

ironbark forest landscape. An increase in the frequency of rare annual herbs

occurring in landscapes burnt in spring compared with autumn is not surprising, as a

larger proportion of the landscape was burnt in spring, potentially opening up niches

and reducing competition from established plants. Propagules of annual species

either found in the soil seed bank or migrating into the area (Bell et al. 1993) could

take advantage of reduced competition, and increased nutrient loads in the ash bed

(Certini 2005). Such a pattern has been observed previously in other sclerophyll

forests (Purdie and Slatyer 1976), with post-fire ephemerals reaching their highest

cover immediately after fire, decreasing as time since fire increased (Gosper et al.

2012). The lower recruitment of rare annuals in unburnt and the lesser-extent autumn

burnt landscapes could be explained, at least in part, by higher competition for

resources such as space, nutrients, and water, especially when common annual and

ground-cover species are filling potential niches.

Recruitment of rare woody-perennial species was comparable between burnt and

unburnt landscapes. Many species are known to require fire to trigger seed

germination (Table S2.5), and it was anticipated that the recruitment of such species

47

would be limited to burnt landscapes. However, species in genera that have a known

post-fire germination response, such as Dillwynia Sm. and Pultenaea Sm. (Auld

1996), recruited in both burnt and unburnt landscapes (Table S2.2), suggesting that

rainfall had the strongest effect on recruitment for many woody species. Some rare

woody species with no known fire response, e.g. Stenanthera pinifolia R.Br.,

recruited only in unburnt sites. By conducting a prescribed burn there is potential for

negatively impacting species that are fire intolerant or require long-unburnt

conditions to recruit or persist (e.g. Morrison et al. 1995; Peterson and Reich 2008).

The response of rare species in autumn-burn landscapes could be attributed to the

landscape heterogeneity produced by the patchy and relatively cool prescribed burns

at that time. It is likely that a mosaic of burnt niches for annuals, unburnt refugia for

fire-intolerant species, and effects of fire to stimulate flowering and seed germination

provides the greatest potential for maximising species recruitment.

Of interest, but impossible under experimental conditions for public safety, is the

effect of landscape-scale summer fires of high intensity; further research following

natural summer wildfires in these landscapes is warranted. It can be speculated that a

summer fire could pose a challenge for species where resprouting ability is

contingent on wildfire intensity, but potentially produce greater effects of soil

heating on germination of deeply buried seed. Greater residence time allows deeper

penetration of heat into the soil that could reach the seed of obligate seeder species

that are deeply buried, stimulating more germination (e.g. Hindrum et al. 2012).

This, combined with greater mortality in species that resprout, may lead to overall

floristic differences between summer-burn sites and the sites observed in this study,

at least temporarily. The effect of fire frequency on rare species and floristic

composition also is an important consideration for vegetation management, and

48

should be considered as part of a longer-term research focus in the landscapes

studied, as well as the effect of disturbance on grasses, orchids and Arthropodium,

which were not included in this study.

Recruitment of rare annual species following prescribed burns in spring follows the

CSR (Competitive, Stress-tolerant, Ruderal) theory of (Grime 1977), insofar as the

presence of ruderal annual species probably was promoted by release from

competition with dominant and stress-tolerant species. It also lends weight to a mass-

ratio hypothesis in this system (Grime 1998), which posits that diversity peaks soon

after disturbance, as rare or less-common species take advantage of temporary gaps

within niches that are available due to a reduction in abundance of dominant species.

Under this hypothesis, as highly competitive dominants recover, they modify

ecosystem conditions to favour their own existence, resulting in a decline in species

diversity as a proportion of species become competitively excluded with time.

The functional contributions of rare and less-common species, whilst important, are

generally thought to be confined to facilitating the recruitment of common dominants

(Grime 1998).

Indeed, rare species were less influential than common and less-common species in

determining floristic similarity among both pre- and post-fire landscapes, and in

terms of productivity, likely play a minor role compared to larger, long-lived

dominant species. However, potentially important short-term ecosystem roles for rare

annual species exist at both the individual species and collective level. These include

facilitating the recovery and recruitment of more common or long-lived species

(Connell and Slatyer 1977; Soliveres et al. 2014) or playing roles in soil stabilisation

(Pohl et al. 2009), nutrient dynamics (Marsh et al. 2000), water dynamics (Rixen and

49

Mulder 2005), providing supplementary or novel resources for biota (Swanson et al.

2010), or having below-ground effects on soil biota (Mariotte 2014).

Prescribed burning in fire-tolerant ecosystems can promote the recruitment of species

that are rare within a landscape, but the context of a disturbance (e.g. season, extent,

and post-fire weather) can dictate their response. Managing disturbance to produce

heterogeneous environments may be most beneficial for maintaining a diversity of

rare species’ life-forms on a landscape-scale. This in turn can promote functional

diversity, which can provide benefits in buffering and stabilising ecosystem

processes in the face of future disturbance.

50  

2.5 Supporting information for chapter two

Table S2.1. Permutational analysis of dispersions among plots based on their floristic composition, with year of survey and season of burn as

fixed factors. Comparisons including all species in the community, as well as species life-form and frequency groups, are shown. Significant

results are provided in bold.

Group Year  Burn  Year*burn 

F(1, 238)  P  F(2,237)  P  F(5, 234)  P 

   All species  0.23  0.650  2.56  0.096  1.66  0.197 

             

Life‐form             

   Woody perennials  <0.01  0.990  1.62  0.240  1.03  0.475 

   Perennial herbs or geophytes  2.91  0.112  1.97  0.175  2.03  0.122 

   Annual herbs  10.52  0.005  1.04  0.465  3.61  0.020 

             

Frequency             

   Common  1.36  0.266  0.85  0.470  0.80  0.610 

   Less‐common  13.74  <0.001  5.01  0.010  6.57  <0.001 

   Rare  95.41  <0.001  2.21  0.266  19.81  <0.001 

51  

Table S2.2. Pairwise comparisons of average dispersion distance (distance-to-

centroid, calculated by permutational analysis of dispersions) among plots within

different treatment groups (survey year, season of prescribed burn). Significant

results are provided in bold.

Pairwise comparisons  Annual herbs  Less‐common  Rare 

  t  P  t  P  t  P 

Within 2010 surveys             

   Control, Autumn  2.14  0.062  3.37  0.003  1.27  0.309 

   Control, Spring  0.81  0.503  2.79  0.010  0.39  0.767 

   Autumn, Spring  1.73  0.139  0.46  0.666  1.95  0.144 

Within 2013 surveys             

    Control,  Autumn  0.51  0.679  1.64  0.128  0.42  0.733 

    Control, Spring  0.78  0.529  1.50  0.169  0.07  0.954 

    Autumn, Spring  0.34  0.785  0.09  0.933  0.48  0.675 

Between 2010 and 2013             

    Control  0.01  0.987  3.99  <0.001  4.38  <0.001 

    Autumn  3.29  0.003  1.86  0.077  6.97  <0.001 

    Spring  1.95  0.099  2.18  0.038  5.15  <0.001 

Within burn treatments             

    Control, Autumn  0.48  0.677  3.12  0.003  0.94  0.480 

    Control, Spring  0.69  0.559  2.66  0.011  0.78  0.541 

    Autumn, Spring  1.42  0.222  0.51  0.632  2.08  0.108 

52

Table S2.3. Average (± SE) dispersion distance (distance-to-centroid, calculated by

permutational analysis of dispersions) among plots within different treatment groups

(survey year, season of prescribed burn). Burn treatments are pooled when

comparing effect of survey year, and survey years are pooled when comparing effect

of burn treatments.

Treatment group Annual herbs    Less‐common    Rare 

Average  SE    Average  SE    Average  SE 

2010  34.92  0.83    44.47  0.83    24.99  1.72 

2013  32.93  0.82    40.53  0.67    44.60  1.04 

                 

Control 2010  35.19  2.11    49.07  1.45    26.56  3.35 

Control 2013  35.24  2.12    41.72  1.14    43.92  2.11 

Autumn 2010  29.04  1.73    42.09  1.27    21.10  2.55 

Autumn 2013  36.58  1.50    39.10  0.98    42.69  1.76 

Spring 2010  33.05  1.55    42.95  1.37    28.23  2.68 

Spring 2013  37.31  1.54    39.23  1.03    43.76  1.33 

                 

Control  35.82  1.38    46.15  1.12    37.03  2.19 

Autumn  35.02  0.97    41.61  0.86    34.29  1.75 

Spring  37.08  1.07    42.23  0.88    39.01  1.50 

53

Table S2.4. Difference in richness of rare species from different plant life-form

groups within autumn-burn, spring-burn and unburnt control plots, before (2010) and

after (2013) prescribed burning (AH, annual herb; PH, perennial herb or geophyte;

WP, woody perennial). Significant results from Wilcoxon signed ranks tests are

provided in bold.

Treatment 

group 

AH    PH    WP 

Z  P    Z  P    Z  P 

Control  ‐0.65  0.518    ‐1.81  0.070    ‐3.47  0.001 

Autumn  ‐4.27  <0.001    ‐3.29  0.001    ‐4.09  <0.001 

Spring  ‐4.44  <0.001    ‐3.45  0.001    ‐2.20  0.028 

54  

Table S2.5. List of rare species and the number of plots in which they were detected

within a box-ironbark forest before (10 = 2010) and after (13 = 2013) prescribed

burning in different seasons (U = unburnt, A = autumn, S = spring). Broad fire

response groups of species are included in column Re (can resprout after fire) and

Se (heat or smoke stimulated germination); references are provided as a footnote.

? = fire response undocumented.

Woody perennials                   

  U  A  S  Re  Se   Species  10  13  10  13  10  13                          Eucalyptus goniocalyx F.Muell. ex Miq.  2  2  ‐  ‐  1  1  ?  ✓1   

Hovea heterophylla A.Cunn. ex Hook.f.  1  5  1  6  1  2  ✓2  ✓1   

Exocarpos cupressiformis Labill.  1  3  1  2  1  ‐  ✓3  x1   

Amyema miquelii (Lehm. ex Miq.) Tiegh.  1  2  ‐  1  ‐  1  ✓4  x1   

Melichrus urceolatus R.Br.  1  2  ‐  ‐  ‐  ‐  ✓3  ?   

Calytrix tetragona Labill.  1  1  2  1  ‐  ‐  ✓2  ?   

Dillwynia sericea A.Cunn.  ‐  5  2  12  1  8  ?  ✓1   

Pultenaea pedunculata Hook.  ‐  4  ‐  3  ‐  1  ?  ✓1   

Pimelea humilis R.Br.  ‐  3  ‐  ‐  5  10  ✓5  ?   

Dillwynia cinerascens R.Br. ex Sims  ‐  2  1  1  ‐  1  ?  ✓1   

Eucalyptus melliodora A.Cunn. ex Schauer  ‐  1  ‐  ‐  1  ‐  ✓6  ✓1   

Pultenaea graveolens Tate  ‐  1  ‐  ‐  ‐  1  ?  ✓1   

Stenanthera pinifolia R.Br.  ‐  1  ‐  ‐  ‐  ‐  X7  x1   

Acacia gunnii Benth.  ‐  ‐  4  5  1  2  ✓8  ✓1   

Boronia anemonifolia A.Cunn.  ‐  ‐  1  8  ‐  ‐  ✓9  ?   

Prostanthera denticulata R.Br.  ‐  ‐  1  3  1  1  ?  ✓1   

Hardenbergia violacea (Schneev.) Stearn  ‐  ‐  1  2  1  3  ✓3  ✓1   

Acacia montana Benth.  ‐  ‐  ‐  2  4  1  ?  ✓1   

Gompholobium huegelii Benth.  ‐  ‐  ‐  2  ‐  ‐  ✓7  ✓1   

Hakea decurrens R.Br.  ‐  ‐  ‐  1  ‐  ‐  x10  ✓1   

Philotheca verrucosa (A.Rich.) Paul G. Wilson  ‐  ‐  ‐  ‐  2  1  ?  ?   

Indigofera australis Willd. subsp. australis  ‐  ‐  ‐  ‐  1  ‐  ✓3,8  ✓1   

                   

55

Table S2.5 (contd.).

Perennial herbs or geophytes                 

  U  A  S  Re  Se Species  10  13  10  13  10  13                      Stylidium graminifolium sensu Willis (1972)  2  2  1  8  1  3  ✓3,8  ✓1 

Juncus subsecundus N.A.Wakef.  2  ‐  1  ‐  2  1  ?  x1 

Leptorhynchos tenuifolius F.Muell.  1  1  ‐  ‐  ‐  ‐  ?  ? 

Gonocarpus elatus (A.Cunn. ex Fenzl) Orchard  1  ‐  ‐  ‐  ‐  ‐  ✓11  ✓1 

Lobelia gibbosa Labill.  ‐  4  2  1  4  ‐  ✓12  ? 

Lagenophora huegelii Benth.  ‐  2  2  ‐  3  ‐  ?  x1 

Cheilanthes austrotenuifolia H.M.Quirk & T.C. Chambers  ‐  2  1  1  1  1  ✓11,13  x1 

Drosera glanduligera Lehm.  ‐  1  2  5  ‐  ‐  ?  ? 

Galium gaudichaudii DC.  ‐  1  ‐  5  2  1  ?  x1 

Senecio hispidulus A.Rich.  ‐  1  ‐  ‐  1  3  x8,14  x1 

Stypandra glauca R.Br.  ‐  1  ‐  ‐  1  1  ✓12  ? 

Brachyscome multifida DC.  ‐  ‐  3  2  2  3  ?  x1 

Senecio quadridentatus Labill.  ‐  ‐  ‐  6  1  20  x8,13  x1 

Brachyscome perpusilla (Steetz) J.M.Black  ‐  ‐  ‐  3  ‐  ‐  ?  x1 

Burchardia umbellata R.Br.  ‐  ‐  ‐  3  ‐  ‐  ✓15  ? 

Drosera peltata sensu Conn (1996)  ‐  ‐  ‐  2  ‐  ‐  ✓5,7  x5 

Epilobium billardiereanum Ser. ex DC.  ‐  ‐  ‐  1  ‐  2  ✓12  x1 

Plantago hispida R.Br.  ‐  ‐  ‐  1  ‐  ‐  ?  x1 

Leptorhynchos squamatus (Labill.) Less.  ‐  ‐  ‐  ‐  1  ‐  ✓5,8  x1 

Euchiton japonicus (Thunb.) Holub  ‐  ‐  ‐  ‐  ‐  3  ?  x1 

Euchiton sphaericus (Willd.) Holub  ‐  ‐  ‐  ‐  ‐  3  x7,13  x1 

Senecio runcinifolius J.H.Willis  ‐  ‐  ‐  ‐  ‐  3  ?  x1 

Lepidosperma laterale R.Br.  ‐  ‐  ‐  ‐  ‐  1  ✓16  ? 

Luzula meridionalis Nordensk.  ‐  ‐  ‐  ‐  ‐  1  ✓5,8  ? 

                 

56

Table S2.5 (contd.).

Annual herbs                 

  U  A  S  Re  Se Species  10  13  10  13  10  13                      Calandrinia calyptrata Hook.f.   3  3  2  4  ‐  2  ?  ? 

Crassula sieberiana sensu Toelken, Jeanes & Stajsic (1996)  2  3  ‐  4  2  16  x3,17  ✓1 

Poranthera microphylla Brongn.  2  3  ‐  ‐  3  4  x12  ✓1 

Siloxerus multiflorus Nees  2  1  ‐  4  1  1  ?  ? 

Stuartina muelleri Sond.  1  ‐  ‐  1  4  4  ?  x1 

Centrolepis strigosa (R.Br.) Roem. & Schult. subsp. strigosa  ‐  2  1  1  1  3  ✓12  ✓12 

Crassula decumbens Thunb.  ‐  1  ‐  4  1  13  x18  ✓1 

Hyalosperma demissum (A.Gray) Paul G.Wilson  ‐  1  ‐  2  ‐  2  ?  ? 

Juncus bufonius L.  ‐  ‐  2  ‐  ‐  2  ?  ? 

Hydrocotyle foveolata H.Eichler  ‐  ‐  ‐  5  ‐  8  ?  ? 

Crassula colorata (Nees) Ostenf.  ‐  ‐  ‐  1  ‐  4  ?  ✓1 

Crassula peduncularis (Sm.) Meigen  ‐  ‐  ‐  1  ‐  ‐  ?  ✓1 

Senecio biserratus Belcher  ‐  ‐  ‐  ‐  ‐  2  ?  ? 

Gnaphalium indutum Hook.f.   ‐  ‐  ‐  ‐  ‐  1  ?  ? 

Senecio glomeratus Desf. ex Poir.  ‐  ‐  ‐  ‐  ‐  1  ?  ? 

Triptilodiscus pygmaeus Turcz.  ‐  ‐  ‐  ‐  ‐  1  ?  ✓1 

                 

1 Ralph M (2003) and references therein; Growing Australian native plants from seed. Murray Ralph / Bushland Horticulture, Fitzroy.

2 Vivian LM & Cary GJ (2011) Relationship between leaf traits and fire-response strategies in shrub species of a mountainous region of south-eastern Australia. Annals of Botany, 109(1), 197-208.

3 Purdie RW & Slatyer RO (1976) Vegetation succession after fire in sclerophyll woodland communities in south‐eastern Australia. Australian Journal of Ecology, 1(4), 223-236.

4 Kelly P, Reid N & Davis I (1997) Effects of experimental burning, defoliation, and pruning on survival and vegetative resprouting in mistletoes (Amyema miquelii and Amyema pendula). International Journal of Plant Sciences, 158(6), 856-861.

5 Morgan JW (1999) Defining grassland fire events and the response of perennial plants to annual fire in temperate grasslands of south-eastern Australia. Plant Ecology, 144, 127-144.

6 Denham AJ, Vincent BE, Clarke PJ & Auld TD (2016) Responses of tree species to a severe fire indicate major structural change to Eucalyptus–Callitris forests. Plant Ecology, 217, 617–629.

7 Wills TJ & Read J (2007) Soil seed bank dynamics in post-fire heathland succession in south-eastern Australia. Plant Ecology, 190, 1–12.

8 Kitchin M, Wright G, Robertson G, Brown D, Tolsma A & Stern SE (2013) Long term monitoring for fire management – 10 years on for the Australian Alps fire plots; technical report no. 26. Environment and Sustainable Development Directorate, ACT Government.

57

9 Benson D & McDougall L (2001) Ecology of Sydney plant species part 8: dicotyledon families Rutaceae to Zygophyllaceae. Cunninghamia, 7(2), 241-462.

10 Enright NJ & Goldblum D (1999) Demography of a non-sprouting and resprouting Hakea species (Proteaceae) in fire-prone Eucalyptus woodlands of southeastern Australia in relation to stand age, drought and disease. Plant Ecology, 144(1), 71-82.

11 Vesk PA, Warton DI & Westoby M (2004) Sprouting by semi-arid plants: testing a dichotomy and predictive traits. Oikos, 107, 72-89.

12 Penman TD, Binns D, Allen R, Shiels R & Plummer S (2008) Germination responses of a dry sclerophyll forest soil-stored seedbank to fire related cues. Cunninghamia, 10, 547-555.

