productivity and diversity patterns in relation to fire...

Influence of fire frequency and resource gradients on the

structure and composition of the jarrah (Eucalyptus

marginata Donn ex Sm.) forest of southwest Australia

Burak Kemal Pekin

B.Sc. Plant Biology

University of California, Davis

This thesis is presented for the degree of Doctor of Philosophy

The University of Western Australia

School of Plant Biology

2010

iii

ABSTRACT

The structure and composition of plant communities is largely dependent on resource

availability. However, fires also strongly influence forest structure and composition

(i.e., relative biomass, cover and abundance) by directly impacting mortality and

regeneration rates of different plant species or by altering the availability of resources.

Australian vegetation communities are highly resilient to fire and generally recover their

pre-fire composition and structure relatively quickly. However, the relative abundance

and biomass of individual species or the relative diversity of different species

assemblages in the community may be altered by the impacts of repeated fires on

species dominance and resource availability. This thesis investigates the impacts of

variable fire frequencies and resource gradients (in terms of soil nutrients and aridity)

on (i) the abundance and biomass of dominant tree species, (ii) overall species and life

form diversity, and (iii) the relative abundance of functional plant species assemblages

in the jarrah (Eucalyptus marginata Donn ex Sm.) forest of southwest Australia.

Sixteen sites with variable fire histories were selected within a specific jarrah

dominated landscape unit (Collis) in the Warren Region of southwest Australia. The

Collis vegetation complex is composed of only two tree species in the overstorey, jarrah

and marri (Corymbia calophylla (Lindl.) K.D.Hill & L.A.S.Johnson), with a diverse

sclerophyllous shrub layer in the understorey. Sites were characterised for a range of

environmental and biological variables. Relationships between density, basal area,

biomass and sapwood area of jarrah and marri, as well as the total leaf area of the stands

with aridity (i.e., potential evapotranspiration divided by annual precipitation) and fire

frequency were analysed using multiple regression analyses. The abundance of all plant

species was estimated and species richness, species evenness and life form evenness

(i.e., the relative abundance of individuals of each life form) were also measured at each

site. Correlations among various diversity measures, site biomass and fire frequency

were tested using simple and multiple regression analyses. Species were classified

according to their life form and functional traits (i.e., resprouter, annual, N-fixer,

mycorrhizal, and cluster root forming). The correlation between the relative diversity of

species with different functional traits and environmental variables (fire regime, aridity

and soil nutrients (including various N and P fractions) was tested using the

Fourthcorner technique. Foliar N and P concentrations of the two overstorey species (E.

marginata and C. calophylla) and three dominant understorey shrub species (Agonis

iv

theiformis, Hakea falcata and Podocarpus drouynianus) were also measured and

regressed against soil nutrients, fire frequency, and species diversity measures using

simple regression.

While the biomass of E. marginata was greater than C. calophylla at more

frequently burnt and more arid sites, total stand biomass, leaf area and sapwood area did

not vary with fire frequency, but were largely explained by aridity. More frequently

burnt sites had greater life form evenness, which was in turn associated with greater

herbaceous species richness along a decreasing biomass gradient. The relative number

and abundance of herbaceous species with fleshy storage organs without N-fixing

capacity was reduced, while the abundance of woody seeders with proteoid/cluster roots

and ectomycorrhizal associations was greater at sites with high organic soil P, and soil

total soil N. While the foliar N:P ratios of all of the dominant species was > 20,

indicating high P-limitation, plant biomass and foliar N:P ratio also increased with total

soil N. Species evenness also increased while species richness decreased with increasing

P-limitation, which was greater at more frequently burnt sites.

While both fire regime and water stress may influence the composition of the

overstorey, overall stand biomass and leaf area are likely to be insensitive to increased

fire frequency. High fire frequency may increase the relative number of herbaceous

species, particularly at greater biomass sites where competition for light, hence life form

dominance is high. Understorey composition is mediated by resource allocation, nutrient

harvesting and growth rate trade-offs among different functional plant assemblages

along soil nutrient (N and P) gradients. Overall species diversity measures, such as

richness and evenness, are dependent on the intensity of competition for nutrients versus

light, which is in turn mediated by an interaction between fire regime and site biomass.

In conclusion, trade-offs among different functional plant assemblages and fire impacts

on competition intensity for nutrients and light may have an important role in mediating

co-existence and maintaining species diversity in the southern jarrah forest.

v

ACKNOWLEDGEMENTS

This research was made possible through funding from the Australian Department of

Environment, Water, Heritage and Arts (DEWHA) with additional support from the

Bushfire Collaborative Research Centre (Bushfire CRC), and the School of Plant

Biology at the University Of Western Australia (UWA). I was also personally supported

by a three-month completion scholarship awarded by the School of Graduate Studies at

UWA. I thank my thesis supervisors, Dr Pauline Grierson, Dr Craig Macfarlane, Dr

Matthias Boer and Dr Roy Wittkuhn, for their guidance as well as their support in the

field. I also thank the Western Australian Department of Environment & Conservation

(DEC), particularly Drs Lachie McCaw and Roy Wittkuhn, for providing logistic

support in the field, making available the fire history data for the Warren Region and for

allowing me use of the Bushfire CRC monitoring plots near Walpole for my research. I

thank all the DEC botanists and field staff who helped to conduct the floral surveys

required for this study, and everyone at the Ecosystems Research Group at UWA as

well as numerous other students and researchers who helped me with my field and

laboratory work. Lastly, I thank Professor Ross Bradstock, Professor Grant Wardell-

Johnson, and Professor Michael Huston for reviewing my thesis and providing many

insightful comments and suggestions on my research.

vii

STATEMENT OF CANDIDATE CONTRIBUTION

The experimental chapters of this thesis (Chapters 3, 4, 5, & 6) have been written as a

series of papers. Chapter 3 was published in the journal of Forest Ecology &

Management; Pekin B.K., Boer M.M., Macfarlane C. and Grierson P.F. (2009) Impacts

of increased fire frequency and aridity on eucalypt forest structure, biomass and

composition in southwest Australia, Forest Ecology and Management 258, 2136-2142.

The contribution of each author to the manuscript is as follows:

Burak Pekin 70%

Craig Macfarlane 10%

Matthias Boer 10%

Pauline Grierson 10%

ix

TABLE OF CONTENTS

Abstract

iii

Acknowledgements

v

Statement of Candidate Contribution

vii

Chapter 1. Fire and plant diversity in southwest Australian forests

1.1 Introduction

1.2 Fire regimes and fire management

1.2.1 Current and historic fire regimes in southwest Australia

1.2.2 Prescribed burning in southwest Australia

1.3 Plant diversity patterns in relation to environmental gradients

1.3.1 Diversity of plant communities in southwest Australia

1.3.2 Role of nutrient limitation in maintaining species diversity

1.4 Ecological impacts of fire in plant communities

1.4.1 Plant adaptations to fire

1.4.2 Impact of fire on soil nutrients

1.4.3 Fire and climate change

1.5 Thesis objectives

1

2

6

10

14

Chapter 2. Vegetation, landforms and fire history of the Warren Region

2.1 Location and description of study sites

2.2 Vegetation and landforms of the Warren Region

2.3 Fire data of the Warren Region

2.4 Field sampling design

17

18

21

21

x

Chapter 3. Impacts of increased fire frequency and aridity on the structure,

composition and biomass of the jarrah forest overstorey

3.1 Introduction

3.2 Methods

3.2.1 Study site description and climate data

3.2.2 Estimating stand density, basal area and biomass

3.2.3 Estimating stand sapwood area from DBH-sapwood area relationship

3.2.4 Estimating leaf area index and litterfall rate

3.2.5 Statistical analyses

3.3 Results

3.3.1 Density, composition, and biomass of stands

3.3.2 Variation in stand structure with aridity and fire frequency

3.4 Discussion

3.4.1 Influence of fire and aridity on stand structure and composition

3.4.2 Structural and physiological response of stands to increased aridity

3.4.3 Conclusions and potential implications for climate change

25

27

32

35

Chapter 4.

Influence of fire regime on plant diversity patterns in the southern

jarrah forest along a productivity gradient

4.1 Introduction

4.2 Methods

4.2.1 Site descriptions

4.2.2 Understorey biomass assessment

4.2.3 Species and life form diversity assessment

4.2.4 Data analyses

4.3 Results

4.3.1 Aboveground plant biomass and composition

4.3.2 Variation in diversity measures with biomass and fire frequency

4.3.3 Variation in species diversity measures with life form evenness

4.4 Discussion

4.4.1 Influence of fire frequency and biomass on woody and

herbaceous species

39

41

44

50

xi

4.4.2 Influence of fire-productivity interactions on species diversity

4.4.3 Diversity of the jarrah forest compared to other temperate ecosystems

4.4.4 Conclusions and implications for fire management

Chapter 5.

Functional plant assemblages of the southern jarrah forest in

relation to fire regime, aridity and soil nutrients

5.1 Introduction

5.2 Methods


5.2.2 Functional trait classification

5.2.3 Soil nutrient sampling and analyses

5.2.4 Statistical techniques and analyses

5.3 Results

5.3.1 Variation in environmental variables across the sites

5.3.2 Composition of species with different functional traits

5.3.3 Variation in functional traits with fire regime and aridity

5.3.4 Variation in regeneration traits and life forms with soil nutrients

5.3.5 Variation in nutritional traits with soil nutrients

5.4 Discussion

5.4.1 Resprouters and seeders in relation to fire and aridity

5.4.2 Resprouters and seeders in relation to soil nutrients

5.4.3 Nutritional traits in relation to soil nutrients and aridity

5.4.4 N-fixers in relation to soil nutrients

5.4.5 Annuals in relation to nutrients, aridity and fire

5.4.6 Conclusions

57

61

65

74

Chapter 6.

Influence of fire-driven changes in nutrient limitation on plant

species diversity

6.1 Introduction

6.2 Methods

6.2.1 Site description

6.2.2 Foliar nutrient sampling and analysis of dominant species

81

83

xii


6.3 Results

6.3.1 Foliar nutrient concentration of the dominant species

6.3.3 Variation in foliar nutrients with fire frequency

6.3.4 Species diversity relationships with foliar N:P ratio and soil nutrients

6.4 Discussion

6.4.1 Foliar nutrients of the dominant species

6.4.2 Foliar N:P and δ15 as indicators of nutrient limitation

6.4.3 Influence of fire regime on nutrient limitation

6.4.4 Influence of nutrient limitations on plant diversity

6.4.5 Conclusions

85

93

Chapter 7. The relationship between nutrient limitation and species richness

in forested ecosystems

7.1 Introduction

7.2 The relationship between diversity and nutrients in forested ecosystems

7.2.1 Influence of nutrient status on species richness

7.2.2 Influence of canopy cover on species richness

7.3 Concluding remarks

99

102

104

References

109

Appendix 1.

Plot level characteristics

133

Appendix 2. Plant species and functional traits

137

1

CHAPTER 1

Fire and plant diversity in southwest Australian forests

1.1 Introduction

There has been a significant increase in the number and size of forest fires around the

globe over the last several decades due to a combination of interdependent factors

including global warming, long term fire suppression, changes in land management

practices and introduction of invasive pyrophytes (Benndorf et al., 2006; Pyne, 2007).

In addition to causing unprecedented losses in human life, infrastructure and wildlife

habitat (Pyne, 2007), this recent increase in wildfires has re-invigorated debate

regarding fire management practices such as prescribed burning as well as the

ecological impacts of fire on the forested landscapes that fuel them. Fire ecology has

been a recognized field of research for more than half a century (Kozlowski and

Ahlgren, 1974). However, relatively little is known of the long term damage caused by

large high intensity forest fires (Keane et al., 2008). In addition, many issues regarding

the impacts of fire in general, even of controlled and low intensity fire, on flora, fauna

and biodiversity have yet to be resolved.

The following discussion summarises fire regimes, fire management practices

and the ecological impacts of fires in fire-prone ecosystems around the world with

particular emphasis on southwest Australian forests. In particular, this thesis examines

both the direct and indirect influence of variable fire regimes on the structure and

diversity of the jarrah (Eucalyptus marginata Donn ex Sm.) forest of southwest

Australia in order to gain insight into the mechanisms by which fires influence

vegetation communities in forested ecosystems. In southwest Australia, low soil

nutrient availability and strong seasonal water stress are major constraints to plant

growth. Consequently, the distribution and abundance of plant species across the

landscape is strongly dependent on resource gradients (Beard, 1983; Specht and Moll,

1983; Inions et al., 1990), which are in turn linked to fire regime (Christensen, 1994;

Pausas and Bradstock, 2007). Thus, the impact of nutrient stress on plant diversity and

its relationship to fire is also reviewed.

2

1.2 Fire regimes and fire management

1.2.1 Current and historic fire regimes in southwest Australia

Any fire regime encompasses the season, intensity and size of individual fire events as

well as the time between individual fires or the total frequency of fires over a given time

period (Gill et al., 1981; McCarthy and Cary, 2002). While many natural environments

around the world experience fires to some extent, fire regimes vary across different

landscapes and climates (Kozlowski and Ahlgren, 1974; Gill et al., 1981; Davis and

Burrows, 1994; Bradstock, 2010). The different attributes that define a fire regime are

often interrelated across space and time. For example, when fires occur frequently in

any particular forest landscape, including the jarrah forests of SW Western Australia,

fuel loads in the understorey and litter layers do not have sufficient time to build up and

the intensity of subsequent fires are usually lower (Davis and Burrows, 1994; McCarthy

and Cary, 2002). Conversely, when fire frequency is low, fuel loads tend to be greater

and more connected across the landscape, particularly in forested landscapes, increasing

the probability of high intensity fires that are difficult to control and likely to burn large

areas (Meyn et al., 2007).

Much of the Australian climate has distinct dry and wet seasons, which

combined with the highly flammable essential oils and resins found within the leaves

and stems of the dominant plant species (Gill et al., 1981) makes Australian landscapes

highly fire-prone (Bradstock et al., 2002). High rainfall during the wet season in

Mediterranean climates also allows for substantial vegetation density, which in turn

provides the fuel for fire during the following dry summer. Fires are thus most common

in landscapes that experience seasonal fluctuations in rainfall patterns that result in a

distinct wetting and drying of the vegetation (Pyne, 2003). For example, ~ 7% (i.e., ~

51,825,000 hectares) of Australia burned annually from 2001 to 2004, where severe

drought conditions prevail either during the summer or winter (Giglio et al., 2005). In

Mediterranean type climates, such as that of southwest Australia, severe drought

conditions following a mild wet winter often create large quantities of dry flammable

vegetation that can result in especially large and frequent fires during the summer

months (Bradstock et al., 2002). For example, there was an average of 44 individual

wildfires per year within ~ 1 million hectares of forest in southwest Australia between

1995 and 2004 (Boer et al., 2008), and more than 100 fires per year in the 1950’s before

the implementation of widespread prescribed burning (Boer et al., 2009). Figure 1.1

3

illustrates the extent and number of fires across the southwest Australian landscape. The

darker shaded gray area in each of the four boxes represents the area burnt within a

single fire season and collectively shows fires that occurred in southwest Australia

through four consecutive fire seasons from 2005 to 2009 (Figure 1.1).

The dry sclerophyllous forests of southwest Australia in particular are highly

prone to fire and can burn by wildfire as often as every 3-4 years (Christensen et al.,

1981). There is some evidence that suggests Australian forests and woodlands

experienced regular fires well before European occupation, i.e. for at least the past

45,000 years (Enright and Thomas, 2008). For example, it has been suggested that fire

intervals in the dry forests of southwest Australia were as short as 3-4 years over the

past several centuries based on studies of grass trees (Ward et al., 2001; Enright et al.,

2005). However, the reliability of data obtained from grass tree cores is arguable and

definite knowledge on long-term historical fire regimes in the region is still lacking and

highly controversial (Ward et al., 2001; Enright et al., 2005; Burrows, 2008).

Nonetheless, regular widespread burning by Aboriginal people is likely to have

supplemented lightning-caused wildfires in southwest Australia to some extent (Abbott,

2003). This traditional practice of patch burning would have provided habitat diversity

and reduced the likelihood of very large, damaging wildfires because of the

discontinuity of flammable fuel across the landscape (Burrows, 2008).

4

Figure 1.1 Annual fires in southwest Australia over four summer fire seasons from 2005 to 2009. Areas burnt during each fire season are indicated in grey. The majority of these fires are prescribed burns. Images were obtained using Web Fire Mapper developed by NASA/University of Maryland (2002).

1.2.2 Prescribed burning in southwest Australia

Hazard control burning

Since the early 1960s, most of the total area burnt annually as well as the majority of

individual fire events in southwest Australian forests has been attributable to prescribed

burning, or the controlled application of fire to a predetermined area under specific

environmental conditions (Boer et al., 2009). Prescribed fires are primarily used by

management agencies to reduce fuel loads and thereby make the suppression of

unplanned fires easier if they occur and reduce risk to life and property (Fernandes and

Botelho, 2003; McCaw et al., 2003). The effect of various environmental factors such

as weather and topography on fire behavior has been extensively researched in the

frame of Project VESTA (Gould et al., 2007) and used to improve existing guides for

prescribed burning practices in southwest Australian forests (Sneeuwjagt and Peet,

1985; Davis and Burrows, 1994; McCaw et al., 2003). For example, when weather

conditions are too cool or damp, poorly burnt patches can remain, while an excessive

removal of ground litter and a high canopy scorch may result if conditions are too hot,

5

dry and/or windy (Sneeuwjagt and Peet, 1985). Consequently, only about 45 days each

year, primarily during the spring season, are suitable for conducting controlled fires in

the open sclerophyllous forests of southwest Australia (McCaw and Burrows, 1989).

Burning for biodiversity

The regular application of prescribed burning for the purpose of reducing fuel loads has

also raised questions regarding the ecological impacts of fires, particularly in Australia

(Morrison et al., 1996; Abbott and Burrows, 2003; Burrows, 2008; Reinhardt et al.,

2008). The consequences of frequently implemented prescribed fires on plant diversity

in particular has raised concern (Morrison et al., 1996; Bradstock et al., 1998) because

decreases in plant species richness or in the abundance of long-lived obligate seeders

have been attributed to high fire frequencies in several cases (Cary and Morrison, 1995;

Morrison et al., 1995; Knox and Morrison, 2005; Spencer and Baxter, 2006). However,

the impacts of both unplanned and planned fires on vegetation structure and diversity

are highly variable and remain largely un-quantified (Burrows, 2008).

Because of the lack of knowledge concerning long-term impacts of variable fire

regimes on ecosystems, several studies have developed models to stimulate and predict

the response of vegetation communities to fire regimes based on known vital attributes

of individual species or plant assemblages (e.g. Bradstock et al., (1998), Bradstock and

Kenny (2003)). These vital attributes include regeneration requirements, post-fire

regeneration syndromes, the length of the juvenile period and the longevity of long-

lived woody species (Gill and McCarthy, 1998). For instance, some woody Australian

plant species, i.e., serotinous seeders, are not favored by short fire intervals because they

are readily killed by low intensity fire and have relatively long juvenile periods (Gill

and McCarthy, 1998). On the other hand, some species, such as short-lived herbaceous

species, may benefit under more frequent burning (Burrows and Wardell-Johnson,

2003; Burrows, 2008). The reproduction and regeneration of many plant species,

particularly those that are endemic to southwest Australia, is also cued or enhanced by

fire making them dependent on regular fires at relatively short intervals (Burrows and

Wardell-Johnson, 2003).

Vegetation communities of southwest Australia tend to recover their pre-fire

composition and diversity relatively quickly after burning when compared to vegetation

communities in other fire-prone regions (Kruger, 1983; Keeley, 1986). This recovery is

6

largely attributed to the post-fire resprouting ability of a large proportion of the plant

species in the region (Bell and Koch, 1980; Gill, 1981). When fire intervals are long,

species richness can also decrease to due to loss of annual and fire ephemeral species,

which remain dormant in the soil as seed or underground organs and regenerate only

after fire (Gill, 1999). For example, a significant decrease in the total number of species

could be seen as little as 3-5 years following fire in the jarrah forest (Bell and Koch,

1980). Thus, species richness can be greater following fire than before on long unburnt

sites (Gill, 1999). In the dry sclerophyllous forests of southwest Australia, species

richness declined up to 20% between 6 and 38 years after fire and increased up to three

times the pre-fire level in the first year following the next fire event (Christensen and

Kimber, 1975). However, fire-free intervals shorter than 6-8 years in these forests may

also reduce the number and abundance of some woody obligate seeders such as Acacia

browniana because these have relatively long juvenile periods (Burrows and Wardell-

Johnson, 2003).

Forested landscapes in southwest Australia also contain a large number of rare

fire-sensitive plant species within riparian zones and on or around rock outcrops (Yates

et al., 2003). The location and frequency of occurrence of assorted rare species

complicates the design of prescribed burning plans as it may be difficult to avoid

burning theses sites when applying fire to the surrounding forest block. While it is

necessary to take these rare and endangered species into account, approaches to burning

practices that aim to conserve and protect single or only few species need to be

monitored closely for adverse effects on other species or communities (Burrows, 2008).

“Fine scale” patch-burning or the frequent (2–3 year intervals) introduction of fire into

the landscape is believed to create, maintain and promote fine-scaled habitat mosaics

incorporating a range of interlocking post-fire seral stages (Burrows, 2008). Thus, active

fire management can be designed to help conserve rare endemic species and to enhance

or maintain general biodiversity by creating a complex patchwork of fire histories

across the landscape and providing habitat and refuge for a large variety of species,

particularly for those that are sensitive to fire (Bradstock et al., 1998; Penman et al.,

2007; Burrows, 2008).

7

1.3 Plant diversity patterns in relation to environmental gradients

1.3.1 Diversity of plant communities in southwest Australia

The southwest of Australia exhibits a high number of endemic and rare plant species

and is considered a world biodiversity hotspot (Wardell-Johnson and Williams, 1996;

Myers et al., 2000; Hopper and Gioia, 2004). The forest-woodland mosaics found in the

very southwest corner of the region contain up to five locally endemic tree species and

several rare tree and herb species confined to small rocky outcrops (Wardell-Johnson

and Williams, 1996). While a lack of topographic belief in the region causes plant

species diversity to be relatively moderate at the landscape scale, namely 1947 taxa

within an area of 8323 km2, plant species diversity at the plot level (up to ~ 100 species

per 100 m2) is among the highest in the world (Lamont et al., 1977; Naveh and

Whittaker, 1979; Lamont et al., 1984; Hopper et al., 1992). The high level of diversity

in this region is not confined to a particular ecosystem type and several localities have

been identified as having exceptionally high species richness within the southwest

Australian floristic region (Hopper and Gioia, 2004). For example, the number of

species in the open sclerophyllous forests of southwest Australia can be as high as 75

per 400 m2 (Gioia and Pigott, 2000), and 76±8 plant species were recorded on average

within 100 m2 plots in a shrubland community in Eneabba north of Perth (Griffin et al.,

1983).

While plant species richness in tropical forests is also exceptionally high within

large plots, i.e., up to 365 species per 1000 m2 (Gentry and Dodson, 1987), the number

of plant species in these ecosystems tends to decrease relatively sharply as area

decreases as illustrated on Figure 1.2 (Lamont et al., 1984). In contrast, species-rich

Mediterranean type ecosystems (MTEs) are known for maintaining exceptionally high

species richness with decreasing plot size (Figure 1.2). For example, open shrublands

and woodlands in the eastern Mediterranean Basin can contain up to 22-25 species in a

single square metre (Naveh and Whittaker, 1979).

8

Area

Num

ber o

f spe

cies

Mediterranean type ecosystems

Tropical ecosystems

XArea

Num

ber o

f spe

cies

Mediterranean type ecosystems

Tropical ecosystems

X

Figure 1.2 Hypothetical species area curves for species-rich Mediterranean type ecosystems and other species-rich ecosystems in the tropics. Up to sample area of size X the number of species (i.e., species richness) in Mediterranean type ecosystems is greater than in tropical ecosystems. X often ranges from 1 m2 to 100 m2 depending on the particular ecosystems considered (Lamont et al., 1984).

The co-existence of a high number of species at a plot scale, i.e., within small a

plot that is for the most part homogenous in resources, is attributed to a combination of

environmental and evolutionary factors. These were reviewed for southwest Australia

by Lamont et al. (1984) as well as Hopper and Gioia (2004), and are summarized here

as follows; southwest Australia has experienced high levels of speciation, which

combined with a relatively old and stable landscape has created an especially rich

species pool; the region experiences regular disturbance events including fires and

severe droughts which reduce dominance of individual species and provide

opportunities for regeneration; there is a high number of niches for individual plant

species to occupy as suggested by the high level of resource partitioning among species

in the region; and southwest Australian plants generally display slow growth rates as a

response to the high levels of environmental stress, particularly due to the low nutrient

status of the soils, which reduces dominance. Thus, low soil nutrient availability

combined with frequent fire may have an especially important role in maintaining plant

diversity in southwest Australian ecosystems.

9

1.3.2 Role of nutrient limitation and fire in maintaining species diversity

Studies in Australia and elsewhere have shown that either the number of species is

greatest at nutrient poor sites or that species richness decreases with reduced nutrient

limitation (Heddle and Specht, 1975; Specht et al., 1977; Huston, 1980; Keith and

Myerscough, 1993; Olde Venterink et al., 2003; Reynolds et al., 2007). The possible

mechanisms behind this negative relationship between species diversity and nutrient

availability have been discussed by Huston (1979) and Tilman (1983; 1994) amongst

others. Generally, soil nutrient concentrations vary slightly over time due to fluctuations

in the rate of nutrient cycling from plant material to humus and mineral soil layers.

Rates are dependent on seasonal changes in temperature, precipitation as well as

disturbances such as fires (Groves, 1983; O'Connell and Menage, 1983; Read and

Mitchell, 1983; Hingston et al., 1989). When soil nutrient pools are very small, even

small amounts of a nutrient released from plant material (e.g., after a fire) can

significantly increase the total soil concentration of that nutrient at a micro-scale. The

competitive advantage of different plant species also varies depending on the

availability of soil nutrients (Grime, 1979; Tilman, 1988). Consequently, high temporal

fluctuation in the rate of cycling of different nutrients allows for more species to co-

exist at nutrient poor sites (Tilman, 1983). In contrast, when soil nutrient pools are

large, temporal fluctuations in nutrient cycling rates do not cause a significant reduction

in nutrient availability, and a few species that take up nutrients fast and grow fast out-

compete slower growing species resulting in reduced species richness (Grime, 1973;

Grime, 1979; Tilman, 1983).

Only a few species can survive when resources are extremely limited or when

productivity or site biomass is very low (Grime, 1979). Consequently, maximum

diversity is often reached at an intermediate level of productivity, where the difference

between the number of species able to survive resource stress and the number of species

competitively excluded is greatest (Grime, 1979; Mittelbach et al., 2001; Pausas and

Austin, 2001). This suggests that species diversity should also be greatest where

nutrient availability is poor, but where a high proportion of the taxa are specialized for

low nutrient condition, i.e., can survive high nutrient stress. Southwest Australia is

remarkable for the large proportion of species that are specialized and adapted to the

low nutrient soils that predominate the highly weathered landscape (Hopper and Gioia,

2004). The cluster root formations found on Proteaceae and many other shrub, rush and

sedge species native to the region enhance the growth and survival of these species by

10

increasing nutrient uptake efficiency and allowing them to tap into novel sources of

nutrients (Lamont, 1983; Lamont, 2003). Host-specific mycorrhizal associations, such

as those found on Ericaceae and orchid species, which are particularly abundant on the

nutrient poor soils of southwest Australia (Lamont, 1984), may also be better at utilizing

nutrients when readily plant available nutrient sources are low (Michelsen et al., 1996;

Read and Perez-Moreno, 2003). However, it is not clear to what extent these specialized

plant traits play a role in maintaining high species diversity in southwest Australian

ecosystems. There is thus a need to explore the relationships among the diversity of

different functional plant assemblages, i.e., assemblages of species displaying specific

nutritional traits, across resource gradients in order to understand the mechanisms that

support diversity in southwest Australia.

