John Wallace Patykowski B.Env.Sci. (hons)
Transcript of John Wallace Patykowski B.Env.Sci. (hons)
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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
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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),
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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.
58
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.
60
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.
61
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.
64
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;
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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
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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.
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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.
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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
74
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).
75
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
76
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).
77
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).
79
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.
83
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
85
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
90
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
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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).
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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.
98
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, [email protected]). Inductively coupled plasma mass spectrometry was used
to measure the concentration of all nutrients except total N, using standard methods
99
(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
100
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
105
106
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.
107
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.
108
109
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.
110
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
114
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).
115
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.
116
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
117
(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
118
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.
119
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
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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
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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.
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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
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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
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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
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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
138
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,
150
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
152
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
153
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
155
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.
157
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
158
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
159
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.
160
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