13 Downe J & Coates F (2004) Recovery of silurian limestone Pomaderris shrubland after the 2003 bushfires in north-east Victoria; Arthur Rylah Institute for Environmental Research Technical Report No. 151. Department of Sustainability and Environment, Victoria.

14 Prober, S.M., Thiele, K.R. & Bramwell, M. (2007) Intense fires promote uncommon fire ephemerals in Currawang Acacia doratoxylon dry scrubs of Little River Gorge, East Gippsland. Victorian Naturalist, 124(6), 320-331.

15 Allen T, Brewster E, Brown M, Drummond D, Elli M, Gurling J, Laby RJ, Wallis G, Burrows F & Jones J (2008) Notes on the post-fire recovery of plants at Wilsons Promontory. Victorian Naturalist, 125(3), 87-91.

16 Benson D & McDougall L (2002) Ecology of Sydney plant species part 9: Monocotyledon families Agavaceae to Juncaginaceae. Cunninghamia, 7(4), 695-930.

17 Venn SE & Morgan JW (2010) Soil seedbank composition and dynamics across alpine summits in south-eastern Australia. Australian Journal of Botany, 58, 349-362.

18 Benson D & McDougall L (1995) Ecology of Sydney plant species part 3: dicotyledon families Cabombaceae to Eupomatiaceae. Cunninghamia, 4(2), 789-100.

 

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Fig. S2.1. Similarity of floristic composition (nMDS, Bray Curtis similarity) in sites

within landscapes before (2010, open symbols), and after (2013, closed symbols)

prescribed burn treatments in autumn (AU), spring (SP) or left unburnt as a control

(UB). ‘YearStudy_site’ displays the code for each landscape, and their corresponding

symbol in the plot.

 

59

Fig. S2.2. Similarity of floristic composition (nMDS, Bray Curtis similarity) of

common species in sites within landscapes before (2010, open symbols), and after

(2013, closed symbols) prescribed burn treatments in autumn (AU), spring (SP) or

left unburnt as a control (UB). ‘YearStudy_site’ displays the code for each

landscape, and their corresponding symbol in the plot.

 

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Fig. S2.3. Similarity of floristic composition (nMDS, Bray Curtis similarity) of less-

common species in sites within landscapes before (2010, open symbols), and after

(2013, closed symbols) prescribed burn treatments in autumn (AU), spring (SP) or

left unburnt as a control (UB). ‘YearStudy_site’ displays the code for each

landscape, and their corresponding symbol in the plot.

 

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Fig. S2.4. Similarity of floristic composition (nMDS, Bray Curtis similarity) of rare

species in sites within landscapes before (2010, open symbols), and after (2013,

closed symbols) prescribed burn treatments in autumn (AU), spring (SP) or left

unburnt as a control (UB). ‘YearStudy_site’ displays the code for each landscape,

and their corresponding symbol in the plot.

 

62

Fig. S2.5. Similarity of floristic composition (nMDS, Bray Curtis similarity) of

woody perennial species in sites within landscapes before (2010, open symbols), and

after (2013, closed symbols) prescribed burn treatments in autumn (AU), spring (SP)

or left unburnt as a control (UB). ‘YearStudy_site’ displays the code for each

landscape, and their corresponding symbol in the plot.

 

63

Fig. S2.6. Similarity of floristic composition (nMDS, Bray Curtis similarity) of

perennial herb and geophyte species in sites within landscapes before (2010, open

symbols), and after (2013, closed symbols) prescribed burn treatments in autumn

(AU), spring (SP) or left unburnt as a control (UB). ‘YearStudy_site’ displays the

code for each landscape, and their corresponding symbol in the plot.

 

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Fig. S2.7. Similarity of floristic composition (nMDS, Bray Curtis similarity) of

annual herb species in sites within landscapes before (2010, open symbols), and after

(2013, closed symbols) prescribed burn treatments in autumn (AU), spring (SP) or

left unburnt as a control (UB). ‘YearStudy_site’ displays the code for each

landscape, and their corresponding symbol in the plot.

 

65

Chapter 3 When functional diversity is not driven by rare species: a

case study in a temperate woodland

This chapter has been written as a manuscript for publication:

Patykowski J, Dell M, Wevill T, Gibson M (2017) When functional diversity is not driven by rare species: a case study in a temperate woodland.

3.1 Introduction

Measuring the effect of disturbance on ecological communities is important for

understanding community resilience (Lavorel 1999), trajectories of recovery (Cramer

et al. 2008) and, thus, how best to manage disturbance for species, biodiversity, and

conservation (Hobbs and Huenneke 1992). Indices of community composition, such

as species richness and diversity, long have been used to such effect (e.g. MacArthur

1965; Whittaker 1972; Peet 1974) although these indices do not always reveal the

full ecological story (Diaz and Cabido 2001; Cadotte et al. 2011). Functional trait

analysis offers a meaningful way to understand differences among biological

communities (e.g. Leitão et al. 2016; Ames et al. 2017; Carmona et al. 2017)

because functional traits—the morphological, physiological and phenological

attributes of plants (Pérez-Harguindeguy et al. 2013)—not only determine how

species respond to their environment, but how they affect ecosystem functioning

(Lavorel and Garnier 2002). By understanding how community trait composition and

functional diversity changes with disturbance, greater insight can be gained into the

effect of disturbance on ecosystem processes (Mouillot et al. 2013b).

Wildfire is a globally relevant disturbance exerting great influence on the distribution

and composition of ecological communities (Bond et al. 2005; Bowman et al. 2009;

 

66

Allen et al. 2010). Climate change is predicted to increase the frequency and

intensity of fire events in many parts of the world (Moritz et al. 2012), so more

advanced and comprehensive solutions for fire management are required. Fire

frequency, intensity, and timing is typically examined in the context of community

composition (Morrison et al. 1995), and the response of species based on life form

group (Heisler et al. 2003; Peterson and Reich 2008) or response syndrome (Pausas

et al. 2004). This approach is justified, as maintaining species diversity is unarguably

important for maintaining ecological stability (Hooper et al. 2005). However, the

effects of fire on the functional diversity of ecosystems may be a greater determinant

of ecosystem functioning than species composition (Diaz and Cabido 2001). Given

recent studies demonstrating the important role of rare species in supporting

vulnerable ecosystem functions (Mouillot et al. 2013a; Leitão et al. 2016), it is

timely that we understand how fire not only affects the presence of rare species, but

how their presence affects functional diversity.

Four independent and complementary indices often are used in combination to

compare different facets of functional diversity among communities (Mouchet et al.

2010; Schleuter et al. 2010; Mouillot et al. 2013b; Boersma et al. 2016). Functional

dispersion (FDis) quantifies the average distance of individual species in a

multidimensional space to a centroid representing a mean trait value for all

combinations of traits in the community (Laliberté and Legendre 2010). As such,

FDis can identify differences in the relative abundance of traits among communities.

Functional divergence (FDiv) measures the distribution of species traits in a

multidimensional space, and whether the most abundant species support extreme or

convergent trait characteristics (Villéger et al. 2008). Functional evenness (FEve)

describes the evenness to which species trait combinations are distributed within a

 

67

multidimensional trait space (Villéger et al. 2008), and functional richness (FRic)

quantifies the volume of multidimensional space occupied by all species in a

community, based on the dissimilarity of their traits (Villéger et al. 2008).

Comparing functional diversity indices in pre- and post-disturbance communities

offers insight into functional change in ecosystems (Mouillot et al. 2013b).

Inappropriate disturbance regimes, such as over- or under-frequent burning, can lead

to erosion of biodiversity (Folke et al. 2004; Bond and Keeley 2005); rare species

often are more susceptible to extirpation than common species (Van Calster et al.

2008). Thus, an important aspect of ecosystem function is understanding the role of

rarer versus more common species in contributing to functional diversity, potentially

highlighting ecosystems that may be more vulnerable to catastrophic collapse. Leitão

et al. (2016) measured the effect of simulated species loss (losing rarest species first,

commonest species first, and random species loss) on three indices of functional

diversity, to understand the functional contribution of rare species in three tropical

ecosystems (trees, birds, and fish). Along with FRic, they calculated indices of

functional specialisation (FSpe; Villéger et al. 2010) and functional originality (FOri;

Mouillot et al. 2008). Functional specialisation is a measure of distinctiveness of

species traits within the community (Mouillot et al. 2013b), whilst functional

originality quantifies how changes in species abundance affect functional redundancy

between species i.e. how closely species share traits with other species (Mouillot et

al. 2008). By simulating species loss and recalculating these functional indices at

each step of loss, Leitão et al. (2016) demonstrated that rare species played a

disproportionately large role in maintaining functional diversity compared to

common species in the ecosystems they studied.

 

68

Our study had two objectives; firstly, to understand the effect of fire on functional

diversity, and, secondly, to understand the effects of biodiversity erosion on

functional diversity in a temperate woodland. As a case study, we compared FRic,

FDiv, FEve and FDis before, and after, prescribed burning of landscapes in a box-

ironbark forest in southeastern Australia. Floristic data were collected for woody

perennial species during spring 2010, during a period of rainfall that ended a decade-

long drought in the region (Bureau of Meteorology 2017). Six landscapes in the

forest were then treated with a low-cover prescribed burn the following autumn, and

six landscapes were treated with a high-cover prescribed burn the following spring

(Holland et al. 2017). Three landscapes were left unburnt as control landscapes.

All 120 sites within the fifteen landscapes were resurveyed in 2013, allowing us to

compare the relative effect of prescribed burns versus the effect of drought-breaking

rainfall on functional diversity. To understand the effects of biodiversity erosion on

functional diversity before, and after, prescribed burning, we simulated the effect of

species loss on FRic, FOri, and FSpe using three species loss scenarios; lose rarest

species first, lose commonest species first, or lose species randomly (null model),

taking the approach outlined by Leitão et al. (2016). Species loss was simulated in

pre- and post-fire communities using the region-wide species pool, and, because

species composition at a local-scale is usually a subset of the region-wide species

pool, we simulated the effect of species loss on functional diversity from

communities at a survey-plot level.

 

69

3.2 Materials and methods

3.2.1 Study site

Data collection occurred within the Heathcote-Graytown-Rushworth forest, a

temperate, sclerophyllous forest in southeastern Australia. The ecosystem has a

Eucalyptus L'Hér. dominated overstorey to 20 m tall, and a patchy understorey of

shrubs (Calder and Calder 2002). The forest regenerated following extensive clearing

and disturbance from alluvial and deep lead gold mining throughout the 19th century

(Newman 1961) and from timber harvesting which occurred until recent decades

(ECC 2001). Based on less-disturbed remnant patches of similar vegetation, it can be

inferred that the present community comprises a subset of species from an original,

more diverse assemblage (Tolsma et al. 2007). Most woody perennial species in the

ecosystem display clear adaptations to fire as a disturbance, such as the ability to

resprout from epicormic or subterranean buds (Tolsma et al. 2007), or fire-stimulated

seed germination (Orscheg and Enright 2011).

Mean annual rainfall in the region is 594 mm; between 1997 and 2009 the region was

in drought, with 480 mm falling, on average, per year (Heathcote weather station;

ID# 088029; Bureau of Meteorology 2017). This had a negative effect on recruitment

(Meers and Adams 2003) and vegetation structure in these forests (Bennett et al.

2013). The drought broke in early 2010 (mean annual rainfall between 2010-2011

was 869 mm), at the beginning of initial floristic surveys. The inclusion of control

landscapes in the study design allowed us to compare the effect of rainfall versus the

effect of rainfall and prescribed fire on different functional indices.

 

70

3.2.2 Experimental prescribed burns

Fifteen landscapes were selected throughout the forest; each was approx. 70–120 ha

in area and separated by a linear distance of >500 m. Areas that had experienced

burning or timber harvesting within the last 30 years were avoided. Six landscapes

received low-cover burns in autumn (mean cover 26%; range 22–51%), six

landscapes received high-cover burns in spring (mean 69%; range 52–89%), and

three landscapes were left as unburnt control landscapes (Holland et al. 2017).

Prescribed burns were conducted by fire management agencies (Department of

Environment, Land, Water and Planning, and Parks Victoria) in autumn (late Feb–

Apr) and spring (Oct–Nov) of 2011.

3.2.3 Measuring species rarity

To determine the frequency of occurrence for woody perennial species, eight

permanent 20 m × 20 m plots were established in each of the fifteen study landscapes

(n=120), to monitor vegetation composition. Surveys were conducted in each plot in

spring (Sep–Oct) of 2010, prior to the prescribed burns. All plots were resurveyed in

spring of 2013, two years after being burnt; this allowed time for species to

germinate and become established. Not only did this aid in their identification, but

also affirmed their contribution to ecosystem functioning; individuals failing to

survive past germination were assumed to make little, if any, functional contribution.

Within each of the 120 plots, five 1 m × 1 m quadrats were established at fixed

locations, and surveyed for woody perennial species. Species occurring within, or

had above-ground vegetative parts overhanging the boundary of all five quadrats

were given a frequency score of 1; species overhanging or occurring within four,

 

71

three or two quadrats received a score of 0.8, 0.6 or 0.4, respectively. Species

overhanging or occurring in a single quadrat, or the 20 m × 20 m plot (but not in a

quadrat), were given a frequency score of 0.2. Rarity values were generated for each

species by multiplying the number of plots they occurred in by their mean abundance

within the plots they occupied and dividing that by the maximum possible frequency

score to place species on a rarity scale of 0–1, with zero representing absence.

3.2.4 Species functional traits

We selected four functional traits relating to key structural, physiological and

phenological attributes of plants governing their influence on the broader ecosystem;

maximum height (cm), specific leaf area (SLA; cm -2 g -1), leaf nitrogen to

phosphorus ratio (mg -1 g -1), and onset of flowering (month). Maximum height is

associated with the structural role of plants, for example, habitat complexity for

fauna (Williams et al. 2002). Specific leaf area relates strongly to attributes such as

potential relative growth rate and leaf lifespan (Wright et al. 2004b), leaf palatability

(Kurokawa et al. 2010), and decomposition (Garnier et al. 2004). Ratios of N:P in

fresh leaves can indicate interspecific differences in nutrient acquisition, especially

amongst species from the same plant community (Güsewell 2004), and also can

indicate leaf litter quality, influencing microbial communities and rates of

decomposition (Güsewell and Gessner 2009). Onset of flowering was included as a

trait because flowering phenology affects the timing and thus abundance of floral

resources for biota (Symes et al. 2008; Hagen and Kraemer 2010).

Species maximum height and onset of flowering data were obtained from the

VicFlora database (Royal Botanic Gardens Victoria 2015). Specific leaf area was

 

72

calculated for each species, using five leaves from each of five plants per species,

collected from plants growing within the study plots. Young, fully expanded, fully

illuminated, hardened and undamaged leaves were used (Garnier et al. 2001). Where

leaves were microscopic, or absent, the functional analogue of the leaf was used

(Pérez-Harguindeguy et al. 2013). Whole stems with leaves attached were collected

in the field and stored in a cool wet environment; stems were recut and stored

underwater once back in laboratory conditions. Samples were left for six hours in a

cool, dark position to improve leaf turgor (Garnier et al. 2001). Leaves were then

detached from their stems and petioles, and placed on a flatbed scanner; single-sided

leaf area was determined from the resulting scan using the software package ImageJ

ver. 1.50b (Rasband 2016). Needle-leaves were assumed to have a circular cross-

section and their area was adjusted by (π/2) (Wright and Westoby 2003). Leaves

were then oven-dried at 70ºC for 48 hours and re-weighed to produce a dry weight

value. The dry-weight of samples, and their measured area, was used to calculate

SLA (cm-2 g-1 dry weight).

Leaf N:P ratios were determined by collecting a minimum of ten fresh leaves from

each of twenty plants per species. We collected fully mature and fully expanded sun

leaves from the most recent year’s growth to minimise the effect of leaf age on

nutrient concentrations (Wright and Westoby 2003). Collections were made between

August and September (late winter to early spring). All leaves were collected

wearing nitrile gloves, and were wiped with damp paper towel to remove potential

surface contaminants. Samples were dried at 60 °C in a fan-forced oven for 72 hours,

until completely dry. Each replicate sample of ten leaves per species was individually

ground into a fine powder using a coffee grinder, and an equal amount of leaf

powder was weighed from each sample and combined to form a representative

 

73

sample per species. Inductively coupled plasma mass spectrometry was used to

measure the concentration of P, using standard methods (APHA 3030 E and 3120 B;

Rice et al. 2017). Total N was measured using the Dumas method of high

temperature combustion (Method 7A5; Rayment and Lyons 2011).

3.2.5 Data analysis

Functional indices (FDis, FDiv, FEve, and FRic) were calculated by applying

principal co-ordinates analysis (PCoA) to a species-species distance matrix, based on

Gower dissimilarity of the species four functional traits. Gower dissimilarity was

used to accommodate the inclusion of categorical data, whilst also standardizing

variables (Gower 1971). The resulting PCoA axes were then used as the new ‘traits’

placing species in a multidimensional space.

FDis was calculated as the average distance of species to the centroid in the

multidimensional space, weighted by species abundance (Laliberté and Legendre

2010). FDiv was calculated as the mean distance of species to the centre of gravity in

the multidimensional space, and the abundance-weighted deviance of species from

the mean distance (Villéger et al. 2008). FDiv is high when abundant species are

distant from the centre of gravity relative to rare species, and low when abundant

species are close to the centre of gravity, relative to rare species (Villéger et al.

2008). FEve was calculated as the regularity with which species were distributed

within the multidimensional space, weighted by abundance, based on the minimum

spanning tree linking all species (points) within the space (Villéger et al. 2008). FRic

was measured as the convex hull volume enclosing species in the multidimensional

space (Villéger et al. 2008). All indices were calculated in R version 3.4.1 (R Core

 

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Team 2017) using the package ‘FD’ (Laliberté et al. 2014). FRic was standardised by

the global FRic that included all species, such that FRic was scaled between 0 and 1.

Each index was calculated for each plot; we then compared each of the functional

indices among plots, using two-way analysis of variance (ANOVA; type III sum of

squares) with burn treatments (control, autumn, and spring) and survey year (2010,

2013) as fixed effects.

To explore the contributions of traits to differences in functional diversity, we

calculated the community weighted mean (CWM) value for each trait. The CWM for

onset of flowering was calculated as the mode month of flowering per study plot.

We used ANOVA to compare the CWM of each trait among study plots, with burn

treatment and survey year as fixed effects.

The effect of biodiversity erosion on FOri, FRic, and FSpe throughout the

landscapes, at a regional and local level, was determined taking the approach of

Leitão et al. (2016). First, a Gower dissimilarity matrix was constructed to determine

the functional distance between species pairs. A PCoA was then applied to the

distance matrix to build a multidimensional functional space. To compute functional

indices, a higher number of species are needed in each sample than PCoA axes

(Villéger et al. 2008). The minimum number of PCoA axes (dimensions) to

accurately represent the initial distances between species pairs was chosen by

quantifying the mean-squared deviation index (mSD; Maire et al. 2015). We kept the

first nine PCoA axes, as this maintained a high quality functional space (Fig. S3.1)

and minimised the number of samples being excluded in order to compute functional

indices (Villéger et al. 2008).