The role of fire in influencing diversity-nutrient relationships is also complex

and depends on the level of competition for light, nutrients and water (Huston, 2003;

Peterson and Reich, 2008). While the intermediate frequency hypothesis suggests that

maximum diversity should be achieved at a medium level of fire frequency, the exact

fire frequencies required to maximize diversity vary with site productivity and resource

availability (Huston, 1979). Fires consume aboveground components of plants and

temporarily increase soil nutrient pools due to ash deposits (Grove et al., 1986). Thus,

frequent nutrient pulses and reduced cover under high fire frequencies may decrease

competition for light and nutrient resources resulting in increased diversity on

landscapes where competition for light or nutrients is high (Specht et al., 1958; Heddle

and Specht, 1975; Peterson and Reich, 2008). Fire impacts on diversity also differ

depending on the fire-adaptive traits of the species present within the vegetation

community (Specht et al., 1958; Gill, 1975; Bell and Koch, 1980; Trabaud, 1994;

Morrison et al., 1995; Knox and Morrison, 2005). For example, the ability of species to

survive fires and regenerate epicormically or from underground storage organs

influences how quick the diversity of the vegetation community recovers following fire

(Bell and Koch, 1980; Kruger, 1983; Burrows and Wardell-Johnson, 2003). Thus, it is

necessary to examine the diversity of species with different fire-traits within the

vegetation community in order to predict how diversity-nutrient relationships may be

influenced by variable fire regimes.

11

1.4 Ecological impacts of fire in plant communities

1.4.1 Plant adaptations to fire

Plant species in fire-prone ecosystems, particularly those of Australia, exhibit a variety

of adaptations to fire including bark protection of aerial buds and cambium, immediate

post-fire flowering and the ability to regenerate from vegetative buds (Gill, 1981; Dixon

and Barrett, 2003). These features allow many Australian tree species (including those

of the jarrah forest) to survive fires with most of their aboveground biomass intact, and

provide the rest of the vegetation community with a high level of resilience to fire, i.e.,

allow it to recover quickly after burning (Gill and Catling, 2002). In contrast to less fire

tolerant forests in the northern hemisphere where even a low intensity burn can kill most

of the trees in the overstorey (Kozlowski and Ahlgren, 1974; Peterson and Reich, 2001),

the open sclerophyllous forests of Australia suffer little physical damage from low

intensity fires (Christensen et al., 1981). While high intensity fires may scorch the

canopy and cause defoliation of eucalypts, tree mortality is generally low (Bell et al.,

1989; Williams et al., 1999; Wardell-Johnson, 2000).

Nonetheless, all forests, including those resilient to fire, contain species that are

more likely to die due to burning or are more vulnerable to extinction under certain fire

regimes. Long-lived woody species that primarily regenerate from seed are especially

susceptible to frequent burning because they have long juvenile periods (Morrison et al.,

1996). Many woody seeders in Australia, such as some Banksia species, rely on plant-

stored seed for their regeneration and may decrease in abundance if the time between

individual fires is shorter than the time it takes for them to mature and set seed (Cowling

et al., 1990; Bell, 2001; Knox and Morrison, 2005). The abundance of woody seeders

that have soil seed stores may also decrease at high fire frequencies, because their soil

seed banks can become depleted over time (Cary and Morrison, 1995; Bell, 2001; Knox

and Morrison, 2005). Consequently, the proportion of woody resprouting species is

often higher than woody seeders in habitats where fires occur relatively frequently

(Bellingham and Sparrow, 2000; Verdu, 2000). However, competitive interactions

between seeders and resprouters are often dependent on interactions between several

environmental factors including water and nutrient availability (Ojeda, 1998;

Bellingham and Sparrow, 2000; Buhk et al., 2007; Pausas and Bradstock, 2007). For

example, while resprouters are found in high abundance on nutrient poor environments,

12

seeders may also be favored on low nutrient soils when fires occur frequently across the

landscape (Grove et al., 1986; Wisheu et al., 2000).

The relative success of species with different regeneration strategies such as

resprouting and seeding under variable environmental conditions is also dependent on

other morphological and life history traits they exhibit. Resprouters generally have high

nutrient and carbohydrate reserves, which can be seen as an adaptation to nutrient stress

(Pate and Dixon, 1981). However, seeders with specialized nutrient acquisition

strategies, such as ectomycorrhizal associations and cluster roots, may be favored over

resprouters without these strategies on nutrient limited soils because these adaptations

increase root surface area, enhance root exudation and solubilisation processes, and

allow uptake of nutrients from non-labile sources (Lamont, 1984; Grierson and Attiwill,

1989; Schmidt and Stewart, 1997; Lamont, 2003; Read and Perez-Moreno, 2003).

Consequently, the relative abundance and distribution of different plant species or

species assemblages is likely to depend on a complex interaction between the functional

adaptations/traits they exhibit, the fire regime and the availability of nutrients and water.

1.4.2 Impact of fire on soil nutrients

Fires play an important role in maintaining soil fertility, particularly in the nutrient-poor

ecosystems of Australia (Humphreys and Craig, 1981). Burning generally removes

organic humus from the topsoil causing nutrients to move from organic layers to

mineral layers in the soil (Humphreys and Craig, 1981; Hingston et al., 1989). This

increases the availability of nutrients, particularly N, for plants because organic N

remains in the ash residue of burnt plant material and acts as a source for N

mineralization (Raison, 1979; Christensen, 1994). However, the net loss of nitrogen due

to volatilization during burning also increases with fire intensity (Raison, 1979;

Christensen, 1994). N volatilization can start at relatively low temperatures (~ 200 °C),

and can vary from 907 kg ha-1 in temperate forests to as much as 1607 kg ha-1 in tropical

forests depending on the fire regime (Wan et al., 2001). Nonetheless, N fertility is often

higher after intense fire than before, particularly in nutrient poor ecosystems, because

fire changes nutrient fractions in the soil to forms more easily available to plants

(Raison, 1979; Humphreys and Craig, 1981; Rundel, 1983). For example, labile

inorganic nitrogen, in the form of NH4+, was found to have increased up to 182%

directly following fire in a southwest Australian eucalypt forest (Grove et al., 1986),

and was more than doubled within one year after fire in a Pinus canariensis forest

13

(Durán et al., 2008). While the increase in labile N is often immediate, soil NH4+ has

been shown to remain elevated for up to four years before returning to pre-fire levels

(Wan et al., 2001; Rau et al., 2007). The colonization of recently burnt sites by nitrogen

fixing species is also thought to have an important role in increasing N availability

following fires, particularly in the forests of southwest Australia (Hingston et al., 1989).

For example, in the jarrah forest, legumes can fix up to 3.3 kg ha-1 yr-1 of N within the

first two years following fire (Hingston et al., 1982), which is reduced to less than 0.5

kg ha-1 yr-1 over the first six years after fire (Hansen et al., 1987).

In contrast to N, the influence of fires on soil P is less well-understood and also

more variable due to differences in the phosphate bonding or fixing capacity of soils and

immobilization of phosphorus by soil microbes (Rundel, 1983; Attiwill and Adams,

1993). Nonetheless, labile or plant available phosphorus can also increase after fire due

to greater penetration of mineralized P into the soil profile (Adams et al., 2003), and

conversion of organic P to orthophosphate (Cade-Menun et al., 2000). For example,

Grove et al. (1986) recorded a 138% increase in labile P from the top 0-3 cm of the soil

profile in the southwest Australian jarrah forest directly following fire. However, these

increases in P availability are often short lived because phosphates quickly bind to Al,

Fe and Mn oxides in acid soils and to Ca minerals in alkaline and neutral soils (Certini,

2005). Labile soil P in the jarrah forest, from the top 0-3 cm, decreased to about half of

the post-fire amount within one year after fire (Grove et al., 1986).

Long absences of fire can also result in changes in understorey species

composition leading to a denser and less fire-tolerant forest understorey (Close et al.,

2009). Such structural changes in the understorey can alter soil microbial community

structure and hence impact nutrient cycling negatively (Hart et al., 2005). Ellis et al.

(1980) showed that tree decline in Eucalyptus delegatensis stands in Tasmania were

related to absence of fire and subsequent invasion of rainforest species into the

midstorey. In the forests of southwest Australia, nutrient losses following fire tend to be

low, and relatively large quantities of organic nutrients are deposited on the soil surface

after burns because the majority of the nutrients are stored in the understorey vegetation

(Hingston et al., 1981; Grove et al., 1986; Hingston et al., 1989; Adams et al., 2003).

Consequently, plant growth and vigour may decline in these ecosystems where fire is

suppressed (Humphreys and Craig, 1981). More recently, canopy decline in tuart (E.

gomphocephala) woodlands and forests of southwestern Australia has been attributed to

14

changes in understorey species density and composition due to long-term fire

suppression (Archibald et al., 2005). However, due to the complex nature of nutrient

dynamics and vegetation responses associated with fires, the impact of a series of fires

over time, i.e., fire regime, on soil nutrient concentration and hence vegetation

composition is highly variable. Both increases and decreases in soil fertility have been

observed with greater fire frequency in forested ecosystems (Reich et al., 2001; Adams

et al., 2003; Certini, 2005; Rau et al., 2007; Durán et al., 2008). Consequently, there is a

need to examine the changes in soil nutrient availability and understorey vegetation

composition and structure associated with different fire histories in order to understand

the link between variable fire regimes and nutrient-diversity relationships.

1.4.3 Fire and climate change

Interactions among controlled burning practices, vegetation cover and climate have

created a highly variable and complex fire history across the southwest Australian

landscape. The fire regimes of this region may be further altered by future changes in

climate due to global warming. Southwest Australia, particularly the more western and

central regions that coincide with the distributions of forest, has already experienced a

15-20% decrease in rainfall during the last century, and is expected to experience a

further decline of up to 60% by 2070 (CSIRO, 2001). The severity of the fire season in

this region is also predicted to increase with a doubling in extreme fire events (Williams

et al., 2001). The impact of fire on plant species diversity is dependent on several

factors including the nutrient status of the soils and the functional attributes of flora.

Changes in chemical soil properties due to the direct impact of burning as well as

indirect changes due to altered vegetation composition and structure under variable fire

regimes can result in a decrease in plant diversity. Consequently, it is necessary to

understand the links between climate, fire regime, resource availability, and vegetation

composition and structure in order to predict the consequences of changes in climate as

well as fire management practices on southwest Australian forests.

1.5 Thesis objectives

This thesis seeks to determine the direct and indirect impact of variations in fire regime,

in terms of fire frequency and interval length, on the structure, composition and

diversity of the southern jarrah forest vegetation community. Given the complexity of

interactions and feedbacks amongst fire, resources and diversity, I investigated the

15

relationships among fire regime, climate and soil nutrients, and assessed how variations

in these relationships/dynamics impact plant structure and diversity. I also examined

how interactions among fire, nutrients, climate and functional plant assemblages help

maintain overall plant species diversity in the jarrah forests as well as in forested

ecosystems in general. The contributions of each of the following chapters to addressing

these overall objectives are as follows:

Chapter 2 provides an overview of the climate, vegetation and landforms of

the study area in the Warren Region of southwest Australia.

Chapter 3 seeks to determine the impact of aridity and fire frequency on

overstorey stand structure and composition. The variation in the density,

basal area and biomass of different overstorey tree species (jarrah and marri)

as well as overall stand biomass, leaf area, sapwood area and litterfall rate

was measured in relation to fire frequency and aridity.

Chapter 4 seeks to determine how fire-biomass interactions influence overall

plant diversity patterns. The variation in the relative number of woody and

herbaceous species as well as overall plant life form and species diversity

was measured in relation to fire frequency and site biomass.

Chapter 5 seeks to assess the role of different functional plant traits in

maintaining diversity under varying environmental conditions. The variation

in the relative number and abundance of species within different plant

assemblages indicative of function was measured in relation to fire

frequency and interval length, aridity and soil nutrients.

Chapter 6 seeks to determine how fire-driven changes in nutrient limitation

influence plant species diversity. The variation in nutrient limitation

(indicated by the foliar N:P ratio of dominant species) was measured in

relation to fire frequency and the variation in plant species richness and

evenness was measured in relation to the foliar N:P ratio of the dominant

species.

Chapter 7 summarizes the findings of the experimental chapters and seeks to

assess the relationship between nutrient limitation and species diversity in

forests in general. The impact of soil nutrient status on species richness was

assessed across different forested ecosystems using data from previously

published studies.

17

CHAPTER 2

Vegetation, landforms and fire history of the Warren Region

2.1 Location and description of the study area

This research was conducted within the Warren Region at the southern extent of the

jarrah (E. marginata) forest of southwest Australia (Figure 2.1). The Warren Region is

defined by an administrative boundary that represents southern forests and heathlands in

the high rainfall zone of SW Australia (700–1400 mm year-1). A total of 0.93 million

hectares within the region is currently managed by the WA State Government’s

Department of Environment and Conservation (DEC).

The broader southwest region of Australia has a Mediterranean type climate

where water is limited during a significant portion of the year (Gentilli, 1989).

Consequently, the regional distribution of plant species and vegetation types is primarily

a function of water availability (Beard, 1983). However, at local scales vegetation

composition, particularly within open forests and woodlands, is influenced by several

other environmental variables including soil acidity and nutrient levels (Inions et al.,

1990). Annual rainfall near the coast is ~1200 mm but decreases sharply towards the

northeast with distance inland even though the landscape is relatively flat (Figure 2.1).

The Warren Region is characterized by undulating terrain. Low areas tend to be

waterlogged during a significant portion of the year, i.e., the winter (Churchward et al.,

1988) and support swamp vegetation with abundant sedges and rushes. Forest and

woodlands dominate the rises and other well-drained sites. While jarrah often co-occurs

with marri (Corymbia calophylla (Lindl.) K.D.Hill & L.A.S.Johnson) across much of

the landscape, forests dominated by jarrah are primarily restricted to shallow gravelly

soils on hill tops (Churchward et al., 1988; Mattiske and Havel, 1998). More fertile

forest sites with high precipitation are dominated by karri (Eucalyptus diversicolor

F.Muell.) and several species of tingle (Eucalyptus jacksonii Maiden, E. guilfoylei

Maiden and E. brevistylis Booker) (Dell and Havel, 1989; Mattiske and Havel, 1998).

18

Figure 2.1 Location of the study area in the Warren Region of southwest Western Australia at the southern extent of the jarrah forest in southwest Australia. Grey areas indicate forested ecosystems. Individual study sites are shown in the inset box and are numbered 1-16. Sites 1-8 correspond to a Collis ‘Coy’ soil type, while sites 9-16 correspond to the Collis ‘Cop’ soil type (Churchward et al., 1988). Mean annual rainfall isohyets are shown as dashed lines.

2.2 Vegetation and landforms of the Warren Region

The greater southwest Australian floristic region contains 7380 native plant species,

49% of which are endemic to the region (Hopper and Gioia, 2004). While, the open

sclerophyllous forest communities of the Warren Region are also highly diverse and

display an exceptionally high number of endemic species (i.e., up to 29 species per 314

km2 in the tingle mosaic), endemism and species richness among localities with

different vegetation associations is highly variable (Wardell-Johnson and Williams,

1996). Wardell-Johnson et al. (1989) identified 12 major vegetation associations within

the Walpole-Nornalup National Park, which includes the western part of the Warren

19

Region. The vegetation associations were defined as heath and shrubland communities

dominated by Pimelea longiflora R.Br., Agonis parviceps Schauer, or Dasypogon

bromelifolius R.Br., sand dune communities dominated by Allocasuarina humilis (Otto

& F.Dietr) L.A.S.Johnson, Jacksonia furcellata (Bonpl.) DC., Banksia littoralis R.Br.,

or Acacia littorea Maslin, and woodland and forest communities dominated by Acacia

browniana H.L.Wendl., Allocasuarina fraseriana (Miq.) L.A.S.Johnson, or Eucaplytus

diversicolor. While the Allocausarina forests and dune communities displayed the

greatest species richness at the plot level (i.e., 26-28 species per 400 m2), inter-dune

communities dominated by Banksia littoralis had the most species at a community

level, i.e., a total of 111 species recorded across 23 plots (Wardell-Johnson et al., 1989).

E. marginata dominated communities in the southern jarrah forest outside of the Warren

Region also displayed high species numbers at larger scales, i.e., up to 100 species per

hectare (Gioia and Pigott, 2000).

Vegetation composition and diversity in the region is strongly dependent on

local soil and other landform features (Wardell-Johnson et al., 1989; Inions et al., 1990;

Wardell-Johnson and Williams, 1996; Havel and Mattiske, 2000). The main features of

the geology, landforms and soils of the greater southwest region, which includes the

Warren Region, were described by Churchward et al. (1988) who distinguished three

broad landscape zones. The first landscape zone includes the landscape units of

Keystone, Collis, Mattaband and Angove and is characterized by granitic gneiss parent

material, sharp delineation between ridges and hilltops. The second is characterized by

swamps and sand dunes on coastal fluvial and aeolian sediments and includes the

Walpole, Blackwater, Owingup, Kordabup and Meerup landform units. The third

landscape zone is composed of narrow landforms associated with drainage lines. Most

of the forested areas in the Warren Region are found on the Keystone, Collis, Mattaband

and Angove landforms within the first landscape zone (Churchward et al., 1988).

The vegetation of southwest Australian forests (Figure 2.2) was mapped by

Mattiske and Havel (1998) using the landscape units described by Churchward et al.

(1988), coupled with various climatic variables and local vegetation associations.

Because of the sharp gradient in rainfall in this region (Figure 2.1), several vegetation

complexes were recorded within each landscape unit creating a high diversity of

vegetation complexes within the Warren Region as illustrated in Figure 2.2 (Havel and

Mattiske, 2000). While vegetation complexes within Keystone, Mattaband and Angove

20

landform units include jarrah in the overstorey, only Collis has jarrah as the dominant

tree species (Mattiske and Havel, 1998). Vegetation found on Collis landform is

considered to be generally representative of the southern jarrah forest and is restricted to

yellow to brownish sandy soils with brownish yellow medium clays appearing at ~60

cm depth that are generally low in mineral nutrient content (Churchward et al., 1988;

McArthur, 1991; Mattiske and Havel, 1998). While there is considerable knowledge on

nutrient cycling, overstorey structure and productivity and understorey succession

following fire in northern jarrah forest vegetation communities (Dell et al., 1989),

compositional and structural responses of the southern jarrah forest to fire and other

environmental impacts is largely undescribed.

Figure 2.2 Vegetation complexes of the Warren Region of southwest Australia. Each colour represents a particular vegetation type defined by Mattiske and Havel (1998) obtained from vegetation, topography and geological features across the landscape. The insert in the middle shows the extent of Collis vegetation in the area sampled for the Warren Region Fire Mosaic study. Each box corresponds to a 10 km Χ 10 km UTM grid. Hatched areas represent areas not mapped by Mattiske and Havel.

21

2.3 Fire data of the Warren Region

This study uses the spatial variation in fire history across the landscape as a proxy for

temporal changes in fire regime, which requires an accurate assessment of historical fire

events in the region. There is some evidence suggesting that Aborigines set fires in the

past in southwest Australia at strategic places and times of the year, which maintained a

mosaic of small patches with different fire histories (Abbott, 2003; Bowman, 2003).

However, while several studies have been conducted on fire history mapping in

southwest Australia, pre-European fire regimes in the region remain unclear (Ward et

al., 2001; Enright et al., 2005).

A more recent fire history of southwest Australia (i.e., since 1950’s) was

collated into a Geographic Information System (GIS) by the Western Australian

Department of Environment and Conservation (DEC) (Wittkuhn et al., 2009). The

location of fires in all southwest forests has been recorded by DEC or its predecessors

since 1953. This information was held in a variety of forms, with most being held as

microfiche images of old paper maps showing fire boundaries (see Wittkuhn et al.

(2009)). All fire boundaries were digitised and information such as fire area, fire type

(prescribed burn or wildfire), season of burn, ignition source, and burn purpose were

incorporated into the database (Wittkuhn et al., 2009). Only the fire history recorded

from maps with definite fire boundaries, i.e., from 1972 onwards, was utilized for the

purposes of this study.

2.4 Field sampling design

In large scale studies across different vegetation types, much of the variation in

ecosystem properties (i.e., plant biomass, canopy leaf area, litterfall rate, nutrient

cycling rates, etc.) is a function of differences in the evolutionary or genetic lineage of

the dominant plant species as well as the variation in local environmental site factors

(Firn et al., 2007). My research specifically sought to determine the impacts of a

variation in fire regime and resource availability on vegetation structure and

composition. A single landscape unit - Collis (Churchward et al., 1988; Mattiske and

Havel, 1998) – with relatively homogenous vegetation was selected for the study, in

order to minimize the variation in ecosystem properties that may be attributable to

22

differences in species lineage. The distribution of Collis type vegetation in the study

area is shown in Figure 2.2.

Due to logistic reasons, field sites used in this study were established by the

Western Australian Department of Environment and Conservation (DEC) for the

Warren Region Fire Mosaic Project (Bushfire Cooperative Research Centre Project

B1.1; http://www.bushfirecrc.com/research/b11/b11.html). The sites were located on

Unallocated Crown Land (UCL) within a designated wilderness area (Wittkuhn et al.,

2009) directly northeast of the township of Walpole, between the 34th and 35th parallels

(Figure 2.1). The Warren Region Fire Mosaic project was restricted to 16 forest sites

because of challenges posed by accessing sites in a wilderness area as well as the

difficulty in finding sites with variable fire histories within the same vegetation

complex. Half of the sites (sites 1-8; Figure 2.1) were located to the south and west of

the region, on a Collis “COy” soil type (Appendix 1), which has a gravely sand or

loamy sand A horizon of ~ 25-35 cm thick with lateritic duricrust common in some

areas (Churchward et al., 1988; Mattiske and Havel, 1998). Sites 9-16 were located to

the north and east of the region, and were on a Collis “COp” soil type (Figure 2.1;

Appendix 1) with a shallow (10-15 cm) gritty sand A horizon, an often bleached A2

horizon, and weathered granite usually within 1.5 m of the surface (Churchward et al.,

1988). Both of these soils support an open eucalypt forest dominated primarily by two

tree species, E. marginata and C. calophylla, in the overstorey. The two soil types also

both have a dense shrub layer in the understorey that includes Hibbertia, Xanthosia,

Conospermum, Agonis, Daviesia, Isopogon, Bossiaea, Hakea, and Acacia spp. to

varying extents and a midstorey which may include Banksia grandis, Persoonia spp.,

Xanthorrhoea preissii, and Kingia australis (Churchward et al., 1988; Mattiske and

Havel, 1998).

Vegetation diversity recovers its pre-fire state quickly in Mediterranean type

ecosystems, particularly those of southwest Australia where a high proportion of the

flora regenerate from rootstock (Kruger, 1983; Bell et al., 1989; Trabaud, 1994).

However, knowledge of the ecological importance of a series of fire events is

incomplete. While vegetation composition was more strongly dependent on the time

since fire than the history of fire, fire frequency had a significant influence on the

abundance of woody obligate seeders in the northern jarrah forest (Burrows and

Wardell-Johnson, 2003). In open eucalypt forests of southeast Queensland, Watson and

23

Wardell-Johnson (2004) found that while both time since fire and fire frequency

influenced vegetation composition, the impact of fire frequency was greater. Thus, a

major objective of this study was to determine the importance of fire frequency or the

sequence of fire interval lengths over time for plant diversity in the southern jarrah

forest. When fire interval lengths are shorter than the juvenile period of fire sensitive

species, a loss of species diversity may occur (Morrison et al., 1996). Loss of fire

sensitive species under high fire frequencies has been observed in Australian plant

communities where the time between individual fire events was less than five years

(Cary and Morrison, 1995; Morrison et al., 1995). In southwest Australia, a minimum

of 10 years between fires is recommended in order to protect fire sensitive plant species

(Burrows and Wardell-Johnson, 2003). The 16 sites chosen for this study represented

either two consecutive short fire intervals of ≤ 5 years (SS); long fire intervals of ≥ 10

years (L); or a mixed fire interval regime (M), while the frequency of fires over the 30

years ranged from 1 to 5 across the sites (Table 2.1; Wittkuhn et al. (2009)). Four out of

the sixteen sites had short intervals (SS) and corresponded to high fire frequencies

(Table 2.1). Five had long intervals (L) corresponding to low fire frequencies and seven

had a mixed interval sequence (M) corresponding to medium fire frequencies (Table

2.1). The fire frequency and interval sequence of each site is shown in Table 2.1.

A 200 m × 100 m plot based on the “Forestcheck” methodology (Robinson,

2004) was established at each field site by DEC (Wittkuhn et al., 2009). These plots

were designed to combine the monitoring of invertebrates, fungi, lichens, flowering

plants and vertebrates with the physical attributes of forest structure in order to

determine the impacts of variable fire regimes on plot scale biodiversity in the southern

jarrah forest (Robinson, 2004). For my study, I used a single sub-plot (30 m × 30 m)

within the corner of each of the Forestcheck plots established by DEC that was most

representative of floristic and soil composition of the larger plot. Areas with large

boulder outcrops and exposed laterite were avoided because they were not

representative of overall vegetation composition and soil characteristics of the sites.

24

Table 2.1 Fire interval sequence, fire frequency and the specific years in which fires occurred for 16 jarrah forest sites in the Warren Region. Fire interval sequences of ‘SS’ represent two consecutive short fire intervals of ≤ 5 years, ‘L’ long fire intervals of ≥ 10 years and ‘M’ represents a mixed fire interval regime. Fire frequency corresponds to the number of fires over a 30 year period, i.e., from 1974 to 2004. Prescribed fires are shown with regular font and wildfire years are shown in italics.

Site Fire interval Fire frequency Fire years 1 SS 4 1981, 1986, 1990, 2002 2 M 3 1981, 1990, 2002 3 M 3 1981, 1990, 2002 4 SS 5 1976, 1980, 1984, 1994, 2002 5 M 4 1976, 1984, 1994, 2002 6 SS 5 1976, 1980, 1984, 1994, 2002 7 M 4 1976, 1984, 1994, 2002 8 SS 5 1974, 1988, 1991, 1994, 2002 9 L 2 1991, 2002 10 L 2 1991, 2002 11 M 3 1981, 1990, 2002 12 L 1 2002 13 L 1 2002 14 M 4 1975, 1984, 1991, 2002 15 M 4 1975, 1984, 1991, 2002 16 L 2 1991, 2002

All field data reported in the following research chapters were collected from

these 16 sub-plots independent from the rest of the Forestcheck plot. Stand level

overstorey structure (Chapter 3), vegetation composition (Chapter 4) and soil nutrients

(Chapter 4) were all assessed within each plot over the course of ~ 2 years, from 2006-

2008. Interpolated climatic data were also obtained each plot location from the

Australian Bureau of Meteorology (Chapter 3). Chapters 3, 4, 5 and 6 are written as

“stand-alone” articles and are presented here in journal format. Consequently, some

repetition in concepts is unavoidable.

25

CHAPTER 3

Impacts of increased fire frequency and aridity on the structure,

composition and biomass of the jarrah forest overstorey

3.1 Introduction

Rainfall patterns, along with the availability of water during the dry season, strongly

influence stand structure and composition across eucalypt dominated landscapes

(Specht and Specht, 1989a; Liedloff and Cook, 2007). Eucalypt forests also exhibit

multiple age cohorts of trees and distinct structural changes due to heterogeneity in fire

history (Wardell-Johnson, 2000; Simkin and Baker, 2008). In climates where high

winter rainfall is followed by a dry summer season, the distribution and abundance of

tree species is primarily determined by their ability to tolerate water stress and to re-

establish after disturbance (Pigott and Pigott, 1993; Zavala, 1999). The southwest of

Australia has a strongly seasonal climate typified by a wet winter and a prolonged

summer drought and the forests of this region are among the most fire prone in the

world (Abbott and Burrows, 2003). Consequently, both drought severity and fire regime

are likely to have an important role in shaping the structure and composition of forests

in southwest Australia.