 

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Functional specialisation (FSpe) measures how species close to the centre of a

functional space (generalist species) and species with extreme trait combinations

(specialist species) change in abundance (Villéger et al. 2010); lower FSpe occurs

when specialist species become relatively less abundant compared with generalist

species (Mouillot et al. 2013b). FSpe was calculated as the mean Euclidean distance

between each species and the barycentre (the average position of all species) in the

multidimensional functional space, scaled between 0 and 1 by dividing values by the

maximum distance observed. FRic was calculated as the minimum convex hull

volume enclosing all species in the multidimensional space. FOri was calculated as

the mean distance between each species and its nearest neighbour in the

multidimensional space; FOri was scaled between 0 and 1 by dividing each value by

the maximum nearest-neighbour distance observed between species pairs.

Scenarios of species loss were simulated at a regional and local level to examine

their effect on functional diversity. At the regional level, all species identified across

all plots were included and ranked from most to least rare. Each species was removed

sequentially from the data set and each of the three functional indices were calculated

at each step of species loss. This was undertaken following three different scenarios

of species loss; losing rarest species first, losing commonest species first, and 1000

simulations of random species loss (to produce a null model).

At a local level, each of the three functional indices were calculated using species

composition data for each plot, and then nine levels of species loss were simulated

(losing 10% to 90% of the species from each plot). Functional diversity indices were

calculated for each plot at each level of species loss. Once the number of species in a

plot was lower than the number of dimensions (PCoA axes), that plot was excluded

from the analysis (Table S3.1), as calculating functional diversity indices required

 

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more species than dimensions. Local species loss was simulated using the

commonest species first, rarest species first, and 1000 random simulations of random

species loss. Each level of species loss was the compared using a Friedman paired

test. All calculations were carried out using R (R Core Team 2017), based on scripts

created by Leitão et al. (2016). Scenarios of regional and local species loss were

calculated for both pre- and post-fire floristic data, to determine if change in species

composition across survey years had changed the relationship between rarity and

functional diversity.

3.3 Results

Between 2010 and 2013, there was a decrease in the functional evenness of plots,

and an increase in functional richness (Fig. 3.1, Table 3.1). Prescribed burning did

not affect the functional diversity of plots differently to unburnt control plots, and

there was no interaction between survey year and burn treatment on functional

diversity (Fig. 3.1, Table 3.1). Based on CWM trait values, the maximum height of

species was lower in 2013, and SLA marginally higher (Fig. 3.2, Table 3.2).

Community-weighted mean ratio of N:P in the fresh leaves of plants was higher in

2013 (Fig. 3.2, Table 3.2). The predominant month of onset of flowering shifted

from June to August for plots that were burnt, whereas it remained at June for

unburnt plots (Fig. 3.2, Table 3.2).

 

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Table 3.1. Functional dispersion (FDis), functional divergence (FDiv), functional

evenness (FEve), and functional richness (FRic) among study plots in a temperate

forest in southeastern Australia, with survey year (2010, pre-burn; 2013, post-burn)

and prescribed burn treatment (unburnt control, autumn burn, spring burn) as fixed

factors.

FD index  Factor  F  df  P FDis  year  0.68  1,1  0.41   burn  2.92  2,1  0.06   Year*burn  0.49  2,1  0.61          

FDiv  year  2.41  1,1  0.12   burn  0.91  2,1  0.40   year*burn  0.79  2,1  0.45          

FEve  year  6.51  1,1  0.01   burn  0.11  2,1  0.89   year*burn  0.72  2,1  0.49          

FRic  year  9.09  1,1  0.003   burn  2.02  2,1  0.14   year*burn  1.35  2,1  0.26 

 

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Fig. 3.1. Functional dispersion (FDis), functional divergence (FDiv), functional

evenness (FEve) and functional richness (FRic) indices (mean ± 95% CI) of study

plots in a temperate forest in southeastern Australia before and after (2010 and 2013,

respectively) prescribed burning in different seasons (● = autumn burn, ■ = spring

burn, ▲ = unburnt reference).

 

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Table 3.2. Community-weighted mean trait values (plant maximum height, cm;

specific leaf area, SLA, cm-2 g-1; leaf nitrogen to phosphorus ratio, N:P ratio) among

study plots in a temperate forest in southeastern Australia, with survey year (2010,

pre-burn; 2013, post-burn) and prescribed burn treatment (unburnt control, autumn

burn, spring burn) as fixed factors.

Trait  Factor  F  df  P Height  year  20.28  1,1  <0.001   burn  2.87  1,2  0.059   Year*burn  0.07  1,2  0.937          

SLA  year  4.54  1,1  0.034   burn  2.56  1,2  0.079   year*burn  0.39  1,2  0.676          

N:P ratio  year  25.04  1,1  <0.001   burn  0.12  1,2  0.981   year*burn  0.95  1,2  0.390 

 

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Fig. 3.2. Community-weighted mean values (± 95% CI) of plant traits (plant

maximum height, cm; specific leaf area, SLA, cm-2 g-1; leaf nitrogen to phosphorus

ratio, N:P ratio; mode month of onset of flowering) in study plots in a temperate

forest in southeastern Australia before and after (2010 and 2013, respectively)

prescribed burning in different seasons (● = autumn burn, ■ = spring burn,

▲ = unburnt reference). Lower frequency values in unburnt plots are a result of

differences in sample size (n = 24 for unburnt, n = 48 for each of autumn and spring

burns).

 

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Simulations of species loss at a regional level revealed that first losing common

species reduced FRic more than would be expected under random species loss,

whereas losing the rarest species first decreased FRic less than expected compared to

random loss (Fig. 3.3). For example, when removing the first 25% of most common

species, FRic dropped an extra 29.6% compared to random species loss, and an extra

68.8% compared to losing rarest species first. FRic declined less rapidly during

initial stages of common species removal in 2013, compared to 2010, probably a

result of FRic being higher in local assemblages during this survey year, although

losing common species first still had a greater than expected effect on reducing FRic

compared to loss of rare species. FOri increased more than expected when losing

common species first from the regional species pool, compared to random species

loss (Fig. 3.3). Thus, the average distance between species and their nearest

functional neighbour increased, demonstrating that by losing rare species, functional

redundancy of species decreased (i.e. functional vulnerability increased).

FSpe followed a similar pattern, with specialist species becoming more abundant

relative to generalist species. These patterns were consistent between 2010 and 2013.

Species loss affected similar change in functional diversity at a local level as it did at

a regional level; loss of common species induced a greater drop in FRic than

expected under random species loss, and loss of rare species reduced functional

richness less than would be expected under random species loss (Fig. 3.4). When

removing common species from local assemblages, a general trend showing an

increase in FOri and FSpe occurred, whereas both of these indices decreased when

rare species were first removed (Fig. 3.4). Differences among species loss scenarios

were significant at each level of species loss (Table S3.1), and patterns were

consistent between 2010 and 2013.

 

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Fig. 3.3. Change in functional originality (FOri), functional specialisation (FSpe),

and functional richness (FRic) after simulations of regional species loss in a

temperate forest in southeastern Australia. Scenarios of losing rarest species first

(black line), losing commonest species first (dotted line) and 1000 simulations of

random species loss (grey line, shaded area represents 95% CI) are compared.

 

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Fig. 3.4. Change in functional originality (FOri), functional specialisation (FSpe),

and functional richness (FRic) after simulations of local species loss (10% to 90% of

species from each local assemblage) in a temperate forest in southeastern Australia.

Scenarios of losing rarest species first (black line), losing commonest species first

(dotted line) and 1000 simulations of random species loss (grey line) are compared.

Bars represent 95% CI. Due to computational requirements, FRic could only be

calculated for plots to 50 and 60 % species loss in 2010 and 2013, respectively. All

comparisons were significant at the p<0.05 level (Friedman paired test; Table S1).

 

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3.4 Discussion

3.4.1 Effect of prescribed burning on functional diversity

When a disturbance affects a community of species with traits selected for resilience

to that disturbance, change in functional diversity may be minimal. We observed that

prescribed burning did not affect the functional diversity of woody perennial species

in fire tolerant landscapes differently to landscapes that were unburnt. Many species

in fire tolerant ecosystems possess the ability to resprout after disturbance (Vesk and

Westoby 2004); when the community is comprised of mainly resprouting species,

post-disturbance change in floristic composition may be minimal. For example, low-

and medium-intensity fire did not directly affect functional diversity in a Neotropical

savannah in Brazil because species turnover was low (Carvalho et al. 2014).

However, often there is a proportion of obligate seeding species in any fire tolerant

community, and these species require disturbance to stimulate germination and

recruitment (Paula and Pausas 2008). As such, species richness often increases in the

first few years after fire, slowly declining as time since fire increases (Gill et al.

1999).

It was expected that a prescribed burn would affect functional diversity differently to

unburnt control plots because of recruitment in obligate seeding species. However,

floristic composition of woody perennial species was previously found to be similar

among burnt and unburnt plots within each survey year before, and after, prescribed

burning in this ecosystem (Patykowski et al. 2018). An increase in species richness

and difference in composition was observed between survey years because of rainfall

(Patykowski et al. 2018). Thus, the changes in functional richness that we observed

were probably the result of species richness increasing in the ecosystem as a result of

 

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increased moisture availability allowing a greater range of functional strategies to

coexist (Spasojevic et al. 2014).

Functional evenness can indicate how evenly resources are being used in a

community. The reduction we observed in functional richness suggests that areas of

trait space were being over-utilised by certain species i.e. competition within some

niches increased, or that some suitable niche space was yet to be filled and areas of

trait-space were underutilised (Mason et al. 2005). We propose the former; it is likely

that the preceding drought acted as an environmental filter causing greater niche

differentiation, as species that are less competitive under drought were excluded

from assemblages. An increase in the availability of a resource (water) when the

drought ended allowed more functionally similar species to coexist through

equalising fitness processes (Chesson 2000), thus decreasing functional evenness in

the community. Further, functional evenness is potentially maximised at lower levels

of functional richness, as lower ranges can artificially result in more even spacing of

trait combinations (de Bello et al. 2013).

Changes in the CWM of traits can explain changes in functional diversity. A

reduction in plant maximum height between 2010 and 2013 can probably be

explained by the increasing richness of understorey species; recruitment of small

shrubs would inevitably reduce the mean maximum height of communities. An

increase in N:P ratios in the fresh leaves of species in the community is not a

surprise, as many of the species that recruited over the survey years were capable of

N-fixation (Patykowski et al. 2018). An increase in the prevalence of N-fixing

species could result in higher N availability for the community (both belowground

and in leaf-litter), and could have a synergistic effect with rainfall to increase

community productivity. Marginal increases in SLA remain to be explained, but are

 

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likely minor effects of sclerophyllous and ericoid shrubs recruiting over the study

period.

3.4.2 Effect of species loss on functional diversity

Simulations of species loss displayed a trend of common species driving functional

diversity both at a local and regional scale, in contrast to patterns observed

elsewhere, where rare species drove functional diversity (Mouillot et al. 2013a;

Leitão et al. 2016). Several hypotheses can be postulated to explain this difference.

Firstly, as the community we studied is likely a subset of the original assemblage

(Tolsma et al. 2007), owing to historic disturbances from land clearing for alluvial

and deep lead mining throughout the 19th century (Newman 1961), it is reasonable to

assume that many extirpated species were likely the rarest (Van Calster et al. 2008).

If findings from diverse assemblages of tropical trees, tropical birds, tropical and reef

fishes, and alpine trees hold true (Mouillot et al. 2013a; Leitão et al. 2016), it is

plausible that the rarest, most functionally unique species already have been lost,

homogenising functional composition and placing greater functional importance on

the common species, relegating the role of remaining, less-common species to that of

providing functional insurance. A second hypothesis is that functional divergence is

high in temperate communities because a wider range of functional strategies coexist

compared to the tropics, where niche-packing is higher (Lamanna et al. 2014).

Certainly, Umaña et al. (2015) determined that, in tropical environments, common

species sat in the centre of functional space being finely tuned to their environment,

whereas rarer species were on the functional periphery and were struggling for

survival in sub-optimal habitat. In the context of the temperate forest we examined,

 

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common species sit along extreme edges of the trait space and their loss would

inevitably result in greater reductions in functional diversity than less-common

species. Despite this, we have demonstrated that the loss of common species in a

temperate woodland would reduce functional diversity more than losing rare species,

or losing species randomly, although rare species likely play important roles in

functional insurance.

3.4.3 Limitations and considerations

Results of functional diversity analysis are sensitive to the identity and number of

traits selected for analysis (Petchey and Gaston 2006); including a greater number of

traits does not necessarily result in more informative outcomes. We opted to include

four traits that strongly related to different effects on ecosystem functioning, rather

than including a larger number of traits but with less targeted or known ecosystem

effects. Trait selection should be dependent on the question being asked. A different

question might require examination of different traits, resulting in a different

outcome. Secondly, three rare species were excluded from our analysis as, although

they were present in original surveys, they were not able to be located to collect trait

data. Despite this, given the strength of the patterns observed we believe it is unlikely

this would have changed our broader conclusions.

 

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3.5 Conclusion

Functional diversity in temperate, fire-tolerant communities is unlikely to be greatly

affected by a single prescribed burn, but disturbances such as prolonged drought, to

which the community may be less adapted, probably acts as an environmental filter

reducing functional diversity. In such systems, common species are likely playing a

greater role in functional diversity, with rarer species being functionally redundant.

Whether this trend is consistent across temperate forests, or whether it is a legacy of

species loss from past disturbances such as extensive land-clearing, remains to be

explored. Management of temperate forests should consider the presence of common

species for their valuable functional roles, but equally should focus on the persistence

of less-common species for their potential roles in providing functional insurance

under future disturbance regimes, including more frequent or intense fire, as loss of

common species could leave large gaps in ecosystem function.

 

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3.6 Supporting information for chapter three

Table S3.1. Comparison of functional originality (FOri), functional specialisation

(FSpe), and functional richness (FRic) after simulations of local species loss (10% to

90% of species from each local assemblage) in a temperate forest in southeastern

Australia. Scenarios of losing rarest species first, losing commonest species first, and

1000 simulations of random species loss were compared at each step using a

Friedman paired test (χ2 and P). N = number of assemblages used at each level of

local species loss.

FD index 

Species loss (%) 

  2010        2013   

    χ 2  P  N    χ 2  P  N FOri  10  136.5  <0.001  120    141.9  <0.001  120   20  132.9  <0.001  120    130.3  <0.001  120   30  120.2  <0.001  120    112.9  <0.001  120   40  144.1  <0.001  120    128.3  <0.001  120   50  160.4  <0.001  120    129.7  <0.001  120   60  176.3  <0.001  120    156.8  <0.001  120   70  169.2  <0.001  120    179.5  <0.001  120   80  152.9  <0.001  120    191.3  <0.001  120   90  178.4  <0.001  117    206.8  <0.001  120                  FSpe  10  140.5  <0.001  120    138.0  <0.001  120   20  146.6  <0.001  120    157.7  <0.001  120   30  132.1  <0.001  120    140.1  <0.001  120   40  136.4  <0.001  120    137.6  <0.001  120   50  169.7  <0.001  120    148.7  <0.001  120   60  180.0  <0.001  120    172.0  <0.001  120   70  163.2  <0.001  120    186.4  <0.001  120   80  200.7  <0.001  120    180.7  <0.001  120   90  193.6  <0.001  117    180.1  <0.001  120                  FRic  10  86.1  <0.001  75    121.2  <0.001  100   20  66.9  <0.001  64    93.8  <0.001  84   30  38.3  <0.001  36    76.2  <0.001  70   40  14.8  0.001  12    35.4  <0.001  34   50  ‐  ‐  1    7.8  0.02  11   60  ‐  ‐  ‐    ‐  ‐  1 

 

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2 4 6 8 10 12 14

Quality of funtional space

Number of dimensions

mS

D

0.0

00

0.0

04

0.0

08

0.0

12

0.0

16

Fig. S3.1. Quality of the functional space based on number of dimensions (PCoA

axes) included in its construction. Mean-squared deviation index (mSD ) for each

number of dimensions included is shown; mSD values <0.001 (grey dashed line)

indicate a high quality functional space (Maire et al. 2015). Nine dimensions were

included in the analysis, as this produced a high quality functional space, and

minimised the number of samples that needed to be removed due to computational

requirements (more species in an assemblage than dimensions in the functional

space).

 

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Chapter 4 Rarity and nutrient acquisition relationships before and after

prescribed burning in an Australian box-ironbark forest

The published version of this chapter can be found at:

Patykowski J, Dell M, Wevill T, Gibson M (In Press) Rarity and nutrient acquisition relationships before and after prescribed burning in an Australian box-ironbark forest. AoB Plants.

4.1 Introduction

The internal resorption and redeployment of nutrients from senescing leaves into

newly developing organs is an important adaptation of plants to conserve nutrients

(Aerts 1996; Wright et al. 2004a), particularly for species in ecosystems with

depauperate soils (Wright and Westoby 2003; Hayes et al. 2013). The movement of

nutrients from deeper soil layers through plant roots, to above ground plant parts and,

ultimately, to surface soils through the decomposition of senesced leaves, influences

the availability of nutrients among soil horizons (Jobbágy and Jackson 2001;

Jobbágy and Jackson 2004), affecting community species richness (Shirima et al.

2016) and productivity (Aerts and Chapin III 1999). Nutrient profiles of senesced

leaves can be species-specific (Hättenschwiler et al. 2008), thus, the rate and volume

that nutrients are returned to the soil in a community through litterfall is dependent

on its constituent species (Cornwell et al. 2008; Hobbie 2015).

Generally the most dominant species, in terms of biomass, have the greatest

influence on ecosystem processes (Gaston 2011), and contribute most to soil nutrient

returns through sheer volume of litterfall; however, less-dominant and even rare

species have been found to play keystone roles in nutrient cycling in some nutrient-

poor environments (Marsh et al. 2000; March and Watson 2010). In such cases, the

 

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less-dominant species have unique nutrient acquisition strategies compared to other

members of their community, enabling better access to nutrients, and a lesser

requirement for resorption (Marsh et al. 2000; March and Watson 2010). The

nutrient-rich litter they provide to otherwise nutrient-poor soils has a bottom-up

effect on other ecosystem properties (Watson and Herring 2012; Fisher et al. 2013).

Disturbance leading to the loss of such species can have profound ecological

consequences.

Fire directly influences nutrient cycling in ecosystems by changing the availability

and distribution of nutrients (Certini 2005), and indirectly influences nutrient cycling

by shaping plant community composition (Bond and Keeley 2005). Fire is a

disturbance process affecting many parts of the world (Krawchuk et al. 2009) but, as

far as we know, the effect of fire on the presence of less-common species and their

post-fire role in nutrient cycling has not been explored. The initial effect of fire on

species abundance can result in four extreme outcomes along a continuum:

1) less-common species are promoted and dominant species are reduced; 2) dominant

species are promoted and less-common species are reduced; 3) both dominant and

less-common species are promoted, or; 4) both dominant and less-common species

are reduced. Within these outcomes, less-common species could play similar

functional roles to the dominant species and facilitate ecosystem recovery through

provision of functional insurance (Yachi and Loreau 1999; Jain et al. 2014), or be

functionally unique and play novel functional roles. Important functions could be lost

should disturbance negatively influence the presence of rare or less-common species

with unique traits (Violle et al. 2017). Of course, a likely impact of any disturbance

is a combination of these outcomes and roles among species. Understanding

contributions to nutrient cycling at both the species and community level, and how

 

93

these contributions change following disturbance, is important for understanding

ecosystem dynamics, especially given recent work highlighting the

disproportionately large contributions made by rare species towards functional

richness in ecosystems around the world (Mouillot et al. 2013a; Leitão et al. 2016;

Umaña et al. 2017).

Patterns of nutrient content, nutrient acquisition strategy, and changes in species

frequency of occurrence following disturbance were explored using a temperate,

evergreen, box-ironbark forest in southeastern Australia as a case study.

Concentrations of six macro- and four micro-nutrients in fresh and senesced leaves

(and thus proportional resorption) were measured for each of 42 box-ironbark species

representing five nutrient-acquisition strategy groups (one carnivorous, two

hemiparasitic, two proteaceous, 12 N-fixing, and 25 mycorrhizal species). Plant

frequency of occurrence was quantified before and three-years after experimental

landscape-scale prescribed burning in autumn and spring. Based on recent evidence

(Mouillot et al. 2013a; Leitão et al. 2016), it was expected that a positive relationship

would exist between species rarity and uniqueness of leaf nutrient profiles. We also

expected that this relationship would change if fire promoted the abundance of rarer

species, and reduced the abundance of common species, indicating novel post-fire

roles for some species that were rare and functionally unique, and functional

insurance roles for species that were rare and functionally redundant. Finally, we

asked if species with similar nutrient acquisition strategies would be similar in their

senesced leaf nutrient profiles, and in their proportional withdrawal of different

nutrients from fresh leaves, compared with species employing different nutrient

acquisition strategies. We compared nutrient concentrations in the upper and lower

soil horizons of the study area to indicate which nutrients were most limiting in the

 

94

system (Jobbágy and Jackson 2004), and thus which species are capable of making

important contributions to soil nutrient pools through the quality of their litter.

4.2 Materials and methods

4.2.1 Study area

Data were collected from within the Heathcote-Graytown-Rushworth forest, the

largest patch of contiguous box-ironbark forest in Victoria, southeastern Australia

(ECC 2001). Soils are nutrient-poor and typically shallow, stony, and skeletal clay

loams forming undulating hills and peneplains, 150–300 m in elevation (Douglas and

Ferguson 1988). The climate of the region is temperate; mean daily maximum

temperatures are warmest in January (29 °C) and coolest in July (12 °C) (Redesdale

weather station ID # 088051; Bureau of Meteorology 2016). The wettest month is

August (69 mm) and the driest month is February (31 mm); mean annual rainfall is

594 mm (Heathcote weather station ID # 088029; Bureau of Meteorology 2016).

The vegetation is sclerophyllous and characterised by an open canopy of Eucalyptus

species to 20 m tall, and a sparse to well-developed understorey of small trees and

shrubs. Most species in this forest exhibit adaptation to fire including strong

resprouting ability and fire-cued seed germination (Tolsma et al. 2007). A variety of

nutrient acquisition strategies exist, including symbiotic associations with

mycorrhizal fungi and N-fixing bacteria, hemiparasitism, carnivory, and proteaceous

rooting; a typical phenomenon in low-nutrient areas (Lambers et al. 2010). The

forests themselves are subject to infrequent and usually small-scale patchy fire,

historically from lightning strike and, more recently, from fuel reduction burning and

deliberately lit fires (DSE 2003).

 

95

4.2.2 Species frequency data

Pre- and post-fire species frequency data were collected as part of a broader study

examining the effect of prescribed burns on ecological attributes within this forest

(see Bennett et al. (2012) and Holland et al. (2017) for rationale and detailed

methodology). Eight permanent 20 × 20 m plots were surveyed during spring

(September–October) of 2010 for frequency of plant species, within each of 15 study

areas within the Heathcote-Graytown-Rushworth forest (Fig. 4.1). All study areas

were dominated by box-ironbark vegetation, and had similar underlying soils and

geology (Douglas and Ferguson 1988). Each study area was a landscape approx. 70–

120 ha in size and separated by a distance >500 m. Within each plot, five permanent

1 × 1 m quadrats were established at fixed locations. Species received a frequency

score for each plot based on the number of 1 × 1 m quadrats that they occurred

within, and were considered present if they were either growing within, or had

foliage overhanging the boundary of the quadrat. Species received a score of 0.2 for

each quadrat they occupied, to a maximum frequency score of 1. Species present in

the 20 × 20 m plot but not in any of the five smaller quadrats received a frequency

score of 0.2. Prescribed burns were conducted in the following autumn and spring of

2011, within six study areas per season; three study areas were left as unburnt,

control areas. Burns in autumn were patchier than those in spring, when a greater

extent of each survey plot was burnt (Holland et al. 2017), and it was expected that

this would affect floristic composition differently. Each plot in each study area was

then resurveyed in spring of 2013, two years after applying burn treatments, to

understand how the vegetation responded to prescribed burns in autumn and

prescribed burns in spring. Using the vegetation survey data, each species received a

pre- and post-fire rarity score by multiplying the number of plots they occurred in by

their mean abundance within the plots they occupied, divided by the maximum

possible frequency score to place species on a rarity scale of 0–1, with zero

representing absence.

 

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Fig. 4.1. Location of study plots within the Heathcote-Graytown-Rushworth forest, southeastern Australia.

 

97

4.2.3 Leaf collections

Forty-two species were selected for leaf-nutrient analysis, chosen to represent a

range of rarity and nutrient acquisition strategies present in the community. We used

published data and literature to assign species to their broad nutrient acquisition

strategy group: carnivorous, hemiparasitic, mycorrhizal, N-fixing, and proteaceous-

root species (Table S4.1). A minimum of 10 fresh leaves were collected from each of

20 plants per species. For widely-distributed, common species we collected leaves

from five plants within each of two unburnt study areas, and one autumn-burnt and

one spring-burnt study area. For sparsely distributed and rarer species, leaf collection

was opportunistic and occurred throughout the study areas where they were known to

be present. Aerial hemiparasites were collected from the same species of host, and

where more than one individual was parasitising a host, samples from only one plant

were collected. As nutrient concentrations are highly dependent on leaf age (Wright

and Westoby 2003), we collected fully mature and fully expanded sun leaves from

the most recent year’s growth. Collections of fresh leaves were made between

August and September (late winter to early spring) of 2014. Senesced leaves were

collected in early summer of 2014 and were considered those with a fully formed

abscission layer preventing further nutrient withdrawal. These were readily

identifiable as the leaves were often yellow-brown in colour and easily detached

from the plant. Leaves already in the litter-layer were not collected. Senesced leaves

were often scarce, so a minimum of 50 senesced leaves were collected in total, from

a minimum of 20 plants, per species. This ensured a representative sample of the

population could be obtained and sufficient material was available for nutrient

analysis. All leaves were collected in the field wearing nitrile gloves, and were gently

wiped with damp paper towel to remove potential surface contaminants such as dust.

 

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Following collection, leaves were dried at 60 °C in a fan-forced oven for 72 hours,

until dry. An equal amount of leaf material was weighed from each plant collected

for a species. This was finely ground into a homogenised powder using a coffee

grinder, to produce a representative sample of fresh and senesced material per

species.

4.2.4 Soil sampling

Soil samples were collected from three plots within each of 15 study areas to assess

soil nutrient concentrations and determine limiting nutrients. Samples of the top-soil

(0–20 cm depth) and of deeper-soil (50–60 cm depth) were collected to capture

potential change in nutrient status between soil horizons (Wigley et al. 2013).

Top- and sub-soil samples were collected at each of three random locations per plot.

Each replicate was dried in a fan-forced oven at 60 °C for 72 hours, until dry. Each

sample was sieved to remove debris and rock, and ground to a fine powder using a

coffee grinder. An equal amount of soil was then weighed from each sample and

bulked to give a representative sample of the top- and sub-soil within each plot.

4.2.5 Nutrient concentration

Soil and leaf samples were analysed for six macronutrients (nitrogen [N], potassium

[K], calcium [Ca], magnesium [Mg], phosphorus [P], sulphur [S]) and four micro-

nutrients (copper [Cu], manganese [Mn], boron [B], zinc [Zn]) by Environmental and

Analytical Laboratories (EAL; Charles Sturt University, Wagga Wagga, NSW,

Australia, eal@cus.edu.au). Inductively coupled plasma mass spectrometry was used

to measure the concentration of all nutrients except total N, using standard methods

 

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(APHA 3030 E and 3120 B) (Rice et al. 2017). For total N, the Dumas method of

high temperature combustion (Method 7A5) was used (Rayment and Lyons 2011).

Proportional resorption of each nutrient was calculated for each species by dividing

the nutrient content of senesced leaves by the nutrient content of fresh leaves.

4.2.6 Data analysis

To determine the uniqueness of species nutrient profiles, we created a similarity

matrix by scaling nutrient variables (subtracting the mean and dividing by the

standard deviation) and using PRIMER ver. 7 (PRIMER-E Ltd, Plymouth, UK) to

calculate Euclidean distances between species pairs. We then ranked species from

most to least similar, such that each species pair received a rank. The mean rank of

each species was then calculated and divided by the maximum possible mean rank

(the maximum mean rank representing a species least similar to the rest of the

community) to place species on a scale from 0–1 (most to least similar to the rest of

the community).

The relationship between the uniqueness of species’ senesced leaf-nutrient profiles

and species’ rarity scores was analysed using linear models in R version 3.3.1 (R

Core Team 2016). Models were produced for senesced leaf nutrient profiles, and for

proportional resorption, as functions of species pre- and post-fire rarity throughout

all study areas, as well as separately within the unburnt, autumn, and spring-burn

study areas.

Principal Component Analysis (PCA) was conducted in PRIMER on standardised

senesced leaf nutrient values, and also proportional resorption values, to determine

which nutrients were important in separating and clustering species based on their

 

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similarity, and to identify emerging patterns based on species nutrient acquisition

strategies.

Differences in nutrient concentrations between soil depths, among burn treatments,

and interactive effects of soil depth and burn treatment on soil nutrient

concentrations, were tested using analysis of variance in R.

4.3 Results

4.3.1 Uniqueness of leaf nutrient profiles and rarity

There was no relationship between species frequency score (either before or after

fire) and the overall nutrient profile in senesced leaves, or in the proportional

resorption of nutrients (P > 0.05; Table S4.2). A number of rarer species were

functionally similar to all other members of the community; however, some less

common species possessed leaf nutrient profiles with a high level of uniqueness, and

so may individually or collectively be important for their nutrient contributions to

soils (Fig. 4.2). Overall, species became more frequent throughout the landscape

following prescribed burn treatments; those that decreased in frequency were

functionally similar in leaf nutrient characteristics to species that increased in

frequency (Fig. 4.3).

 

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Fig. 4.2. Relationship between species pre-fire frequency and uniqueness of their

senesced leaf nutrient profile. Letters represent species identified as important

contributors of sampled nutrients (outlier species) in PCA analysis. A = Amyema

miquelii, B = Brunonia australis, C = Bursaria spinosa, D = Cassinia arcuata,

E = Exocarpos cupressiformis, F = Ozothamnus obcordatus, G = Prostanthera

denticulata. Symbols represent species’ nutrient acquisition strategy

(■ = carnivorous, ▲ = proteaceous roots, ○ = mycorrhizal, + = N-fixing rhizobium

bacteria, ✱ = hemiparasitic).

 

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Fig. 4.3. Uniqueness of leaf nutrient profile and percent change in species frequency

score in the landscape following disturbance. Dashed line represents zero change in

frequency score before and after prescribed burning as a disturbance. Symbols

represent species’ nutrient acquisition strategy (■ = carnivorous, ▲ = proteaceous

roots, ○ = mycorrhizal, + = N-fixing rhizobium bacteria, ✱ = hemiparasitic).

 

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4.3.2 Nutrient acquisition strategies and leaf nutrient profiles

Two hemiparasitic species—Amyema miquelii (Lehm. ex Miq.) Tiegh and Exocarpos

cupressiformis Labill.—separated from all other species in the PCA plot comparing

senesced leaf nutrient content (Fig. 4.4), with the first two principal components

explaining 53.1% of variation among species (Table S4.3). Concentrations of P, K,

and Cu were substantially higher in the senesced leaves of these hemiparasites than

the community average for these nutrient concentrations (Table 4.1), and were the

most influential nutrients in causing separation of these species in the PCA plot

(Fig. 4.4). Five mycorrhizal species also separated out from the main group,

predominantly due to their high concentrations of Mn, Zn and Cu (Table 4.1).

Clustering of N-fixing species occurred when plotting principal components three

and four (explaining 23% of the variation among species; Fig. 4.4). Concentrations

of N in the senesced leaves of N-fixing species was generally higher than

concentrations found for species with other nutrient acquisition strategies

(Table S4.1), and this nutrient was greatly responsible for the clustering observed in

Fig. 4.4.

 

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Table 4.1. Leaf nutrient concentrations (mg-1 / kg-1) for box-ironbark species identified as important contributors of sampled nutrients through

PCA analysis (Fig. 4.3) for senesced leaf nutrient concentration. Nutrient concentration and proportional resorption of nutrients for all 42 species

sampled is provided in Tables S4.1 and S4.2. Figures in bold indicate values that are greater than double the community mean, which is derived

from all 42 species sampled in this study. Nutrient acquisition strategy (NAS): H = hemiparasitic, M = mycorrhizal.

Species  NAS  N  K  Ca  Mg  P  S  B  Mn  Zn  Cu 

Amyema miquelii  H  5940  28900  6940  3250  553  1510  94  380  24.6  14.4 

Brunonia australis  M  3620  10300  6860  5440  210  606  56  408  130  7.4 

Bursaria spinosa  M  7420  4800  9360  3860  226  1260  91  1040  140  4.3 

Cassinia arcuata  M  7500  6030  5880  2690  406  2070  150  1040  99.6  22.1 

Exocarpos cupressiformis  H  10200  12500  6020  3140  841  1500  36  429  19.2  14.3 

Ozothamnus obcordatus  M  7060  5340  11800  2100  271  2410  118  1702  68.5  10 

Prostanthera denticulata  M  10700  2840  4820  1500  342  1370  87  751  103  8.4 

Community mean    7513  3726  6424  2088  224  1104  55  365  27  5 

 

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Fig. 4.4 (previous page). PCA ordination of 42 box-ironbark species by similarity of

senesced leaf nutrient content for six macro- and four micro-nutrients. Principal

components one and two (panel A) and three and four (panel B) are displayed

(explaining 53.1% and 23% of variation in the data, respectively), showing important

contributors of sampled nutrients (outlier species). Length of line indicates relative

strength of the influence of the nutrient. Symbols represent species’ nutrient

acquisition strategy (■ = carnivorous, ▲ = proteaceous roots, ○ = mycorrhizal, + =

N-fixing rhizobium bacteria, ✱ = hemiparasitic). Important contributors of sampled

nutrients (outlier species): a = Amyema miquelii, b = Brunonia australis, c =

Bursaria spinosa, d = Cassinia arcuata, e = Exocarpos cupressiformis, f =

Ozothamnus obcordatus, g = Prostanthera denticulata.

 

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When comparing proportional withdrawal of nutrients from senesced leaves, both

hemiparasitic species separated from the main cluster of species, and no clear

separation occurred for any other nutrient acquisition strategy (Fig 4.5). The

concentration of K in the senesced leaves of Exocarpos cupressiformis was 671%

higher than in fresh leaves. This influenced its position on the PCA plot relative to

other species (Fig. 4.5) when considering the first two principal components

(explaining 78.3% of variation among species; Table S4.3). When considering

principal components two and three, which explained 32.5% of the variation in the

data and where K was less influential, Amyema miquelii separated from the group

(Fig. 4.5) and E. cupressiformis joined the main cluster. The concentration of all

nutrients increased in the senesced leaves of A. miquelii (except for N, which it

withdrew similarly to all other species in the community), which influenced its

position in the plot. Notably, all species except A. miquelii withdrew P. Values of

macro- and micro-nutrient concentration in fresh and senesced leaves for each

species is provided in Table S4.1.

 

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Fig. 4.5 (previous page). PCA ordination of 42 box-ironbark species by similarity of

resorption of six macro- and four micro-nutrients prior to leaf drop. Principal

components one and two (panel A) and two and three (panel B) are displayed

(explaining 78.3% and 32.5% of variation in the data, respectively), Length of line

indicates relative strength of the influence of the nutrient. Symbols represent species’

nutrient acquisition strategy (■ = carnivorous, ▲ = proteaceous roots, ○ =

mycorrhizal, + = N-fixing rhizobium bacteria, ✱ = hemiparasitic). Important

contributors of sampled nutrients (outlier species): a = Amyema miquelii,

b = Exocarpos cupressiformis.

 

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4.3.3 Soil nutrients and fire

Soil nutrient profiles generally differed between the upper (0–20 cm) and lower

(50–70 cm) horizons (Table 4.3). Soil nutrient profiles did not differ between burn

treatments three years post-fire, except for boron, which was higher in unburnt

landscapes (Fig. 4.7); there was no interaction between season of burn and sample

depth on nutrient concentrations. Three nutrients were found in higher concentrations

in the upper soil horizon (Ca, N and P), and four were more concentrated in the

lower soil horizon (Cu, Mg, K, and Zn; Table 4.2 and Fig.4.6). Mean nutrient

concentrations for each nutrient, by depth and burn treatment, are provided in

Table S4.4.

 

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Table 4.2. Difference in concentration of nutrients between soil depths (0–20 cm,

50–70 cm), among burn treatments (unburnt control, autumn burn, spring burn), and

their interaction, in a box-ironbark forest. Soil samples were collected three years

after landscape-scale prescribed burn treatments. Significant results from ANOVA

are highlighted in bold.

Nutrient  F  df  P    Nutrient  F  df  P                  

Boron          Nitrogen (total)       

       Depth  0.19  1,1  0.66           Depth  78.89  1,1  <0.001 

       Season  4.97  1,2  0.009           Season  0.52  1,2  0.59 

       Depth*season  0.04  1,2  0.96           Depth*season  1.68  1,2  0.19 

Calcium          Phosphorus       

       Depth  4.90  1,1  <0.001           Depth  35.79  1,1  <0.001 

       Season  0.16  1,2  0.19           Season  1.67  1,2  0.19 

       Depth*season  0.04  1,2  0.63           Depth*season  0.09  1,2  0.91 

Copper          Potassium       

       Depth  47.11  1,1  <0.001           Depth  7.03  1,1  0.01 

       Season  0.10  1,2  0.90           Season  0.70  1,2  0.50 

       Depth*season  0.32  1,2  0.73           Depth*season  0.793  1,2  0.46 

Magnesium          Sulphur       

       Depth  37.65  1,1  <0.001           Depth  1.22  1,1  0.27 

       Season  0.47  1,2  0.64           Season  0.85  1,2  0.43 

       Depth*season  0.41  1,2  0.67           Depth*season  0.06  1,2  0.94 

Manganese          Zinc       

       Depth  2.57  1,1  0.11           Depth  19.70  1,1  <0.001 

       Season  0.11  1,2  0.90           Season  0.80  1,2  0.45 

       Depth*season  0.59  1,2  0.56           Depth*season  0.10  1,2  0.90                  

 

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Fig. 4.6. Concentration (log mg-1 kg-1 ± SE) of key macro- and micro-nutrients

nutrients in upper (0–20 cm; open symbols) and lower (50–70 cm; closed symbols)

soil horizons in a box-ironbark forest in southeastern Australia. Samples were

collected three years after landscape-scale prescribed burn treatments.