Fires often reduce stem density and total standing biomass and can influence

competitive interactions among different tree species (Wirth et al., 1999; Peterson and

Reich, 2001; Diaz-Delgado et al., 2002). Eucalypt stands are highly resilient to fire

disturbance, i.e., have a short and efficient recovery period following fire, and may

experience different structural changes under high fire frequencies compared to other

forests owing to their capacity to re-sprout as well as regenerate from seed (Bell et al.,

1989). However, the survival of individual eucalypt trees following fire is dependent on

their size and physiognomic attributes (Wardell-Johnson, 2000; Vivian et al., 2008), and

resprouting capacity or vigour can decrease when fires occur too frequently due to a

depletion of carbohydrate reserves (Pate et al., 1990). Thus, while the total amount of

biomass and leaf area of eucalypt stands will largely depend on water availability, fire

regime may also influence the density and relative biomass of different eucalypt species,

depending on the cumulative impact of fires on tree mortality and the regeneration and

growth rate of new trees.

26

Annual potential evapotranspiration (PET) is greater than annual precipitation

(P) in forests with a strong summer drought, which results in seasonal water limitation

and reduces tree growth (Sabate et al., 2002). The ability of trees to grow under water

stress is largely dependent on their leaf area to sapwood area ratio, a reduction in which

can facilitate the growth and survival of trees when water is limiting (White et al., 1998;

Delucia et al., 2000; Martin et al., 2001; Zeppel and Eamus, 2008). Thus, although tree

leaf and sapwood area are correlated within forest stands, the relative amount of leaf to

sapwood area of individual trees can decrease with reduced water availability (Callaway

et al., 1994; Mencuccini and Grace, 1995; Vertessy et al., 1995; Enquist et al., 1998).

Leaf area to sapwood area ratio has also been shown to influence the growth capacity of

Eucalyptus globulus trees during water stress (White et al., 1998). Consequently, leaf

area may decrease relative to sapwood area at stand level in eucalypt forests at more

arid sites as a response to water limitation.

I investigated relationships between stand structure, aridity and fire frequency in

the jarrah forest, which is the most widely distributed forest type in southwest Australia.

The overstorey of this forest is comprised mainly by two tree species, jarrah and marri

(C. calophylla) (Churchill, 1968; Dell and Havel, 1989). While jarrah tends to dominate

over marri throughout most of their distribution (Churchill, 1968), the influence of

water stress and fire regime on competitive interactions between the two species is not

known. Jarrah is well adapted to high levels of water stress (Abbott et al., 1989).

However, the growth rate of jarrah and marri stands is limited by water over a

substantial portion of the year (i.e., summer dry season) (Stoneman et al., 1996), when

evaporation can be up to 15-fold greater than precipitation (Crombie, 1997). Jarrah and

marri can also survive fires with most of their aboveground biomass intact, and both

species can re-sprout either epicormically or from belowground lignotubers (Bell et al.,

1989). However, jarrah is more resistant to fire (i.e., has a higher survival rate following

fire) than marri due to its ability to more effectively avoid damage to its cambium

(Burrows, 1987; Bell et al., 1989). Thus, high fire frequencies combined with increased

drought stress may favour jarrah over marri.

The objective of this study was to determine the influence of fire frequency and

water availability on the structure, biomass and composition of mixed jarrah and marri

stands in order to assess how the jarrah forest ecosystem may respond to future changes

27

in climate and fire regime. I thus measured how the relative density and biomass of

jarrah and marri trees, as well as overall structural attributes of the stands such as total

biomass and leaf to sapwood area ratio respond to increased aridity and fire frequency. I

hypothesized that:

1. More frequent fire and greater aridity would (i) reduce overall stand biomass,

leaf and sapwood areas, and litterfall, and (ii) reduce the density, basal area, and

biomass of marri relative to that of jarrah.

2. The ratio of stand leaf area to sapwood area would be smaller at more arid sites

compared to higher rainfall sites.

3.2 Methods

3.2.1 Study site description and climate data

Sixteen (30 m × 30 m) plots were established within the southern jarrah forest of

southwest Australia (Chapter 2). This region has a slightly undulating landscape with a

gradient of increasing annual rainfall ranging from 700 mm in the northeast to 1200 mm

in the southwest (Figure 2.1; Bureau of Meteorology (2008)). All forest stands were

confined to yellow duplex soils, sensu Collis (1988), and were composed of E.

marginata and C. calophylla in their overstorey (Chapter 2). At the time of sampling,

the time since fire was four years across all sites. The total number of prescribed and

wild fires since 1974 was used to calculate overall fire frequency (Chapter 2). Climatic

data were predicted for the precise GPS location of each site for the past ~35 years

(1970 to 2008) from the Australian Bureau of Meteorology (BOM,

http://www.bom.gov.au/climate). Mean annual precipitation (P) was adjusted for

elevation using BIOCLIM (Nix, 1986), and mean annual potential evapotranspiration

(PET) was obtained with data drill from SILO (Jeffrey et al., 2001). In order to estimate

site water availability, an aridity index (AI) was calculated at each site as the ratio of

PET over P, which is especially useful in seasonally dry environments such as the jarrah

forest where rainfall does not supply the full water requirement for optimum growth of

plants (UNEP, 2007).

28

3.2.2 Estimating stand density, basal area and biomass

Stand level structure of the overstorey was measured in a single 30 m × 30 m plot at

each site. Diameter at breast height (~1.3 m) over bark (DBH) of all marri and jarrah

trees greater than 2.5 cm in diameter was recorded at each plot. No other overstorey

species were present in the plots. Stem density (SD) was calculated as the number of

stems per hectare. A tree size classification was also made based on the overall size

distribution of trees across the sites. Small trees were selected to represent the smallest

5% of DBH recorded at all plots. Stem density of small trees was thus determined as the

total number of trees with DBH of 2.5 – 4 cm per plot. Stand basal area (Ab, m2 ha-1)

was calculated as the sum of the basal area of all trees within each plot divided by plot

area. Aboveground biomass of individual trees was estimated from DBH where DW is

dry weight (kg) using existing allometric equations (equations 1 and 2).

jarrah ln (DW) = – 3.680 + 2.84 × ln (DBH) Hingston et al. (1981) (1)

marri ln (DW) = – 3.370 + 2.74 × ln (DBH) Hingston et al. (1981) (2)

Stand biomass (B) of jarrah and marri was calculated for all sites as the sum of the

biomass of all individuals of each species at each plot. Total B (both species) was also

calculated at each site as the sum of the biomass of all individual jarrah and marri trees.

Stand level proportion of the basal area, stem density, and biomass of marri relative to

that of jarrah, were calculated as the basal area of marri divided by basal area of jarrah

(Ab marri: Ab jarrah), the density of marri stems divided by the density of jarrah stems (SD

marri: SD jarrah), and the biomass of marri divided by biomass of jarrah (B marri: B jarrah)

respectively at each plot.

3.2.3 Estimating stand sapwood area from DBH-sapwood area relationship

Tree cores were taken from five sites that encompassed the variation in stand structure

across the study region in order to obtain a relationship to predict sapwood area from

DBH for jarrah and marri trees. A total of 64 trees (46 jarrah and 18 marri) with DBH

ranging from 25 – 690 mm for jarrah, and 25 – 820 mm for marri, were sampled. DBH

of individual trees was recorded and two to four stem wood cores and bark thickness

measurements (depending on the diameter of the tree) were taken at breast height from

each tree. Cores were stained with methyl orange to distinguish the heartwood-sapwood

boundary and the depth of sapwood was measured as the radial distance from the bark-

sapwood boundary to the sapwood-heartwood boundary. Sapwood area of each tree was

29

calculated as the difference between the total under-bark area and the heartwood area

that were obtained from the averaged diameter measurements.

While sapwood area does not always increase linearly with tree size (Bond-

Lamberty et al., 2002), a linear model was used to describe the relationship between

DBH and sapwood area because the relationship was obviously linear for both species

sampled in this study (Table 3.1). Marri had greater sapwood area than jarrah for any

given tree size (Figure 3.1) even though same sized trees had a similar bark thickness.

Consequently, separate allometric equations were developed for each species. Because

trees with 0 DBH should also have no sapwood area, and neither of the regression

intercepts were significantly different from zero, the models were forced through the

origin to develop the final allometric equations used to estimate total stand sapwood

area (As) for jarrah (equation 3) and marri (equation 4). The following equations were

applied to all trees at each plot to estimate As of each species as well as total As (sum of

both species) in m2 ha-1:

jarrah As = 34×DBH r2 = 0.88 P = <0.0001 n = 46 (3)

marri As = 61×DBH r2 = 0.93 P = <0.0001 n = 18 (4)

Table 3.1 Linear regression equations relating diameter at breast height (DBH) to sapwood area (As) for individual marri (C. calophylla) and jarrah (E. marginata) trees sampled across the study region. The regression coefficients (a & b) and their standard errors (SE) are given.

Species Equation A SE b SE r2 P value

jarrah As = a(DBH) + b 40 1.9 1854 509 0.91 < 0.0001

marri As = a(DBH) + b 71 3.5 4177 1121 0.96 < 0.0001

3.2.4 Estimating leaf area index and litterfall rate

Leaf area index (LAI) is a good proxy for canopy growth and is strongly related to net

primary productivity (Burrows et al., 2002). Digital cover photography was used to

obtain LAI at each plot as an estimate of stand leaf surface area (Al). This approach for

estimating LAI has been thoroughly calibrated for jarrah forest using destructive

methods (Macfarlane et al., 2007a; Macfarlane et al., 2007b). As one of the objectives

of this study was to determine the structural response of stands to water stress associated

30

with a landscape aridity gradient, digital cover photography was undertaken during the

summer (dry season) from December 2006 to March 2007. Sixteen cover photos (Figure

3.2) were taken in each plot using four parallel transects starting at the corner with 10 m

between each photo. The photos were taken with a tripod-mounted digital Nikon

Coolpix 4500 camera as FINE JPEG images with maximum resolution. The camera,

without fisheye converter, was set to F2 lens, automatic exposure, Aperture-Priority

mode and minimum aperture (f/9.6). The lens was pointed directly upwards and the

camera lens was levelled at each photo location using a bubble level placed above the

lens. The images were then analysed using WinSCANOPY Pro 2006a (Regent

Instruments, Ste-Foy, Quebec) as described by Macfarlane et al. (2007c). LAI was

calculated automatically by WinSCANOPY assuming a light extinction coefficient of

0.5 (Macfarlane et al., 2007b; Macfarlane et al., 2007c). LAI was divided by both basal

area and sapwood area to estimate a leaf area to basal area (Al:Ab) ratio as well as leaf

area to sapwood area (Al:As) ratio; As was divided by Ab to estimate sapwood area to

basal area (As:Ab) ratio.

0 200 400 600

050

0015

000

2500

0

Diameter at breast height (cm)

Sapw

ood

area

(cm

2 )

0 200 400 600

050

0015

000

2500

0

Diameter at breast height (cm)

Sapw

ood

area

(cm

2 )

Figure 3.1 The relationship between diameter at breast height and sapwood area for individual marri and jarrah trees sampled across the study region. Data for marri trees are shown as black circles and for jarrah trees as white circles.

31

Figure 3.2 Examples of a cover photos taken of the southern jarrah forest canopy. The images were taken using a tripod-mounted Nikon Coolpix 4500 camera and converted to black and white before analysis. Image a) is equivalent to an LAI of 2.16, image b) to an LAI of 1.45 and image c) to an LAI of 0.98.

32

Litterfall from the canopy was measured over the course of 18 months using

circular litter traps with an area of 0.24 m2 at a height of ~1 m above the forest floor.

Each trap consisted of a steel wire hoop and polyester fly mesh, and had a diameter of

~55 cm and a depth of ~25 cm. My objective was to compare representative rates

among stands rather than quantify seasonal patterns in litterfall. Consequently, four

fixed litter traps were used per stand with one in each plot corner, and litter was

collected once at the beginning and once at the end of the dry season between December

2007 and April 2009. Litter was oven dried at 60°C for 24 hours to remove any

remaining moisture, and then separated into leaf and other components (stems, fruit,

bark etc.) before weighing. Litterfall rate (Rl) was calculated from the average weight of

leaves of four traps per plot (ton ha-1 yr-1). All stand level structural traits for all sites are

given in Appendix 1.


The open source statistical graphics and computing environment R version 2.6.2 (R

Development Core Team, 2009) was used for all statistical analyses. Multiple linear

regression was used to separately test the effect of the explanatory variables, aridity and

fire frequency, on the responses SD, B, As, LAI, Rl, Ab marri: Ab jarrah , SD marri: SD jarrah ,

and B marri: B jarrah ; main effects and interactions of the variables were initially examined

and removed from the analysis if not significant (P < 0.1). Simple linear regression was

used to test the relationships between structural traits (As:Ab, Al:As and Al:Ab) and aridity

at the stand level as well as between aridity and fire frequency.

3.3 Results

3.3.1 Density, composition, and biomass of stands

The diameter (DBH) of individual trees within the study plots ranged from 2.5 cm to

over 60 cm. Total Ab (both jarrah and marri together) ranged from 23 to 59 m2 ha-1 and

total SD from 467 to 3467 stems ha-1. Total B varied from 85 to 414 tons ha-1. Total As

ranged from 5 to 15 m2 ha-1 while LAI ranged from 0.8 to 2.4. Jarrah was more abundant

than marri throughout the region and had greater SD, Ab and B (see Appendix 1). The

relative proportion of marri to jarrah, in terms of Ab marri: Ab jarrah , SD marri: SD jarrah , and

B marri: B jarrah, was also highly variable across the stands (Appendix 1). However, on

average marri accounted for 38% of total As suggesting that it makes a more significant

33

contribution to the productivity and water use of the stands than might be expected from

its relatively low abundance.

3.3.2 Variation in stand structure with aridity and fire frequency

Less arid stands had burnt more frequently over the preceding 30 years than more arid

stands (r2 = 0.50, P = 0.002). This is largely a result of fire management: since the 1960s

most of the fires in the Warren Region have been prescribed fires, accounting for 85-

90% of the total annual fire extent (Boer et al., 2009). Return intervals of prescribed

fires are adapted to fuel accumulation rates, which decrease with site aridity, such that

fire frequencies in the wetter areas tend to be slightly higher than in the drier areas.

Aridity and fire frequency together explained more than half of the variation in

the density of small trees across the stands (Table 3.2). The number of small tree stems

(DBH < 4 cm) was strongly and positively related to both aridity and fire frequency

(Table 3.2). Stem density (SD), especially of jarrah, was higher at more arid stands;

however, total B and LAI were inversely related to aridity (Table 3.2). Total SD and the

density of jarrah stems increased with higher fire frequency (Table 3.2). Total stand

biomass and biomass of jarrah did not vary with fire frequency; in contrast, biomass of

marri was reduced at more frequently burnt sites (Table 3.2). About half of the variation

in total SD, density of jarrah stems and biomass of marri across the sites was explained

by aridity and fire frequency together, while aridity alone explained 31% of the

variation in total B across the stands (Table 3.2).

The number of marri stems relative to jarrah stems did not vary significantly

with aridity or fire frequency (data not shown). However, aridity and fire frequency

together explained about a quarter of the variation in Ab marri: Ab jarrah as well as B

marri: B jarrah and both Ab

marri: Ab jarrah and B marri: B jarrah decreased slightly with aridity as well as

fire frequency (Table 3.2). As and Rl were not correlated with aridity or fire frequency,

nor did LAI differ among sites of varying fire frequency (data not shown). However, LAI

was strongly and negatively correlated with aridity (Table 3.2). The relative proportions

of Al and As also varied significantly with aridity. Stand level Al:Ab ratio was negatively

correlated (Figure 3.3a), and As:Ab ratio was positively (Figure 3.3b) correlated with

aridity, indicating that while Al was reduced relative to total wood area at more arid

sites, Al:As decreased even more strongly with aridity (Figure 3.3c).

34

r2 = 0.75P < 0.001

r2 = 0.25P = 0.049

r2 = 0.26P = 0.045

34

5

Leaf

to b

asal

are

a ra

tio

0.15

0.25

0.35

Sapw

ood

to b

asal

are

a ra

tio

Aridity index

Leaf

to s

apw

ood

area

ratio

6

a)

b)

c)

1.30 1.35 1.40 1.45 1.50

0.10

0.15

0.20

0.25

r2 = 0.75P < 0.001

r2 = 0.25P = 0.049

r2 = 0.26P = 0.045

34

5

Leaf

to b

asal

are

a ra

tio

0.15

0.25

0.35

Sapw

ood

to b

asal

are

a ra

tio

Aridity index

Leaf

to s

apw

ood

area

ratio

6

a)

b)

c)

1.30 1.35 1.40 1.45 1.50

0.10

0.15

0.20

0.25

Figure 3.3 The relationship between aridity and a) leaf area to basal area ratio (Al:Ab), b) sapwood area to basal area ratio (As:Ab), and c) leaf area to sapwood area ratio (Al:As) across sixteen study sites in the southern jarrah forest. Aridity index is annual potential evapotranspiration divided by mean annual precipitation.

35

Table 3.2 Effect of aridity and fire frequency on stand level structural traits across sixteen study sites in the southern jarrah forest. Estimates (Est.), standard errors (SE), and P-values are given for each linear multiple regression, and r2 is given for the overall multiple regression model. Structural traits include stem density (SD), biomass (B), the relative basal area (Ab

marri: Ab jarrah) and biomass (B marri: B jarrah) of marri to jarrah, and

leaf area index (LAI). A negative (-) sign in front of an estimate indicates a negative relationship. When a main effect (in this case fire frequency) did not make a significant contribution to the variation of a structural trait, it was removed from the model.

Aridity index Fire frequency

Structural traits Est. SE P Est. SE P r2

SD total (stem ha-1) 1035 15 0.006 37 16 0.035 0.46

SD jarrah (stem ha-1) 891 249 0.003 33 12 0.018 0.50

SD small (stem ha-1) 773 204 0.002 31 10 0.010 0.53

B total (tons ha-1) -1074 381 0.025 -- -- -- 0.31

B marri (tons ha-1) -580 164 0.004 -17 8 0.054 0.49

Ab marri: Ab jarrah -1.91 0.90 0.054 -0.08 0.04 0.096 0.26

B marri: B jarrah -2.62 1.37 0.078 -0.13 0.07 0.082 0.25

LAI -5 1.40 0.004 -- -- -- 0.47

3.4 Discussion

3.4.1 Influence of fire and aridity on stand structure and composition

This study demonstrates that while the total overstorey biomass and LAI of the jarrah

forest are influenced by aridity, they are relatively insensitive to fire frequency, even

though aridity and fire frequency were themselves inversely correlated. Hence, my first

hypothesis was only partially supported and aridity appears to be a more significant

determinant of biomass than fire frequency in this forest. Although there was less

biomass of marri at more frequently burnt sites, total stand biomass (both jarrah and

marri together) was not reduced because stem density, particularly of jarrah, increased

with more frequent fires. The increased number of small jarrah stems appeared to

compensate for the total biomass lost due to a reduction in marri biomass at high fire

frequencies. Studies of northern Australian eucalypt savannas also showed that an

increase in growth of surviving stems and/or recruitment of new stems compensated for

reduced live basal area from stem death (Williams et al., 1999). While regeneration by

resprouting is generally thought to provide tree stands with the ability to recover quickly

following fires (Diaz-Delgado et al., 2002; Vivian et al., 2008), both my study and that

36

of Williams et al. (1999) contrast with examples from other fire prone ecosystems. In

North American oak woodlands, increased fire frequency was associated with an overall

reduction in basal area and stem density (Peterson and Reich, 2001), and Diaz-Delgado

et al. (2002) also found that stand resilience or the ability of stands to recover green

biomass after disturbance decreased over time with greater fire frequency in mixed oak-

pine forests of Mediterranean Spain. Consequently, I suggest that mixed open eucalypt

forests of Australia are particularly resilient to fire disturbance, or efficient at

compensating for lost biomass and leaf area after fire.

The strong inverse correlation of LAI with aridity suggests that aridity is also an

important determinant of productivity, not just biomass, in this forest although the lack

of correlation between litterfall rate and aridity does not support this conclusion. This

may reflect the relatively short period during which litter was collected or the small

number of traps deployed in this study. The influence of seasonal water stress may also

impact litterfall differently than overall stand biomass. The study region has a temperate

climate but with a relatively short intense dry season, so most of the litterfall is likely to

occur over a short period of time, i.e., beginning of the dry season (Specht et al., 1983).

Consequently, any variation in litterfall due to differences in drought severity among the

sites would be expected to be small. The variation in litterfall among the stands may

have also been influenced by changes in the proportion of marri to jarrah because these

species had different amounts of sapwood relative to their size (DBH) (Figure 3.1) and

may also exhibit different litterfall rates under similar drought conditions.

The study also provides evidence that jarrah has a competitive advantage over

marri with greater fire frequency as well as aridity. The number of marri stems relative

to jarrah did not vary with the frequency of fires suggesting that marri and jarrah do not

have significantly different regeneration capacities. However, both the basal area and

biomass of marri was smaller relative to jarrah at more frequently burnt sites, even

though aridity was generally less at these sites, implying marri has a higher mortality

rate than jarrah under high fire frequencies. Although fires rarely kill mature marri and

jarrah trees, even when those fires are very intense, marri is more likely to suffer stem

damage than jarrah during burns (Burrows, 1987). Thus, the cumulative damage caused

by a series of fires over time on larger marri trees is likely to be greater than the damage

caused to jarrah trees. More arid sites also displayed lower basal area and biomass of

37

marri relative to jarrah; hence, I speculate that the relative abundance of marri may

decrease especially when high fire frequency is combined with high water stress.

3.4.2 Structural and physiological response of stands to increased aridity

Mixed jarrah and marri stands exhibited lower Al and higher As relative to total wood

area at more arid sites compared to wetter sites as hypothesized. Canopy LAI and B were

more strongly related to aridity than As, which may reflect the uneven tree size

distribution of the forest stands in the study region. More of the biomass at drier stands

could be attributed to small trees, which have proportionately more sapwood than larger

trees (Kostner et al., 2002; Macfarlane, 2008). Although total As is expected to decrease

with reduced water availability (Zeppel and Eamus, 2008), the relative proportion of

tree sapwood to biomass has been shown to increase at more arid stands (Gracia et al.,

1999). Consequently, drier stands display relatively higher sapwood area than wetter

stands despite a decrease in overall site biomass. This type of response to differences in

water availability has previously been observed in pine forests in North America

(Callaway et al., 1994) and Europe (Mencuccini and Grace, 1995). A higher sapwood

area, or area of overall conducting xylem, improves the capacity of trees to provide

water to foliage without escalating the risk of xylem embolisms (Delucia et al., 2000),

and leaf area can decline in response to drought while sapwood area remains stable

(Vertessy et al., 1995).

Even small increases in aridity can strongly influence the severity and duration

of summer drought and hence water limitation (Gracia et al., 1999). Thus, while the

aridity range observed in this study was relatively small (1.3-1.5), water stress may vary

enough to cause a significant decrease in total stand leaf area relative to total wood or

sapwood area at more arid sites. The decrease in Al:As ratio with aridity may also be

explained by the higher abundance of small trees at more arid sites. Grigg et al. (2008)

found that stands of jarrah dominated by small trees had a lower Al:As ratio than stands

dominated by large jarrah trees. Thus, mortality of larger trees may result in an overall

decrease in stand level Al:As ratio at more arid sites over time.

3.4.3 Conclusions and potential implications for climate change

An increase in aridity and fire frequency may lead to a greater dominance of jarrah over

marri, as well as a general increase in the distribution of small trees and loss of larger

marri trees. Mature marri trees produce more hollows than jarrah, and these hollows

38

provide important habitat for native mammal populations (Whitford and Williams,

2002). Marri trees also flower prolifically and have dense foliage, which makes them an

important nesting and foraging tree for birds (Michael, 2002) as well as a significant

habitat for a range of invertebrates. Thus, while overall stand biomass may not be

impacted, a high frequency fire regime may influence overstorey tree composition and

structure resulting in other ecological and hydrological consequences.

39

CHAPTER 4

Influence of fire regime on plant diversity patterns in the southern

jarrah forest along a productivity gradient

4.1 Introduction

While plant diversity patterns are highly variable across different scales and habitats,

they are largely dependent on productivity or biomass gradients (Scheiner and Rey-

Benayas, 1994; Mittelbach et al., 2001; Pausas and Austin, 2001). The relationship

between site biomass and diversity is also influenced by the frequency of disturbances

such as fire (Huston, 1979; Huston, 1994). The open eucalypt forests of Australia are

especially fire-prone, and prescribed burning is commonly implemented in these

ecosystems to reduce fuel loads and hence the overall intensity of wild fires (Chapter 1).

Where frequent prescribed burning is primarily implemented to reduce fuel loads (i.e.,

as hazard reduction burns) there has been some concern over the impacts of these

repeated fires on vegetation diversity (Morrison et al., 1996; Bradstock et al., 1998).

While species richness in Australian forests and shrublands tends to peak within a few

years following fire, the relative number of species with different life forms can change

significantly with longer time since fire (Specht et al., 1958; Bell and Koch, 1980;

Wardell-Johnson et al., 2004; Wardell-Johnson et al., 2007). Long lived and woody

obligate seeders in particular require a longer time to set seed after fire because they

have longer juvenile periods than short lived herbaceous species (Morrison et al., 1996).

Thus, if the time between individual fires is shorter than the time required for long lived

woody seeders to mature and set seed, the abundance of these species may be reduced,

and herbaceous species may be favoured (Cary and Morrison, 1995; Morrison et al.,

1995; Burrows and Wardell-Johnson, 2003). Frequent fires can also deplete

carbohydrate stores of long lived woody species that have lignotubers (Pate et al.,

1990), which may result in reduced vigour and abundance of woody resprouters further

providing a competitive advantage for herbaceous species.

Understorey plant species richness decreases with increasing overstorey cover

and productivity in the temperate eucalypt forests of Australia (Specht and Morgan,

1981; Specht and Specht, 1989b; Specht and Specht, 1993). Higher productivity or

biomass is also associated with lower species richness in temperate forests in North

40

America and Europe, where large woody life forms such as trees and shrubs

competitively exclude herbaceous species in the understorey (Thompson et al., 2005;

Hérault and Thoen, 2008; Peterson and Reich, 2008). Because woody species gradually

suppress short-lived understorey species with increasing time since fire (Specht et al.,

1958; Specht et al., 1983), a high fire frequency may increase species richness by

reducing dominance by woody life forms, particularly at more productive sites where

woody species cover is greater (Huston, 1994). For example, species richness was

nearly two-fold greater at more frequently burnt oak stands in North America compared

to stands burnt rarely; fire prevented shrubs and trees from competitively excluding a

significant portion of the flora that was composed of grasses and forbs (Peterson and

Reich, 2008). In the jarrah forests of southwest Australia, the abundance of woody

obligate seeders decreases at high fire frequencies while the number and abundance of

smaller life forms with short juvenile periods such as grasses can increase (Burrows and

Wardell-Johnson, 2003). Consequently, the effect of fire frequency on overall plant

species diversity in this ecosystem is likely to depend on how herbaceous species

respond to fire impacts on woody species dominance in terms of total biomass.

Although plant diversity is often measured as species richness, i.e., the number

of species within a given area, or species evenness, i.e., the distribution of abundance

across different species (Magurran, 2004), competitive interactions among plant species

are mediated by their growth or life forms (Pate et al., 1984; Mabry et al., 2000).