Circles = unburnt reference landscapes, triangles = autumn burn landscapes, squares

= spring burn landscapes.

 

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4.4 Discussion

We did not observe a linear relationship between species rarity and the uniqueness of

leaf-nutrient profiles, and this relationship did not change as the result of a prescribed

fire. However, we found that a number of rarer species possessed relatively unique

leaf nutrient profiles, and that nutrient uptake strategy could indicate similarity

among species nutrient profiles.

4.4.1 Uniqueness of leaf nutrient profiles and rarity

Studies exploring relationships between rarity and functional uniqueness generally

find weak to moderate, positive trends (e.g. Mouillot et al. 2013a). This includes

greater than expected functional uniqueness of rare species relative to common

species (Leitão et al. 2016) and, thus, their roles in ecosystem functioning are of

greater importance than would be expected based on abundance. These studies

incorporated broader suites of functional traits in their analysis than our own.

A positive relationship between rarity and functional uniqueness may exist within the

ecosystem we studied, but it does not occur when looking at leaf nutrient content

alone.

It is not surprising that the relationship between rarity and uniqueness of leaf nutrient

profiles did not change as a result of fire in the forest we studied. In most fire-

tolerant ecosystems around the world, a proportion of the species are adapted to

resprout after burning, and indeed, no substantial change in abundance was observed

among species in this study, many of which are able to resprout (Tolsma et al. 2007).

The effect of a disturbance that is more intense than an autumn or spring burn (i.e.

a summer burn that scorches or burns the canopy) may have wider implications for

 

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the persistence of some species. For example, Amyema miquelii (an aerial

hemiparasite with a unique senesced leaf nutrient profile), does not have fire-

stimulated germination and recovers weakly, if at all, after burning (Kelly et al.

1997), should a fire reach it in the canopy. Recolonization of burnt sites with

A. miquelii would require seed dispersal from surrounding populations in the

landscape. Consequently, the extent and severity of the disturbance becomes an

important factor governing the rate that a unique functional role is restored.

4.4.2 Nutrient acquisition strategies and leaf nutrient profiles

The two hemiparasites (Amyema miquelii and Exocarpos cupressiformis) present in

the study areas had unique leaf nutrient profiles because of their concentrations of K

and P (up to 7.75 times higher than the community mean for K, and 3.75 times for

P). High concentrations of K have previously been noted in the leaves of the aerial

hemiparasite A. miquelii (March and Watson 2010). We add to this by demonstrating

the root hemiparasite E. cupressiformis also has high levels of K in senesced leaves,

and had the highest levels of P in the community. Hemiparasitic plants are important

contributors to soil nutrient cycles in ecosystems around the world, through litterfall

rich in nutrients such as K and P (Quested et al. 2003; March and Watson 2010;

Ndagurwa et al. 2014; Scalon et al. 2017). Litterfall from hemiparasitic species has

positive effects on community productivity (March and Watson 2007; Spasojevic

and Suding 2011; Fisher et al. 2013) and bottom-up effects on diversity (Watson

2002), leading some to describe them as a keystone resource (Watson and Herring

2012).

 

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It is concerning that the two hemiparasitic species observed in our study were among

the least frequently encountered species. Should a disturbance cause the loss of the

hemiparasitic species or their hosts, their functional contribution will be lost as well.

Amyema miquelii is fire-sensitive and does not resprout after being burnt (Kelly et al.

1997), whereas E. cupressiformis is known to resprout after fire (Kubiak 2009).

Hemiparasites have a nearly cosmopolitan distribution and often very specific

germination requirements (Watson 2001). Understanding hemiparasite and host

tolerances to stress and responses to disturbance is necessary to highlight which

ecosystems are vulnerable to loss of this functional group in the future, and what

processes may cause this loss.

Nitrogen fixing species contribute N to community nutrient pools through below-

ground fixation of N (Adams and Attiwill 1984a), and leaf-fall. Resorption of N is

generally lower in N-fixing species than those that do not fix nitrogen (Killingbeck

1996) and, indeed, we found that N-fixing species generally had higher

concentrations of N in senesced leaves than other members of the community. Many

nitrogen fixing species (e.g. members of Fabaceae) from fire-prone environments use

fire as a cue for mass germination (Adams and Attiwill 1984b). Such changes to the

abundance of N-fixing species can confer an enhanced ecosystem role in the years

following fire, particularly for providing N nutrition to non N-fixing species

(Pfautsch et al. 2009). Although we observed a small change in species abundance

following disturbance, several N-fixing species were relatively common even before

prescribed burning (Fig. 4.2). Further investigation of the ecophysiological

tolerances to disturbances (e.g. fire, drought, flood, temperature, disease) possessed

by the N-fixing species observed in this study is warranted to determine how stable

the process of nitrogen fixation is in this ecosystem.

 

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There was no clear segregation of mycorrhizal species in our analysis. We opted for

a broad mycorrhizal category and did not split species into different associations

(e.g. into ectomycorrhizas, ericoid mycorrhizas, or vesicular-arbuscular

mycorrhizas), as they are uncharacterised for many species in this community.

However, mycorrhizal associations can offer plants enhanced access to different

nutrients, depending on the type of association and species (Marschner and Dell

1994; Read and Perez-Moreno 2003), and affect the functional traits of plants.

For example, ectomycorrhizal fungi are able to breakdown organic matter, whereas

vesicular-arbuscular mycorrhiza predominantly scavenge inorganic nutrients that are

released by microbes (Read and Perez-Moreno 2003; Richard et al. 2013). Thus,

plants with vesicular-arbuscular mycorrhiza often have leaf litter that is more easily

broken down by microbial action than plants with ectomycorrhizal associations.

Studies investigating such differences between co-occurring species are few (Richard

et al. 2013), but the implication of differences among mycorrhizal associations, and

their functional roles in nutrient cycling, is worthy of further attention.

The functional significance of species with high concentrations of micronutrients in

their leaves remains to be explored, as these nutrients, although essential for plant

physiology, are usually less limiting to plants than macronutrients and are thus less

studied (Marschner and Marschner 2011). Several species in our study stood out for

the high concentrations of micronutrients (B, Cu, Mn, and Zn) in their senesced

leaves. Three of the more common species (Cassinia arcuata, Ozothamnus

obcordatus and Bursaria spinosa) had high levels of Mn and Zn in their leaves;

up to 4.7 and 5.2 times the community average, respectively. In plantations, species

of Eucalyptus (which also forms the canopy of the ecosystem we studied) have been

shown to translocate Mn from lower to upper soil layers and change soil chemistry

 

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(Jobbágy and Jackson 2004). The apparent hyper-accumulation of Mn in B. spinosa,

C. arcuata, and O. obcordatus (soil concentrations were on average 20 mg-1 kg-1 and

senesced leaves were up to 1700 mg-1 kg-1) could be a physiologic adaptation to cope

with Mn accumulation in soils through Eucalyptus leaf litter, and should be explored.

4.4.3 Soil nutrients and fire

Over three years had passed between burn treatments and soil sampling in this study,

so overall similarity in soil nutrient levels between burnt and unburnt areas was

unsurprising. An ephemeral increase in soil nutrients is a well-known consequence of

fire, resulting from the combustion of plant material. Often, soil nutrients quickly

return to pre-fire levels, within several months (Adams and Boyle 1980; Macadam

1987; Weston and Attiwill 1990; Tomkins et al. 1991) to several years (Simard et al.

2001), as they are quickly taken up by living cells or lost through wind and water

erosion, or soil leaching (Thomas et al. 1999; Certini 2005).

Soil nutrients that are most strongly cycled through the community tend to aggregate

in the upper layers of the soil. This is because they are either translocated and

quickly absorbed by plant roots before they can leach into lower soil layers (Jobbágy

and Jackson 2001), or because some dominant species in the community have a high

requirement for a mineral and thus transform the deposition of nutrients in soil layers

(Jobbágy and Jackson 2004). We found that Ca, N and P were concentrated in the

upper layers. Calcium deficiency in soils is generally rare in natural systems (White

and Broadley 2003), and although plants generally have a high requirement for Ca

(Marschner and Marschner 2011), it is likely Ca is in ample supply in this system

and is less limiting. Deficiency in soil N and P is common in nutrient-poor systems

 

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around the world, thus, species contributing litter rich in these nutrients likely play

important roles in community productivity and diversity, particularly for species with

shallow root systems. Indeed, all but three species withdrew N from senescing

leaves, with a community mean of 41.7% withdrawal. Three species did not

withdraw N – these species were small to prostrate shrubs with leaves that, when

fallen, appear to become trapped and accumulate underneath the plant. Further study

is required to understand if there is adaptive significance to this trait.

4.5 Conclusion

Community-wide relationships between rarity and the uniqueness of species’ leaf

nutrient profiles do not occur in Australian box-ironbark forest. However, some of

the rarest species (those with low abundance) with uncommon strategies for nutrient

acquisition (such as hemiparasitism) can support some of the most unique leaf

nutrient profiles. Species capable of making unique contributions to soil nutrient

pools deserve greater attention, so that their functional roles can be better understood

and conserved.

 

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4.6 Supporting information for chapter four

Table S4.1. Concentration of macro- and micro-nutrients (mg-1/ kg-1) in fresh (F) and senesced (S) leaves of species growing in a box-ironbark forest

in southeastern Australia, and proportional resorption of nutrients (%). NAS = Nutrient acquisition strategy assigned to each species (N = N-fixing,

M = mycorrhizal, H = hemiparasite, C = carnivorous, Pt = proteaceous roots); references are provided as a footnote. Where no nutrient acquisition

strategy could be cited, species were assumed to be mycorrhizal.

Species  NAS  Nitrogen (N)  Potassium (K)  Calcium (Ca)  Magnesium (Mg)  Phosphorus (P)  Sulphur (S) 

     F  S  %  F  S  %  F  S  %  F  S  %  F  S  %  F  S  % 

Acacia acinacea  N1,2  25000  18300  ‐26.8  4630  4160  ‐10.2  2520  2010  ‐20.2  2650  2530  ‐4.5  499  217  ‐56.5  1450  1150  ‐20.7 

Acacia aspera  N1,2  16900  10800  ‐36.1  3620  2580  ‐28.7  3920  4600  17.3  1990  2010  1.0  486  170  ‐65.0  1420  998  ‐29.7 

Acacia genistifolia  N1,2  17800  7290  ‐59.0  8250  2840  ‐65.6  7460  7810  4.7  3160  3550  12.3  511  50  ‐90.2  2480  1650  ‐33.5 

Acacia gunnii  N1,2  14900  9430  ‐36.7  4670  2360  ‐49.5  4210  5480  30.2  2280  2860  25.4  446  118  ‐73.5  1030  704  ‐31.7 

Acacia montana  N1,2  19400  8970  ‐53.8  4320  2740  ‐36.6  4640  9500  104.7  1540  1700  10.4  614  134  ‐78.2  1500  1180  ‐21.3 

Acacia paradoxa  N1,2  20400  13500  ‐33.8  4990  2370  ‐52.5  7340  6790  ‐7.5  2700  2490  ‐7.8  519  136  ‐73.8  1840  1250  ‐32.1 

Acacia pycnantha  N1,2  21200  10000  ‐52.8  7270  1990  ‐72.6  4640  7990  72.2  2890  2660  ‐8.0  740  72  ‐90.3  1790  929  ‐48.1 

Acrotriche serrulata  M1  8400  8560  1.9  4220  2210  ‐47.6  11400  9130  ‐19.9  1240  1100  ‐11.3  494  307  ‐37.9  1420  1190  ‐16.2 

Amyema miquelii  H1  8360  5940  ‐28.9  14500  28900  99.3  2470  6940  181.0  1340  3250  142.5  451  553  22.6  603  1510  150.4 

Astroloma humifusum  M1  8380  8450  0.8  4260  1840  ‐56.8  11400  10400  ‐8.8  985  887  ‐9.9  460  317  ‐31.1  1390  1030  ‐25.9 

Boronia anemonifolia  M3  11000  5540  ‐49.6  7100  3590  ‐49.4  7130  8630  21.0  3030  3180  5.0  739  230  ‐68.9  1400  1190  ‐15.0 

Brachyloma daphnoides   M1  10500  4220  ‐59.8  3460  2720  ‐21.4  4200  4030  ‐4.0  1920  1700  ‐11.5  547  124  ‐77.3  1130  929  ‐17.8 

Brunonia australis  M4  24800  3620  ‐85.4  28600  10300  ‐64.0  6970  6860  ‐1.6  5030  5440  8.2  1070  210  ‐80.4  1630  606  ‐62.8 

Bursaria spinosa  M  13200  7420  ‐43.8  8750  4800  ‐45.1  8090  9360  15.7  3970  3860  ‐2.8  734  226  ‐69.2  1360  1260  ‐7.4 

 

120

Table S4.1 (contd.).

Species  NAS  Nitrogen (N)  Potassium (K)  Calcium (Ca)  Magnesium (Mg)  Phosphorus (P)  Sulphur (S) 

     F  S  %  F  S  %  F  S  %  F  S  %  F  S  %  F  S  % 

Cassinia arcuata  M5,6  17300  7500  ‐56.6  17300  6030  ‐65.1  4130  5880  42.4  2840  2690  ‐5.3  1110  406  ‐63.4  2170  2070  ‐4.6 

Daviesia leptophylla  N1,2,4  18100  10200  ‐43.6  6840  2680  ‐60.8  1690  2770  63.9  2110  1830  ‐13.3  431  67  ‐84.5  1250  755  ‐39.6 

Daviesia ulicifolia  N1,2,4  17300  15000  ‐13.3  4810  1960  ‐59.3  2790  3630  30.1  1970  2270  15.2  375  134  ‐64.3  1430  1380  ‐3.5 

Dianella admixta  M7  10800  5520  ‐48.9  9580  1580  ‐83.5  5640  5920  5.0  1190  825  ‐30.7  688  207  ‐69.9  1580  823  ‐47.9 

Dillwynia sericea  N1,4  13800  9460  ‐31.4  4450  1290  ‐71.0  4860  4270  ‐12.1  1330  876  ‐34.1  370  108  ‐70.8  1090  731  ‐32.9 

Drosera peltata  C1,3  15400  7960  ‐48.3  7980  2670  ‐66.5  2290  3080  34.5  2020  2150  6.4  1930  525  ‐72.8  1090  554  ‐49.2 

Eucalyptus goniocalyx  M3,4  11000  4130  ‐62.5  4860  2580  ‐46.9  8350  7310  ‐12.5  2180  2390  9.6  538  150  ‐72.1  881  522  ‐40.7 

Eucalyptus macrorhyncha  M3,4  9650  4140  ‐57.1  4390  1540  ‐64.9  3860  7400  91.7  2180  2430  11.5  488  118  ‐75.8  763  450  ‐41.0 

Eucalyptus melliodora  M3,4  13600  7220  ‐46.9  8850  3710  ‐58.1  6880  8000  16.3  2320  1960  ‐15.5  834  234  ‐71.9  1440  1040  ‐27.8 

Eucalyptus microcarpa  M3,4  11200  7230  ‐35.4  6620  2630  ‐60.3  4610  7480  62.3  3010  2400  ‐20.3  567  246  ‐56.6  1050  804  ‐23.4 

Eucalyptus polyanthemos  M3,4  13500  6210  ‐54.0  9970  4360  ‐56.3  5340  11000  106.0  2180  2420  11.0  661  160  ‐75.8  1180  957  ‐18.9 

Eucalyptus tricarpa  M3,4  11900  6620  ‐44.4  5330  2460  ‐53.8  4560  3460  ‐24.1  1820  1500  ‐17.6  565  238  ‐57.9  1120  888  ‐20.7 

Euryomyrtus ramosissima  M3,4  7150  7500  4.9  4110  2620  ‐36.3  3520  4370  24.1  1830  1190  ‐35.0  334  264  ‐21.0  856  979  14.4 

Exocarpos cupressiformis  H1  14700  10200  ‐30.6  1620  12500  671.6  5790  6020  4.0  3650  3140  ‐14.0  1540  841  ‐45.4  1530  1500  ‐2.0 

Grevillea alpina  Pt1  6990  6100  ‐12.7  5600  3210  ‐42.7  3300  2580  ‐21.8  1870  1180  ‐36.9  480  279  ‐41.9  746  578  ‐22.5 

Hakea decurrens  Pt1  6630  1900  ‐71.3  3010  1100  ‐63.5  1900  4360  129.5  983  1490  51.6  283  77  ‐72.8  943  706  ‐25.1 

Hibbertia crinita  M3  8730  7180  ‐17.8  6100  1850  ‐69.7  7010  6550  ‐6.6  2010  1330  ‐33.8  414  210  ‐49.3  1960  1480  ‐24.5 

Hibbertia exutiacies  M3  10500  8750  ‐16.7  5000  1750  ‐65.0  7850  7080  ‐9.8  4220  1860  ‐55.9  495  269  ‐45.7  4660  2800  ‐39.9 

Leucopogon rufus  M1,3  8370  2670  ‐68.1  2830  1530  ‐45.9  6440  7240  12.4  936  894  ‐4.5  491  122  ‐75.2  1340  1290  ‐3.7 

Melichrus urceolatus  M1  9760  3330  ‐65.9  3210  1600  ‐50.2  5800  4870  ‐16.0  1280  1170  ‐8.6  670  239  ‐64.3  1140  836  ‐26.7 

Ozothamnus obcordatus  M6  14100  7060  ‐49.9  13600  5340  ‐60.7  9080  11800  30.0  2270  2100  ‐7.5  664  271  ‐59.2  3230  2410  ‐25.4 

Philotheca verrucosa  M3  12200  3760  ‐69.2  5410  4490  ‐17.0  6720  8510  26.6  2940  3590  22.1  599  179  ‐70.1  1420  1280  ‐9.9 

 

121

Table S4.1 (contd.).

Species  NAS  Nitrogen (N)  Potassium (K)  Calcium (Ca)  Magnesium (Mg)  Phosphorus (P)  Sulphur (S) 

     F  S  %  F  S  %  F  S  %  F  S  %  F  S  %  F  S  % 

Prostanthera denticulata  M  12100  10700  ‐11.6  5970  2840  ‐52.4  4340  4820  11.1  2010  1500  ‐25.4  588  342  ‐41.8  1390  1370  ‐1.4 

Pultenaea graveolens  N1,2,4  19700  16000  ‐18.8  4010  2240  ‐44.1  4590  6950  51.4  1810  2260  24.9  347  213  ‐38.6  1670  1620  ‐3.0 

Pultenaea largiflorens  N1,2,4  8460  5280  ‐37.6  2490  1460  ‐41.4  6710  7560  12.7  1220  1060  ‐13.1  500  186  ‐62.8  1100  948  ‐13.8 

Stenanthera pinifolia  M1  8460  5280  ‐37.6  2490  1460  ‐41.4  6710  7560  12.7  1220  1060  ‐13.1  500  186  ‐62.8  1100  948  ‐13.8 

Stypandra glauca  M  14500  4960  ‐65.8  11600  3770  ‐67.5  3260  4880  49.7  1370  1280  ‐6.6  749  164  ‐78.1  1040  627  ‐39.7 

Xanthorrhoea glauca  M3  6980  1660  ‐76.2  5000  1860  ‐62.8  3220  4930  53.1  1130  1620  43.4  397  92  ‐76.8  711  435  ‐38.8 

Mean     13170  7513  ‐41.7  6706  3726  ‐31.8  5420  6424  27.4  2158  2088  ‐1.1  617  224  ‐62.2  1436  1104  ‐20.0 

1 Pate JS (1994) The mycorrhizal association: just one of many nutrient acquiring specializations in natural ecosystems. Plant and Soil, 159(1), 1–10. 2 de Faria SM, Lewis GP, Sprent JI & Sutherland JM (1989) Occurrence of nodulation in the Leguminosae. New Phytologist, 111(4), 607–619. 3 Brundrett MC & Abbott L (1991) Roots of jarrah forest plants. I. Mycorrhizal associations of shrubs and herbaceous plants. Australian Journal of

Botany, 39(5), 445–457. 4 Warcup JH (1980) Ectomycorrhizal associations of Australian indigenous plants. New Phytologist, 85(4), 531–535. 5 Kramadibrata K (2002) The mycorrhizal status of plants in the Gresswell Nature Reserve, Melbourne, Victoria, Australia. Berita Biologi, 6(3), 431–

439. 6 Warcup JH (1990) The mycorrhizal associations of Australian Inuleae (Asteraceae). Muelleria, 7(2), 179–187. 7 Wang B & Qiu YL (2006) Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza, 16(5), 299–363.

 

122

Table S4.1 (contd.).

Species  Boron (B)  Manganese (Mn)  Zinc (Zn)  Copper (Cu) 

  F  S  %  F  S  %  F  S  %  F  S  % 

Acacia acinacea  46  40  ‐13.0  137  95  ‐30.6  18.9  18.9  0.0  5.9  5.8  ‐1.7 

Acacia aspera  46  51  10.9  195  187  ‐4.1  22.2  17.1  ‐23.0  5.4  3.5  ‐35.2 

Acacia genistifolia  45  58  28.9  233  320  37.3  16.9  16.2  ‐4.1  5  2.8  ‐44.0 

Acacia gunnii  51  55  7.8  293  234  ‐20.1  13.6  26.3  93.4  5.6  2.3  ‐58.9 

Acacia montana  58  48  ‐17.2  185  231  24.9  17.4  23.2  33.3  6.5  2.1  ‐67.7 

Acacia paradoxa  52  41  ‐21.2  235  291  23.8  17.4  13  ‐25.3  6.7  3.4  ‐49.3 

Acacia pycnantha  46  38  ‐17.4  102  115  12.7  13.8  12.3  ‐10.9  8.2  5.6  ‐31.7 

Acrotriche serrulata  60  30  ‐50.0  268  292  9.0  19.2  12.8  ‐33.3  6.6  8  21.2 

Amyema miquelii  55  94  70.9  145  380  162.1  11.2  24.6  119.6  6.5  14.4  121.5 

Astroloma humifusum  23  22  ‐4.3  170  230  35.3  11.6  11.4  ‐1.7  10.7  6.9  ‐35.5 

Boronia anemonifolia  49  53  8.2  116  124  6.9  20.5  27.3  33.2  4.4  3.4  ‐22.7 

Brachyloma daphnoides   103  97  ‐5.8  268  207  ‐22.8  15.7  13.8  ‐12.1  5.7  4.3  ‐24.6 

Brunonia australis  57  56  ‐1.8  249  408  63.9  105  130  23.8  11.8  7.4  ‐37.3 

Bursaria spinosa  99  91  ‐8.1  939  1040  10.8  103  140  35.9  5.7  4.3  ‐24.6 

Cassinia arcuata  118  150  27.1  893  1040  16.5  107  99.6  ‐6.9  29  22.1  ‐23.8 

Daviesia leptophylla  59  56  ‐5.1  209  151  ‐27.8  33.1  21.9  ‐33.8  5.9  2.8  ‐52.5 

Daviesia ulicifolia  44  30  ‐31.8  185  156  ‐15.7  26.2  31.5  20.2  5.6  7.7  37.5 

Dianella admixta  44  30  ‐31.8  235  136  ‐42.1  74.2  31.6  ‐57.4  5.3  3.3  ‐37.7 

Dillwynia sericea  35  23  ‐34.3  149  139  ‐6.7  20.6  17.1  ‐17.0  4.1  3  ‐26.8 

Drosera peltata  33  33  0.0  52.9  89  67.3  23  13.4  ‐41.7  4.4  3  ‐31.8 

Eucalyptus goniocalyx  104  79  ‐24.0  459  436  ‐5.0  8.8  5.8  ‐34.1  5.2  2.6  ‐50.0 

Eucalyptus macrorhyncha  36  44  22.2  167  450  169.5  7.6  6.9  ‐9.2  4.4  2.1  ‐52.3 

Eucalyptus melliodora  75  69  ‐8.0  444  414  ‐6.8  16.4  12.8  ‐22.0  7  4  ‐42.9 

Eucalyptus microcarpa  57  49  ‐14.0  246  485  97.2  16.4  10.7  ‐34.8  7.4  4.6  ‐37.8 

Eucalyptus polyanthemos  43  68  58.1  271  641  136.5  10.8  6.6  ‐38.9  5.9  3.3  ‐44.1 

Eucalyptus tricarpa  53  64  20.8  166  143  ‐13.9  13.4  4.2  ‐68.7  4.4  2.5  ‐43.2 

Euryomyrtus ramosissima  74  55  ‐25.7  397  509  28.2  8.9  9.4  5.6  3.2  3.5  9.4 

Exocarpos cupressiformis  45  36  ‐20.0  431  429  ‐0.5  12.4  19.2  54.8  12  14.3  19.2 

Grevillea alpina  44  18  ‐59.1  539  250  ‐53.6  7.8  4.6  ‐41.0  2.7  1.9  ‐29.6 

Hakea decurrens  48  40  ‐16.7  197  484  145.7  5.8  3.6  ‐37.9  3.2  0.9  ‐71.9 

Hibbertia crinita  47  30  ‐36.2  201  187  ‐7.0  18.4  14.7  ‐20.1  4.5  4.6  2.2 

Hibbertia exutiacies  78  35  ‐55.1  144  162  12.5  38.2  25.2  ‐34.0  5.9  4.9  ‐16.9 

Leucopogon rufus  22  50  127.3  314  335  6.7  13.4  12.7  ‐5.2  7.9  4.4  ‐44.3 

Melichrus urceolatus  33  25  ‐24.2  542  521  ‐3.9  12.5  7.5  ‐40.0  5.7  3.5  ‐38.6 

Ozothamnus obcordatus  109  118  8.3  1320  1720  30.3  63.5  68.5  7.9  12.8  10  ‐21.9 

Philotheca verrucosa  74  111  50.0  108  184  70.4  10.7  7.8  ‐27.1  6.6  3.6  ‐45.5 

Prostanthera denticulata  92  87  ‐5.4  825  751  ‐9.0  82.5  103  24.8  8.7  8.4  ‐3.4 

Pultenaea graveolens  49  59  20.4  175  148  ‐15.4  29.3  32.9  12.3  5  5.1  2.0 

Pultenaea largiflorens  41  40  ‐2.4  620  400  ‐35.5  13.3  8.6  ‐35.3  7.5  5.8  ‐22.7 

Stenanthera pinifolia  41  40  ‐2.4  620  400  ‐35.5  13.3  8.6  ‐35.3  7.5  5.8  ‐22.7 

Stypandra glauca  42  57  35.7  239  273  14.2  41.1  41.7  1.5  4  5.2  30.0 

Xanthorrhoea glauca  41  42  2.4  61.1  138  125.9  11.9  14.7  23.5  2.6  1.4  ‐46.2 

Mean  57  55  ‐0.86  327  365  22.7  27  27  ‐6.2  7  5  ‐23.7 

 

123

Table S4.2. The relationship between species rarity, and the uniqueness of leaf

nutrient profile, in senesced leaves, and in proportional resorption of nutrients from

leaves, for 42 species from a box-ironbark forest in southeastern Australia. Linear

models were produced considering species frequency within 15 landscapes (all sites)

in 2010 (pre-burn) and 2013 (post-burn), as well as separately for sites which

experienced a burn treatment in 2010 spring autumn, spring, or left unburnt.

Burn season  Leaf nutrients  F  P  Adj. R2 

         All sites              Pre‐burn  Senesced  0.03  0.87  ‐0.02   Resorption  0.16  0.69  ‐0.02      Post‐burn  Senesced  0.04  0.84  ‐0.02   Resorption  0.08  0.79  ‐0.02 Unburnt control              Pre‐burn  Senesced  0.04  0.85  ‐0.02   Resorption  0.03  0.86  ‐0.02      Post‐burn  Senesced  0.07  0.79  ‐0.02   Resorption  0.08  0.78  ‐0.02 Autumn burn              Pre‐burn  Senesced  0.02  0.88  ‐0.02   Resorption  0.22  0.64  ‐0.02      Post‐burn  Senesced  0.00  0.99  ‐0.02   Resorption  0.38  0.54  ‐0.02 Spring burn              Pre‐burn  Senesced  0.11  0.74  ‐0.02   Resorption  0.19  0.66  ‐0.02      Post‐burn  Senesced  0.11  0.74  ‐0.02   Resorption  0.34  0.56  ‐0.02 

 

124

Table S4.3. Eigenvector scores and percent variation explained by five principal components for the similarity of leaf nutrient profiles, and

proportional resorption, among 42 evergreen species in a box-ironbark forest in southeastern Australia. All ten macro- and micro-nutrients

sampled are included in the PCA as eigenvectors. Proportional resorption was calculated as the ratio of nutrients in fresh and senesced leaves.

  Senesced    Proportional resorption   PC1  PC2  PC3  PC4  PC5    PC1  PC2  PC3  PC4  PC5 

Percent variation  36.9  16.2  12.2  10.8  8.6    54.6  23.7  8.8  4.6  3.1 Cumulative variation  36.9  53.1  65.3  76.1  84.7    54.6  78.3  87.1  91.7  94.8                        

Eigenvectors                       

B  0.38  ‐0.27  0.05  ‐0.07  0.23    0.01  ‐0.27  0.02  ‐0.68  0.58 Ca  0.21  ‐0.41  ‐0.15  0.22  ‐0.68    0.03  ‐0.51  0.03  0.03  ‐0.40 Cu  0.43  0.28  ‐0.06  0.16  0.16    0.12  ‐0.10  ‐0.55  0.22  0.22 Mg  0.28  ‐0.04  0.31  ‐0.62  ‐0.38    0.05  ‐0.34  ‐0.12  ‐0.17  ‐0.12 Mn  0.37  ‐0.35  ‐0.14  0.21  0.29    0.01  ‐0.66  0.40  0.43  0.20 N  0.02  0.44  ‐0.54  ‐0.43  ‐0.08    0.03  0.08  ‐0.28  0.34  0.07 P  0.29  0.48  0.16  0.35  0.01    0.06  ‐0.06  ‐0.30  0.26  0.19 K  0.33  0.31  0.46  0.06  ‐0.23    0.97  0.11  0.19  0.00  0.02 S  0.32  0.07  ‐0.55  0.12  ‐0.21    0.10  ‐0.22  ‐0.43  0.02  0.24 Zn  0.35  ‐0.17  ‐0.02  ‐0.41  0.36    0.14  ‐0.21  ‐0.38  ‐0.32  ‐0.55 

 

125

Table S4.4. Mean (±SD) concentrations of nutrients (mg-1 kg-1) in soil samples from

a box-ironbark forest in southeastern Australia, collected three years after landscape-

scale experimental prescribed burn treatments in autumn, spring, or left as unburnt

reference landscapes. Samples were taken from upper (0–20 cm) and lower (50–70

cm) soil profiles.

Sample  Total N  K  Ca  Mg  P 

Upper                     

    Reference  831.1  ± 297.0  628.6  ± 224.9  186.7  ± 155.6  344.8  ± 150.5  56.4  ± 22.3 

    Autumn  1227.6  ± 682.2  664.8  ± 284.4  256.5  ± 260.0  503.7  ± 384.9  78.0  ± 31.1 

    Spring  1127.9  ± 322.7  749.7  ± 346.7  240.8  ± 141.2  560.0  ± 470.6  73.3  ± 25.0 

    Mean  1108.4  ± 516.4  691.5  ± 304.9  236.3  ± 201.3  494.4  ± 398.3  71.8  ± 28.3 

Lower                     

    Reference  456.3  ± 119.1  935.4  ± 312.0  57.5  ± 29.6  1260.0  ± 985.7  31.8  ± 12.7 

    Autumn  438.2  ± 161.8  753.4  ± 249.7  54.4  ± 32.7  1239.8  ± 1026.2  44.7  ± 32.3 

    Spring  434.9  ± 159.3  839.9  ± 320.8  93.2  ± 95.8  1174.7  ± 950.9  44.1  ± 36.1 

    Mean  440.5  ± 153.4  824.4  ± 300.2  70.5  ± 67.9  1217.8  ± 989.2  41.9  ± 31.6 

                     

Sample  S  B  Mn  Zn  Cu 

Upper                     

    Reference  81.2  ± 27.3  13.7  ± 4.3  20.0  ± 31.6  6.5  ± 4.1  5.4  ± 4.4 

    Autumn  110.1  ± 71.3  10.3  ± 2.8  19.7  ± 21.4  6.3  ± 5.8  5.7  ± 3.1 

    Spring  104.7  ± 30.1  10.2  ± 4.8  21.7  ± 22.4  9.6  ± 9.2  5.7  ± 2.7 

    Mean  102.2  ± 51.6  10.9  ± 4.2  20.5  ± 24.2  7.6  ± 7.3  5.6  ± 3.3 

Lower                     

    Reference  122.5  ± 100.9  13.0  ± 4.3  31.6  ± 23.8  22.0  ± 21.1  13.9  ± 7.5 

    Autumn  131.9  ± 80.9  10.1  ± 2.3  26.2  ± 53.0  18.6  ± 23.2  12.5  ± 5.7 

    Spring  139.8  ± 83.9  9.6  ± 4.4  13.0  ± 14.2  24.4  ± 26.7  14.2  ± 9.6 

    Mean  133.2  ± 86.7  10.5  ± 3.9  20.4  ± 39.3  21.6  ± 24.4  13.5  ± 7.9 

 

 

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Chapter 5 Seasonal physiological patterns among sympatric trees

during water-deficit, and implications of climate change

This chapter has been written as a manuscript for publication:

Patykowski J, Dell M, Wevill T, Gibson M (2017) Seasonal physiological patterns among sympatric trees during water-deficit, and implications of climate change.

5.1 Introduction

An increase in the frequency and duration of drought over the next century will be a

consequence of climate change for large parts of the world (Collins et al. 2013).

Many tree species are threatened by more frequent, hotter droughts (Niu et al. 2014;

Allen et al. 2015), such that the future distribution and composition of vegetation is

likely to change world-wide (Walther et al. 2002; McDowell and Allen 2015).

Species found in already dry, inland forest ecosystems are at particular risk of losing

suitable habitat (Hamer et al. 2015). As dominant trees often characterise vegetation

types and have an overwhelming influence on ecosystem processes (Grime 1998;

Schwartz et al. 2000), climate change will affect the stability of ecosystem-level

processes and the capacity of terrestrial systems to perform important functions

(Oliver et al. 2015).

Ecosystem processes can be maintained under fluctuating environmental conditions

because of trait-redundancy between species (Hooper et al. 2005). Changing climatic

conditions that are detrimental to the presence of a common species in a system also

may confer a competitive advantage to a functionally similar, less common species,

allowing the latter to proliferate. In this scenario, functional insurance is theoretically

provided against the loss of the common species, as ecosystem processes can be

 

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maintained by the promoted species despite the changing floristic composition

(McNaughton 1977; Yachi and Loreau 1999).

Photosynthetic rate, transpiration, and instantaneous water-use efficiency (WUEi)

often are used as proximal response traits exploring performances of plant species

under changing moisture and temperature regimes (Berry and Bjorkman 1980; Duan

et al. 2013; Grossiord et al. 2017), and as indicators of inter-specific competitive

ability (Montgomery and Givnish 2008; Morris et al. 2011). Studies using such traits

to compare the effect of climatic variables on plants often use tree species at the

seedling- and juvenile-stage in glasshouse experiments; less studies focus on mature

trees in situ (Way and Oren 2010), and this remains the case in 2018. Examining

inter- and intra-specific variability in mature tree physiology across different seasons

is a stepping-stone towards advancing our understanding of their performance under

a changing climate (Aspinwall et al. 2016) and, thus, likely changes to competitive

interactions, vegetation composition, and ultimately, ecosystem functioning

throughout the world. We examined seasonal physiological patterns among co-

dominant, sub-dominant, and uncommon Eucalyptus species forming the canopy of a

temperate, inland woodland in southeastern Australia, as a model system to predict

likely interactions among species in dry inland forest ecosystems. Whilst the likely

impacts of climate change (warming, reduced/increased moisture availability,

elevated CO2) have been evaluated for many Eucalyptus species in settings both

within and outside of Australia, studies comparing co-occurring species under

climate change are limited (Booth et al. 2015), especially concerning mature

individuals.

Broadly, increased temperatures and drought-stress can interact to suppress

photosynthetic performance of Eucalyptus seedlings (Duan et al. 2013), but

 

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responses to these variables are usually species-specific. For example, the negative

effects of increased temperatures on the photosynthetic performance and survival of

Eucalyptus saligna Sm. and Eucalyptus sideroxylon A.Cunn. ex Woolls seedlings

were offset by the effects of elevated CO2, such that their susceptibility to drought

under future climates may be the same as past and present climates (Lewis et al.

2013). Conversely, elevated CO2 did not ameliorate negative effects of elevated

temperature and drought for Eucalyptus radiata Sieber ex DC. seedlings (Duan et al.

2014). Water-stressed seedlings of Eucalyptus polyanthemos Schauer and Eucalyptus

tricarpa (L.A.S.Johnson) L.A.S.Johnson & K.D.Hill exhibited a reduced

photosynthetic rate (Merchant et al. 2007). Seedlings of the co-occurring Eucalyptus

microcarpa (Maiden) Maiden, E. polyanthemos and E. tricarpa exhibited reduced

growth in height under water stress and, when combined with a 4 °C rise in growing

temperature, their rate of stem diameter growth also was reduced (Rawal et al. 2014).

However, E. microcarpa and E. polyanthemos displayed a greater ability to maintain

growth under moisture-limited conditions than E. tricarpa, and so the latter was at

risk of being out-competed by the two former species if warmer and drier conditions

persisted (Rawal et al. 2014). Extreme drought led to greater die-back of mature

Eucalyptus marginata Donn ex Sm. than the co-dominant Corymbia calophylla

(Lindl.) K.D.Hill & L.A.S.Johnson; however, E. marginata was more likely to

resprout than C. calophylla after crown-death (Matusick et al. 2013), highlighting

variability between species in both their response and ability to cope with drought.

We compared rates of photosynthesis, transpiration, and WUEi among six co-

occurring Eucalyptus species in a temperate woodland, across four seasons (over a

12-month period). We used established trees (> 30 years old) in their natural habitat.