Consequently, measuring functional aspects of diversity, such as life form evenness

(i.e., the relative abundance of individuals with different life forms) reveal greater

insight into how competitive interactions in vegetation communities may be influenced

by environmental changes than traditional species diversity measures (Diaz and Cabido,

1997; Mouillot et al., 2005). I previously examined the biomass, structure and

composition of the jarrah forest overstorey in relation to fire frequency and aridity

(Chapter 3). Here, my objective was to determine how fire frequency and site biomass

(overstorey plus understorey) are related to dominance among different woody and

herbaceous life forms and how life form dominance impacts overall understorey

diversity. I thus measured the variation in life form evenness with fire frequency across

sites varying from high to low aboveground biomass where standing biomass is

considered a surrogate for productivity. I also measured differences in species richness,

species evenness, and the relative number of woody and herbaceous species with life

form evenness as well as biomass and fire frequency. I hypothesized that:

41

1. Woody species richness decreases with higher fire frequency, while herbaceous

species richness, overall species diversity and life form evenness all increase

with higher fire frequency.

2. Sites with greater total plant biomass and sites with greater life form dominance

(i.e., lower life form evenness) have fewer herbaceous species relative to woody

species and lower overall species diversity (richness and evenness).

4.2 Methods


This study was conducted across the same sixteen sites described in Chapters 2 and 3

(Figure 2.1). All sites were selected to have the same time since the last fire event,

which was ~ four years at the time of sampling, in order to minimize any variation due

to post-disturbance succession. However, the sites had a variable fire history in terms of

the frequency of fires or the length of time between individual fire events over 30 years

(Chapter 2). Site biomass (tonnes ha-1) was calculated as sum of the total aboveground

component of the overstorey and understorey. Overstorey biomass was estimated as

described in Chapter 3.

4.2.2 Understorey biomass assessment

Understorey biomass was estimated from allometric equations relating the green-cover

of vegetation to biomass. Plot-based estimates using percentage cover or crown area

have been used in several studies in order to estimate understorey biomass (Ludwig et

al., 1975; Halpern et al., 1996; Sah et al., 2004). I developed a relationship between

percentage green cover and biomass based on measurements from 2 m × 2 m quadrats

with varying species composition (different dominant shrubs) using a digital cover

photography technique based on Bennett et al. (2000). Two to four quadrats were

established at five locations to give a total of 15 quadrats across the study region

representing the range of cover and species across all 16 study sites. Six digital photos

were taken at each quadrat facing vertically downwards from a height of 3.5 m to cover

the whole quadrat. The Nikon D80 digital camera was fixed via a square bracket to a

sturdy extension pole and levelled according to a bubble level attached to the shaft of

the pole. Subsequent photos were cropped using Adobe Photoshop 7.0 to ensure that

each picture contained only the area within the sample quadrat, which was delineated

42

with yellow tape. The % green cover of the photos was determined using

WinSCANOPY 2006 (Regent Instruments, Ste-Foy, Quebec) based on a colour scale

where green shades were defined as foliage and brown shades defined as ground. The

sampled patches were then destructively harvested, dried at ~ 60o C for 10 days and

weighed to obtain the total dry weight of the understorey biomass for each quadrat.

Linear and exponential regressions were tested to determine the relationship

between understorey green-cover and biomass from harvested quadrats. The best fit

(lowest P-value and highest r2) was observed under a linear model (Figure 4.1), which

was used to develop equation 1. An additional 25 digital photos were taken at each site

(i.e., within 30 m × 30 m plots) with the same technique mentioned above in order to

estimate total site understorey green-cover. Five transects were run across the plots

starting at the corner, and five photos spaced roughly 7 m apart were taken along each

transect. The photography was conducted at the same time of the year for all sites, in

late summer, from March to April of 2007.

20 30 40 50 60 70 80

12

34

56

78

Green-cover (%)

Bio

mas

s (to

ns h

a-1)

20 30 40 50 60 70 80

12

34

56

78

Green-cover (%)

Bio

mas

s (to

ns h

a-1)

Figure 4.1 The relationship between understorey biomass (tons ha-1) and green-cover (%) from 15 (2 m × 2 m) quadrats in the southern jarrah forest of southwest Australia. Each point represents a individual harvested quadrat with variable species composition.

43

The 25 photos taken at each site were averaged to obtain mean site green-cover,

which was converted to total site understorey biomass using equation 1.

Bu = 0.103 × (Cover) - 1.15 r2 = 0.94 P <.0001 n=15 (1)

4.2.3 Species and life form diversity assessment

Floral surveys were conducted in conjunction with the Western Australian Department

of Environment and Conservation (DEC) in the spring of 2007 from 4th – 26th of

October to determine plant species composition. Data were collected for all vascular

plant species within each plot at all 16 sites through a visual assessment of the total

number of individuals of each species using the following classes: 1, 2-10, 11-50, 51-

100, 101-500, or > 500 individuals. From these data, two species composition matrices

(presence/absence and abundance) were created with species as columns and sites as

rows. The species abundance matrix used an integer code from 0 to 500, with each code

corresponding to the mean abundance value indicating the approximate abundance of

each species at a given site. The presence/absence matrix used a 0 to indicate an absence

and a 1 to indicate a presence of a species at each site.

Species diversity was determined as both species richness, using the

presence/absence matrix, and Simpson’s evenness index from the species abundance

matrix (Smith and Wilson, 1996; Magurran, 2004). Simpson’s evenness index is

derived from Simpsons index, and passes all the requirements for evenness indices

proposed by Smith & Wilson (1996) including: i) independence from species richness,

ii) response to changes in abundance and addition of minor species, and iii)

independence from the units used to measure abundance. Species richness was

calculated as the total number of species per site. Species evenness was calculated as the

reciprocal of the Simpson’s index (D, equation 2) divided by total number of species at

each site:

D = ∑=

S

i 1ni (ni - 1) / N (N - 1) (2)

where ni is the estimated abundance of each species i, given as either 1 for 1 individual,

5 for 2-10 individuals, 25 for 11-50 individuals, 75 for 51-100 individuals, 250 for 101-

44

500 individuals, or 1000 for > 500 individuals, and N is the total sum of all abundances

of all species per site.

All species were also classified according to a combination of their growth and

life form attributes, which is especially useful in describing vegetation communities in

the nutrient impoverished and fire-prone ecosystems of southwest Australia (Pate et al.,

1984; Clarke, 2002). Thus each species was classified as a tree, shrub (>30 cm), dwarf

shrub (<30 cm), fern, cycad, geophyte, grass, herb, rush, sedge, vine, or grass tree. Life

form evenness was determined from the abundance of individuals with each life form

(Mouillot et al., 2005) in an analogous fashion to species evenness where life form

evenness is the reciprocal of D divided by the total number of different life forms

present at each site. D for life forms was calculated using equation 2; but in this case ni

was the abundance of individuals within each functional group i, and N was the total of

all abundances of all life forms per site. Woody species richness was calculated as the

total number of tree, shrub, dwarf shrub, cycad, vine, or grass tree species, while

herbaceous species richness was calculated as the total number of herb, geophyte, rush,

sedge, grass, and fern species within each plot. Herbaceous to woody species ratio was

calculated as the total number of herbaceous species divided by the total number of

woody species at each site.

4.2.4 Data analyses

All data were analysed using the open resource statistical graphics and computing

environment R version 2.6.2 (R Development Core Team, 2009). Multiple regression

analyses were used to separately test the effect of the explanatory variables (overstorey

plus understorey biomass and fire frequency) on the responses of species richness,

species evenness, herbaceous species richness, woody species richness, ratio of

herbaceous to woody species, and life form evenness. Main effects and interactions of

the variables were initially examined and removed from the analysis if not significant (P

> 0.1). Simple regression analyses were also used to test the correlation among plant

diversity measures (species richness, herbaceous species richness, woody species

richness, ratio of herbaceous to woody species, species evenness, and life form

evenness). Both linear and exponential regressions were tested and the best fit model

(lowest P-value) was applied in all cases. A principle components analysis (PCA), with

PRCOMP command in R, was also conducted to illustrate the floristic relationship

45

among the study sites. Separate PCAs were conducted for presence/absence and

abundance data. Abundance data was transformed prior to analysis.

4.3 Results

4.3.1 Aboveground plant biomass and composition

Understorey biomass ranged from 3 to 6 tonnes ha-1 and was relatively low when

compared to overstorey biomass, which ranged from 85 to 414 tonnes ha-1 (Chapter 3;

Appendix 1). Consequently, understorey biomass contributed to < 5 % of total

aboveground biomass (both overstorey and understorey), which ranged from 89 to 420

tonnes ha-1.

While only two species comprised the overstorey (E. marginata and C.

calophylla), a total of 183 different plant species were found across the sites, a large

proportion of (~ 39 %) of which were shrubs (Table 4.1). Herbs, dwarf shrubs, and

geophytes together also made up a significant (~ 44 %) portion of the taxa (Table 4.1).

In contrast, only ~ 16 % of taxa were comprised of trees, sedges, rushes, vines, grasses,

and grass trees (Table 4.1). Only one fern and one cycad species was observed, which

together comprised < 1% of total abundance across the sites (Table 4.1). Herbaceous

species, i.e., geophytes, grasses, herbs, ferns, rushes and sedges, composed about 40%

of the taxa, while woody species (trees, shrubs, dwarf shrubs, cycads, grass trees and

vines) made up the remaining 60%. The number of plant species across the sites was

highly variable, ranging from 43 to 88, and mean species richness across all sites was 61

± 3.3 (Appendix 1). Herbaceous species richness varied up to three-fold among sites,

whereas woody species richness was relatively consistent (Appendix 1). While the

variation in species evenness across the sites was slight (1.15 - 1.35), life form evenness

was highly variable ranging from 1.9 to 7.6 (Appendix 1).

46

Table 4.1 The total number of species with different life forms across the 16 southern jarrah forest study sites. The percent of taxa composed by species in each life form is also given.

Life form Number

of species % of Taxa

Cycad 1 0.5 Dwarf Shrub 26 14.2 Fern 1 0.5 Geophyte 24 13.1 Grass 2 1.1 Grass Tree 3 1.6 Herb 30 16.4 Rush 6 3.3 Sedge 10 5.5 Shrub 71 38.8 Tree 4 2.2 Vine 5 2.7

The first two principle components together (PC1 and PC2) explained 26% of

the floristic variation across the sites for presence/absence data and 27% of the variation

for abundance data. The sites showed more clustering for abundance data than

presence/absence data (Figure 4.2). Site 16 and 3 had similar fire frequencies (2 and 3

respectively over the past 30 years) and were clustered together, separate from the rest

of the sites on the presence/absence PCA (Figure 4.2a). Both site 16 and 3 also

displayed similar biomass (Appendix 1). Although site 11 had medium fire frequency

(i.e., three fires over the past 30 years) and displayed biomass close to mean among the

sites (Appendix 1), it was relatively distant from all the rest of the sites on both the

presence/absence (Figure 4.2a) and abundance PCA (Figure 4.2b). The closely clustered

group of sites (1, 2, 5, 8 and 9) as well as the larger clusters in general on both the

presence/absence and abundance PCAs displayed variable fire frequencies and biomass

(Figure 4.2; Appendix 1). However, sites 1, 2, 5 and 8 all had a COy soil type

(Appendix 1) suggesting that this floristic clustering is more strongly influenced by soil

factors at the sites.

47

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

PC2

1

2

34

5

14

15 76

8

11 10

9

1213

16

-0.4 -0.2 0.0 0.2 0.4

-0.4

-0.2

0.0

0.2

0.4

PC2

1

2

3

4

1415 76

8

11

10

9

12

13

16

5

PC1

a)

b)

FF = 3Medium biomass

FF = 2-3Medium biomass

FF = 2-5Mixed

biomass

FF = 4-5Medium biomass

FF = 1-5 Mixed

biomass

FF = 2-5Mixed

biomass

FF = 3Mediumbiomass

Figure 4.2 Principle components analysis (PCA) showing the floristic relationships among the 16 southern jarrah forest sites for a) presence/absence and b) transformed abundance data. Fire frequency (FF) and total plant biomass of sites grouped closely together are also shown. See table 2.1 for specific fire frequency and fire interval length of each site.

48

4.3.2 Variation in diversity measures with biomass and fire frequency

Multiple regression analyses did not reveal a significant effect of total standing biomass

and fire frequency on overall species richness and evenness, herbaceous species

richness or woody species richness (Table 4.2). However, life form evenness decreased

with total aboveground biomass while increasing with greater fire frequency (Table

4.2). The ratio of herbaceous species richness to woody species richness also decreased

with increasing total biomass (both overstorey and understorey) but did not significantly

vary with fire frequency (Table 4.2).

Table 4.2 Effect of site biomass (total aboveground plant biomass in ton ha-1) and fire frequency on plot level diversity measures (life form evenness and the ratio of herbaceous to woody species richness) across 16 sites in the southern jarrah forest. Estimates (est.), standard errors (SE), P-values are given for each multiple regression when significant (P < 0.05). An adjusted r2 for the overall regression model is also provided. A negative (-) sign in front of an estimate indicates a negative relationship. When a main effect (in this case fire frequency) did not make a significant contribution to the variation of a diversity measure, it was removed from the model.

Site Biomass Fire Frequency

est. SE P est. SE P r2

Life form Evenness -2.95 0.76 0.002 1.40 0.60 0.036 0.53 Ratio of herbaceous to woody species -0.24 0.10 0.031 Non significant 0.24 Species Evenness Non significant Non significant -- Overall species richness Non significant Non significant -- Herbaceous species richness Non significant Non significant -- Woody species richness Non significant Non significant --

49

r2 = 0.34P = 0.017

r2 = 0.15P = 0.136

r2 = 0.35P = 0.015

r2 = 0.01P = 0.767

r2 = 0.07P = 0.315

r2 = 0.30P = 0.027

3035

4045

5055

Woo

dy s

p. ri

chne

ss

1520

2530

35

Her

bace

ous

sp. r

ichn

ess

2 3 4 5 6 7

5060

7080

Life form evenness

Spec

ies

richn

ess

1.15 1.20 1.25 1.30 1.35

Species evenness

a)

b)

c)

d)

e)

g)

r2 = 0.34P = 0.017

r2 = 0.15P = 0.136

r2 = 0.35P = 0.015

r2 = 0.01P = 0.767

r2 = 0.07P = 0.315

r2 = 0.30P = 0.027

3035

4045

5055

Woo

dy s

p. ri

chne

ss

1520

2530

35

Her

bace

ous

sp. r

ichn

ess

2 3 4 5 6 7

5060

7080

Life form evenness

Spec

ies

richn

ess

1.15 1.20 1.25 1.30 1.35

Species evenness

a)

b)

c)

d)

e)

g)

Figure 4.3 The relationship between life form and a) woody species richness, b) herbaceous species richness, and c) overall species richness, as well as the relationship between species evenness and d) woody species richness, e) herbaceous species richness, and g) overall species richness across sixteen sites in the southern jarrah forest of southwest of Australia. When a significant trend is observed (P < 0.05), r2 and P- values are shown in bold.

50

4.3.3 Variation in species diversity measures with life form evenness

While neither overall species (Figure 4.3a) nor woody species richness (Figure 4.3c)

was significantly correlated with life form evenness, herbaceous species richness

increased with life form evenness (Figure 4.3b). Both overall species richness (Figure

4.3g) and herbaceous species richness (Figure 4.3e) were lower at sites with greater

species evenness, and the number of herbaceous species relative to woody species was

greater at more species rich sites (Figure 4.4)

r2 = 0.23P = 0.061

0.3

0.5

0.7

0.9

Her

bace

ous

spec

ies

: woo

dy s

peci

es

50 60 70 80

Total species richness

r2 = 0.23P = 0.061

0.3

0.5

0.7

0.9

Her

bace

ous

spec

ies

: woo

dy s

peci

es

50 60 70 80

Total species richness

Figure 4.4 The relationship between the relative number of herbaceous species to woody species and overall species richness across the southern jarrah forest of southwest of Australia.

4.4 Discussion

4.4.1 Influence of fire frequency and biomass on woody and herbaceous species

The results reveal that overall floristic composition (Figure 4.2) and specifically the

number of woody species (Table 4.2) is not significantly influenced by fire regime in

the southern jarrah forest. High fire frequency has been shown to reduce the abundance

of some obligate seeding woody shrub species in the northern jarrah forest (Burrows

and Wardell-Johnson, 2003) as well as in some Australian shrublands (Morrison et al.,

1995; Knox and Morrison, 2005). However, significant changes in overall species

richness due to fire regime are rare in forested ecosystems. While fire frequency has

51

been shown to influence floristic composition in open forests in south-east Queensland,

a significant effect on overall species richness was not observed (Watson and Wardell-

Johnson, 2004). In forests where a reduction in woody species richness has been

observed at high fire frequencies, the time between individual fire events was as low as

2 years (Peterson and Reich, 2008). In my study, sites with high fire frequency (i.e., five

fires over a 30 year period) had up to two consecutive 3-4 year fire intervals, which is

the minimum amount of time required for fuel loads to build up enough to burn in the

jarrah forest (Christensen et al., 1981). Thus, while fire-free intervals shorter than 6-8

years may reduce the abundance of some woody obligate seeders (e.g., Acacia

browniana) in the jarrah forest (Burrows and Wardell-Johnson, 2003), current fire

frequencies are not likely to be high enough to cause local extinction of a large number

of woody species in this habitat.

While herbaceous species richness was not directly related to fire frequency, the

number of herbaceous species relative to woody species decreased with greater biomass

(Table 4.2; Figure 4.5). Other studies from forested landscapes in North America

(Peterson and Reich, 2008) and Western Europe (Hérault and Thoen, 2008) have also

shown a reduction in the number of smaller non-woody species with increasing woody

plant cover, which reflects greater competition for light. In this study, sites with low

biomass and high fire frequency had high life form evenness (Table 4.2; Figure 4.5),

which was associated with an increase in herbaceous species richness (Figure 4.3b).

Thus, frequent fires may increase herbaceous species richness in the jarrah forest by

reducing dominance by woody life forms, particularly at sites with greater biomass. In

southwest Australian forests, the number of herbaceous species is greatest within a few

years after fire, and decreases gradually as woody shrub species in the understorey

become more established and increase in cover (Bell and Koch, 1980; Wardell-Johnson

et al., 2007). Consequently, the response of herbaceous species to fire regime in these

ecosystems is likely to be dependent on the level of site biomass, which reflects the

extent of dominance among different plant life forms.

52

Figure 4.5 Theoretical relationships among site biomass (total aboveground plant biomass), fire frequency and plant diversity measures in the southern jarrah forest. Significant correlations observed in this study are shown with a (+) or a (–) sign indicating the sign of the relationship and a black arrow suggesting the direction of the effect. Dashed arrows suggest the direction of effects that were hypothesized but not observed.

4.4.2 Influence of fire-productivity interactions on species diversity

Floristic composition, in terms of the relative presence/absence and abundance of

species, did not show any clear trends with regard to fire frequency or site biomass

(Figure 4.2), and overall species diversity measures were not directly related to either of

these variables (Table 4.2). However, there were fewer herbaceous species relative to

woody species at species poor sites (Figure 4.4), suggesting that overall species richness

may decrease in the southern jarrah forest as woody species dominate over herbaceous

species at more productive sites (Figure 4.5). Results from experimental but mature

forest communities in the UK (Thompson et al., 2005) also showed that sites with

greater biomass dominated by large woody perennials exhibit lower overall species

richness when compared to less productive sites. Increasing overstorey biomass reflects

greater overstorey leaf area and cover in the southern jarrah forest (Chapter 3). Because

Species evenness

Biomass

Life form evenness

Fire frequency

Species richness

+

+

-

Herbaceous to woody

species ratio

-

+ - Species evenness

Life form evenness

Fire frequency

Species richness

+

+

-

Herbaceous to woody

species ratio

-

+ -

53

> 95 % of the total aboveground biomass is attributable to the overstorey trees in this

ecosystem, site biomass is likely to largely reflect overstorey cover, hence competition

for light. Consequently, the findings of this study provide support for the hypothesis that

the number of understorey species decreases with reduced light penetration from the

overstorey canopy in Australian forests (Specht and Morgan, 1981; Specht and Specht,

1993).

A high frequency fire regime may also increase species richness by allowing

more herbaceous species to co-exist along with woody species (Figure 4.5), particularly

at sites with greater biomass where herbaceous species richness is low. The total

number of species was also greater at more frequently burnt sites in North American

oak woodlands where reduced cover by shrubs and trees allowed more grass and forb

species to co-exist (Peterson and Reich, 2008). In contrast, reduced dominance and

cover of woody species at higher fire frequencies in eucalypt forests in Fraser Island,

Queensland, Australia was associated with greater dominance by a single herbaceous

fern species, Pteriduim, which out-competed other herbaceous species in the

understorey resulting in decreased overall species richness (Spencer and Baxter, 2006).

Consequently, species diversity patterns in forested ecosystems are influenced by fire-

productivity interactions on dominance among different woody and herbaceous life

forms to some extent. However, the lack of a direct biomass and fire effect on species

diversity suggests that fire-productivity interactions only have a minor role in

controlling overall plant diversity in this ecosystem. Soil nutrients are thought to have a

strong role in maintaining high species richness in the southwest Australian plant

communities (Chapter 1). The association between floristic composition and soil type

observed on the presence/absence and abundance PCAs in this study suggests that local

soil factors may also influence plant diversity in the southern jarrah forest. Furthermore,

due to their course nature, overall diversity measures, such as species and life form

evenness, may mask other compositional changes in vegetation communities. The

diversity of species assemblages with specific functional traits such as resprouting

ability and cluster root formation may better represent the response of jarrah forest

vegetation to changes in fire regime and nutrient availability (Chapter 1). The relative

abundance of different functional plant assemblages with regard to soil nutrients and

fire regime are examined in greater detail in Chapter 5 in order to assess the impacts of

possible fire-nutrient interactions on plant diversity patterns in the southern jarrah

forest.

54

The competitive exclusion of rare herbaceous species by woody life forms at

sites with greater biomass may also explain the inverse relationship between species

evenness and species richness (Figure 4.3g) observed in this study. While species

evenness was not directly related to fire frequency or biomass, loss of rare herbaceous

species at sites with greater biomass may result in a more even distribution of

abundance across the remaining species, i.e., greater species evenness (Figure 4.5).

Conversely, frequent fires may reduce species evenness by allowing more herbaceous

species to co-exist. Negative relationships between different diversity measures have

been observed in other studies where species richness and species evenness responded

differently to ecological processes including changes in competition intensity (Stirling

and Wilsey, 2001; Spencer and Baxter, 2006; Perry et al., 2008). Consequently,

different species diversity measures may respond differently to fire regime in the

southern jarrah forest depending on how fire-productivity interactions impact

herbaceous species richness, and how this may in turn influence the distribution of

abundance across individual species (i.e., species evenness). I thus suggest that

measuring functional or life form diversity, or how abundance is distributed across

different life forms (life form evenness), as well as the relative changes in the diversity

of woody versus non-woody species, is crucial to understanding the impacts of altered

fire frequency on overall species diversity patterns in forests or other structurally and

functionally complex vegetation communities.

4.4.3 Diversity of the jarrah forest compared to other temperate ecosystems

While species diversity in tropical forests is known for being exceptionally high, even

within the understorey (Gentry and Dodson, 1987), the southern jarrah forest displays

high plant species richness when compared to other temperate forests. The mean species

richness per 900 m2 was 61±3 across the sixteen sites, which is consistent with values

(50−68 species / 900 m2) obtained from other E. marginata dominated sites in the

region (Gioia and Pigott, 2000). The number of species per 1000 m2 in the temperate

forests of North America is closer to 30 on average and generally does not exceed 50

(Whittaker, 1977). The average number of plant species (per 400 m2) in closed canopy

forests in eastern Australia varies from 21 up to 30 (Keith and Sanders, 1990). In the

karri (Eucalyptus diversicolor) forest, which is an open but taller forest located directly

adjacent to the jarrah forest on more fertile soils, the number of species within 400 m2

plots was 29 on average (Wardell-Johnson et al., 2004), which is less than half the mean

55

(~ 61) observed in this study. Plant species diversity in temperate Australia tends to be

higher in open forests when compared to more productive forests with greater canopy

cover (Specht and Morgan, 1981; Le Brocque, 1998). However, the number of species

recorded per 1000 m2 in forests with similarly open canopies (~ 40−60%) as the jarrah

forest, was 45-50 maximum in southeast Australia (Rice and Westoby, 1983) and 35- 42

on average in South Australia (Specht and Specht, 1989b). This suggests that other

factors (i.e., other than canopy cover and light competition) may also have a role in

maintaining high species richness in the jarrah forest.

The majority (> 50 %) of the plant species in the southern jarrah forest are

understorey shrubs and dwarfs shrubs (Table 4.1), which are similar in structure to the

species found in highly species-rich Mediterranean type shrublands. The range of

species richness in this study was comparable to values provided by Naveh and

Whittaker (1979) from Mediterranean shrublands, i.e., 65 species in southwest

Australian heath and 77 species in South African fynbos per 1000 m2 on average. The

exceptionally high species richness of southwest Australian and South African

shrublands is attributed to the low fertility of the soils as well as strong seasonal water

stress induced by the Mediterranean type climate (Chapter 1). The jarrah forest has an

exceptionally dry summer relative to its generally high rainfall (Gentilli, 1989), which

ranged from ~ 800 to 1100 in this study (Chapter 3; Appendix 1), as well as very low

nutrient soils (Hingston et al., 1981). Consequently, the higher plot scale species

richness observed in the southern jarrah forest compared to other forests in Australia or

elsewhere may be explained by interactions between climate and nutrient availability,

which is addressed in chapter 5.

4.4.4 Conclusions and implications for fire management

The number of species in the southern jarrah forest is generally high compared to other

eucalypt forests in Australia, and is comparable to Mediterranean type shrublands in

southwest Australia and South Africa. Fire frequency impacts on plant diversity patterns

in forests are likely to be mediated by interactions between woody and herbaceous

species along productivity gradients. Increased dominance among life forms at higher

biomass sites reduces the relative number of herbaceous species, which may lead to a

reduction in overall species richness (Figure 4.5). While the number of woody species is

not significantly reduced at higher fire frequencies, dominance among life forms

decreases, which may result in an increase in herbaceous species richness as well as

56

increase in overall species richness (Figure 4.5). The loss of rare herbaceous species at

sites with greater biomass and lower fire frequency may also result in a more even

distribution of abundance across the remaining species, i.e., greater species evenness

(Figure 4.5). These findings demonstrate that frequently implemented prescribed fires

may have variable diversity consequences on forests depending on the biomass potential

of the sites. Fire frequency may not impact plant species diversity in more open forest

sites where overstorey biomass and cover, hence dominance among life forms, is low.

Nonetheless, a high frequency fire regime of up to 5 fires over a 30 year period will

favour herbaceous species and may achieve an increase in overall species richness when

site biomass is high in southwest Australian forests as previously hypothesized by

Huston (2003).

57

CHAPTER 5

Functional plant assemblages of the southern jarrah forest in relation

to fire regime, aridity and soil nutrients

5.1 Introduction

Plants exhibit different architectural and physiological attributes in response to

environmental stress or disturbance that is often reflected by their growth or life forms

(Gill, 1981; Pate et al., 1984; Diaz and Cabido, 1997; Mabry et al., 2000). These

attributes are collectively referred to as functional traits and provide plants with

mechanisms to increase their growth and reproductive performance (Read and Stokes,

2006). In Mediterranean type ecosystems (MTEs), such as those of southwest Australia,

spatial and temporal variation in resource availability is pronounced and disturbance, in

the form of fire is frequent imposing harsh conditions for plant survival (Beard, 1983;

Pate and Beard, 1984; Cowling and Lamont, 1998). However, species lost from a

community due to frequent fire or resource stress may be replaced by others displaying

a more suitable life form or functional trait to those conditions. Thus, measuring the

diversity of plant species based on their life forms or functional traits across

environmental gradients can provide a better understanding of the mechanisms that

support diversity, and of the resilience of vegetation communities to changes in

environmental conditions (Diaz and Cabido, 1997; Lavorel et al., 1997).