We hypothesised that the seasonal physiological patterns of dominant species within

 

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the woodland would be similar, whereas sub-dominant and uncommon species would

display physiological patterns that potentially explain their degree of rarity. Based on

the differences observed among species, implications of a changing climate are

discussed in the context of competitive interactions, functional insurance, and global

vegetation-shift.

5.2 Methods

5.2.1 Study area

This study was undertaken within box-ironbark woodland of the Heathcote-

Graytown-Rushworth forest in southeastern Australia. Underlying soils are skeletal

clay loams that are shallow, stony and of low fertility (Mikhail 1976), forming gentle

hills and peneplains ranging from 150–300 m in elevation (Douglas and Ferguson

1988).

Within this forest, Eucalyptus microcarpa and E. tricarpa characterise the vegetation

and are widespread on the slopes and plains of this landscape. Sparsely-distributed

species within the landscape are found in other less extensive habitat types. For

example, Eucalyptus melliodora A.Cunn. ex Schauer favours colluvial loams of

lower slopes and drainage lines with higher available nutrients and moisture than

soils occupied by the dominant species (Boland et al. 2006). In its preferred habitat,

E. melliodora can verge towards co-dominance with E. microcarpa and E. tricarpa,

but the number of sites where it occurs are few. Conversely, Eucalyptus goniocalyx

F.Muell. ex Miq., also sparsely distributed in this forest, are found mostly on dry,

elevated sites with shallow, depauperate soils (Boland et al. 2006); in the few areas it

occurs in this forest it can form a co-dominant part of the canopy.

 

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The climate is temperate; January is the warmest month (mean maximum 29 °C) and

July the coldest (mean maximum 12 °C) (Bureau of Meteorology 2016; Station ID:

088051). Mean annual rainfall is 594 mm; February is the driest month (31 mm) and

August the wettest (69 mm) (Bureau of Meteorology 2016; Station ID: 088029).

During the period of this study (autumn 2015 – summer 2016), the region was in

severe drought; monthly rainfall was substantially lower than the long-term seasonal

averages, and mean spring and summer temperatures were higher than the long-term

averages (Table 5.1).

 

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Table 5.1. Seasonal weather (mean ± SE) of the Heathcote region in southeastern Australia. All temperature data were recorded at the Redesdale

weather station (Station ID: 088051), and all rainfall data at the Heathcote weather station (Station ID: 088029; Bureau of Meteorology 2016).

Long-term values using standard ‘climate normal’ data from 1961–1990, as well as values recorded during the study period (2015–2016), are

shown. Summer values (Dec–Feb) include rainfall and temperature data from December of the previous year. Temperature records for this

region exist only from 1994 onwards.

Year summer  autumn  winter  spring 

annual total (Dec – Feb)  (Mar – May)  (Jun – Aug)  (Sep – Nov) 

Monthly rainfall (mm)           

     1961–1990  42.1 (35.3)  36.4 (27.9)  49.4 (33.5)  56.7 (30.5)  594 (164.9) 

     2015  26.5 (15.6)   19.2 (5.5)  29.2 (14.1)  23.4 (12.3)  299.2 

     2016  35.1 (15.8)         

           

Monthly temp (°C)          annual mean      1994–2015d  28.3 (1.2)  20.6 (3.7)  12.8 (0.7)  20.1 (3.1)  20.5 (0.9) 

     2015  29.9 (1.0)   20.8 (3.7)  12.6 (0.8)  23.6 (4.5)  21.9 (7.1) 

     2016  30.3 (0.7)         

 

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5.2.2 Quantifying species dominance

One hundred and twenty study plots randomly located throughout the forest were

examined to determine the presence/absence of each tree species, with study

boundaries defined by the presence of Eucalyptus tricarpa as the dominant species.

Species present in ≥ 50% of plots were classed as dominant, species present in

5–50% of study plots were classed as sub-dominant, and species present in ≤ 5% of

the study plots were classified as uncommon. Six tree species occurred in the study

plots; three were co-dominant (Eucalyptus macrorhyncha F.Muell. ex Benth.,

E. microcarpa, and E. tricarpa, detected in 50.9, 67.5, and 96.7% of plots,

respectively) one was sub-dominant (E. polyanthemos, detected in 43.4% of plots),

and two were uncommon (E. goniocalyx and E. melliodora, detected in 2.5 and 0.8%

of plots, respectively).

5.2.3 Tree selection for physiological measurements

The closest occurring population of each uncommon species was separated by a

distance of ≈ 3 km. Sampling of the remaining four species was undertaken among

and between each population of uncommon species, to limit site-specific effects.

Individuals within each of the six species were selected for sampling on the basis that

they did not show signs of stress or damage, for example, from insect attack. Five

trees per species were selected and each was revisited during autumn (2 – 12 April

2015), winter (29 June – 22 July 2015), spring (28 September – 8 October 2015) and

summer (15 December – 10 January 2015/16) to collect measurements of

photosynthesis, transpiration of H2O, and WUEi. Leaves selected for study were

visually determined to be fully developed, sun-hardened and from the most recent

 

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season’s growth. Three leaves were selected from each tree. Leaves also were free

from insect attack and damage, and were collected from the eastern aspect of the

outer canopy, from a height of 6 m. Leaves were selected only if they had been in

sufficient light for 10 min prior to sampling, to ensure leaf induction status and

stomatal closure were not a concern as a result of shading (Pérez-Harguindeguy et al.

2013). Stems were detached from the tree immediately prior to sampling, and were

re-cut under water to prevent embolism of the xylem; leaves were left attached to

stems and stems were kept in water while physiological measurements were

determined.

5.2.4 Physiological measurements  

We recorded photosynthetic rate (µmol CO2 m-2 s-1) and transpiration (mmol H2O

m-2 s-1) using an LCPro-SD (ADC Bioscientific Limited) infrared gas analyser

(IRGA) equipped with a 6.25 cm-2 broad-leaf cuvette. Instantaneous water use

efficiency (µmol CO2 / mmol H2O m-2 s-1) was calculated as the ratio of

photosynthesis to transpiration. Measurements were taken between the hours of 0800

and 1600. Sampling of species and trees was stratified throughout each season and

day to produce daily averages for species, minimising any effect of reduced

photosynthetic rates during mid-day vapour pressure deficits (Raschke and

Resemann 1986). Temperature within the leaf chamber was set to mimic the mean

maximum daily temperature during each respective season of sampling (summer

28 °C, autumn 20 °C, winter 13 °C, and spring 20 °C). Ambient concentrations of

CO2 (392.8 ± 4.7 ppm) were used. Photosynthetic photon flux density (PPFD) within

the chamber was set at 2000 µmol photons of photosynthetically active radiation

 

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(PAR) m-2 s-1 (full irradiance). Each leaf was placed in the chamber and exposed to

these conditions for three minutes, to allow it time to equilibrate, and for gas-

exchange measurements to stabilise. Three recordings of physiological parameters

were then taken, with a one-minute interval between each, to account for any minor

fluctuations in gas-exchange measurements. PPFD within the chamber was then

reduced by 250 µmol PAR m-2 s-1, and the leaf allowed to equilibrate for a further

two minutes before another three readings were taken, each one minute apart. This

process was repeated, reducing PPFD in increments of 250 µmol PAR m-2 s-1 until

leaves were in complete darkness (0 µmol PAR m-2 s-1). An average of the three

measurements at each level of PPFD was used, and an average of the three leaves per

tree was then used for analysis. Leaves that did not completely fill the chamber of the

cuvette were scanned on a flat-bed scanner and the surface area of the leaf enclosed

by the cuvette was measured using the software package ImageJ ver. 1.50b (Rasband

2016). Readings taken on the IRGA were then scaled accordingly to the leaf area

enclosed.

5.2.5 Data analysis

Preliminary analysis demonstrated non-linear effects of PPFD on both

photosynthesis, and transpiration. Therefore, we opted to analyse the data using

generalised additive mixed models (GAMM; Hastie and Tibshirani 1990, Wood

2006), using the Mixed GAM Computation Vehicle (mgcv) package (Wood 2011) in

R version 3.3.1 (R Core Team 2016). First, we modelled the relationship between

PPFD and each of photosynthetic rate, transpiration, and WUEi, with season, species,

and their interaction as fixed effects. A unique identifier was included for each

 

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individual tree, as a random factor to account for repeated measurements on trees

over time that can lead to non-independence (Zuur et al. 2009). Photosynthetic

photon flux density was entered as a smoothed term and separate response curves

were produced for each season. Maximum likelihood estimation was used to

internally determine the amount of smoothing (Wood 2008). Parametric terms from

the GAMM were compared using Wald tests of significance to generate P-values for

the effect of factors with more than two levels (Wood 2013). Given the flexible

nature of GAMMs we set an a priori significance level of α = 0.01 when interpreting

the effects of the treatments.

A significant interaction was observed between season and species; we then

modelled each season separately using GAMMs. Adjusted R2 values were used to

explain the proportion of variance explained by the models. Treatment effects on

physiological parameters, and differences between species, were then inferred from

non-overlapping 95% confidence intervals from the predicted models.

During preliminary analysis for WUEi, two data points were identified where

transpiration was < 0.001 mmol H2O m-2 s-1. When divided into photosynthetic rate,

extreme WUEi values were produced. These were highly influential in the autumn

and summer models for E. polyanthemos (as identified by Cook distance, a leave-

one-out measure of influence; Fox 2002) and including these points produced

response curves for E. polyanthemos that were biologically unrealistic. Excluding

these data points did not affect overall hypothesis testing, but improved curve form

and model fit for this species.

 

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5.3 Results

5.3.1 Photosynthesis

There was an interaction between photosynthetic rate and PPFD across seasons;

species were least active in autumn and summer, and most active in winter and

spring (Table 5.2; Fig 5.1). The relationship between photosynthetic rate and PPFD

did not differ among species in autumn or summer, but differed among species in

both winter and spring (Table 5.2). Variance in photosynthetic rate was best

explained in the models for winter and spring (R2 = 0.74 and 0.50, respectively;

Table 5.2). Eucalyptus goniocalyx, E. macrorhyncha, and E. microcarpa had similar

photosynthetic rates that were high in winter (Fig. 5.1), and which were slightly

reduced in spring. Eucalyptus polyanthemos, and E. tricarpa had similarly low

photosynthetic rates in winter, which increased in spring. Eucalyptus melliodora did

not appear to markedly change photosynthetic rates throughout the seasons and was

consistently lower than all other species. Thus, the two uncommon species

(E. goniocalyx and E. melliodora) exhibited different patterns of photosynthetic rates

from each other throughout the year. There was no consistent pattern among the

common species, although two of the three species displayed highest rates of

photosynthesis in winter, whereas E. tricarpa, and the subdominant E. polyanthemos,

were more active in spring.

 

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Table 5.2. Seasonal relationship between light and physiological parameters of trees. Results of generalised additive mixed models describing

the effect of season and species on the relationship between photosynthetic photon flux density and each of photosynthetic rate (A), transpiration

(E), and instantaneous water-use efficiency (WUEi) for Eucalyptus species in Heathcote-Graytown-Rushworth forest, southeastern Australia. 

Results under ‘full model’ include each fixed factor in a single model; ~ designates an interaction term. Results under ‘season by species’

include species as a fixed factor in separate models for each season.

Model         A        E        WUEi     df    F  p  R2    F  p  R2    F  p  R2 

Full model                             

     Species    5    2.02  0.073  0.60    2.27  0.046  0.40    14.88  <0.001  0.48 

     Season    3    105.03  <0.001      41.73  <0.001      49.90  <0.001   

     Season ~ Species    15    23.73  <0.001      22.27  <0.001      8.14  <0.001   

Species by season                             

     Autumn    5    1.49  0.194  0.26    2.11  0.064  0.23    3.83  0.002  0.17 

     Winter    5    12.86  <0.001  0.74    9.28  <0.001  0.63    3.99  0.002  0.48 

     Spring    5    3.11  0.009  0.50    2.58  0.027  0.43    2.31  0.045  0.50 

     Summer    5    2.88  0.015  0.43    2.03  0.074  0.31    1.28  0.271  0.50 

 

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Fig. 5.1. Seasonal relationship between photosynthetic photo flux density (PPFD)

and photosynthetic rate (95% CI) for Eucalyptus species in Heathcote-Graytown-

Rushworth forest, southeastern Australia.

 

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5.3.2 Transpiration

Transpiration was generally lowest in autumn; the relationship between transpiration

and PPFD differed only among species in winter (Table 5.2). Variance in

transpiration was best explained in the model for winter and spring (R2 = 0.63 and

0.43, respectively; Table 5.2). Eucalyptus goniocalyx, E. macrorhyncha and

E. microcarpa had marginally higher rates of transpiration in winter than all other

species (Fig. 5.2). There was no pattern among species’ dominance groups, with one

dominant (E. tricarpa), one sub-dominant (E. polyanthemos), and one uncommon

species (E. melliodora) having similarly lower rates of transpiration in winter.

 

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Fig. 5.2. Seasonal relationship between photosynthetic photo flux density (PPFD)

and transpiration (95% CI) for Eucalyptus species in Heathcote-Graytown-

Rushworth forest, southeastern Australia.

 

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5.3.3 Instantaneous water-use efficiency

Instantaneous water-use efficiency differed among seasons and species, and an

interaction between season and species occurred (Table 5.2). The highest values for

WUEi occurred in winter, and were lowest in summer for all species. There was no

difference in WUEi among species in spring and summer, but a difference occurred

in autumn and winter (Table 5.2). Variance in WUEi was explained poorly in autumn

(R2 = 0.17), but moderately in each of the winter, spring and summer models

(R2 ≈ 0.5; Table 2). Eucalyptus goniocalyx, E. macrorhyncha, and E microcarpa had

the highest WUEi in winter (Fig. 5.3), although at maximum irradiance (2000 µmol

PAR m-2 s-1) there appeared to be little difference among species.

 

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Fig. 5.3. Seasonal relationship between photosynthetic photo flux density (PPFD)

and instantaneous water-use efficiency (WUEi; 95% CI) for Eucalyptus species in

Heathcote-Graytown-Rushworth forest, southeastern Australia.

 

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5.4 Discussion

Physiological differences among mature, co-occurring trees can indicate how they

will interact and compete in the future. Photosynthetic rates were highest in winter

and spring, which is typical of temperate mediterranean-type ecosystems, where

water availability during autumn and summer is more limiting to growth than cold

winter temperatures (Kramer et al. 2000). Observed photosynthetic rates were

similar to those previously recorded for Eucalyptus species growing under drought

stress (Merchant et al. 2007), and low compared to the maximum rates observed for

Eucalyptus species growing under optimal conditions (Whitehead and Beadle 2004),

indicating rainfall deficit probably affected species physiological performance in this

study. Transpiration of H2O was highest in summer, likely due to vapour pressure

deficit increasing evaporation through stomata (Eamus et al. 2013), or through leaf-

cooling mechanisms preventing damage to cells from excess heating (Wahid et al.

2007). This is reflected in WUEi, which was generally highest in winter, and lowest

in summer. Stability of WUEi may be a good predictor of genotypic tolerance to

drought; seedlings of Eucalyptus globulus Labill. and E. globulus × Eucalyptus

nitens H.Deane & Maiden that showed little change in WUEi in response to drought,

or those that showed an increase in WUEi after drought, exhibited higher rates of

survival than individuals that exhibited a decrease in WUEi during drought

(Navarrete-Campos et al. 2013). Such patterns also may be applicable at the inter-

species level. The large unexplained variance in the models of our study, particularly

in autumn and summer, suggests either an effect of genotype (Warren et al. 2005;

Navarrete-Campos et al. 2013), or that the effect of small-scale habitat conditions

could influence a plants’ ability to cope during water-deficit. As we included a

random effect for individual tree, we suggest that the latter is more likely. In winter

 

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and spring, where moisture is less limiting, the response of individuals within a

species appears more homogenous. Under limiting moisture conditions of summer

and autumn, site-specific factors such as topography, soil depth and position within

the landscape affect moisture availability just as much as mean rainfall values

(Gómez-Plaza et al. 2000), and thus a trees’ response to drought may be as

individual as it is species specific.

5.4.1 Patterns among species

Seasonal physiological patterns could explain, in part, why some species are less

common than others; there were similarities among several dominant species, and the

uncommon E. melliodora was clearly distinct. The dominant E. macrorhyncha and

uncommon E. goniocalyx are found on drier, hilly sites in the study landscape. It is,

therefore, unsurprising that they utilised winter, as did the dominant E. microcarpa,

as a key period to increase photosynthetic activity, where sunlight is less intense and

the soils wetter. By doing so, longer periods of cool temperatures and increased

moisture availability allow them to persist in less-favourable sites and extend their

possible growing period into spring. For this reason, it was unexpected that the

dominant E. tricarpa was most active in spring, as with the sub-dominant

E. polyanthemos, although arid land typically has variable resources that can

fluctuate year to year, and competing dominant species may have to access different

resources or have complementary strategies to co-exist (Silvertown 2004). The

uncommon E. melliodora appeared physiologically inactive throughout the year

compared to the other species. Eucalyptus melliodora is predominantly restricted to

gullies and ephemeral drainage lines within this forest. The effect of water-deficit on

 

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species restricted to such areas could be more pronounced than for other species in

the community, as species with prolonged water supply compared to species from the

remainder of the landscape are likely to have fewer strategies to cope with drought

(Wright et al. 2001).

5.4.2 Interactions under a changing climate

Species with a high stress tolerance generally may be less competitive under

optimum conditions in a community, but may become highly competitive when

resource limitation supresses the competitive ability of more common species

(Liancourt et al. 2005). Eucalyptus goniocalyx, thus, may be at a competitive

advantage under extreme drought in comparison to other species in the community,

displaying the highest rates of photosynthesis, comparable rates of transpiration (and

therefore high WUEi) and with an apparent preference for drier areas in this forest

(Boland et al. 2006). Conversely, E. melliodora could be competitively excluded by

other species from this forest due to loss of suitable micro-habitats under a drying

climate.

A more subtle pattern is that of species using winter versus spring for their main

period of activity. Temperature is a key factor differentiating the physiology of some

eucalypts (Whitehead and Beadle 2004). If hotter, drier weather persists, it is likely

that conditions conducive to winter growth (cool temperatures and ample moisture)

are going to become shorter. Species such as E. macrorhyncha and E. microcarpa,

which had higher rates of photosynthesis in winter, may be at a disadvantage if

winters become shorter. Further, the effect of persistent drought can lead to mortality

of species broadly considered drought-tolerant (Matusick et al. 2013); conditions

 

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exceeding thresholds of physiological tolerance remain to be defined for species in

this community (Niu et al. 2014). Although spring-like weather conditions favoured

by E. tricarpa and E. polyanthemos could increase in duration in the future, and thus

favour their persistence, this could be negated by their overall lower rates of

photosynthesis compared to the winter group. Under water-stress, seedlings of

E. microcarpa had greater rates of photosynthesis than E. tricarpa, and

E. polyanthemos had higher WUEi than E. tricarpa (Rawal et al. 2014).

We examined mature trees and found that only one of the six species examined,

E. microcarpa, had higher WUEi in winter. Thus, future climates may change species

dominance based on competition between species and survival of seedlings (Rawal et

al. 2014), but those competitive interactions will differ once plants become

established. Seedlings of dominant trees found in the box-ironbark region should

have a competitive advantage over species restricted to more mesic areas under a

changing climate, as they are better adapted to cope under water deficit (Merchant et

al. 2007; Rawal et al. 2014). Of further consideration is the interaction of multiple

stressors, such as water-deficit, increased temperatures, and the effects of insects and

pathogens on mortality of mature individuals within and between species (Mitchell et

al. 2013). As no in situ physiological measurements are available for mature trees in

this community from previous studies, we suggest this data may serve as a baseline

for future comparisons.