Plants display a wide range of strategies for regenerating after fire including

germination from seed and resprouting from a rootstock, lignotuber or other

underground storage organs (Gill, 1981; Pate and Dixon, 1981; James, 1984; Lloret et

al., 1999; Bond and Midgley, 2001). Consequently, the impact of a given fire regime on

vegetation composition depends on the particular regeneration traits of the plants within

the community. For example, high fire frequencies or consecutive short fire intervals

may provide a competitive advantage for resprouters by depleting seed banks of

obligate seeders (Figure 5.1), whereas seeders may out-compete resprouters at lower

fire frequencies because they have higher growth rates (Keeley, 1986; Cowling et al.,

1990; Morrison et al., 1995; Bellingham and Sparrow, 2000; Clarke and Knox, 2002;

Knox and Morrison, 2005). However, fire frequency is dependent on and often

correlated with regional climatic factors. In seasonally dry ecosystems such as MTEs,

58

high fire frequencies typically occur in areas with relatively high fuel accumulation

rates and correspondingly high moisture availability (Pausas and Bradstock, 2007;

Pekin et al., 2009). Thus, while resprouters may benefit from more arid conditions due

to increased mortality of seeders, less arid or more humid sites in seasonally dry forests

and shrublands may also be associated with high abundance of resprouting species due

to adverse effects of relatively short fire intervals on obligate seeders (Lamont and

Markey, 1995; Pausas, 1999; Meentemeyer and Moody, 2002) as shown in Figure 5.1.

Competitive interactions between resprouting and seeding species are also

dependent on nutrient availability due to trade-offs in resource allocation between the

two strategies (Bellingham and Sparrow, 2000; Buhk et al., 2007). While resprouters

store nutrients in underground storage organs, which is generally seen as an adaptation

to low nutrient availability (Pate and Dixon, 1981), the metabolic costs associated with

maintaining a storage organ may make resprouting species inefficient when fires are

frequent because soil nutrient availability is temporarily but significantly elevated after

fire (Grove et al., 1986; Wisheu et al., 2000). The temporary increases in nutrient

availability may allow short-lived seeders to establish more successfully than

resprouters on poor substrates in highly fire-prone ecosystems because seeders are more

efficient at utilizing nutrients and carbohydrates for growth (Keeley, 1986; Pate et al.,

1990; Bellingham and Sparrow, 2000; Verdu, 2000). Thus, while frequent fires favour

resprouting species due to depletion of soil seed banks, seeders may also gain an

advantage relative to resprouters as fire frequency increases in ecosystems where the

soil substrate is very low in nutrients (Figure 5.1). Consequently, the relative

distribution of resprouters and seeders in MTEs is likely to be mediated by complex

interactions between fire frequency or interval length, climate and nutrient availability

(Clarke et al., 2005) as schematised in Figure 5.1.

59

Figure 5.1 Hypothetical influence of environmental variables on the richness and abundance of plant species that regenerate from seed and by resprouting in Mediterranean type climates. Arrows indicate direction and (-/+) signs indicate type of effect. Potential mechanisms for the effect are also given.

Similarly, species with an annual life cycle allocate relatively large proportions of their

production to growth and require more nutrients per unit of biomass. Nonetheless,

annuals are generally favoured over perennials in drought-prone climates because they

can avoid the dry season and complete their life cycle when conditions are more

favourable, i.e., when both water and nutrient availability is high (Danin and Gideon,

1990; Holzapfel et al., 2006). While MTEs display seasonally arid conditions,

perennials are much more common than annuals in the highly infertile soils of

Mediterranean South Africa and southwest Australia where the scarcity of annual

species is often attributed to the fact that soil resources, particularly nutrients, are too

low for them to complete their life cycles and set seed in one year (Keeley, 1986;

Wisheu et al., 2000). Thus, the abundance of annual species relative to perennials is

likely to increase with site aridity provided nutrient availability remains high and may

be influenced by fire-nutrient interactions in highly nutrient depleted landscapes.

However, the impact of nutrient availability on the richness and abundance of

plant species is likely to be primarily mediated by their nutrient harvesting strategies.

The ability to form symbiotic associations with nitrogen fixing bacteria and mycorrhizal

fungi in particular enable plants to more effectively fix N or forage for nutrients

Fire frequency

Nutrient availability

Resprouter r ich/ abun

Site aridity

+

- Seeder rich/ abun

+

- -

-

Increased competition

Depletion of seed banks

60

(Lamont, 1983; Lamont, 1984; Pate, 1994). For example, species that have

ectomycorrhizal and ericoid mycorrhizal associations can access organic soil nutrients

which may provide them with an advantage over other mycorrhizal species when

inorganic nitrogen (e.g., ammonia and nitrate) or phosphorus availability is low (Read

and Perez-Moreno, 2003). Nitrogen fixing ability is also highly advantageous when soil

nitrogen is limited because it allows plant species to access nitrogen from air as well as

solutes in the soil (Lamont, 1983). Furthermore, specialized cluster root structures such

as proteoid, capillaroid, dauciform, and sand-binding roots help plants better utilize soil

nutrients by increasing root surface area, and enhancing root exudation and

solubilisation processes as well as nutrient uptake (Lamont, 1984; Grierson and Attiwill,

1989; Grierson, 1992; Lambers et al., 2006). Hence, specialized roots may provide an

advantage when readily available or soluble (inorganic) soil nutrient concentrations are

low. Cluster roots, particularly the proteoid roots found on Proteaceae species, are

especially common in the MTEs of South Africa and southwest Australia where a high

proportion of soil phosphorus is stored in organic or other insoluble forms (Lamont,

1984; Lamont, 1993; Lambers et al., 2008). Thus, cluster root species may be favoured

by higher concentrations of organic soil phosphorus, particularly when inorganic

sources of phosphorus are scarce.

The jarrah forest of southwest Australia has exceptionally high plant diversity

when compared to other temperate forests (Chapter 4). The flora displays a wide variety

of mycorrhizal fungal associations and specialized cluster root structures, and contains a

high proportion (~ 70 %) of resprouters (Bell and Koch, 1980; Bell et al., 1989;

Brundrett and Abbott, 1991). Previously, I assessed the influence of fire frequency and

aridity on the biomass and density of jarrah and marri stands in order to gain insight into

the resilience of the jarrah forest overstorey (Chapter 3). I also determined how species

diversity responds to changes in dominance among life forms due to increased fire

frequency and site biomass (Chapter 4). While a direct impact of fire on floristic

composition was not observed (Figure 4.2), soil type, hence nutrient availability, may

have a significant influence on the relative richness and abundance of different plant

species or assemblages in the southern jarrah forest (Chapter 4). Thus, here I sought to

assess: (i) whether certain functional traits are favoured over others under a range of

environmental conditions; and (ii) the role of functional traits in supporting plant species

diversity in this ecosystem. I measured the relative richness and abundance of species

with different plant life forms, and regeneration (i.e., resprouting, seeding, annual life

61

cycle) and nutritional strategies (i.e., N-fixing ability, mycorrhizal associations and

cluster root types) in relation to key environmental gradients of aridity, fire regime and

substrate fertility. I hypothesised that:

1. The abundance and richness of resprouters is (a) positively correlated with fire

frequency and the occurrence of short fire intervals, (b) negatively correlated

with aridity, and (c) positively correlated with soil nutrient availability. I further

hypothesised that richness and abundance of seeders in relation to these factors

show opposite correlations to those of resprouters.

2. The abundance richness of annuals is positively correlated with aridity and soil

nutrient availability.

3. The abundance and richness of ectomycorrhizal, ericoid mycorrhizal, proteoid

and other cluster root-forming species are negatively correlated with the

availability of inorganic soil nutrients (N and P) and positively correlated with

organic soil P.

4. The abundance and richness of N-fixing species is negatively correlated with

soil N availability.

5.2 Methods


The sixteen study sites used for this study have been described previously in Chapter 2

(Figure 2.1). Fire regime, i.e., fire frequency as well as the interval length between

individual fires, was also determined at each site as described in Chapter 2 (Table 2.1)

and site aridity (potential evapotranspiration divided by mean annual precipitation) as

described in Chapter 3.

5.2.2 Functional trait classification

All plant species were categorized into functional groups based on an assessment of

their life form (Chapter 4) as well as functional (regeneration and nutritional) traits (see

Appendix S1 for the full list of all species and their traits). Five functional groups, three

categorical and two binary, were chosen including life form, regeneration strategy, root

structure, life cycle and nitrogen fixing ability. Life form classification was made based

on a combination of the growth and life form as described in Chapter 4 and life cycle

was defined as perennial or annual (Table 5.1). Regeneration strategies were determined

62

according to dominant method for regenerating after fire as either from fleshy

underground storage organ (including bulbs, corms, rhizomes, and tubers), woody

rootstock/lignotuber, or seed (Table 5.1) (Burrows and Wardell-Johnson, 2003; Burrows

et al., 2008). Although the dominant tree species, E. marginata and Corymbia

calophylla, often regenerate from seed, they were excluded from regeneration strategy

classification because their ability to survive fires with their aboveground biomass intact

is likely to make them insensitive to a depletion of their seed banks (Abbott et al., 1989;

Lewis, 2003). However, both E. marginata and C. calophylla were included in the life

form classification described in Chapter 4 as trees along with several less dominant

midstorey tree species.

Root structure classification was made either according to the known

mycorrhizal root association of each species (Table 5.1) as ectomycorrhizal, arbuscular

mycorrhizal (AM), orchid mycorrhizal and ericoid mycorrhizal, or according to

specialized non-mycorrhizal root types as proteoid and other non-mycorrhizal root

(Brundrett, 2008). Some species were variably described in the literature as both AM

and non-mycorrhizal; where such conflicts were noted, the species were classified as

mycorrhizal since they have the potential to form the association. Other non-

mycorrhizal root species included rushes and sedges that are known for having

capillaroid, dauciform, or sand-binding roots, as well as a few carnivorous and parasitic

plant species which are known to not form associations with mycorrhizal fungi

(Brundrett and Abbott, 1991; Brundrett, 2008). Classification for nitrogen fixing ability

was made either as fixer or non-fixer; all leguminous species in the Papilionaceae and

Mimosaceae families, as well as one cycad species (Macrozamia riedlei) were classified

as nitrogen fixing (Lamont, 1984).

63

Table 5.1 Functional groups and all traits within each group used to classify plant species recordd at the 16 southern jarrah forest study sites (Figure 2.1).

Functional group

Trait

Regeneration Strategy

Fleshy underground organ Woody rootstock/lignotuber

Seed Life cycle Annual Perennial N- fixing ability Nitrogen fixer Non nitrogen fixer Root structure Ectomycorrhizal Arbuscular mycorrhizal Orchid mycorrhizal Ericoid mycorrhizal Proteoid root Other non-mycorrhizal

Species data classified into the functional groups presented in Table 1 were

organized into a vegetation trait matrix (Q) with species listed as rows and functional

groups as columns (Legendre et al., 1997). Across all species, the proportionate

abundance of functional traits within functional groups was calculated as the sum of the

abundance of all individuals of each species with a particular functional trait divided by

the total abundance of all species. The number of different species with each trait was

then divided by the total number of species within the whole group to obtain the

proportionate contribution of each trait to overall species composition.

5.2.3 Soil nutrient sampling and analyses

Three paired soil samples were collected from three depths (0-5 cm, 5-10 cm and 10-20

cm) along a diagonal transect across each of the 16 plots. Samples were sieved to < 2

mm and subsequently analysed for pH, major nutrients and carbon. Soil pH was

measured in water (soil:water, 1:5) (Kuo, 1996). Total carbon (% C) and nitrogen (N %)

were analysed using an Automated Nitrogen Carbon Analyser-Mass Spectrometer

consisting of a Roboprep connected with a Tracermass isotope ratio spectrometer

(Europa Scientific Ltd., Crewe, UK). All samples were standardized against a secondary

reference of Radish collegate (3.167 % N, δ15 N 5.71 ‰, 41.51 % C, δ13 C -28.61 ‰)

that was in turn standardized against primary analytical standards (IAEA, Vienna). Soil

64

C:N ratio was calculated as total C divided by total N (Nt). Natural abundance of carbon

and nitrogen isotope values will not be considered further in this chapter, but are

included in the analysis presented in Chapter 6.

Labile inorganic N (Ni = NH4+-N + NO3

--N) was extracted by shaking soil (5.0

g) in 50 ml of 1M KCL for 1 h (Kuo, 1996), filtered (Whatman #40 filter paper) and

stored at 4 °C. NH4+-N and NO3

--N concentrations were determined by automated

colorimetric methods (Technicon, 1977). Hydroxide-extractable P was measured by

shaking soil for 16 hours in 0.1 M NaOH using a 10 g soil:50 ml solution (Bowman and

Cole, 1978). NaOH extractions were immediately filtered and an aliquot acidified with

HCl to precipitate organic matter was re-filtered (Whatman # 40) and analysed for

inorganic P (OH-Pi). An aliquot was also digested (H2SO4/H2O2) to obtain total labile P

(OH-Pt). OH-extractable P is indicative of labile (potentially available) P and is related

to productivity in the jarrah forest (Grierson and Adams, 2000). Phosphorus in all

extracts was analysed using a modified ascorbic acid method (Kuo, 1996). Organic P

(OH-Po) was estimated as the difference between the digested and acidified samples and

soil N:P ratio as Nt divided by OH-Pt. The three replicates from each depth as well as

the three depths at each site were averaged to obtain a single mean site value for all

nutrient fractions. Soil nutrient concentrations and ratios were then organized together

with all other environmental data (see Table 5.2) into a single matrix (R) with sites as

rows and environmental variables as columns (Legendre et al., 1997).

5.2.4 Statistical techniques and analyses

All statistical analyses were conducted using the open source statistical graphics and

computing environment R version 2.6.2 (R Development Core Team, 2009). The

correlation between the plant functional traits and environmental variables described

above was determined using the Fourthcorner technique available through the ade4

package (Legendre et al., 1997; Dray and Legendre, 2008). The Fourthcorner technique

is a permutational method that measures the link between three separate matrices; a

species composition matrix (L) with the abundance or richness of species at each site,

an environmental variable matrix (R) containing fire regime, aridity, and soil nutrient

content at each site (see Table 5.2), and a species trait matrix (Q) containing the

functional trait codes of all species. The significance of the link was determined by the

probability of the correlation adjusted using Holm’s procedure with the environmental

control over individual species model (Model 1) that permutes each row (species) of

65

matrix L independently (Legendre et al., 1997). Model 1 assumes that the presence or

abundance of a species in a given location is primarily determined by environmental

conditions at the site independent of the presence of other species, or species

assemblages (Legendre et al., 1997; Dray and Legendre, 2008). This is the most

appropriate model for this study since all of the sampled species were from a single

vegetation or forest type located within a particular geographical locality. Thus, while

competitive interactions among species may also influence species abundances, the

species pool is consistent across the study habitat and site-specific environmental

variables are likely to be the main factor limiting the presence and abundance of

individual species.

A principle components analysis (PCA), with PRCOMP command in R, was

also conducted to better illustrate the relationships observed in the Fourthcorner

analysis. The relative abundance of individuals with different functional (regeneration

and nutritional) traits that were found to be significantly correlated with particular

environmental variables across the sites were plotted together on the same PCA. The

relative abundance of individuals within each functional trait was determined by the

total percentage of abundance composed by species possessing that trait at each site.

Both the relative percentage of trait abundance and environmental data were normalized

prior to principle components analysis. Simple regression analysis was also used to test

the correlation among the different environmental variables (fire frequency, fire interval

length, aridity, and soil nutrient fractions) in order to assess the potential interaction

between them and better explain their impacts on the distributions of plant traits.

5.3 Results

5.3.1 Variation in environmental variables across the sites

The soils of the study habitat were slightly acidic with pH values ranging from 4.8 to

5.6. Soils were amonifying, where NH4+ ranged from 28 to 109 μg g-1, and was more

than 10 times greater than soil NO3- concentration on average (Table 5.2). At least 50%

of the labile P was organic; OH-Po ranged from 0-33 μg g-1 and was also much more

variable than OH-Pi, which ranged from 4-18 μg g-1 across the sites (Table 5.2). Soil

C:N ratio varied from 31 to 49, while the soil N:P ratio varied up to seven-fold across

the sites (Table 5.2).

66

Soil OH-Pt concentration was lower (r2 = 0.21, P = 0.073), while soil Ni was

higher (r2 = 0.29, P = 0.031) at more frequently burnt sites than less frequently burnt

sites. However, neither soil Nt (r2 = 0.13, P = 0.176) nor OH-Pi (r2 = 0.05, P = 0.416)

were significantly correlated with fire frequency. Fire frequency was also negatively

correlated with soil C:N ratio (r2 = 0.20, P = 0.078), which was slightly positively

correlated with aridity (r2 = 0.19, P = 0.094).

Table 5.2 Type and range of environmental data across the sixteen study sites in the southern jarrah forest. All environmental variables were included in (matrix R) the Fourth-corner analyses. The number of replicates (n) is shown for categorical data and the minimum to maximum range of values is given for continuous data.

Environmental variable

Data type

Code/ Value

Fire interval sequence

categorical SS (n = 4) M (n = 7)

L (n = 5) Fire Frequency categorical 1 (n = 2) 2 (n = 3) 3 (n = 3) 4 (n = 5) 5 (n = 3) Aridity Index continuous 1.28 – 1.51 Soil C:N ratio continuous 31 – 49 Soil N:P ratio continuous 1 – 7 Soil OH-Pi (μg g-1) continuous 4 – 18 Soil OH-Po (μg g-1) continuous 0 – 33 Soil NH4

+ (μg g-1) continuous 28 – 109 Soil NO3

- (μg g-1) continuous 9 – 18 Soil Nt (μg g-1) continuous 42 – 142

67

5.3.2 Composition of species with different functional traits

The number of resprouting species (combination of species that regenerate from fleshy

storage organ and rootstock/lignotuber) was slightly greater than the number of species

that regenerate from seed (Table 5.3). Only 4% of the taxa had an annual life cycle

(Table 5.3). With respect to root structure, the majority of the species were arbuscular

mycorrhizal (~ 53% of abundance), while a smaller percentage (~ 17%) could

potentially form other mycorrhizal associations (ectomycorrhizal, ericoid, and orchid)

(Table 5.3). Species that form proteoid (cluster) roots and other non-mycorrhizal species

made up ~ 29% of taxa. Approximately 17% of species were potentially nitrogen fixing

(Table 5.3).

Table 5.3 The total number of species as well as the percent of total species with each functional trait across the sixteen southern jarrah forest study sites.

Functional Traits

no. of taxa

% of Taxa

Regeneration strategy Fleshy storage organ 53 29 Woody rootstock 53 29 Seed 75 42 Life cycle Annual 7 4 Perennial 176 96 N - fixing ability Nitrogen fixing 31 17 Non-fixing 152 83 Root structure Arbuscular mycorrhizal 88 48 Ectomycorrhizal 12 7 Ericoid mycorrhizal 12 7 Orchid mycorrhizal 18 10 Proteoid root 27 15 Other non-mycorrhizal root 26 14

68

5.3.3 Variation in functional traits with fire regime and aridity

The overall abundance and richness of species with different regeneration strategy, life

form, N-fixing ability and root structure were not significantly related to fire frequency

or fire interval sequence (Table 5.4 & 5.5). In contrast, the abundance of annual species

was negatively correlated to fire frequency (Table 5.4 & 5.5). The abundance of annuals

and richness of proteoid root and ecto-mycorrhizal species was positively correlated

with aridity, whereas the richness of arbuscular mycorrhizal species negatively

correlated with aridity (Table 5.4 & 5.5). However, neither the abundance nor the

richness of non-mycorrhizal, ericoid mycorrhizal, and orchid mycorrhizal species varied

significantly with aridity (Table 5.4 & 5.5).

5.3.4 Variation in regeneration traits and life forms with soil nutrients

The relative diversity of species with different regeneration traits were significantly

related to soil OH-Po, N:P, and C:N ratio (Table 5.4 & 5.6), but not to soil Ni or OH-Pi

(data not presented). The abundance and richness of species with fleshy storage organs,

which included geophytes, sedges, rushes and some grasses, was negatively correlated

with soil OH-Po and C:N ratio, while the abundance of seeders was positively correlated

with soil OH-Po (Table 5.4 & 5.6). The abundance and richness of seeders decreased

with increasing soil N:P ratio, while the abundance of woody rootstock/lignotuber

species increased (Table 5.4 & 5.6). The number of annual species also decreased with

increasing soil C:N ratio (Table 5.4 & 5.6). However, with the exception of trees which

showed an increase in richness with higher OH-Po, the relative abundance of individuals

and richness of species with different life forms did not vary with any soil nutrient

fractions (Table 5.4 & 5.6).

The cumulative proportion of variation explained by component axes 1 and 2

showing the relationships amongst the relative abundance of individuals with different

regeneration traits and soil nutrients (OH-Po, N:P and C:N) was 69%. The PCA showed

that the abundance of seeders was positively related to soil OH-Po and negatively related

to soil N:P ratio (Figure 5.2). In contrast, the relative abundance of woody root species

was positively related to soil N:P ratio but negatively related to soil OH-Po, while the

abundance of fleshy organ and annual species decreased relative to soil C:N ratio across

the study sites (Figure 5.2).

69

Principle component axis 1

Prin

cipl

e co

mpo

nent

axi

s 2

-0.5 0.0 0.5

-0.5

0.0

0.5

FlOr

Seeders

WdRt

Annuals

Po

N:P

C:N


Prin

cipl

e co

mpo

nent

axi

s 2

-0.5 0.0 0.5

-0.5

0.0

0.5

FlOr

Seeders

WdRt

Annuals

Po

N:P

C:N

Figure 5.2 Principle components analysis showing the relationships among the relative abundance of individuals with different regeneration traits and environmental variables across sixteen southern jarrah forest study sites. Species with an annual life cycle, species that regenerate from woody rootstock (WdRt), fleshy storage organ (FlOr), and seed are shown in italics, and environmental variables, soil N:P ratio, C:N ratio and organic P (Po) content, are shown in bold.

5.3.5 Variation in nutritional traits with soil nutrients

The relative diversity of species with different nutritional traits also varied significantly

with soil OH-Po, N:P, and C:N ratio (Table 5.4 & 5.6) but not with soil Ni or OH-Pi

(data not presented). The richness and abundance of species with proteoid roots and the

richness of ectomycorrhizal species was positively correlated with soil OH-Po (Table 5.4

& 5.6). In contrast, the richness of species with proteoid roots and the abundance and

richness of ectomycorrhizal species was negatively correlated with soil N:P ratio (Table

5.4 & 5.6). Both the abundance and richness of N-fixing species also decreased with

increasing soil N:P ratio (Table 5.4 & 5.6). The richness and abundance of species with

proteoid roots increased, while the richness of orchid and AM species decreased with

increasing soil C:N ratio (Table 5.4 &5.6). However, neither the abundance nor richness

of other non-mycorrhizal and ericoid mycorrhizal species varied with soil nutrients

(Table 5.4 & 5.6).

70


Prin

cipl

e co

mpo

nent

axi

s 2

-0.5 0.0 0.5

-0.5

0.0

0.5

Po

N:PC:N

N-fixer

Pro

ECM

Eri

NM

Orc

AM


Prin

cipl

e co

mpo

nent

axi

s 2

-0.5 0.0 0.5

-0.5

0.0

0.5

Po

N:PC:N

N-fixer

Pro

ECM

Eri

NM

Orc

AM

Figure 5.3 Principle components analysis showing the relationship amongst the relative abundance of individuals with different nutritional traits and environmental variables across sixteen southern jarrah forest study sites. Nutritional traits including nitrogen fixing (N-fixer), ectomycorrhizal (ECM), Arbuscular mycorrhizal (AM), ericoid mycorrhizal (Eri), orchid mycorrhizal (Orc), nonmycorrhizal (NM), and proteoid roots (Pro) are shown in italics, and environmental variables, soil N:P ratio, C:N ratio, and organic P (Po) content, are shown in bold.

The cumulative proportion of variation explained by principle component axes 1

and 2 showing the relationship among the relative abundance of individuals with

different nutritional traits and soil nutrients (OH-Po, N:P and C:N) was 60 %. The PCA

showed a negative relationship between AM species and soil C:N, and a positive

relationship between abundance of proteoid and ectomycorrhizal species and the soil

C:N ratio (Figure 5.3). The abundance of N-fixers was also negatively related to soil

N:P ratio across the sites. The relative abundances of proteoid and ectomycorrhizal

species were positively correlated and both were inversely related to the abundance of

AM species (Figure 5.3).

71

Table 5.4 Summary of Fourthcorner analysis results. The relationship between each functional trait and environmental variables across the sixteen jarrah forest sites is shown. Positive relationships are indicated with a + sign and negative relationships with a - sign; o indicates a non-significant relationship. Column Ab is the relationship for abundance data and Pr is the relationship for presence/absence data.

Soil organic P

Soil N:P

Soil C:N

Aridity

Fire Frequency

Pr Ab Pr Ab Pr Ab Pr Ab Pr Ab Regen. Strategy Woody rootstock o o o + o o o o o o Fleshy organ - - o o - - o o o o Seed + + - - o o o o o o Root structure Arbuscular myc. o o o o - o - o o o Ectomycorrhizal + o - - o o + o o o Ericoid myc. o o o o o o o o o o Orchid myc. o o o o - o o o o o Proteoid root + + - o + + + o o o Other non-myc. o o o o o o o o o o Life cycle Annual o o o o - o o + o - N -fixing ability Nitrogen fixing o o - - o o o o o o Life form Tree + o o o o o o o o o

72

Table 5.5 Correlations between environmental variables (aridity, fire frequency, and fire interval sequence) and all functional traits across the southern jarrah forest study sites. The relationship between each trait and environmental variable is shown separately for abundance and presence/absence data. The correlation coefficient (F, Χ2, r) of the global tests of significance and their probability (P) are given. The correlation coefficient (D) and probability (Pr) adjusted using Holm’s procedure is also shown for each functional trait when significant. A (-) in front of a correlation coefficient indicates a negative relationship. All probabilities are calculated from 999 permutations and are shown in bold when P < 0.1.

Aridity

Fire Frequency

Fire Interval Sequence

Abundance data Root structure

F = 2.45 P = 0.122

F = 0.57 P = 0.858

Χ2 = 13.1 P = 0.512

Regen. Strategy F = 0.15 P = 0.93 F = 1.14 P = 0.364 Χ2 = 7.79 P = 0.322 Life form F = 2.98 P = 0.12 F = 2.03 P = 0.361 Χ2 = 41.7 P = 0.422 Annual life cycle r = 0.036 P = 0.070 r = -0.044 P = 0.051 F = 2.13 P = 0.420 N-fixing ability

r = -0.015 P = 0.237

r = 0.004 P = 0.419

F = 0.86 P = 0.567

Presence/absence data Root structure F = 1.11 P = 0.047 F = 0.31 P = 0.629 Χ2 = 6.47 P = 0.178 D Pr(D) Arbuscular myc. -0.062 0.006 Ectomycorrhizal 0.038 0.088 Proteoid root 0.048 0.060 Regen. Strategy F = 0.243 P = 0.434 F = 0.038 P = 0.886 Χ2 = 1.33 P = 0.570 Life form F = 0.570 P = 0.347 F = 0.46 P = 0.506 Χ2 = 14.8 P = 0.129 Annual life cycle r = 0.023 P = 0.184 r = -0.021 P = 0.189 F = 0.175 P = 0.738 N-fixing ability

r = -0.017 P = 0.228

r = 0.008 P = 0.36

F = 0.288 P = 0.505

73

Table 5.6 Correlations of plant functional traits with environmental variables (organic P, N:P and C:N) across all sixteen study sites. The relationship between each trait and environmental variable is show separately for a) abundance and b) presence/absence data. The correlation coefficient (F, r) of the global tests of significance and their probability (P) are given. The correlation coefficient (D) and probability (Pr) adjusted using Holm’s procedure is also shown for each functional trait when significant. A (-) in front of a correlation coefficient indicates a negative relationship. All probabilities are calculated from 999 permutations and are shown in bold when significant P < 0.1.

a) Abundance

Soil organic P

Soil N:P

Soil C:N

Root structure F = 4.38 P = 0.008 F = 3.96 P = 0.021 F = 4.27 P = 0.012 D Pr(D) D Pr(D) D Pr(D) Arbuscular myc. -0.009 0.531 0.016 0.430 -0.036 0.140 Ectomycorrhizal 0.033 0.176 -0.052 0.018 0.033 0.140 Ericoid myc. -0.020 0.531 0.015 0.528 -0.002 0.740 Orchid myc. -0.015 0.531 0.023 0.528 -0.045 0.135 Proteoid root 0.062 0.012 -0.036 0.170 0.060 0.012 Other non-myc. -0.046 0.112 0.031 0.320 -0.007 0.740 Regen. strategy F = 3.78 P = 0.045 F = 6.55 P = 0.002 F = 3.71 P = 0.036 D Pr(D) D Pr(D) D Pr(D) Fleshy organ -0.052 0.048 0.029 0.184 -0.058 0.024 Seed 0.048 0.039 -0.078 0.004 0.037 0.174 Woody rootstock -0.006 0.762 0.056 0.024 0.010 0.456 Life form F = 3.14 P = 0.102 F = 2.57 P = 0.178 F = 1.96 P = 0.407 Annual Life cycle r = 0.013 P = 0.291 r = -0.027 P = 0.151 r = -0.004 P = 0.423 N-fixing ability r = 0.030 P = 0.091 r = -0.045 P = 0.029 r = 0.023 P = 0.141

74

Table 5.6 Continued

b) Presence/absence

Soil organic P

Soil N:P

Soil C:N

Root structure F = 1.82 P = 0.005 F = 1.82 P = 0.002 F = 2.09 P = 0.001 D Pr(D) D Pr(D) D Pr(D) Arbuscular myc. -0.018 0.294 0.009 0.351 -0.052 0.030 Ectomycorrhizal 0.042 0.035 -0.056 0.010 0.030 0.114 Ericoid myc. -0.026 0.244 0.031 0.296 0.023 0.272 Orchid myc. -0.044 0.172 0.032 0.296 -0.075 0.020 Proteoid root 0.073 0.006 -0.055 0.006 0.058 0.012 Other non-myc. -0.036 0.148 0.030 0.296 0.012 0.282 Regen. Strategy F = 1.25 P = 0.015 F = 0.99 P = 0.030 F = 0.83 P = 0.054 D Pr(D) D Pr(D) D Pr(D) Fleshy organ -0.056 0.028 0.033 0.152 -0.049 0.044 Seed 0.042 0.048 -0.055 0.024 0.011 0.608 Woody rootstock 0.009 0.343 0.035 0.105 0.032 0.162 Life form F = 0.83 P = 0.077 F = 0.55 P = 0.388 F = 0.76 P = 0.132 D Pr(D) Tree 0.027 0.036 Annual life cycle r = 0.015 P = 0.268 r = -0.024 P = 0.182 r = -0.033 P = 0.082 N-fixing ability r = 0.022 P = 0.159 r = -0.037 P = 0.041 r = 0.010 P = 0.341

5.4 Discussion

5.4.1 Resprouters and seeders in relation to fire and aridity

My findings do not support the hypothesis that resprouters increase in abundance and

richness relative to seeders with greater fire frequency or short fire interval length. In

seasonally dry fire-prone ecosystems such as the jarrah forest, it is unclear if fire or

aridity is the primary factor determining seeder and resprouter abundance. The

abundance of resprouting species in these ecosystems is often higher at wetter sites,

which are more productive, have greater and spatially more connected fuel loads and

therefore tend to burn more frequently (Lamont and Markey, 1995; Pausas, 1999;

Meentemeyer et al., 2001; Clarke and Knox, 2002; Pausas and Bradstock, 2007). While

fire frequency was also greater at relatively humid sites than at more arid sites in this

study, I did not observe a significant correlation between fire frequency and resprouter

and seeder diversity (Table 5.4). This may be explained by the ability of species that

75

generally regenerate from seed to also resprout after fire if the intensity of that fire was

low. In fact, many Australian plant species that regenerate from seed can also resprout

after low intensity fires, thus reducing the probability of their seed banks becoming

depleted (Gill, 1981; Vesk and Westoby, 2004). While there was no quantitative data on

the intensity of the fires across my sites, most of the fires in the study region (i.e., 85-

90%) since the 1960s have been prescribed fires (Abbott and Burrows, 2003; Boer et

al., 2009). Prescribed fires are generally lit under mild conditions to avoid damage to

overstorey trees and result in a low intensity burn (Catchpole, 2002). In addition, the

repetition of short fire intervals (such as for sites with a SS-regime) are likely to result

in low fire intensities due to low levels of fuel accumulation. Hence, I assume that low

intensity fire has prevailed at my sites since 1972. Consequently, relationships between

resprouting and seeding dynamics and the history of fires within this landscape may

have been obscured by seeders that were not “obligate” but survived and resprouted

after low intensity fires.

5.4.2 Resprouters and seeders in relation to soil nutrients

Nutrient availability, while being correlated to fire frequency, is likely to have a more

direct role than fire in mediating competition between resprouters and seeders in the

southern jarrah forest. Seeders were favoured over species that regenerate from fleshy

organs, and to a lesser extent over species that regenerate from woody

rootstock/lignotubers, with increasing soil OH-Po and decreasing N:P (Table 5.4).

Species with fleshy storage organs also decreased in both abundance and richness with

greater soil C:N, i.e., reduced N fertility (Table 5.4). This agrees with findings from the

New England Tablelands in eastern Australia, where resprouter abundance was shown

to increase with soil N fertility across a eucalypt forest-scrubland continuum (Clarke et

al., 2005). Both the southern jarrah forest and the New England Tablelands (Clarke et

al., 2005) experience regular fires that temporarily increase nutrient availability for

plants. Plant available soil N, particularly in the form of NH4+, can increase up to 182%

following fire in the jarrah forest (Grove et al., 1986), and has been shown to remain

elevated for up to 4 years in other fire-prone ecosystems (Wan et al., 2001; Rau et al.,

2007). While resprouters have greater storage of carbohydrates, which allows them to

continue to grow when nutrient availability is extremely low, seeders can grow faster

during post-fire nutrient flushes (Grime, 1979; Pate and Dell, 1984; Pate et al., 1990;

Verdu, 2000). Furthermore, the nutrients released by fire tend to remain high in the soil

profile inaccessible to new growth from large subterranean roots when compared to

76

seedlings (Grove et al., 1986; Wisheu et al., 2000). Thus, investment in underground

storage organs may be inefficient at jarrah forest sites with low N fertility where seeders

can utilize N released from fires and grow faster, especially if total labile soil P is not

limiting. This agrees with findings of Cruz et al. (2002) who showed that resprouting

vigour in Mediterranean-type shrublands was lower following fire at sites with greater

nitrogen limitation. While no direct fire impacts on resprouting and seeding dynamics

were observed in this study, sites with high fire frequency, i.e., where the time between

individual fires was as low as 3 years, had greater inorganic N and lower total labile soil

P (OH-Pt). Thus, fire frequency may indirectly influence resprouting and seeding

dynamics in the southern jarrah forest by altering the relative availability of N and P.

5.4.3 Nutritional traits in relation to soil nutrients and aridity

Proteoid and other cluster rooted species are thought to be favoured when readily

available sources of P such as inorganic phosphorus are low (Lamont, 2003; Lambers et

al., 2008). However, the relative richness and abundance of proteoid and other non-

mycorrhizal root species did not vary with labile inorganic soil P (OH-Pi) in this study,

largely because OH-Pi was consistently low across all sites (Table 5.2). Nevertheless,

species with proteoid and ectomycorrhizal roots were favoured at sites with greater

labile organic P (OH-Po; Table 5.4), which suggests that they are more successful at

utilizing soil organic P fractions than other mycorrhizal and non-mycorrhizal species,

giving them a competitive advantage at sites where there is little soluble inorganic P.

Tree species richness also increased with greater OH-Po (Table 5.4) since two of the tree

species (E. marginata and C. calophylla) have ectomycorrhizal associations, while the

other two (Banksia grandis and Persoonia elliptica) have proteoid (cluster) roots (see

Appendix 2). While the relative function of cluster and mycorrhizal roots on nutrient

poor soils has been reviewed extensively (Bowen, 1981; Lamont, 1983; Lamont, 1993;

Read and Perez-Moreno, 2003; Lambers et al., 2006), there are as yet no published

studies that have examined the competitive interactions between proteoid or other

cluster root species and mycorrhizal species.

Similarly, ectomycorrhizal and proteoid root species were favoured when soil

N:P was lowest, i.e., when total P availability was high relative to total N (Table 5.4).

This suggests that proteoid root and ectomycorrhizal species, and to a lesser extent other

non-mycorrhizal and ericoid mycorrhizal species, may have a competitive advantage

over arbuscular and orchid mycorrhizal species at sites with low N fertility (i.e., high

77

C:N; Table 5.4), particularly when total labile P is not limiting. Studies from subarctic

heathlands in Sweden as well as temperate forests in the UK also suggest that

ectomycorrhizal and ericoid species dominate over arbuscular mycorrhizal species when

nutrient cycling and soil N availability is low (Michelsen et al., 1996; Cornelissen et al.,

2001), which may be explained by the greater ability of these species to utilize alternate

forms of nitrogen such as simple amino acids (Schmidt and Stewart, 1997; Nordin et al.,

2001; Read and Perez-Moreno, 2003).

Proteoid and ectomycorrhizal species richness also increased while arbuscular

mycorrhizal species richness decreased with greater aridity (Table 5.4). Root clusters on

southwest Banksia species might also enhance water uptake (Burgess et al., 2000).

However, it is not known if cluster root species are better competitors for water than

mycorrhizal species (Lamont, 2003). In soils such as that of the jarrah forests where the

permeable surface layer overlies either clay or parent rock, nutrient stress varies strongly

with seasonal fluctuations in water availability (Groves, 1983). However, the formation

of cluster roots under field conditions is highly dependent on water availability and they

do not persist in dry soils (Grierson and Adams, 2000). In this study, C:N increased with

aridity, suggesting that nitrogen limitation is likely greater at drier sites than at more

humid sites. Litter decomposition and hence nutrient mineralization rates decrease as

water becomes more limiting, particularly during the summer when temperatures are

optimal for microbial activity (O'Connell and Grove, 1996). Thus, proteoid and

ectomycorrhizal species are likely to be particularly favoured at drier jarrah forest sites

where seasonal nutrient stress, in terms of N limitation, is high but organic P availability

is high.

5.4.4 N-fixers in relation to soil nutrients

N-fixing flora of the jarrah forest is highly diverse, particularly directly following fire

(Hingston et al., 1989). While N-fixers were not significantly influenced by fire

frequency or interval length in this study (Table 5.4), the high number of N-fixers

observed across the sites (~ 17 % of taxa) suggests that this functional group makes a

significant contribution to the diversity of the southern jarrah forest at four to five years

after fire (i.e., at the time of sampling). N-fixers are thought to have a particularly

important role in seasonal climates such as those of MTEs because they colonize early

after fires and significantly increase soil N pools (Lamont, 1983; Rundel, 1983). In the

jarrah forest, mean rate of N-fixation can be as high as 3.3 kg ha-1 yr-1 in the first two

78

years following fire (Hingston et al., 1982). However, the ability to fix nitrogen is only

advantageous under N limitation when P availability is relatively high, because nitrogen

fixation is a highly phosphorus demanding process (Read and Mitchell, 1983; Vitousek

et al., 2002). In this study, the abundance and number of N-fixing species was higher at

sites with low soil N:P compared to sites with high soil N:P, i.e., when total labile P

availability is high relative to N (Table 5.4). This agrees with findings of Hingston et al.

(1982) who showed that phosphate addition to soil leads to a nearly 3-fold increase in

N-fixation by jarrah forest legumes. Thus, N-fixers may gain a competitive advantage

over species that do not have the capacity to fix N as N availability decreases, provided

that P availability is high enough for them to carry out N-fixation.

5.4.5 Annuals in relation to nutrients, aridity and fire

The low number of annual species found in this study, ~ 4 % of taxa, indicates that

having an annual life cycle is a relatively rare strategy in the southern jarrah forest,

which is likely to be largely attributable to the extremely low nutrient status of the soils.

Annual species richness decreased with greater soil C:N (Table 5.4), suggesting that

high N limitation may make it difficult for annuals to establish and regenerate

successfully within one year. In most fire-prone ecosystems, ephemeral species stay

dormant in the seed bank and regenerate only after fire when a large amount of nutrients

is released (Wisheu et al., 2000). In this study, annual species abundance decreased with

greater fire frequency (Table 5.4). This finding may be explained by the fact that annual

species are often less variable in their emergence time than perennial species making it

more likely for fire to coincide with their seedling emergence (Maret and Wilson, 2000).

Thus, while annuals may thrive directly after fires in southwest Australian forests (Bell

and Koch, 1980; Wardell-Johnson et al., 2007), they are nevertheless disadvantaged by

high fire frequencies. There were also more annual species at more arid sites (Table

5.4), supporting the hypothesis that greater water limitation during summer may reduce

dominance by perennials and favour species with an annual life cycle (Holzapfel et al.,

2006). Thus, annuals may gain an advantage with increased seasonal water limitation

only when fire frequency and nutrient stress, in the form of N limitation, is low.

5.4.6 Conclusions

This study demonstrates that the success of species with different functional traits such

as regeneration strategy, life cycle, root structure and N-fixing ability are strongly

linked to resource gradients, particularly in terms of nutrient availability. This suggests

79

that trade-offs among growth rate, resource allocation and nutrient harvesting strategies

mediate competitive interactions among plant assemblages, and may provide

mechanisms for the vegetation community to adapt to temporal as well as spatial

changes in resource limitation caused by interactions among fire regime, soil nutrients

and water availability. Faster growing proteoid and ectomycorrhizal seeders,

particularly those that can fix nitrogen, will likely dominate over resprouters (especially

those with fleshy storage organs) with arbuscular and orchid mycorrhizal associations

when N availability is low but organic labile P availability is high. Proteoid and

ectomycorrhizal species are also favoured at sites with high aridity where seasonal N

limitation is also greater. In contrast, despite being favoured at more arid sites where

seasonal water limitation is high, annual species are disadvantaged by high N limitation.

While only annual species richness is directly influenced by fire frequency, the relative

richness and abundance of other functional traits may also be influenced by fire-induced

fluctuations in nutrient availability.

81

CHAPTER 6

Influence of fire-driven changes in nutrient limitation on plant species

diversity in the jarrah forest

6.1 Introduction

While the direct impact of fires on vegetation composition and succession have been

reasonably well studied, particularly in southwest Australian forests (Specht et al.,

1958; Bell and Koch, 1980; Morrison et al., 1995; Burrows and Wardell-Johnson, 2003;

Wardell-Johnson et al., 2004), little is known of how fire-driven changes in nutrient

limitation influences plant species diversity. In the southern jarrah forest, the relative

number and abundance of species within different functional plant assemblages is

strongly related to soil N and P availability (Chapter 5), which is in turn strongly

influenced by fire (O'Connell et al., 1979; Grove et al., 1986). Both increasing N and P

limitation have been associated with greater plant species richness in several ecosystems

around the world as well as Australia (Heddle and Specht, 1975; Huston, 1980; Specht

and Specht, 1989b; Keith and Myerscough, 1993; Olde Venterink et al., 2003). High

nutrient limitation allows more species to co-exist by creating greater niche space and

preventing competitive exclusion of low-nutrient specialized species (Chapter 1; Tilman

(1983)). Phosphorus limitation in particular may increase in the southern jarrah forest at

greater fire frequencies, because total labile soil P is negatively related to fire frequency

in this ecosystem (Chapter 5). A high proportion of southwest Australian plant species

are specialized for soils that are poor in P (Lamont, 1984; Cowling and Lamont, 1998).

Consequently, higher fire frequencies may increase plant species diversity in the jarrah

forest by creating more niche space for low-P specialized species.

The extent of nutrient limitation in vegetation communities can be assessed by

analyzing the foliar N and P concentrations of the dominant species (Koerselman and

Meuleman, 1996). Although the amount of nutrients in plant foliage often increases

with greater soil nutrient availability, the relative limitation in N and P is often reflected

by the ratio of N to P in the foliage (Koerselman and Meuleman, 1996; Vitousek and

Farrington, 1997; Gusewell, 2004). Ecosystems where the foliar N:P ratio of the

dominant vegetation is greater than 16 are generally limited by P (Gusewell, 2004). The

82

mean foliar N:P ratio of southwest Australian vegetation, particularly of the dominant

woody species, is extremely high (> 20) indicating a high level of P limitation (Specht

and Moll, 1983; Pate and Dell, 1984). However, responses to both N and P fertilization

have been observed in forests of southwest Australia, particularly when water is not

limiting for growth (Stoneman et al., 1989; Stoneman et al., 1996). Thus, it is possible

for plant species with high foliar N:P ratios to be limited by both N and P (O'Connell

and Grove, 1996; Olde Venterink et al., 2003; Gusewell, 2004).

Alternatively, foliar δ15N signatures of dominant plant species can be used to

determine the level of nutrient limitation in ecosystems (Austin and Vitousek, 1998).

While more depleted foliar δ15N signatures are generally indicative of poor N supply,

hence greater N-limitation (Austin and Vitousek, 1998), they may also reflect greater P-

limitation (Clarkson et al., 2005). Different species within the same habitat can also

have depleted or enriched foliar δ15N signatures depending on how they utilize and

partition nutrients (Michelsen et al., 1996; Schmidt and Stewart, 2003; Beyschlag et al.,

2009). For example, in N-limited ecosystems, species with N-fixing ability tend to have

high foliar N concentrations and δ15N signatures closer to 0 ‰ and are thus more

enriched than species without N-fixing ability which often have negative δ15N

signatures (Townsend et al., 2007; Beyschlag et al., 2009). Species with different types

of mycorrhizal and non-mycorrhizal associations also have different δ15N signatures,

which reflect the sources of soil N they utilize (Michelsen et al., 1996; Schmidt and

Stewart, 2003). Thus, a large variation in δ15N signatures among species at a given site

often indicates a high level of niche differentiation or nutrient partitioning (Robinson,

2001; Beyschlag et al., 2009).

The objective of this study was to assess the indirect influence of variable fire

regimes on plant diversity through impacts on nutrient limitation in the southern jarrah

forest. I thus first sought to determine the extent to which the vegetation is limited by

nutrients by measuring the variation in aboveground biomass (overstorey and

understorey) as well as the foliar N and P concentrations and δ15N signatures of the

dominant plant species in relation to soil nutrient availability. I then determined the

relationship between the foliar N:P ratios and fire frequency to assess fire impacts on

nutrient limitation. Previously, I examined the direct impact of fire regime and

environmental gradients on species and life form richness and evenness (Chapter 4) as

well as the relative richness and abundance of different plant assemblages (Chapter 5).

83

Thus, here I investigate the relationships among the foliar N:P ratios of the dominant

vegetation and plant species richness and evenness in order to gain further insight into

how increased nutrient limitation may influence the diversity of the southern jarrah

forest vegetation community. I hypothesized that:

1. Foliar nutrient concentrations (N and P) and foliar δ15N signatures of the

dominant species as well as aboveground biomass increase with greater soil

nutrient availability.

2. The foliar N:P ratio of dominant species increase with greater fire frequency

owing to greater P limitations.

3. Plant species richness and evenness increase as the foliar N:P ratios of the

dominant species increase (i.e., when P is more limiting).

6.2 Methods

6.2.1 Site description

The same sites and experimental design have been used for this study as described in

previous Chapters. Total standing biomass, overstorey biomass and understorey

biomass were calculated at each site (Appendix 1) as described in Chapter 3 and

Chapter 4. Species diversity measures (richness and evenness; Appendix 1) were also

calculated as described in Chapter 4 and soil nutrient fractions (Appendix 1) were

determined as explained in detail in Chapter 5.

6.2.2 Foliar nutrient sampling and analysis of dominant species

Two tree (Eucalyptus marginata and Corymbia calophylla) and three shrub species

exceeding one metre in height (Agonis theiformis, Hakea falcata and Podocarpus

drouynianus) were sampled for foliar N and P based on their high abundance and wide

distribution within the forest understorey. These species were the most dominant plant

species across the study habitat. Of the shrub species, only A. theiformis was recorded in

all 16 of the study sites, while the other two shrub species (H. falcata and P.

drouynianus) were among the most abundant understorey plant species at most sites. E.

marginata and C. calophylla, were found at all sites, and together comprised over 95 %

of the total aboveground plant biomass across the sites (see Chapter 4). Consequently,

the foliar N and P of these five species are likely to provide a good indication of nutrient

limitations in this ecosystem.

84

Leaf samples of each indicator species (~ 100 g) were collected at random

within a 16 m2 quadrat at the four plot corners. As my objective was to determine the

variation in foliar N and P and δ15N with soils and fire frequency rather than within-site

variation, samples from all four corners were pooled so that there was one sample of

each species representative of each site. All leaves were collected within arms reach (<

2 m) from the forest floor. Because this habitat is an open forest, the leaves received a

mixture of sun and shade. All leaf samples were dried at ~ 60° C and ground to a

powder before being digested in concentrated H2SO4, prior to colorimetric analysis for

total nitrogen and phosphorus using an auto-analyser (Technicon 1977). The foliar N:P

ratio was determined for each species at each site as total foliar N divided by total foliar

P. Samples were also analysed independently for natural abundance of 15N (δ15N) using

an Automated Nitrogen Carbon Analyser-Mass Spectrometer consisting of a Roboprep

connected with a Tracermass isotope ratio spectrometer (Europa Scientific Ltd., Crewe,

UK). The samples were standardized against a secondary reference of Radish collegate

(3.167 % N, δ15 N 5.71 ‰, 41.51 % C, δ13 C -28.61 ‰) that was in turn standardized

against primary analytical standards (IAEA, Vienna).


All statistical analyses were conducted using the open source statistical graphics and

computing environment R version 2.6.2 (R Development Core Team, 2009). Simple

regression analyses were used to test the relationships among soil nutrients (Nt, Ni, OH-

Pt, OH-Pi), soil δ15N, foliar nutrients (N and P) and δ15N of the five dominant species,

species diversity measures (richness and evenness), aboveground biomass (overstorey

and understorey) and fire frequency across the study sites. Although the study sites

encompassed two soil types (Chapter 2), there was an equal number of high and low

frequency fires across both soil types (Appendix 1). Thus, the correlations between soil

and foliar nutrients and fire frequency were assumed to represent fire frequency effects

on nutrient availability. Both linear and logarithmic models were tested and the best fit

model (lowest P-value) was applied in all cases. Interactions between soil type and the

variables were tested and removed if not significant. When obvious patterns were

observed, regressions with P-values between 0.075 and 0.05 were also considered

significant and discussed accordingly.

85

6.3 Results

6.3.1 Foliar nutrient concentration of the dominant species

Mean foliar N:P ratios of the dominant plant species across all sites ranged from 22 to

31 (Table 6.1). While the average foliar P concentration of all five dominant species

was 0.026 %, the average N concentration was 0.65 %. Consequently, the average foliar

N:P ratio of all species across all sites was ~ 26 indicating high level of P-limitation.

Species with the highest foliar N:P ratio did not necessarily have the highest N

concentrations or the lowest foliar P. For example, H. falcata had both the lowest foliar

P (0.021 %) and N (0.573 %) concentration with a foliar and N:P ratio of 28, while P.

drouynianus had the lowest foliar N:P ratio (22) and a N concentration of 0.595 % and a

P concentration of 0.028 %. C. calophylla had the highest foliar P (0.031 %) and N

(0.729 %) concentration but had a foliar N:P ratio of 24, while A. theiformis had both

lower foliar N and P concentrations and a foliar N:P ratio of 31 (Table 6.1). The two

overstorey tree species (E. marginata and C. calophylla) did not significantly differ in

foliar N and P, N:P ratio or δ15N across the sites (Table 6.1). In contrast, the foliar P and

N:P ratio of H. falcata was significantly different to that of P. drouynianus (Table 6.1).

The mean foliar N of A. theiformis was also significantly different to that H. falcata

(Table 6.1), and all of the understorey species (A. theiformis, H. falcata and P.

drouynianus) had significantly different foliar δ15N concentrations (Table 6.1).

All species with the exception of H. falcata had foliar δ15N signatures that were

similarly depleted across the sites of ~ -5.5 ‰ to -3.4 ‰ (Table 6.1). Consequently, the

mean δ15N signature of all five species together was ~ -4.6. H. falcata, interestingly the

only dominant species with cluster roots, had a significantly more enriched foliar δ15N

of ~ 2.4 ‰ (Table 6.1). The species with the highest foliar N:P ratio (A. theiformis, 31),

also had the most depleted foliar δ15 N signature of ~ -5.5 (Table 6.1).

86

Table 6.1 Mean foliar N and P (%), N:P and δ15N (‰) and their standard errors for five dominant species as well as for all five species together in the southern jarrah forest. The values shown are the average of samples collected from 9-16 sites. Means of species with the same superscripts do not differ significantly at the P < 0.05 level.

Species

Foliar N (%)

Foliar P (%)

Foliar N:P

Foliar δ15N (‰)

Sample no. Agonis theiformis

n = 16

0.679AB ± 0.014

0.023BC ± 0.0013

31A ± 1.19

-5.47A ± 0.26

Corymbia calophylla

n = 16

0.729A ± 0.016

0.031A ± 0.0018

24BC ± 0.73

-4.58AB ± 0.14

Eucalyptus marginata

n = 16

0.688AB ± 0.012

0.026ABC ± 0.0012

27AB ± 0.93

-5.33A ± 0.11

Hakea falcata

n = 9

0.573C ± 0.085

0.021C ± 0.0043

28AB ± 1.40

2.37C ± 0.38

Podocarpus drouynianus

n = 13

0.595BC ± 0.008

0.028AB ± 0.0011

22C ± 0.79

-3.82B ± 0.24

All species

n = 70

0.650 ± 0.014

0.026 ± 0.0009

26 ± 0.58

-4.55 ± 0.22

6.3.2 Variation in foliar nutrients and aboveground biomass with soil nutrients

Foliar N and P concentrations were related to both total soil N and total labile soil P, but

not to labile inorganic N (Ni) or inorganic P (OH-Pi). The foliar N:P ratio of all species

except H. falcata also decreased with increasing total soil N (Table 6.2). In contrast,

foliar N:P of only two species (C. calophylla and A. theiformis) was negatively

correlated with soil OH-Pt (Table 6.2). The average foliar N:P of the three species found

across all 16 sites (C. calophylla, E. marginata and A. theiformis) also decreased with

both total soil N and OH-Pt (Table 6.2). Only the foliar P concentration of C. calophylla

and the average of three species (C. calophylla, E. marginata and A. theiformis)

increased with greater soil OH-Pt, whereas the foliar P concentrations of all five

dominant species as well as the mean of three dominant species (C. calophylla, E.

marginata and A. theiformis) increased with greater soil Nt (Table 6.2).

Foliar N concentrations of C. calophylla, H. falcata and the average of three

species (C. calophylla, E. marginata and A. theiformis) increased with soil Nt, while

only the foliar N of C. calophylla was higher at sites with greater soil OH-Pt (Table 6.2).

87

Foliar δ15N signatures of A. theiformis as well as the mean of three species (C.

calophylla, E. marginata and A. theiformis) decreased (were more depleted) as soil OH-

Pt increased. However, foliar δ15N did not vary with total soil N, regardless of the

species (Table 6.2). Interestingly, the foliar δ15N signature of H. falcata was

significantly and positively correlated to the soil δ15N signature (Table 6.3). Overstorey

biomass as well as total aboveground biomass (overstorey and understorey together)

was greater at sites with higher inorganic soil N (NH4+ + NO3

-) compared to sites with

low inorganic soil N (Table 6.4). However, aboveground biomass did not significantly

vary with total and labile soil N and P fractions or with soil δ15N (Table 6.4).