5.4.3 Functional insurance and global vegetation shift  

In situations where the species composition of a forest changes due to climatic shifts,

it is likely that functional roles and interactions of species will change concomitantly.

 

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A range-expansion of shorter xeric plants is predicted under a warming, drying

climate (McDowell and Allen 2015). In the forest we studied, E. goniocalyx was

uncommon but appeared physiologically better adapted to cope under limiting

moisture than the uncommon E. melliodora. Thus, species such as E. goniocalyx may

expand their range and encroach further into drying areas, such as lower slopes and

gullies, as they become stronger competitors with dominant species. Conversely,

species such as E. melliodora are at risk of competitive exclusion over time and loss

of suitable habitat. This presents a potential functional shift in terms of forest

structure. Eucalyptus goniocalyx is the shortest species examined in this study,

reaching maximum heights of up to 15 m; species such as E. melliodora, and the

dominant E. tricarpa, reach heights in excess of 30 m (Boland et al. 2006). Changes

to canopy structure can in turn affect the suitability of habitat for fauna (Cork and

Catling 1996), including species-specific characteristics such as hollow formation

(Fox et al. 2008). Climate change also will affect the flowering phenology of plants

(Rawal et al. 2015), not only affecting plant reproduction but the provision of nectar

and floral resources for other biota (Butt et al. 2015). Furthermore, changing

composition can affect the season of floral resource availability and quality of floral

resources, with flowers of some species being favoured over others (Morrant et al.

2010). Unfortunately, these traits need comprehensive study to understand how

changing composition will affect these and other functions. It is clear that changing

composition is not as simple as like-for-like substitution, and poses a world-wide

problem. Migration pressure further challenges species that are sensitive to drying

conditions, but the introduction of new species into forests also poses interesting

questions of novel interactions, and potential for substitution of important functional

roles (Alexander et al. 2016).

 

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Understanding physiological traits is fundamental for indicating species reaching

their thresholds of suitable habitat, so that we can predict if future conditions may

favour or inhibit their persistence in a given area. With this understanding, we can

predict likely future interactions among species, and how these interactions may

translate into ecosystem functions. We also can prioritise the inclusion of select

species in land restoration projects where the potential for future persistence is

shown, and offer assisted migration to species that are demonstrated to be climate-

sensitive in their current range (Dumroese et al. 2015). By doing so, we stand to

enhance the success of species conservation and land restoration efforts, thereby

promoting long-term functional stability in ecosystems.

 

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Chapter 6 Discussion

Rare species can play novel functional roles (Marsh et al. 2000), and

disproportionately contribute to functional diversity in ecological communities

(Mouillot et al. 2013a; Leitão et al. 2016). As such, loss of rare species can

negatively affect the way ecosystems function (Naeem et al. 2012; Soliveres et al.

2016). Disturbances to ecosystems can change the presence and abundance of

species, and rare species may be more susceptible to extirpation than those that are

common (Van Calster et al. 2008). Drought and fire are disturbances that are likely

to increase in frequency and intensity in many parts of the world as a result of

climate change (Smith et al. 2009; Moritz et al. 2012; Batllori et al. 2013; Collins et

al. 2013), but the effect these disturbances have on species rarity, and likely effects

on ecosystem functioning, are poorly understood. This thesis investigated the effect

of disturbance (fire, drought) on plant rarity, and the roles of rare plants in ecosystem

functioning. A temperate, box-ironbark ecosystem in southeastern Australia was used

as a case study.

6.1 Effect of disturbance on plant rarity

A majority of species in the ecosystem studied possessed fire-tolerant traits, such as

an ability to resprout after fire, or fire-cued seed germination (Tolsma et al. 2007;

Cheal 2010). It was anticipated that a prescribed fire would stimulate germination of

obligate seeding species, including those absent or rare in long-unburnt landscapes

(>30 years unburnt), but persistent in the soil seed bank. It was expected that the

floristic composition of burnt and unburnt sites would differ following fire, however,

 

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similar patterns of species recruitment were observed in burnt versus unburnt

landscapes following prescribed burning at the end of a decade-long drought (chapter

two). Fire had a significant effect only on the presence and frequency of occurrence

of annual herbs that were rare in landscapes prior to burning.

When propagules are available, successful recruitment of annual species depends on

adequate moisture, and gaps within niches where germinants can successfully

compete for light and space (Berlow et al. 2003; Price and Morgan 2008; Albrecht

and McCarthy 2009). It is likely that many of the annual herbs studied were rare in

the ecosystem as an artefact of drought. Increases in the abundance and richness of

annual herbs is common following fire in fire-prone ecosystems (Guo 2001; Calvo et

al. 2008; Capitanio and Carcaillet 2008), with their increased presence likely

amplifying their functional role, including facilitating community recovery.

The frequency of plant-plant interactions that are facilitatory, rather than

competitive, are known to increase in ecosystems under high abiotic stress (Gallien

et al. 2018). Interactions that facilitate plant coexistence include the amelioration of

water and heat stress on plants via shading (Hastwell et al. 2003), and the protection

of palatable plants from herbivory, due to the presence of unpalatable neighbours

(Callaway et al. 2005). Identifying the extent that facilitation occurs in the

community, the functional groups that are responsible, and the functional groups that

benefit, requires further study.

Exploring the effect of more intense disturbance (i.e. summer fire) would be

worthwhile. In the Australian landscape, intense summer fires are predictably less

patchy than a fire in autumn or spring, and provide greater effects of soil heating and

combustion of aboveground parts for resprouting species. In such a case, reduction in

 

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the abundance of both common and rare species will be higher than in an autumn or

spring burn. When intense summer fires affected large parts of southeastern Australia

in 2009, some woody perennial species that were rare before the fire were observed

to recruit in vast numbers (Just and Beardsell 2011; Just and Beardsell 2013). For

example, the rare and threatened swamp bush pea Pultenaea weindorferi Reader

temporarily became a dominant component of the understorey in some areas, with

patches of up to 250,000 plants occurring due to post-fire recruitment (Tolsma et al.

2012a). Based on abundance alone, they are likely making a large contribution to

post-fire ecosystem processes. However, drought-breaking rainfall also occurred

following the 2009 fires in southeastern Australia, so disentangling the effects of fire

versus the effects of above-average rainfall remains problematic.

The effects of drought and moisture availability appeared to be an overarching factor

influencing recruitment in the community explored in this study (chapter two),

beyond the effects of fire. At the very least, when drought-breaking rain was

combined with prescribed burning, the effect of fire on species rarity was masked by

the effects of moisture. A number of obligate seeding species in this forest are known

to produce a proportion of seed that is non-dormant, or a proportion of seed that will

become non-dormant during inter-fire periods, and germinate (Orscheg and Enright

2011), but successful recruitment may be low without sufficient rainfall (Meers and

Adams 2003). Furthermore, a fire that occurs during a drought can lead to enhanced

mortality of species (Brando et al. 2014) or weaken their ability to recover from

further disturbance (Fensham et al. 2003; Twidwell et al. 2016) if sufficient rainfall

does not occur shortly after the fire. This suggests that, for woody perennial species,

drought acts as an environmental filter limiting recruitment, survival and, thus, the

occurrence of species. The abundance of species that are less tolerant to drought

 

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could also be limited through competition, for example, competition among

seedlings of species that germinate during intermittent periods of favourable weather

while the ecosystem is in drought, or competition among mature individuals.

This is exemplified by the physiological comparison among eucalypts in the

ecosystem (chapter five). During a period of severe rainfall deficit, one species rare

in the landscape exhibited very low rates of photosynthesis throughout the entire year

of observations. This species occurred within drainage lines and gullies; areas in the

landscape that often contain higher soil moisture than other areas, due to downslope

movement of water. A second species rare in the landscape, found on drier sites such

as slopes and hillcrests, exhibited the highest rates of photosynthesis and water-use

efficiency in the community. When faced with drought, hydraulic failure can occur if

species maintain pre-drought growth rates and do not moderate their water use (Duan

et al. 2013; Mitchell et al. 2014). Conversely, species face carbon starvation under

prolonged drought if they adopt a conservative water-use strategy and slow their

growth rates (Lewis et al. 2013; Mitchell et al. 2014). Closing stomata to avoid

desiccation reduces CO2 uptake and the production of carbohydrates, but demand for

carbohydrates to maintain metabolic processes remains (McDowell et al. 2008). As

carbohydrate reserves become depleted, the species becomes less likely to recover

from an additional disturbance, such as insect attack or fire. Species with a

conservative approach to drought—lowering rates of photosynthesis—are at greater

risk of being out-competed from the sites they occupy if drought becomes more

frequent and intense in the future. Species that maintain higher rates of carbon

assimilation along with high water-use efficiency under drought are likely to expand

their frequency in a landscape, because of improved competitive ability and release

 

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from competition (Rawal et al. 2014), provided drought intensity does not reach

critical thresholds for germination and survival (Choat et al. 2012; Urli et al. 2013).

Drought also amplifies the effect of other disturbances on species, such as insect

attack, infection, disease, and fire (Mitchell et al. 2013; Booth et al. 2015). Species

maintaining higher rates of photosynthesis are likely to be more resilient and recover

from disturbance, and are thus at a further competitive advantage. Additionally,

drought can reduce the quality and quantity of flower and seed production (Ashton

1975), such that species with higher drought-tolerance potentially have greater

reproductive success and, thus, can expand their populations through higher levels of

recruitment. The ability to germinate during drought can differ among species

(Hegarty 1978), and physiological differences between seedlings and mature plants

can affect survival even within a species (Niinemets 2010). Nonetheless, innate

tolerance to drought can be expressed at a seedling stage (Zhang et al. 1997; Poorter

and Markesteijn 2008), resulting in reduced drought-induced mortality. For example,

seedlings of Eucalyptus sideroxylon Woolls are less susceptible to drought than

Eucalyptus saligna Sm. because of an ability to maintain higher stomatal

conductance and photosynthesis under high soil water potentials (Lewis et al. 2013).

Indeed, as temperature and moisture regimes shift because of climate change,

widespread mortality of forest trees within their current distribution pattern is likely

to occur (Lewis et al. 2013; Allen et al. 2015), being replaced by species that are

shorter, and better adapted to xeric conditions in regions that become warmer and

drier (McDowell and Allen 2015). Thus, for any given area, a decline in common

species is likely, with new or currently rare species replacing them as the ecosystem

dominants, creating novel communities of plants (Dunlop et al. 2012) with different

functional characteristics. Although the main focus was on canopy trees, understorey

 

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species also will face drought stress, along with different biotic and abiotic

conditions associated with changing canopy structure that will affect competitive

interactions (Engelbrecht and Kursar 2003; Sack 2004) and, ultimately, functional

composition. Overall, further research into the physiological adaptations and

thresholds associated with drought stress is warranted for species in box-ironbark and

surrounding ecosystems to determine how communities may change in species and

functional composition in the future.

Interactions among species with close relationships are an important consideration

for future research and management. For example, the distribution of hemiparasites

such as box mistletoe Amyema miquelii is likely to change because they depend on

the presence of suitable hosts, predominantly in the genus Eucalyptus. Eucalypts

have evolved a range of defences to avoid parasitism by mistletoe (e.g. different bark

types and shedding of bark, chemical defences, and physiological responses such as

shedding limbs), and other factors such as light availability due to canopy

architecture (Yan and Reid 1995) make some species more susceptible to become

hosts than others (Ward 2005). Thus, the presence, abundance and distribution of

mistletoe will likely change depending on the abundance of their most suitable hosts.

Considering the importance of hemiparasites for nutrient cycling (chapter four), this

requires considerable attention.

6.2 Contributions of species to ecosystem function

Considering recent evidence demonstrating that rare species can contribute

disproportionately to functional diversity (Leitão et al. 2016) and play unique

functional roles (Mouillot et al. 2013a)—particularly in tropical ecosystems with

 

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high species diversity—similar patterns were expected for woody perennial species

in this study (chapter three). If a disturbance such as fire increased the presence and

abundance of rare species that were functionally unique, their contributions to

functional processes and functional diversity would also increase. However, fire did

not affect the relative abundance of woody perennial species, and rare species were,

generally, functionally similar to common species. Thus, common species drove

functional diversity both before and after a prescribed burn. Determining if this

pattern is consistent across temperate forests is of future interest.

When common species are the main contributors to functional diversity within an

ecosystem, the general contribution of rare species could be towards providing

functional insurance (Jain et al. 2014) based on the functional insurance hypothesis

(McNaughton 1977; Yachi and Loreau 1999). However, annual herbs that were rare

in the landscape prior to burning were the only group of species obviously promoted

by fire (above the effects of rainfall). Their post-fire role was not explored in this

study, so an account of their traits in the future could be used to determine if they are

playing unique roles in stabilising the understorey following disturbance, substituting

for the functional role of species that were lost, or collectively increasing functional

diversity. Furthermore, this study clearly identified rare species with unique traits

that play novel functional roles, such as the contributions made by hemiparasites

towards soil nutrient inputs through enriched leaf litter (chapter four). The

precautionary principle should be applied towards species conservation as a matter of

course, to prevent the loss of key functional contributors with roles that we do not yet

appreciate. This is urgent as we are in the sixth mass extinction. The precautionary

principle should extend to herbaceous species, including grasses and orchids that

were not included in this study.

 

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In a number of ecosystems, it is apparent that rare species drive functional diversity

(Mouillot et al. 2013a; Leitão et al. 2016), but, in ecosystems where common species

drive functional diversity, rare species still play unique or novel roles. The finding

that common species are the main drivers of functional diversity in a box-ironbark

forest does not imply that conservation priorities should rest with ensuring dominant

species are managed and protected, with less emphasis on uncommon species.

Certainly, when common species are the main contributors to a particular ecosystem

process, the loss of rare species can have little effect on maintaining that function

(Schwartz et al. 2000), at least in the short-term (Smith and Knapp 2003). However,

there is consensus that maintaining high levels of biodiversity increases the

efficiency and stability of ecosystems, and that change in ecosystems accelerates as

biodiversity loss increases (Cardinale et al. 2012). The collective value of rare

species lies with their ability to enhance the capacity at which a particular function

can operate. For example, common species (10% of the total species in the

community, comprising 90% of all individuals) in a grassland could support

ecosystem functions at 50% of their maximum capacity (Soliveres et al. 2016). The

addition of the remaining locally rare species caused ecosystem functions to operate

upwards of 90% of their maximum observed capacity (Soliveres et al. 2016). As

previously discussed, understanding how disturbance affects the presence of rare

species is essential for understanding their roles, but also how their roles can change,

and thus provides a basis indicating how to best manage disturbance to ensure vital

contributors to ecosystem function are not lost.

 

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6.3 Managing disturbance for ecosystem functionality

Prescribed burning of vegetation is often used to reduce the risk of wildfire that is

negatively impacting human life and economic assets (Penman et al. 2011) and,

within some areas, to manage the spatial and temporal patterns of fire regimes to

maintain biodiversity at different post-fire stages of succession (Burrows and

McCaw 2013). By considering the minimum and maximum tolerable intervals

between successive fires for the persistence of fire-sensitive species in a community,

fire regimes can be managed to minimise the risk of local species extinctions (Cheal

2010). However, a limited understanding of the fire ecology of many rare species

means prescribed burning is applied less often for their specific management.

Further, the effect of prescribed burning on plant functional trait diversity—a more

important determinant of ecosystem functioning than species diversity (Cadotte et al.

2011)—has been of low consideration.

In some ecosystems, prescribed burning can be used to maintain species and

functional richness (Carvalho et al. 2014), and increase functional dispersion (Silva

et al. 2013). Further, reintroducing fire into long-unburnt landscapes can promote

species that were becoming rare because of fire suppression (Ames et al. 2017).

Despite this, prescribed burning is not recommended as an essential tool for

managing species and functional diversity in box-ironbark ecosystems. There was a

positive effect of rainfall on species recruitment (chapter two) and functional

richness (chapter three), but there are inherent difficulties in predicting post-fire

weather, and poor post-fire species recruitment has been observed in box-ironbark

forest during drought (Meers and Adams 2003). If post-fire weather is not conducive

to species survival, there is a substantial risk of recruitment failure and local species

extinctions, even for species that display adaptation to fire. Because fire did not

 

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affect the relationships between species rarity and functional diversity, and rainfall

had a relatively homogenous effect on species recruitment across the landscapes,

arguments for the use of fire alone as a management tool for biodiversity

conservation are too simplistic. Instead, managers should consider changing how

they monitor in the future to include functional diversity, and plan timing to ensure

greater likelihood of favourable conditions following fire. In particular, policy needs

to change during a drought cycle.

Mitigating or minimising the impacts of drought on vegetation communities is

challenging at a landscape level. The extent, frequency and duration of droughts are

likely to increase in southeastern Australia in the future (Smith et al. 2009; Collins et

al. 2013), but the timing and nature of such disturbance events is difficult to predict.

One potential option to help vegetation under a changing climate is to assist the

migration of species from their current range into areas modelled to be more suitable

into the future (McLachlan et al. 2007), provided modelling is unbiased and of high-

quality (Boiffin et al. 2017). Species could be used to restore currently degraded

areas, as well as introduced into remnant patches of vegetation outside their current

range. Although some species may be able to recede or expand into favourable

microclimates within their current range, natural migration of most species is

unlikely to keep pace with climate change (Corlett and Westcott 2013), especially

where suitable habitats and landscapes are highly fragmented. For example, the

likely potential rate of dispersal for species of Eucalyptus is equivalent to 1-2 m per

year (Booth 2017). Gradual introductions of species into new areas could be a viable

option to provide insurance populations for their persistence—particularly for species

that are rare—and to provide functional trait insurance for change in community

composition at recipient sites. However, assisted migration must be considered with

 

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caution. Further study of novel interactions among species from different habitats is

required to understand how introduced species, both planted and those that naturally

disperse, may affect new environments (Ricciardi and Simberloff 2009). This

extends to understanding how novel assemblages of functional traits will affect

ecosystem processes.

6.4 Conclusion

Disturbance such as fire can promote the presence of rare species in a temperate

forest, but characteristics of the disturbance, such as season and post-fire weather,

will promote different lifeform groups. In this study, patterns of common species

supporting functional diversity differed to studies from tropical systems that found

rare species were most influential for functional diversity, but in either case, rare

species such as hemiparasites can play unique functional roles. Understanding the

effect of disturbance such as drought on ecosystem function requires more than

understanding impacts on species composition. Rare canopy trees can possess

physiological traits that make them more tolerant to water-deficit, and thus they

could be functionally important under future climates or disturbance regimes for

stabilising ecosystem processes where patterns of species dominance, and thus trait

distribution, is altered. Given the inherent difficulties in managing disturbances such

as drought and fire, conservation of rare species is paramount to ensuring the

likelihood that important functional processes are maintained and trait combinations

are available for ecosystem functioning into the future.

 

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