Table 6.2 The relationship of foliar N (%), δ15N (%), P (%), N:P ratio and δ15N (‰) of five dominant species, as well as the average of three species found at all sites (E. marginata, C. calophylla, and A. theiformis) with soil nutrients. Estimate, standard error (SE), P-value, and r2 are given for each linear regression. A negative sign (-) in front of an estimate indicates a negative relationship. P-values (< 0.075) suggesting a significant correlation are shown in bold.

Species a) Total soil N

Mean of

three sp.* Agonis

theiformis Corymbia calophylla



Hakea falcata

Foliar N (%) Est. 9.2E-05 2.0E-05 1.8E-04 7.6E-05 4.3E-05 7.1E-04 SE 4.1E-05 6.1E-05 5.0E-05 4.7E-05 3.1E-05 1.3E-04 P 0.040 0.748 0.003 0.130 0.196 0.001 r2 0.27 0.01 0.49 0.16 0.15 0.82 Foliar P (%) Est. 1.7E-05 1.6E-05 5.5E-06 1.5E-05 1.0E-05 3.6E-05 SE 3.3E-06 3.5E-06 2.0E-05 3.2E-06 3.3E-06 6.6E-06 P <0.001 0.001 0.003 <0.001 0.011 0.001 r2 0.65 0.59 0.49 0.62 0.46 0.80 Foliar N:P (%) Est. -0.010 -0.014 -0.006 -0.009 -0.005 -0.006 SE 0.002 0.004 0.003 0.003 0.003 0.004 P 0.001 0.001 0.043 0.014 0.073 0.242 r2 0.53 0.53 0.26 0.36 0.26 0.19 Foliar δ15N (‰) Est. -4.9E-04 -9.4E-04 -7.5E-04 2.0E-04 -2.3E-04 1.8E-03 SE 6.1E-04 1.1E-03 5.8E-04 4.8E-04 1.1E-03 1.4E-03 P 0.428 0.399 0.213 0.678 0.835 0.258 r2 0.05 0.05 0.11 0.01 <0.01 0.18 * Mean of E. marginata, C. calophylla, and A. theiformis

88

Table 6.2 Continued b) Total OH-extractable soil P

Mean of

three sp.* Agonis

theiformis Corymbia calophylla



Hakea falcata

Foliar N (%) Est. 1.2E-03 4.9E-04 3.4E-03 -2.5E-04 5.9E-04 2.9E-03 SE 1.3E-03 1.7E-03 1.7E-03 1.4E-03 9.1E-04 8.6E-03 P 0.358 0.777 0.064 0.862 0.528 0.743 r2 0.06 0.01 0.22 0.00 0.04 0.02 Foliar P (%) Est. 2.8E-04 2.3E-04 4.7E-04 1.5E-04 1.6E-04 2.3E-04 SE 1.4E-04 1.4E-04 1.7E-04 1.4E-04 1.1E-04 4.3E-04 P 0.059 0.119 0.017 0.302 0.179 0.607 r2 0.23 0.16 0.34 0.08 0.16 0.04 Foliar N:P (%) Est. -0.177 -0.231 -0.168 -0.132 -0.104 -0.119 SE 0.085 0.127 0.074 0.105 0.081 0.136 P 0.057 0.091 0.038 0.228 0.222 0.410 r2 0.24 0.19 0.27 0.10 0.13 0.10 Foliar δ15N (‰) Est. -0.036 -0.079 -0.017 -0.013 -0.020 0.045 SE 0.014 0.022 0.016 0.013 0.029 0.043 P 0.022 0.003 0.321 0.339 0.502 0.324 r2 0.32 0.49 0.07 0.07 0.04 0.14

* Mean of E. marginata, C. calophylla, and A. theiformis

Table 6.3 The correlation between foliar δ15N (‰) of five dominant species and soil δ15N (‰) in the southern jarrah forest. Estimate, standard error (SE), P-value, and r2 are given for each linear regression. A negative sign (-) in front of an estimate indicates a negative relationship. P-values (< 0.075) suggesting a significant correlation are shown in bold.

Soil δ15N (‰)

Species

Foliar δ15N (‰)

Agonis theiformis

Corymbia calophylla



Hakea Falcate

Est. -0.169 -0.200 0.018 -0.046 1.138 SE 0.358 0.192 0.158 0.379 0.420 P 0.644 0.317 0.912 0.907 0.030 r2 0.02 0.07 < 0.00 < 0.00 0.51

89

Table 6.4 The correlation between soil nutrients (total soil nitrogen, inorganic soil nitrogen, OH-extractable total soil phosphorus, and OH-extractable inorganic soil phosphorus) and overstorey biomass, understorey biomass, and total above ground standing biomass across 16 jarrah forest sites in southwestern Australia. P and r2 values are shown for each separate linear regression model. P-values (< 0.075) suggesting a significant correlation are shown in bold and represent a positive relationship.

Total standing biomass (ton ha-1)

Overstorey biomass (ton ha-1)

Understorey biomass (ton ha-1)

r2 P r2 P r2 P Soil N total (%) 0.11 0.225 0.10 0.224 0.02 0.578 Soil N inorganic (μg g-1) 0.42 0.007 0.42 0.007 0.01 0.789 Soil OH-Pt (μg g-1) <0.01 0.952 <0.01 0.952 0.13 0.168 Soil OH-Pi (μg g-1) 0.17 0.112 0.17 0.112 < 0.01 0.950 Soil δ15N (‰) 0.06 0.345 0.06 0.351 0.12 0.196

6.3.3 Variation in foliar nutrients with fire frequency

The foliar N:P ratio of A. theiformis was higher at more frequently burnt sites (Figure

6.1). The foliar N:P of H. falcata and E. marginata also showed an increasing trend

with greater fire frequency (Figure 6.1), although this was not significant at P > 0.05.

Foliar N:P ratio of all five species together increased significantly with greater fire

frequency, albeit slightly (Figure 6.1).

6.3.4 Species diversity relationships with foliar N:P ratio and soil nutrients

Overall species richness was greater at sites where the overstorey tree species, C.

calophylla, had a higher foliar N:P ratio (Figure 6.2). In contrast, species evenness

decreased as the foliar N:P of three of the dominant species (E. marginata, C.

calophylla, and H. falcata) increased (Figure 6.3). Species richness and species

evenness were not related to soil Nt or Ni (data not shown). However, while species

richness was not correlated with soil OH-Pt or OH-Pi (data not shown), species evenness

was greater at sites with high soil OH-Pt compared to sites with low OH-Pt (r2 = 0.23, P

= 0.059).

90

r2 = 0.31P = 0.026

r2 = 0.26P = 0.163

r2 = 0.14P = 0.224

r2 = 0.17P = 0.113

r2 = 0.05P = 0.428

r2 = 0.10P = 0.007

1020

3040

C. calophylla E. marginata

1020

3040

Folia

r N:P

ratio

P. drouynianus

1 2 3 4 5

1020

3040

A. theiformis

1 2 3 4 5

Fire frequency

All speciesr2 = 0.31P = 0.026

r2 = 0.26P = 0.163

r2 = 0.14P = 0.224

r2 = 0.17P = 0.113

r2 = 0.05P = 0.428

r2 = 0.10P = 0.007

1020

3040

E. marginata

1020

3040

H. falcata

P. drouynianus

1 2 3 4 5

1020

3040

A. theiformis

1 2 3 4 5

r2 = 0.31P = 0.026

r2 = 0.26P = 0.163

r2 = 0.14P = 0.224

r2 = 0.17P = 0.113

r2 = 0.05P = 0.428

r2 = 0.10P = 0.007

1020

3040

C. calophylla E. marginata

1020

3040

Folia

r N:P

ratio

P. drouynianus

1 2 3 4 5

1020

3040

A. theiformis

1 2 3 4 5

Fire frequency

All speciesr2 = 0.31P = 0.026

r2 = 0.26P = 0.163

r2 = 0.14P = 0.224

r2 = 0.17P = 0.113

r2 = 0.05P = 0.428

r2 = 0.10P = 0.007

1020

3040

E. marginata

1020

3040

H. falcata

P. drouynianus

1 2 3 4 5

1020

3040

A. theiformis

1 2 3 4 5

Figure 6.1 The foliar N:P ratio of five dominant plant species separately as well as all five species together against fire frequency in the southern jarrah forest. Species showing a significant trend are labelled in bold. Significance was determined as P < 0.075 because many of the obvious patterns displayed P-values between 0.075 and 0.05.

91

18 20 22 24 26 28

5060

7080

C. calophyllar2 = 0.23P = 0.059

20 22 24 26 28 30 32

E. marginatar2 = 0.01P = 0.816

24 26 28 30 32 34

5060

7080

Spe

cies

rich

ness

H. falcatar2 = 0.11P = 0.374

18 20 22 24 26

P. drouynianusr2 = 0.02P = 0.688

20 25 30 35 40

5060

7080

A. theiformisr2 = 0.02

P = 0.651

Foliar N:P ratio

18 20 22 24 26 28

5060

7080


20 22 24 26 28 30 32

E. marginatar2 = 0.01P = 0.816

24 26 28 30 32 34

5060

7080

Spe

cies

rich

ness

H. falcatar2 = 0.11P = 0.374

18 20 22 24 26


20 25 30 35 40

5060

7080

A. theiformisr2 = 0.02

P = 0.651

Foliar N:P ratio

Figure 6.2 Overall species richness against the foliar N:P ratio of five dominant plant species across the southern jarrah forest sites. Species showing a significant trend are labelled in bold. Significance was determined as P < 0.075 because many of the obvious patterns displayed P-values between 0.075 and 0.05.

92

20 25 30 35 40

1.20

1.25

1.30

1.35

Foliar N:P ratio

A. theiformisr2 = 0.15P = 0.133

24 26 28 30 32 34

1.20

1.25

1.30

1.35

Spe

cies

eve

nnes

s

H. falcatar2 = 0.71P = 0.004

18 20 22 24 26


20 22 24 26 28 30 32

E. marginatar2 = 0.30

P = 0.027

18 20 22 24 26 28

1.20

1.25

1.30

1.35


20 25 30 35 40

1.20

1.25

1.30

1.35

Foliar N:P ratio

A. theiformisr2 = 0.15P = 0.133

24 26 28 30 32 34

1.20

1.25

1.30

1.35

Spe

cies

eve

nnes

s

H. falcatar2 = 0.71P = 0.004

18 20 22 24 26


20 22 24 26 28 30 32

E. marginatar2 = 0.30

P = 0.027

18 20 22 24 26 28

1.20

1.25

1.30

1.35


Figure 6.3 Overall species evenness against the foliar N:P ratio of five dominant plant species across the southern jarrah forest sites. Species showing a significant trend are labelled in bold. Significance was determined as P < 0.075 because many of the obvious patterns displayed P-values between 0.075 and 0.05.

93

6.4 Discussion

6.4.1 Foliar nutrients of the dominant species

While the three dominant shrub species of the understorey were highly variable in their

foliar N, P, N:P ratio and δ15N signature, the two overstorey eucalypts (E. marginata

and C. calophylla) were remarkably consistent (Table 6.1). A range of other eucalypts

have previously been noted to exhibit similar foliar N and P concentrations under

similar soil nutrient conditions (Judd et al., 1996), which is in contrast to other

temperate as well as tropical forests where the foliar N and P concentration of the

dominant overstorey tree species can vary drastically (Martinelli et al., 1999; Townsend

et al., 2007). Plant species with different nutrient acquisition strategies often display

different foliar nutrient concentrations and foliar δ15N signatures, and tend to access

different sources of nutrients (Cornelissen et al., 2001; Nordin et al., 2001; Beyschlag et

al., 2009). Both of the tree species (C. calophylla and E. marginata) in this study are

ectomycorrhizal (Appendix 2), which suggests they utilize similar soil nutrient fractions

(Chapter 5). In contrast, overstorey tree species in other forests display variable nutrient

acquisition strategies such as N-fixation and different mycorrhizal associations

(Martinelli et al., 1999; Townsend et al., 2007). Thus, the more similar foliar nutrient

concentrations of overstorey species in the southern jarrah forest, as well as in other

eucalypt dominated forests, compared to forests elsewhere may be explained by the fact

that eucalypts generally access nutrients from the same sources.

The greater variability of foliar nutrients within the understorey compared to the

overstorey may also be explained in a similar way. Two of the dominant understorey

shrub species (A. theiformis and P. drouynianus) are arbuscular mycorrhizal, while the

third (H. falcata) is non-mycorrhizal with cluster (proteoid) roots (Appendix 2;

Brundrett and Abbott (1991)), which may have greater capacity to utilize different soil

N and P fractions, in particular organic N (Schmidt and Stewart, 1997; Turner, 2008),

than mycorrhizas (see Chapter 5). H. falcata was the only species to have a foliar δ15N

signature that is correlated with soil δ15N (Table 6.3), and also had a highly enriched

foliar δ15N (+2.37‰) when compared to the foliar δ15N signatures of the other dominant

species which ranged from -3.82 to -5.47‰ (Table 6.1). These findings are consistent

with those from different forested ecosystems in the tropics where cluster root species

had significantly higher foliar δ15N (+2.9 to +4.1‰) on average than ectomycorrhizal

and arbuscular mycorrhizal species which had foliar δ15N signatures closer to 0‰

94

(Schmidt and Stewart, 2003). Thus, while the southern jarrah forest overstorey species

utilize similar nutrient sources, there is likely to be greater partitioning of nutrient

fractions among the dominant understorey species.

6.4.2 Foliar N:P and δ15 as indicators of nutrient limitation

The variable foliar N and P concentrations and N:P ratios among the five dominant

species in relation to available soil N and P observed in this study agrees with other

studies across a range of ecosystems that have shown that plant species within the same

habitat can be differentially limited by N and P (Koerselman and Meuleman, 1996;

Townsend et al., 2007). While the average foliar N:P ratio of all the five dominant

species indicate a high level of P-limitation across all sites (i.e., N:P ratio > 20), foliar

N:P ratios decreased with greater soil N as well as P (Table 6.2). Total soil N also

explained a greater proportion of the variation in foliar nutrient concentrations

(particularly for foliar P) than soil P (Table 6.2), and total aboveground biomass

increased strongly with greater N availability (Ni) (Table 6.4). These findings are

consistent with studies from N and P limited tropical forests, where foliar P

concentration as well as overstorey tree biomass increased with both greater soil N and

P (Davidson et al., 2004). In contrast, foliar N and P concentrations of tree species were

more strongly influenced by soil P than N in Mediterranean pine-oak forests where N

mineralization rates, and hence N fertility, is high (Sardans et al., 2006). These results

suggest that N supply can have a strong influence on how efficiently P is acquired by

plants (Treseder and Vitousek, 2001), particularly in ecosystems that are limited by both

N and P.

The hypothesis that more depleted foliar δ15N signatures indicate greater nutrient

limitation is also partially supported by this study. The species with the highest foliar

N:P ratio (A. theiformis) had the most depleted foliar δ15N (Table 6.1) and also

displayed a significant increase in foliar δ15N with total labile soil P (Table 6.2). This

agrees with findings from heath bogs in New Zealand where the foliar δ15N signature of

dominant shrub species increased with soil and foliar P concentration and decreased

with foliar N:P ratio (Clarkson et al., 2005). Consequently, in ecosystems where the

vegetation is limited by both N and P, an increase in the availability of N may actually

reduce P-limitation just as much, if not more than, an increase in P availability (Treseder

and Vitousek, 2001).

95

6.4.3 Influence of fire regime on nutrient limitation

While fires tend to increase the availability of nutrients for plants, at least in the short

term (Humphreys and Craig, 1981; Adams et al., 2003), P released by fire may be

relatively short-lived such that within a few years any P that has been released has either

been taken up or is held in relatively recalcitrant pools (Humphreys and Craig, 1981).

Labile soil P concentration has been shown to decrease following fire, as well as with

increasing fire frequency (Chapter 5), in the jarrah forest due to increased adsorption of

P by inorganic surfaces exposed by burning of soil organic matter (Grove et al., 1986).

While studies demonstrating fire frequency impacts on soil P availability are few,

increases in P-limitation with greater fire frequency have been observed in generally P-

poor Pinus canariensis forests (Durán et al., 2008). Consequently, greater fire frequency

may further increase the extent to which the jarrah forest vegetation is limited by P, as

evidenced by the higher foliar N:P ratios observed at greater fire frequencies in this

study (Figure 6.1; Figure 6.4).

foliarN:P- -

Pt Nt

E S

+

-

-+

FireFreq

+-

NiPi

+

foliarN:P- -

Pt Nt

E S

+

-

-+

FireFreq

+-

NiPi

+

Figure 6.4 Summary of the relationships among fire frequency, foliar N:P ratio, total soil nitrogen (Nt) and inorganic soil nitrogen (Ni = NO3

- + NH4+), total (Pt) and

inorganic (Pi) sodium hydroxide extractable soil phosphorus, species richness (S) and species evenness (E) observed in the southern jarrah forest. Significant correlations observed in this study are shown with a (+) or a (–) sign indicating the sign of the relationship and a black arrow suggesting the direction of the effect. Dashed arrows suggest the direction of effects that were hypothesized but not observed.

96

6.4.4 Influence of nutrient limitations on plant diversity

Different attributes of diversity (species evenness and species richness) are likely to

respond differently to changes in nutrient availability in the jarrah forest. Species

evenness was reduced at sites with low labile soil P (Figure 6.4) and at sites where the

dominant species had higher foliar N:P ratios (Figure 6.3) suggesting that increased P-

limitation results in a less even distribution of abundance across species, i.e., greater

dominance by any one species. However, species richness is inversely related to species

evenness in the southern jarrah forest (Chapter 4; Figure 4.2g) and, in contrast to species

evenness, was slightly greater at sites where the dominant species had higher foliar N:P

ratios (Figure 6.2). These findings suggest that species evenness may decrease as

species richness increases along a gradient of increasing P-limitation which may be

exaggerated at greater fire frequencies.

Species richness generally tends to increase with greater N and P limitation in

nutrient-limited ecosystems (Heddle and Specht, 1975; Huston, 1980; Specht and

Specht, 1989b; Keith and Myerscough, 1993; Ostertag and Verville, 2002; Olde

Venterink et al., 2003; Reynolds et al., 2007). This can be explained by greater temporal

fluctuations in nutrient availability at nutrient poor sites compared to nutrient rich sites

which allows for low-nutrient specialized species to co-exist among faster growing non-

nutrient specialized species (Chapter 1; Tilman (1983)). However, negative impacts of

increased nutrient limitations on species evenness are less common. Generally, species

richness is expected to be greatest where competition and dominance among species is

lowest, i.e., when species evenness is high (Grime, 1979). This is in contrast to my

study as well as to recent studies of heath communities in southwest Australia, where

dominance among species was lower at nutrient rich sites that also had low species

richness (Perry et al., 2008). The heathlands and open forests of Australia are often

limited by moisture, nutrients as well as light (Specht and Morgan, 1981; Beard, 1983;

Specht and Moll, 1983). Under these circumstances, overstorey trees and dominant

shrubs can increase in abundance and cover with greater resource availability (i.e., water

and nutrients) and reduce light penetration through the canopy, which may result in a

loss of rare (i.e., less abundant) species in the understorey (Specht and Specht, 1989b;

Keith and Myerscough, 1993).

In the southern jarrah forest, the response of different functional plant

assemblages to changes in nutrient availability is highly variable (Chapter 5).

97

Dominance, hence competition among different life forms also increases with greater

site biomass (Chapter 4), which in turn increases with greater nutrient availability

(inorganic N) (Table 6.4). I thus suggest that as competition for nutrients decreases with

reduced P-limitation (i.e., greater availability of labile P, particularly organic labile P),

tree species increase in abundance (Chapter 5) resulting in greater cover and hence

greater competition for light among the understorey species. This in turn leads to a loss

of rare species, and hence a more even distribution of abundance among the remaining

species, i.e., greater species evenness. Tilman (1985) hypothesized that the competitive

advantage and abundance of species during different stages of succession following

disturbances such as fire is dependent on the relative availability of light versus

nutrients. Similarly, this study demonstrates that the relative abundance of different

species (i.e., species evenness) is dependent on the relative intensity of competition for

nutrients versus light, which may shift significantly across a relatively short nutrient

gradient induced by fire regime, particularly in ecosystems with poor nutrient

availability such as the jarrah forest.

6.4.5 Conclusions

While the southern jarrah forest overstorey is highly conservative in its foliar nutrients,

the nutrient content of the understorey foliage is more variable suggesting greater

partitioning of soil nutrient fractions among the dominant shrub species. Despite the

high foliar N:P ratio (> 20) of the dominant species, both greater soil N and P reduce the

extent to which the vegetation is limited by P in this ecosystem. Species evenness

decreases as species richness increases along a gradient of increasing P-limitation,

which in turn increases with greater fire frequency (Figure 6.4). Thus, the relationships

between different diversity measures, such as species evenness and richness, in the

southern jarrah forest are likely to depend on how fire regime influences the relative

competition for light versus nutrients among individual plant species.

99

CHAPTER 7

The relationship between nutrient limitation and species richness in

forested ecosystems

7.1 Introduction

This thesis has demonstrated that the diversity of the southern jarrah forest is largely

dependent on resource gradients, which are in turn related to fire regime. While overall

stand structure (i.e., total stem density, biomass and leaf area to sapwood area ratio) is

related to water availability to a large extent, the composition of the overstorey (i.e., the

relative basal area and biomass of jarrah versus marri) is a function of both aridity and

fire frequency (Chapter 3). The composition of the understorey is influenced by

interactions among fire, aridity and soil nutrients, and is mediated by trade-offs among

different functional plant assemblages (Chapter 5). Overall plant diversity measures

such as species richness and evenness are likely to dependent on the intensity of

competition for nutrients versus light, which is strongly influenced by an interaction

between fire regime and site biomass (Chapter 4 & 6).

The southern jarrah forest displays high plant species richness at a plot scale

when compared to other forested ecosystems in temperate regions (Chapter 4), which

may be attributable to strong nutrient limitation (Chapter 6). The nutrient-poor

shrublands of southwest Australia and South Africa are also known for having

exceptionally high number of species at small spatial scales when compared to more

fertile environments including other shrublands (Lamont et al., 1977; Griffin et al.,

1983; Specht and Moll, 1983; Lamont et al., 1984; Cowling et al., 1996). The high

levels of species diversity in other species-rich ecosystems, such as tropical forests, has

also been attributed to high nutrient stress (Huston, 1980; Tilman, 1983). However,

competitive interactions between plant species are also influenced by the availability of

light (Chapter 4). In temperate regions, forests with greater canopy cover often display

lower species richness than forests with more open canopies (Specht and Morgan, 1981;

Specht and Specht, 1989b; Specht and Specht, 1993). I thus hypothesized that nutrient-

poor forests would have greater species richness than nutrient-rich forests with similar

light competition intensity indicated by cover. To explore the validity of this hypothesis,

100

I examined previously published studies from a range of forest habitats around the

world for information on plant species richness and indications of soil nutrient status

and canopy cover (see Box 1 for details).

Box 1. Species richness in nutrient-poor versus nutrient-rich forests

Published data on species richness and soil nutrient status Published data from 34 forested habitats, including broadleaf- and conifer-dominated stands, from different temperate regions were collated (Table 7.1). The mean plot level species richness of each habitat was recorded as obtained by the authors from several sites within each habitat. The standard errors for those means are shown (Table 7.1) when they were provided in the original publication. While mean species richness was given per 1000 m2 for most habitats, some of the studies recorded values from smaller plots (400-500 m2; Table 7.1). All plot sizes were recorded and analysed for significant effect as mentioned below. Quantitative data on canopy cover was difficult to obtain, thus, the habitats were classified as having a closed or open canopy as suggested by the authors. A few woodland habitats were also included in the analysis and were classified as having a very open canopy (Table 7.1). While some of the habitats were located within the same geographic region, each habitat had a distinct phytosociological or plant association. Major tree and shrub species associated with each habitat are also listed in Table 7.1.

While I measured soil nutrients (Chapter 5) and assessed the extent of nutrient limitation in the southern jarrah forest (Chapter 6), most studies reporting species richness values in plant communities do not provide quantitative data on the nutrient status of the soils. Further, different studies use different indices of nutrient availability because of the variability in the types of nutrient fractions that limit different habitats. Consequently, I categorized the different habitats I examined as nutrient-poor or nutrient-rich based on quantitative soil nutrient data when available, according to the description of the soils and geology and/or additional information provided by the authors. I only included habitats that were clearly nutrient-poor or nutrient-rich, i.e., had high or low concentrations of key nutrients required for plant growth and function (e.g. N, P, K, Ca, Mg). I excluded habitats that had intermediate soil fertility, such as the karri (E. diversicolor) forest of southwest Australia, or when the geology or substrate did not give a clear indication of the nutrient status of the soils. The specific soil nutrient indicators or indices used to classify the soil nutrient status of each habitat are shown in Table 7.1.

Because the size of plots used to obtain species richness values differed among some sites, a three-way analysis of variance (ANOVA) was conducted to test the effect of nutrient status, canopy cover and plot size on species richness using R version 2.6.2 (R Development Core Team, 2009). Interaction effects were initially tested and were removed from the analysis if not significant.

101

Box 1. Continued

Description of the examined habitats Of the 34 examined forests and woodlands 24 were from Australia, 9 were from western United States and 1 was from southern Europe (Table 7.1). Only three of the forest habitats were reported as having a truly closed canopy; the Blechnum fern / Ceratopetalum apetalum forest in Ku-ring-gai Chase National Park, NSW (habitat 17; Table 7.1) and the two Acmena dominated rain forest habitats from the Eden Region of New South Wales (habitats 3 and 4; Table 7.1). Despite the open canopy classification for the rest of the forest habitats, there is likely to be a large variation in percent cover among them, particularly among the douglas-fir (Pseudotsuga) forests of western United States which ranged across highly mesic to semi-arid climates (Whittaker, 1960). Eleven of the habitats had very open canopies and were closely associated with woodland habitats rather than true forests (Table 7.1). These were included in this study because they were forested or tree dominated ecosystems. Open savannahs with scattered trees were excluded.

Soil P was the most commonly measured nutrient fraction across the habitats (Table 7.1), particularly for the Australian habitats, because P is one of the most common nutrients limiting plant growth in Australian ecosystems (Specht and Moll, 1983; McLaughlin, 1996) as well as in other terrestrial ecosystems around the world (Vitousek and Howarth, 1991). However, many of the habitats were also listed as having low availability of N or trace elements such as Mg, K and Ca (Table 7.1).

Species richness with regard to nutrient status, cover and plot size Overall, nutrient-poor habitats had more species than nutrient-rich habitats (P = 0.0003; Figure 7.1), and nutrient status explained 34% of the variation in species richness across the 34 habitats (r2 = 0.34). While species richness was highly variable across the nutrient-poor habitats, nutrient-rich habitats had consistently low species richness (Table 7.1). The most species rich habitat with high nutrient status (habitat 5; Table 1) only had 37 species, which was less than the mean species richness of all nutrient-poor habitats (42 ± 4).

The most species poor habitat was a closed forest (habitat 17; Table 7.1), and the most species rich one was a woodland with very open canopy (habitat 25; Table 7.1). However, a three-way ANOVA did not reveal a significant effect of canopy cover or plot size on species richness (data not shown). Species richness across habitats with very open canopies was also highly variable ranging from 20 to 77 across 11 habitats (Table 7.1). Mean species richness of the southern jarrah forest (61±3) was also higher than the mean of nutrient-rich habitats as well the mean of nutrient-poor habitats (Figure 7.1). Only the Causarina woodland from southeast Australia (habitat 25; Table 7.1) displayed greater species richness (77±3) than the southern jarrah forest (habitat 1; Table 7.1).

102

2030

4050

6070

10

Southern jarrah forest .

Nutrient-richhabitats

Nutrient-poor habitats

Spec

ies

richn

ess

2030

4050

6070

10

Southern jarrah forest .

Nutrient-richhabitats

Nutrient-poor habitats

Spec

ies

richn

ess

Figure 7.1 Species richness at a plot scale across 17 nutrient-rich versus 17 nutrient-poor forested habitats. Boxes represent the standard deviation of species richness values across habitats within each nutrient status category. The mean richness of habitats within each category is indicated by a bold line within the boxes and the overall range is indicated by a dashed line. The mean species richness of nutrient-poor habitats is significantly higher than the mean of nutrient-rich habitats (P = 0.0003). The species richness of the southern jarrah forest (habitat 1 on Table 7.1) is indicated by a black square.

7.2 The relationship between diversity and nutrients in forested ecosystems

7.2.1 Influence of nutrient status on species richness

The findings of this meta-analysis suggest high nutrient stress is linked to high species

richness across a range of forested ecosystems. Plant species richness has been shown to

increase with reduced soil nutrient content, particularly in P, in tropical forests (Huston,

1980). While a poor soil substrate or greater nutrient limitation is also generally

associated with high species richness in shrublands in California (Moody and

Meentemeyer, 2001) and wetlands in North America (Bedford et al., 1999) and Europe

(Olde Venterink et al., 2003), species richness can decrease when nutrients are

extremely limited, resulting in a unimodal or ‘hump-shaped’ curve (Mittelbach et al.,

2001; Pausas and Austin, 2001; Olde Venterink et al., 2003). However, a unimodal

relationship between species richness and nutrient availability is rarely observed in

forested ecosystems. Laanisto et al. (2008) found that only 9 out of 54 studies from

ecosystems dominated by woody species showed a unimodal curve for species richness,

103

and this was with regard to a general index of productivity which may reflect several

types of environmental stress, such as water as well as nutrient limitation (Chapter 3 &

6). The rareness of a unimodal relationship between species richness and nutrient

limitation in forests may be explained by the fact that landscapes that are extremely

nutrient impoverished generally do not support trees and are rather dominated by less

productive shrublands (Beard, 1983; Specht and Moll, 1983).

The consistently low number of species at nutrient-rich habitats versus nutrient-

poor habitats found in the present meta-analysis agrees with Huston’s (1980) findings in

tropical forests. Huston (1980) concluded that while soils of low or high fertility may

support low species richness, high species richness occurs only in nutrient poor forests.

This suggests that while other factors such as increased competition for light may

reduce species numbers, the mechanisms that allow a high number of species to co-exist

within vegetation communities are strongly linked to nutrient stress (Chapter 1). The

southern jarrah forest also had a generally greater number of species when compared to

other nutrient-poor forests from eastern Australia as well as North America (Table 7.1;

Figure 7.1). The number of plant species displaying specialized adaptations to nutrient

limitation, such as different types of cluster roots, in southwest Australia is very high

when compared to other landscapes in Australia as well as other parts of the globe

(Lamont, 1984; Lamont, 1993; Lambers et al., 2008). This is often attributed to the old

age of the soils and lack of relief in SW Australia, which results in a vast area with

consistently low soil nutrients and a large species pool that is adapted to high nutrient

stress (Hopper, 1992; Hopper and Gioia, 2004). Thus, the exceptionally high number of

plant species in the southern jarrah forest compared to other temperate forests can also

be attributed to the presence of a large number of low nutrient specialized species.

These findings provide support for the hypothesis that species diversity is greatest in

ecosystems where nutrient availability is poor, but a high proportion of the taxa are

specialized for low nutrient conditions (Chapter 1).

7.2.2 Influence of canopy cover on species richness

This meta-analysis also suggests that other environmental factors may contribute more

significantly to competitive interactions in woodlands than in forested ecosystems with

higher cover. For example, grazing pressure from livestock is thought to have an

important role in influencing competitive interactions among species in Mediterranean

woodlands, where the majority of the plant species are herbaceous (Naveh and

104

Whittaker, 1979). The tabor oak (Quercus ithaburensis) woodlands of the eastern

Mediterranean in particular are known for displaying exceptionally high species

richness at moderately grazed sites (Naveh and Whittaker, 1979). These woodlands

were not included in this study because the dark rendzina soils on which they occur are

of variable or intermediate fertility. However, some of the woodlands included in this

study, particularly those from Australia, may have been exposed to high grazing

pressure from marsupials or invertebrates. Because woodlands are generally among the

less productive forested ecosystems, other environmental stresses such as low water

availability may more strongly influence competition in these habitats (Beard, 1983).

7.3 Concluding remarks

The southern jarrah forest is among the most nutrient-impoverished as well as species

rich forest ecosystems in the world. While competitive interactions among plant species

are influenced by many factors in the environment, the mechanisms that allow the co-

existence of a large number of species in forested ecosystems are likely to be largely a

function of nutrient stress. The relationship between nutrient limitation and plant species

diversity observed in the southern jarrah forests as well as across forested ecosystems in

general demonstrates that nutrient limitation plays an important role in maintaining

species diversity in this ecosystem as well as in other forests in Australia and elsewhere.

Furthermore, a species pool with plants displaying a wide variety of functional

adaptations or traits, particularly to nutrient low nutrient availability, combined with

strong nutrient limitation is likely to create exceptionally high species diversity. While

impacts of fire regime on vegetation composition and diversity are more subtle than

those created by resource gradients, fire may also have an important indirect role in

mediating competitive interactions among plant species by influencing nutrient

limitation, particularly in highly nutrient impoverished ecosystems.

105

Table 7.1 Mean plot level species richness of 36 forested habitats with high and low nutrient status from around the globe.

Habitat

Mean no. of species (SE)

Plot size (m2)

Canopy cover

Associated Species

Nutrient status

Nutrient index used

Location

Reference

1 61 (3) 900 Open Eucalyptus marginata Low N-P Western Australia (Chapter 4)

2 22 (2) 1000 Open Cladium mariscus Fraxinus oxycarpa High N Southern Europe (Gerdol et al., 1985)

3 26 (2) 400 Closed Acmena smithii Pteris umbrosa High P Eastern Australia (Keith and Sanders, 1990)

4 30 (3) 400 Closed Acmena smithii Eupomatia laurina High P Eastern Australia (Keith and Sanders, 1990)

5 37 (2) 400 Open Eucalyptus viminalis E. fastigata High P Eastern Australia (Keith and Sanders, 1990)

6 29 (5) 400 Open Eucalyptus ovata Juncus australis High P Eastern Australia (Keith and Sanders, 1990)

7 35 (3) 400 Open E. cypellocarpa Angophora floribunda Low P Eastern Australia (Keith and Sanders, 1990)

8 38 (1) 400 Open E. angophoroides Gahnia radula Low P Eastern Australia (Keith and Sanders, 1990)

9 37 (1) 400 Open Eucalyptus globoidea Senecio sp. Low P Eastern Australia (Keith and Sanders, 1990)

10 38 (4) 400 Open Eucalyptus viminalis Dichondra repens Low P Eastern Australia (Keith and Sanders, 1990)

11 27 (5) 400 Very open Kunzea ambigua Phebalium ralstonii Low P Eastern Australia (Keith and Sanders, 1990)

12 30 (3) 400 Very open Angophora floribunda Eucalyptus sieberi Low P Eastern Australia (Keith and Sanders, 1990)

106

Table 7.1 Continued

Habitat


Plot size (m2)

Canopy cover

Associated Species

Nutrient status

Nutrient index used

Location

Reference

13 20 (5) 400 Very open E. consideniana Persoonia chamaepeuce Low P Eastern Australia (Keith and Sanders, 1990)

14 33 (30-35)1 500 Open Allocasuarina torulosa Persoonia linearis High N-P-Ca-K-Mg Eastern Australia (Le Brocque, 1998)2

15 26 (25-27)1 500 Open Angophora floribunda Allocasuarina torulosa High N-P-Ca-K-Mg Eastern Australia (Le Brocque, 1998)2

16 27 (26-28)1 500 Open Syncarpia glomulifera Allocasuarina torulosa High N-P-Ca-K-Mg Eastern Australia (Le Brocque, 1998)2

17 14 (11-16)1 500 Closed Ceratopetalum apetalum Blechnum cartilagineum High N-P-Ca-K-Mg Eastern Australia (Le Brocque, 1998)2

18 48 (42-52)1 500 Open Eucalyptus piperita Eucalyptus haemastoma Low N-P-Ca-K-Mg

(sandstone) Eastern Australia (Le Brocque, 1998)2

19 51 (45-59)1 500 Open Angophora costata Eucalyptus sp. Low N-P-Ca-K-Mg


20 22 (20-24)1 500 Very open Allocausarina torulosa Acmena smithii High N-P-Ca-K-Mg Eastern Australia (Le Brocque, 1998)2

21 55 (43-62)1 500 Very open Eucalyptus gummifera Eucalyptus haemastoma Low N-P-Ca-K-Mg


22 52 (46-57)1 500 Very open Banksia seratta Eucalyptus gummifera Low N-P-Ca-K-Mg


23 51 (50-52)1 500 Very open Angophora costata Eucalyptus hameastoma Low N-P-Ca-K-Mg


24 36 (1) 1000 Open Syncarpia glomulifera High Volcanic Eastern Australia (Rice and Westoby, 1983)

25 77 (3) 1000 Very open Causarina sp. Low Sandstone Eastern Australia (Rice and Westoby, 1983)

107

Table 7.1 Continued

Habitat


Plot size (m2)

Canopy cover

Associated Species

Nutrient status

Nutrient index used

Location

Reference

26 21 1000 Open Chamaecyparis lawsoniana Rhododendron occidentale High Ca-Mg

(olivine-gabbro) Western USA (Whittaker, 1960)

27 22 1000 Open Pseudotsuga menziesii Quercus chryolepis High Ca-Mg


28 20 1000 Open Pseudotsuga menziesii Arbutus menziesii High Ca-Mg

(quartz-diorite) Western USA (Whittaker, 1960)

29 24 1000 Open Pseudotsuga menziesii Lithocarpus densiflora High Ca-Mg


30 26 1000 Open Corylus rostrata Chamaecyparis lawsoniana High Ca-Mg


31 34 1000 Open Chamaecyparis lawsoniana Pinus monticola Low Ca-Mg

(serpentine) Western USA (Whittaker, 1960)

32 26 1000 Very open Pseudotsuga menziesii Arctostaphylos cinerea High Ca-Mg


33 23 1000 Very open Pinus jeffreyi Arctostaphylos viscida Low Ca-Mg

(serpentine) Western USA (Whittaker, 1960)

34 31 1000 Very open Pinus monticola Libocedrus decurrens Low Ca-Mg

(serpentine) Western USA (Whittaker, 1960) 1 a range of species richness values across sites was provided instead of standard errors for this habitat 2 nutrient status and associated species for this habitat was obtained from Le Brocque and Buckney (1995)

109

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Appendix 1. Site characteristics obtained from 16 (900 m2) plots in the southern jarrah forest. Data are ordered by site. Location of each site is

illustrated in Figure 2.1. Fire frequency, mean annual precipition and potential evapotranspiration are given for a 30 year period. Aridity index was

calculated as evapotranspiration divided by mean annual precipitation. Stem density (SD), basal area (Ab), Overstorey biomass (B), and sapwood area

(As) are given for Eucalyptus marginata and Corymbia calophylla, as well as both species together. Leaf area index and leaf area to sapwood area ratio

(Al: As) is given for both species together. The relative basal area (Abmarri: Ab

jarrah), stem density (SD marri: SD jarrah) and sapwood area (Asmarri: As

jarrah) of

marri to jarrah are also provided. Foliar N:P ratio ratios are given for five dominant species. Plant diversity measures and soil nutrient concentrations

and ratios are also provided. Soil C:N ratio was calculated as total soil N divided by total soil N, and N:P ratio as total soil N divided by total labile

(hydroxide extractable) soil P.

Site 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Soil type COy COy COy COy COy COy COy COy COp COp COp COp COp COp COp COp

Fire interval sequence SS M M SS M SS M SS L L M L L M M L

Fire frequency 4 3 3 5 4 5 4 5 2 2 3 1 1 4 4 2

Mean annual precip. (mm) 888 911 937 1011 1015 988 988 1000 954 929 901 934 938 950 951 918

Potential evapotrans. (mm) 1280 1280 1347 1300 1300 1334 1335 1335 1358 1358 1358 1358 1358 1343 1343 1354

Aridity index 1.44 1.41 1.44 1.29 1.28 1.35 1.35 1.33 1.42 1.46 1.51 1.45 1.45 1.41 1.41 1.48

SD total (stem ha-1) 3467 2056 1511 467 1533 1311 1378 2689 1478 2644 2444 2000 1322 3222 2800 2167

SD C. callophylla (stem ha-1) 944 678 744 56 656 356 300 978 656 722 278 611 300 511 956 989

SD E. marginata (stem ha-1) 2511 1378 744 356 878 956 1067 1711 822 1878 2156 1378 1022 2667 1844 1178

Ab total (m2 ha-1) 25 23 42 31 59 36 37 40 33 27 29 36 43 28 31 36

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Appendix 1. Continued

Site 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Ab C. callophylla (m2 ha-1) 4 5 12 7 26 3 10 10 11 10 4 5 9 6 8 10

Ab E. marginata (m2 ha-1) 21 18 30 24 33 33 27 30 22 17 25 30 34 22 23 26

Bo total (ton ha-1) 112 113 265 227 414 251 224 187 85 105 154 198 259 134 137 179

Bo C. callophylla (ton ha-1) 11 16 49 52 173 10 72 32 42 42 17 16 49 23 25 32

Bo E. marginata (ton ha-1) 101 97 215 176 241 241 152 155 42 62 137 183 210 111 112 147

As total (m2 ha-1) 9.46 7.84 9.82 4.23 12.53 6.89 8.21 12.87 10.22 10.04 7.99 9.79 8.68 9.62 11.45 11.37

As C. callophylla (m2 ha-1) 3.20 3.52 5.60 1.16 7.47 2.06 2.75 5.99 5.11 4.68 1.76 3.62 3.04 3.11 5.27 6.18

As E. marginata (m2 ha-1) 6.26 4.33 4.22 3.07 5.05 4.84 5.46 6.87 5.11 5.36 6.23 6.17 5.64 6.51 6.17 5.20

Leaf area index 0.81 1.30 1.09 1.15 2.44 1.38 1.71 2.38 1.52 0.91 0.77 1.04 1.26 0.99 1.38 1.41

Al: As 9 17 11 27 19 20 21 18 15 9 10 11 14 10 12 12

Ab C. calophylla : Ab E. marginata 0.18 0.29 0.41 0.28 0.80 0.10 0.35 0.33 0.48 0.61 0.15 0.18 0.27 0.26 0.35 0.38

SD C. calopyhlla : SD E. marginata 0.38 0.49 1.00 0.16 0.75 0.37 0.28 0.57 0.80 0.38 0.13 0.44 0.29 0.19 0.52 0.84

B C. calophylla : B E. marginata 0.11 0.16 0.23 0.29 0.72 0.04 0.48 0.20 1.00 0.68 0.12 0.09 0.24 0.21 0.22 0.22

Litterfall rate (tons ha-1 yr-1) 1.38 1.31 1.61 1.11 2.00 0.81 1.38 2.28 1.81 1.37 1.41 1.56 1.48 1.59 1.13 1.89

Underst. biomass (ton ha-1) 3.24 5.83 4.77 3.12 5.21 5.40 3.96 5.19 4.02 3.56 3.68 2.95 4.23 5.40 3.98 3.20

Foliar N:P E. marginata 27.5 27.2 26.5 28.1 21.1 31.1 28.6 31.3 27.9 32.6 29.5 25.9 19.5 23.1 30.3 23.3

Foliar N:P C. callophylla 23.5 23.8 23.9 24.0 25.1 25.3 27.1 24.8 23.8 26.0 25.6 28.8 17.4 20.6 25.2 18.9

Foliar N:P A. theiformis 30.9 30.5 30.0 33.8 29.0 37.9 40.8 28.5 29.3 29.4 33.3 32.6 18.8 31.4 29.3 26.8

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Site 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Foliar N:P P. drouynianus 22.5 20.8 19.4 - 19.4 23.7 27.0 21.1 22.2 26.2 24.0 - 17.0 - - 19.7

Foliar N:P H. falcata - - - 25.9 31.1 34.2 32.8 - - - 31.4 - 22.4 25.2 24.2 26.9

Total species richness 63 76 47 59 71 49 57 54 74 55 91 81 53 43 60 48

Herbaceous species richness 27 33 14 20 25 12 17 25 36 17 34 28 14 12 22 17

Woody species richness 36 43 33 39 46 37 40 29 38 38 57 53 39 31 38 31

Species evenness 1.19 1.18 1.24 1.28 1.22 1.17 1.22 1.17 1.15 1.19 1.20 1.15 1.31 1.32 1.28 1.35

Lifeform evenness 7.55 5.62 1.93 2.32 2.89 3.49 4.18 6.39 6.32 3.43 4.50 2.91 1.96 4.01 5.22 2.04

Soil pH 4.8 5.3 4.5 5.4 5.4 5.1 5.3 5.4 4.1 5.2 5.1 5.1 5.6 5.4 5.0 4.7

Soil NO3- (μg g-1) 9.2 9.6 17.9 9.6 10.3 9.7 9.8 10.7 8.8 8.8 12.3 9.7 8.7 9.1 10.3 8.5

Soil NH4+ (μg g-1) 56 44 34 109 82 74 67 61 28 29 30 39 67 33 33 36

Total soil N (%) 0.053 0.053 0.042 0.048 0.091 0.045 0.043 0.059 0.060 0.065 0.061 0.048 0.142 0.065 0.054 0.042

Total soil C (%) 1.83 1.65 1.50 1.88 2.96 1.52 1.54 1.91 2.24 3.19 2.65 1.97 6.88 2.90 2.26 1.38

Soil C:N 34 31 35 39 33 34 36 32 38 49 43 41 48 44 42 33

Inorganic soil P (μg g-1) 4.0 8.6 9.7 9.2 11.8 4.5 3.7 8.7 10.1 8.5 18.2 6.9 8.1 8.6 12.6 11.0

Organic soil P (μg g-1) 5.3 1.6 4.2 6.1 1.2 8.4 12.8 0.0 6.3 7.0 11.0 6.9 8.0 10.4 15.4 32.8

Soil N:P 5.7 5.2 3.1 3.1 7.0 5.1 3.3 5.2 2.7 3.8 2.1 4.7 3.7 2.5 5.1 1.0

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Appendix 2. 183 plant species surveyed at 16 sites in the southern jarrah forest. Species are listed in alphabetical order. The taxonomic family, life form, life cycle, regeneration strategy, root structure, and nitrogen fixing ability are given for each species.

Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Acacia alata Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia applanata Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia browniana Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia divergens Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia drummondii Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia extensa Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia myrtifolia Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia pentadenia Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Acacia pulchella Mimosaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Agonis theiformis Myrtaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Amphipogon amphipogonoides Poaceae Grass Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Anarthria prolifera Restionaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Anarthria scabra Restionaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Andersonia caerulea Epacridaceae Dwarf shrub Perennial Seed Ericoid mycorrhizal Non N-fixer Astroloma drummondii Epacridaceae Dwarf shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Astroloma pallidum Epacridaceae Dwarf shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Astroloma sp. unknown Epacridaceae Dwarf shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Banksia dallanneyi Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Banksia formosa Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Banksia grandis Proteaceae Tree Perennial Woody rootstock Proteoid root Non N-fixer Banksia sphaerocarpa Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Billardiera drummondii Pittosporaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Billardiera variifolia Pittosporaceae Vine Perennial Seed Arbuscular mycorrhizal Non N-fixer Boronia crenulata Rutaceae Dwarf shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Boronia gracilipes Rutaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Boronia spathulata Rutaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Bossiaea linophylla Papilionaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Bossiaea ornata Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Caladenia brownii Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Caladenia flava Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Caladenia macrostylis Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Caladenia magniclavata Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Caladenia reptans Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Caladenia sp. unknown Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Cassytha racemosa Lauraceae Vine Perennial Seed Other non-mycorrhizal Non N-fixer Chamaescilla corymbosa Anthericaceae Geophyte Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Chorizema rhombeum Papilionaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Comesperma virgatum Polygalaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Comesperma volubile Polygalaceae Vine Perennial Seed Arbuscular mycorrhizal Non N-fixer Conospermum caeruleum Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Conospermum capitatum Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Conostylis setigera Haemodoraceae Herb Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Conostylis sp. unknown Haemodoraceae Herb Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Corymbia calophylla Myrtaceae Tree Perennial Seed Ectomycorrhizal Non N-fixer Crowea angustifolia Rutaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Cyathochaeta avenacea Cyperaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Cyrtostylis huegelii Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Dampiera linearis Goodeniaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Dasypogon bromeliifolius Dasypogonaceae Herb Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Daviesia cordata Papilionaceae Shrub Perennial Woody rootstock Proteoid root Nitrogen fixer Daviesia incrassata Papilionaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Daviesia preissii Papilionaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Desmocladus fasciculatus Restionaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Desmocladus flexuosus Restionaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Drosera menziesii Droseraceae Geophyte Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Drosera pallida Droseraceae Geophyte Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Drosera stolonifera Droseraceae Geophyte Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Elythranthera brunonis Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Eucalyptus marginata Myrtaceae Tree Perennial Seed Ectomycorrhizal Non N-fixer Gompholobium confertum Papilionaceae Shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Gompholobium knightianum Papilionaceae Dwarf shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Gompholobium marginatum Papilionaceae Dwarf shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Gompholobium ovatum Papilionaceae Dwarf shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Gompholobium polymorphum Papilionaceae Shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Gompholobium preissii Papilionaceae Dwarf shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Goodenia eatoniana Goodeniaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Grevillea pulchella Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Grevillea pul. subsp. ascendens Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Grevillea quercifolia Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Grevillea trifida Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Haemodorum simplex Haemodoraceae Geophyte Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Hakea amplexicaulis Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hakea falcata Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hakea oleifolia Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hakea trifurcata Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hakea undulata Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hakea varia Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Hemigenia rigida Lamiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Hibbertia amplexicaulis Dilleniaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Hibbertia commutata Dilleniaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Hovea chorizemifolia Papilionaceae Dwarf shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Hovea elliptica Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Hybanthus debilissimus Violaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Hydrocotyle callicarpa Apiaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Isopogon axillaris Proteaceae Dwarf shrub Perennial Seed Proteoid root Non N-fixer Isopogon formosus Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Isopogon sphaerocephalus Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Johnsonia acaulis Anthericaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Johnsonia lupulina Anthericaceae Geophyte Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Kennedia coccinea Papilionaceae Vine Perennial Seed Proteoid root Nitrogen fixer Kingia australis Dasypogonaceae Grass tree Perennial Woody rootstock Other non-mycorrhizal Non N-fixer Kunzea recurva Myrtaceae Shrub Perennial Seed Ectomycorrhizal Non N-fixer Lepidosperma leptostachyum Cyperaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Lepidosperma squamatum Cyperaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Leptomeria cunninghamii Santalaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Leucopogon australis Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon capitellatus Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon glabellus Epacridaceae Dwarf shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon gracillimus Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon pendulus Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon propinquus Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Leucopogon verticillatus Epacridaceae Shrub Perennial Woody rootstock Ericoid mycorrhizal Non N-fixer Levenhookia preissii Stylidiaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Levenhookia pusilla Stylidiaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Lindsaea linearis Lindsaeaceae Fern Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Logania serpyllifolia Loganiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Lomandra hermaphrodita Dasypogonaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Lomandra integra Dasypogonaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Lomandra pauciflora Dasypogonaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Lomandra sericea Dasypogonaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Lomandra sonderi Dasypogonaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Luzula meridionalis Juncaceae Rush Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Macrozamia riedlei Zamiaceae Cycad Perennial Fleshy organ Arbuscular mycorrhizal Nitrogen fixer Marianthus sp.Walpole Pittosporaceae Vine Perennial Seed Arbuscular mycorrhizal Non N-fixer Melaleuca thymoides Myrtaceae Shrub Perennial Woody rootstock Ectomycorrhizal Non N-fixer Mesomelaena stygia Cyperaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Mirbelia dilatata Papilionaceae Shrub Perennial Seed Ectomycorrhizal Nitrogen fixer Olax phyllanthi Olacaceae Dwarf shrub Perennial Seed Other non-mycorrhizal Non N-fixer Opercularia hispidula Rubiaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Orchid sp. unknown 1 Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Orchid sp. unknown 2 Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Orchid sp. unknown 3 Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Orchid sp. unknown 4 Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Patersonia occidentalis Iridaceae Herb Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Patersonia umbrosa Iridaceae Herb Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Pentapeltis peltigera Apiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Pentapeltis silvatica Apiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Persoonia elliptica Proteaceae Tree Perennial Woody rootstock Proteoid root Non N-fixer Persoonia longifolia Proteaceae Shrub Perennial Woody rootstock Proteoid root Non N-fixer Petrophile diversifolia Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Petrophile serruriae Proteaceae Shrub Perennial Seed Proteoid root Non N-fixer Pimelea rosea Thymelaeaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Pimelea sylvestris Thymelaeaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Pithocarpa pulchella Asteraceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Platysace filiformis Apiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Platysace tenuissima Apiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Platytheca galioides Tremandraceae Shrub Annual Seed Arbuscular mycorrhizal Non N-fixer Podocarpus drouynianus Podocarpaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Poranthera huegelii Euphorbiaceae Dwarf shrub Annual Seed Arbuscular mycorrhizal Non N-fixer Pterostylis pyramidalis Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Pterostylis recurva Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Pterostylis vittatus Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Pultenaea reticulata Papilionaceae Shrub Perennial Seed Arbuscular mycorrhizal Nitrogen fixer Pyrorchis nigricans Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Scaevola striata Goodeniaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Sphaerolobium alatum Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Sphaerolobium fornicatum Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Sphaerolobium grandiflorum Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Sphaerolobium medium Papilionaceae Shrub Perennial Woody rootstock Arbuscular mycorrhizal Nitrogen fixer Stackhousia monogyna Stackhousiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Stirlingia tenuifolia Proteaceae Dwarf shrub Perennial Woody rootstock Proteoid root Non N-fixer Stylidium aff. junceum Stylidiaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Stylidium amoenum Stylidiaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Stylidium calcaratum Stylidiaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Stylidium luteum Stylidiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Stylidium scandens Stylidiaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Stylidium schoenoides Stylidiaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Stylidium sp. unknown Stylidiaceae Herb Annual Seed Arbuscular mycorrhizal Non N-fixer Styphelia tenuiflora Epacridaceae Shrub Perennial Seed Ericoid mycorrhizal Non N-fixer Synaphea petiolaris Proteaceae Dwarf shrub Perennial Fleshy organ Proteoid root Non N-fixer Taxandria parviceps ms Myrtaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer

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Species Family Life form Life cycle Regeneration strategy Root structure N-fixing ability Tetraria capillaris Cyperaceae Sedge Perennial Fleshy organ Other non-mycorrhizal Non N-fixer Tetraria octandra Cyperaceae Sedge Perennial Seed Other non-mycorrhizal Non N-fixer Tetrarrhena laevis Poaceae Grass Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Tetratheca affinis Tremandraceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Tetratheca hispidissima Tremandraceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Thelymitra crinita Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Thelymitra sp. unknown Orchidaceae Geophyte Perennial Fleshy organ Orchid mycorrhizal Non N-fixer Thomasia heterophylla ms Sterculiaceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Thysanotus tenellus Anthericaceae Herb Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Tremandra diffusa Tremandraceae Shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Tremandra stelligera Tremandraceae Dwarf shrub Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Tricoryne elatior Anthericaceae Herb Perennial Fleshy organ Arbuscular mycorrhizal Non N-fixer Trymalium ledifolium Rhamnaceae Shrub Perennial Seed Ectomycorrhizal Non N-fixer Velleia trinervis Goodeniaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Xanthorrhoea gracilis Xanthorrhoeaceae Grass tree Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Xanthorrhoea preissii Xanthorrhoeaceae Grass tree Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Xanthosia candida Apiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Xanthosia eichleri Apiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Xanthosia huegelii Apiaceae Herb Perennial Seed Arbuscular mycorrhizal Non N-fixer Xanthosia rotundifolia Apiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer Xanthosia singuliflora Apiaceae Herb Perennial Woody rootstock Arbuscular mycorrhizal Non N-fixer Xanthosia tasmanica Apiaceae Dwarf shrub Perennial Seed Arbuscular mycorrhizal Non N-fixer

productivity and diversity patterns in relation to fire...

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